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Identification of Soil Property Variation Using Spectral and Statistical

Analyses on Field and ASTER Data.

A Case Study of Tunisia

Mila I. Luleva

February, 2007

Course Title: Geo-Information Science and Earth Observation

for Environmental Modelling and Management

Level: Master of Science (MSc)

Course Duration: September 2005 - March 2007

Consortium partners: University of Southampton (UK)

Lund University (Sweden)

University of Warsaw (Poland)

International Institute for Geo-Information Science

and Earth Observation (ITC) (The Netherlands)

GEM thesis number: 2005-26

Identification of Soil Property Variation Using Spectral and Statistical

Analysis on Field and ASTER Data.

A Case Study of Tunisia

by

Mila I. Luleva

Thesis submitted to the International Institute for Geo-information Science and Earth

Observation in partial fulfilment of the requirements for the degree of Master of

Science in Geo-information Science and Earth Observation: Specialisation:

Environmental Modelling and Management

Thesis Assessment Board:

Chairperson Prof. Dr A. K. Skidmore

External examiner Prof. P. M. Atkinson

Supervisor Prof. Dr F. D. van der Meer

Member 1 Prof. P. Pelesjö

Member 2 Dr. H. M. A. van der Werff

International Institute for Geo-Information Science and

Earth Observation, Enschede, The Netherlands

Disclaimer

This document describes work undertaken as part of a programme of study at

the International Institute for Geo-information Science and Earth Observation.

All views and opinions expressed therein remain the sole responsibility of the

author, and do not necessarily represent those of the institute.

i

Abstract

Identifying the most suitable and cost- time effective technique for studying soil

properties variation has been a challenge for many soil scientists. Even though the

spectral characteristics of the soil and its constituents have been widely examined,

the use of satellite remote sensing data and ASTER imagery in particular, has been

limited. The aim of this study was to investigate soil properties such as moisture

content, organic matter, electroconductivity, pH, Calcium, Carbonates, Magnesium,

Manganese, Sodium, Potassium, and Iron within similar soil types by ASTER. The

study area was located in South-East Tunisia, chosen mainly due to the limited

presence of vegetation and extensive regions of bare soil. The methodology consists

of chemical and physical composition determination, moving through fine resolution

soil laboratory spectra measurements and development of Partial Least Squared

Regression (PLSR) models to link these results to ASTER imagery. Two ASTER

scenes were selected- winter season, years 2003 and 2005, in terms of gathering

better overview of the current situation. These two have been pre-processed and

classified by Maximum Likelihood (two different band combinations), using the

same techniques and the same regions of interest (training/testing sites), identified

from reference soil map and ISODATA unsupervised algorithm. Three accuracy

assessment techniques were applied in terms of identifying the performance of the

classifications as well as probability levels for correctly classified pixels. It was

established that with 95% confidence, only one out of ten samples was ‘probably’

correctly classified. The spectra collected under laboratory conditions using ASD

FIELDSPEC spectrometer were stored in a spectral library, which was then

resampled to ASTER and, together with the derived spectra from the ASTER scenes,

was input into the PLSR model collectively with the determined concentrations. The

PLSR models resulted in a set of correlations between the properties and the

reflectance values (R2 >0.9 for EC, Ca, Mg). The PLSR coefficients were plotted in

terms of identifying important wavelengths related to the properties. With the

decrease of spectral resolution, these wavelengths were reduced and the final set was

observed mainly in the shortwave infrared region. Even though the findings were

based on limited data available, the conclusions were optimistic, acknowledging the

potential use of ASTER in soil properties prediction and determination. Further work

can emphasise on the use of more soil samples, implementation of different terrain

parameters and selection of number of homogeneous, but with different nature, soil

clusters for sampling.

ii

Acknowledgements

There are so many people that contributed to the completion of this thesis but

unfortunately I have to limit myself to mentioning only some of them.

First of all, I would like to express my greatest gratitude to the academic team

involved in this research. Prof. Freek Van der Meer, thank you very much for all the

help, support, patience and constructive criticism. Your professionalism has

contributed greatly not only to this project but also to the development of my interest

and knowledge in the area of remote sensing. Dr. Ulrik Martensson and Dr. Harald

Van der Werff, thank you for the help and support throughout this journey. Drs.

Boudewijn de Smeth, thank you for the hours you spend in the laboratory

The Tunisian team- Ali, Nejeh and Abderrazak, thank you for making me feel at

home during my stay in your beautiful country. The long and very hot hours we spent

in the field as well as all the help in general, have been much appreciated.

Enormous ‘Thank you’ goes to everyone involved in the development of GEM MSc.

I am extremely grateful that you gave me the opportunity to travel, study and meet so

many great people at the same time.

To all my amazing GEM classmates: I really cannot believe one year and a half has

gone so fast. You guys made it unforgettable. Thank you for everything! I wish you

all lots of luck and great success! Sarika, Yan, Shawn and Chen- I will miss you guys

a lot, especially when I want a nice coffee with a great company in the “Sandy

place”, or a portion of these amazing mushrooms with Chinese leaves- NO spices!! I

will keep quiet about the long hours we spent cooking and baking...

I would like to thank my family and my friends for all the support during those

difficult little moments that everyone has once in a while. Mom and Dad, without

you none of these would have been possible- ‘Благодаря ви за всичко’. Ivo, thanks

for being the best brother ever! Stef and Iain- you guys—there are no words to

explain how grateful I am to you—if one day I can help somebody at least half as

much, I will be the happiest person on Earth!!!

Everyone who helped me, supported me and believed in me- Thanks for everything,

you people made it possible!

iii

Table of contents

Abstract........................................................................................................................ i

Acknowledgements..................................................................................................... ii

Table of contents .......................................................................................................iii

1. Introduction........................................................................................................ 1

1.1. Background.............................................................................................. 1

1.2. Problem Statement................................................................................... 5

1.3. Research Questions.................................................................................. 6

1.4. Research Objectives ................................................................................ 6

1.5. Research Hypotheses ............................................................................... 7

2. Materials and Methods....................................................................................... 9

2.1. Study Area ............................................................................................... 9

2.2. Soil Types and Characteristics Within the Region of Interest ............... 10

2.3. Field Data Collection............................................................................. 12

2.4. Data Processing ..................................................................................... 13

2.4.1. Laboratory Samples Analyses........................................................... 13

2.4.2. Spectral analysis ............................................................................... 14

2.4.3. Image analysis................................................................................... 17

2.4.4. Soil types identification per sample .................................................. 22

3. Results.............................................................................................................. 25

3.1. Laboratory analysis results .................................................................... 25

3.2. Spectral and image analysis ................................................................... 26

3.2.1. Laboratory spectra and CIE diagram ................................................ 26

3.2.2. PLSR on laboratory spectral library.................................................. 27

3.2.3. Image analysis................................................................................... 28

3.3. Soil types and properties identification per sample ............................... 35

4. Discussion........................................................................................................ 39

4.1. Spectral library analysis......................................................................... 39

4.2. Partial Least Squared Regression models .............................................. 40

4.3. ASTER scenes analysis ......................................................................... 44

4.3.1. Spectral profiles per sample.............................................................. 44

4.3.2. Classifications................................................................................... 45

4.4. Soil types identification within the study area ....................................... 49

5. Conclusions...................................................................................................... 55

6. Recommendations............................................................................................ 57

7. References........................................................................................................ 59

8. Appendices....................................................................................................... 63

iv

8.1. Appendix 1: Selected figures and tables ................................................63

8.1.1. Figures ..............................................................................................63

8.1.2. Tables................................................................................................70

8.2. Appendix 2: Field work report...............................................................76

8.3. Appendix 3: Samples photographs ........................................................83

8.4. Appendix 4: Procedures for soil analysis...............................................84

8.5. Appendix 5: Classification Accuracies ..................................................87

v

List of figures

In Text:

Figure 1: Location of the Study Area

Figure 2: Flowchart of data processing methodology

Figure 3: PLSR coefficients from laboratory spectral library

Figure 4: PLSR coefficients: laboratory spectra vs. resampled to ASTER spectra

Figure 5: PLSR coefficients: ASTER 2003 vs. ASTER 2005

Figure 6: Aster classifications outputs

Figure 7: Soil type coverage defined by the classification techniques at different

confidence levels

Figure 8: Soil types according to classification for the sampling points

Figure 9: Soil types according to classification for the test points

In Appendix:

Figure A1: Soil maps

Figure A2: Flowchart of PLSR data arrangement procedure (Wold et al. 2001)

Figure A3: Distribution of sampling points

Figure A4: Eigen values from MNF transformed bands

Figure A5: a) CIE tristimulus curves; b) CIE Chromaticity diagram

Figure A6: CIE diagram and soil sample distribution according to their colour

Figure A7: Spectral signatures derived under laboratory conditions

Figure A8: Spectral profiles extraction from ASTER at each sampling location

Figure A9: Analysis of the three spectral libraries for selected sampling points

Figure A10: Misclassified pixels associated with Lithosols, Brown Lithosols, and

Lithosols/Regosols

vi

List of tables

In Text

Table 1: Results from laboratory analysis and precision assessment

Table 2: Albedo and soil colour analysis

Table 3: Spectral wavelength regions important for the PLSR prediction model of

the properties

Table 4: Image classifications overall accuracy, kappa coefficients and most

successfully classified soil types

Table 5: Validation matrix for sampling points against soil types from ASTER

scenes, 2 classification methods and different probability levels

In Appendix

Table A1: PLSR prediction model assessment from laboratory results and spectral

library

Table A2: PLSR prediction model assessment for the resampled to ASTER library

Table A3: PLSR prediction model assessment for spectra derived from ASTER scene

2003

Table A4: PLSR prediction model assessment for spectra derived from ASTER scene

2005

Table A5: Extraction of soil types from each classification per sample point

Table A6: Extraction of soil types from each classification per test point

IDENTIFICATION OF SOIL PROPERTY VARIATION USING SPECTRAL AND STATISTICAL ANALYSIS

1

1. Introduction

Over the last couple of decades, it has been a challenge to find the most appropriate

technique for studying soil properties effectively and, at the same time, reducing the

time and effort involved in field sampling and laboratory analysis. This has been of a

major concern not only for soil scientists but also environmental managers. In this

respect, the scope of remote sensing has been widely studied, looking more closely at

how the spectral soil response can be linked to various soil properties and

characteristics. Furthermore, as stated by Palacios-Orueta and Ustin (1998), the

relatively high spectral resolutions as well as contiguous placement of bands,

together with the shortwave infrared region of the reflected spectrum offered by most

of the newly launched sensors, create opportunities for the specialists in the field.

Even though, the heterogeneous nature of soil creates a lot of difficulties in terms of

assessing its properties from reflectance spectra, if the spectral response is well-

known, multispectral imagery could be successfully used for this purpose (Ben-Dor

et al 2002).

1.1. Background

As stated by Udelhoven et al. (2003), soil parameters are neither static nor

homogenous in space and time; however, the costs of the analytical procedures are

often a limiting factor when address spatial soil variability especially in large-scale

applications. Reflectance spectra have been used for many years as a source of

information about the variation in the Earth's surface composition (Van der Meer et

al., 2001). Consequently, the soil properties derived from spectra have been studied

long before the 1980s. Many studies have drawn their main emphasis on the visible

and infrared regions of the spectrum- between 0.3 and 2.8 µm (Baumgardner et al.

1985), and the trend has been followed to date, with some relationships established

from data in the thermal and microwave regions (Barnes et al. 2003). The majority

of the absorption features, diagnostic for mineral composition, occur in the short-

wave infrared portion of the wavelength spectrum ranging from 2 to 2.5 µm.

Moreover, Shepherd and Walsh (2002) state that most of the basic physical and

chemical soil properties show high correlation with derivative reflectance values

within the visible and short-wave infrared wavelengths. These authors also suggest

that subtle differences in the spectral shape can serve as a valuable base for


2

identifying soil properties mainly due to the fact that the soil spectra forms as a result

of the overlap between absorption features of many organic and inorganic

compounds (Shepherd & Walsh, 2002). To a large extent, the changes in the spectral

response, according to Ben-Dor et al. (2003), occur due to changes in soil albedo

and soil chromophores, where the former is strictly related to the physical soil

properties, while the latter – to the chemical. Many researchers have identified clear

relationships between the soil colour, organic matter and iron content, which are also

strongly correlated with the soil albedo. There has been a general consensus that soil

albedo is determined by three main factors: moisture content, surface roughness and

soil colour (Post et al., 2000). In many cases, however, other factors may also

strongly influence the albedo. Soil electroconductivity for example is a property of

the soil affected by not only soil moisture but also dissolved salts content, clay

content, mineralogy and soil temperature. A relationship established between this

soil property and soil albedo can indicate that the latter is influenced by one of the

factors affecting the electroconductivity. Baumgardner et al. (1985) identified that

the differences in moisture content, organic matter, soil texture, iron content as well

as salinity determine most of the spectral responses in this spectrum range.

The presence of organic matter in the soil has been found to decrease the overall

reflectance and result in decrease in the spectral contrast. Consequently, this makes

the relationship between spectral and physicochemical properties much more

difficult to examine (Palacios-Orueta and Ustin, 1998). Furthermore, as outlined by

Palacios-Orueta et al. (1999), organic matter is a crucial indicator not only of soil

erosion but also of soil water holding capacity and permeability. Measurements of

organic matter content in soil have been included in many studies. The wavelengths

identified by Barnes et al. (2003) as ones having strong correlation with organic

matter, are between 0.425 and 0.695 µm, however this is only a case if the soils have

the same parental material. The authors also identified that this relationship could be

affected by the iron and manganese oxides present in the soil. If the soils are from

different parental material, it has been suggested that the middle infrared region

should be examined (Barnes et al. 2003).

On the other hand, the shape of soil reflectance curves, according to Baumgardner et

al. (1985), is affected by the presence of strong water absorption bands at 1.45 and

1.95 µm. Recently, Barnes et al. (2003) have observed that soil moisture is most

effectively correlated to visible and near infrared reflectance of bare soil fields if the

data are collected a few days after rainfall. According to Van der Meer et al. (2001),

the increasing moisture content decreases the overall reflectance of the soil. The


3

microwave part of the spectrum has also been suggested by some; however, the

process could be highly limited by the presence of vegetation (Barnes et al. 2003).

Salinity of soils has been studied from spectral reflectance as well, where most of the

features seen in the spectrum of saline minerals can be quantitatively measured by

laboratory or field spectroradiometers (Farifteh et al., 2006). It has been found that

saline soils give lower reflectance than non-saline throughout the range between 0.5

and 2.38 µm (Baumgardner et al., 1985). In the case of salt-affected soils, the white

crust that forms on the surface tends to have higher reflectance in the visible and near

infrared part of the spectrum (Barnes et al. 2003). However, as stated by Barnes et

al. (2003), this should not be considered as a clear indicator due to the fact that soils

with high sand content will have a response curve, which has similar shape, in the

visible and near infrared region, to the saline soils.

Soil texture has been studied by many using analysis of spectral reflectance. To

minimise the effect of other properties, Barnes et al. (2003) suggest the use of

combined data from the visible, near infrared and thermal infrared spectra. The

authors also recommended direct field sampling in terms of improving the accuracy

of interpretation of the bare soil imagery. Particle size has been identified to

influence strongly the soil reflectance. As stated by Baumgardner et al. (1985)

referring to an earlier study done by Bowers and Hanks (1965), with the decrease in

particle size, a strong exponential increase in reflectance can be noted, throughout all

wavelengths in the range of 0.4 to 1.0 µm. Although several techniques have been

found relatively efficient in studying soil texture from reflectance properties, it is

important to note that very often only textural classes can be identified from the

spectral response (Barnes et al., 2003).

Many studies have been conducted relating Potassium (K) to spectral response.

Shepherd and Walsh (2002) applied a non-linear regression technique known as

MARS (multivariate adaptive regression spline), to predict the concentration of

exchangeable K and P (Phosphorus) from soil reflectance. The results from the study

were relatively poor however the authors suggest that the relationships are strong

enough to permit discrimination of soils falling above or below a specific threshold

value. It is known, however, that non-polluted soil has concentrations of around 2-

3% for exchangeable K and P. Therefore, the change in the concentration will not be

easily detected using spectral response curves, unless high amounts of these elements

have been artificially added to the soil.


4

As indicated so far, there is a great amount of literature covering the in-situ

laboratory procedures for estimating and predicting soil properties. These

procedures, however, cannot always be applied, especially when it comes to large or

remote areas. Interestingly, not much has been done on the use and applications of

remote sensing techniques in this field in terms of identifying soil properties, despite

the fact that, according to Baumgardner et al. (1985), studying soil for mapping

purposes with aerial photographs dates from year 1929. Ben-Dor et al. (2002)

identify two general, but very important limitations related to the use of remote

sensing in soil science: only the top few centimetres can be studied and soil

vegetation masks the response from the soil surface. Recently it has been concluded

that multispectral remote sensing is an advanced technique that allows the

identification of targets, in this case soil properties, based on their extensively

studied spectral absorption features, with a near-laboratory quality detection of

reflectance spectra (Ben-Dor et al. 2002). There have been many efforts to include

aerial photographs and satellite images of bare soil in terms of studying organic

matter and phosphorus levels. Lopez-Granados et al. (2005) pointed out that these

approaches were mainly based on linear regression with brightness values from the

blue, green and near infrared bands. Chabrillat et al. (2002), on the other hand,

outline the fact that, unlike multispectral imagery, hyperspectral remote sensing

provides a continuous spectrum for each pixel, which enables the spectral

identification of minerals, rocks, or soils with laboratory-like reflectance

spectroscopy at the remote sensing scale (Chabrillat et al. 2002). Ben-Dor et al.

(2002) also note that since soil is a very heterogeneous material it is more difficult to

apply quantitative analysis to multispectral remotely sensed data.

ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) is a

cooperative effort between NASA and Japan's Ministry of Economy and Industry.

The instrument captures high spatial resolution data in 14 bands from the visible to

thermal infrared wavelengths and provides stereo viewing capability for digital

elevation model creation. It consists of three separate subsystems- VNIR, SWIR and

TIR. The VNIR (visible and near infrared) operates at the visible and near infrared

wavelengths with a resolution of 15m, while the SWIR (short wave infrared) has a

resolution of 30m and operates in six spectral bands in the near infrared region. The

TIR (thermal infrared) has a resolution of 90m and obtains data in 5 bands all in the

thermal infrared region (California Institute of Technology, 2006).

In a study by Chang & Islam (2000), soil moisture and brightness temperature have

been determined from ASTER imagery, in terms of identifying different soil types by


5

examining a sequence of remotely sensed images. A great disadvantage of the

procedure, however, is the fact that the results contain information only about the

near-surface soil properties. The procedure is valuable when performed on remote or

large areas. ASTER imagery has also been used in a study by Apan et al. (2002),

identifying different soil attributes, soil colour and texture in particular, as well as

land cover types. Reasonable separability has been indicated in ASTER Bands 2 and

8, for both soil colour and texture. The authors also point out that SWIR band ratios

are not useful for separability purposes.

1.2. Problem Statement

Spectrometry under laboratory conditions has been proven to be rather effective, and

therefore has been used in many soil related studies. Remote sensing techniques,

however, have not been applied to such extent, especially when it comes to

multispectral imagery such as ASTER. This is mainly due to the difficulties related

to the choice of study area clear from vegetation and containing most of the variation

in the top 10 cm as well as the coarser resolution provided by the sensor compared to

the laboratory spectra. Moreover, as stated by De Jong & Epema (2001), reflection

measurements, when taken in the field, represent a complex mixture of soil

components, varying soil surface conditions and atmospheric effects. The main

purpose of this study, therefore, is to explore the possibilities of using this type of

imagery in identifying direct and indirect relationships between soil constituents

measured in the laboratory, spectrometer measurements also performed in the

laboratory, and ASTER data.


6

1.3. Research Questions

• Can soil properties, such as moisture content, organic matter and Calcium,

Potassium, Magnesium, Manganese and Sodium concentrations, at trace

level, be determined from the spectral response derived under laboratory

conditions as well as from ASTER images?

• Can relationships obtained from laboratory analysis, both direct and

indirect- via the spectral response, between the above mentioned soil

properties, be established with analyses of ASTER images?

• Is ASTER imagery suitable for detecting variation of the chosen soil

properties over small areas, considering the similar nature of different soil

types?

1.4. Research Objectives

• To identify the most suitable soil properties for analysis according to the

characteristics of the study area as well as the literature.

• To analyse two ASTER scenes in terms of identifying variation in soil types

and respectively soil properties associated with them.

• To create spectral libraries of all soil samples in terms of identifying unique

absorption features and variation between the samples, from spectral

responses obtained under laboratory conditions as well as these extracted

from ASTER imagery.

• To identify relationships between certain wavelengths from the spectrum

and the absorption features associated with them, using PLSR models and

assess their applicability.

• To evaluate and combine the results from the image analysis with those

derived under laboratory conditions, in terms of achieving maximum

performance of the technique.


7

1.5. Research Hypotheses

• The presence of soil properties such as moisture content, organic matter and

Calcium, Potassium, Magnesium, Manganese and Sodium concentrations in

soil can be derived from the spectral response measured in the laboratory as

well as from ASTER images.

• Relationships can be derived from laboratory analysis, both direct and

indirect, between the above mentioned soil properties, and be established

with analyses of ASTER images.

• ASTER imagery is suitable for detecting variation of the chosen soil

properties over small areas.


9

2. Materials and Methods

2.1. Study Area

The study site is located in South-Eastern Tunisia (see figure 1). The area is 16.206

km2, with top left and top right coordinates respectively x: 601530 y: 3729154 and x:

602546 y: 3728787 (WGS 84, UTM zone 32). It is located in the Matmata

Mountains through the Jeffara plain into the Gulf of Gabes. The region has been

chosen not only due to the great interest from governmental and non-governmental

organisations, but also because of the relatively large areas of bare soil. It is

characterised by typical Mediterranean climate with maximum temperatures reached

in the period between June and August (48ºC), whereas the coldest temperatures are

measured between December and February. Due to the proximity to the sea, the

climate of the study area slightly differs from the typical arid or semi-arid. The

rainfall is very irregular and ranges between 150-240mm with an average of 30 rainy

days per year (September/October). This low rate of rainfall combined with the high

temperatures in the summer result in very high potential evapotranspiration up to

around 1321mm. The yearly climatic water balance is almost negative. There are two

major geological landscapes- the Djabel Matmata and the Jeffara. The former is

characterized by an average altitude of 500 meters (northern altitudes reach more

than 600). The latter, on the other hand, reaches the highest level of 100m above sea

level. The geology is comprised of continental and marine formations, the most

recent one dating from recent Quaternary. There are two main soil groups- Aridisols

and Entisols, with very small depth, with big patches of bare rock due to extensive

erosion. The area is mainly rural, with livestock and rain fed farming activities. A lot

of the land is agricultural, mainly olive trees plantations. The population of the

region is estimated at around 62 00 inhabitants spread across 13 imadas

(administrative areas) divided into 5 ethnic groups.


10

Figure 1: Location of the Study area.

Source of map: http://www.africa-expedition.com/images/ct/tunisia-map.jpg [accessed 5/01/2006]

2.2. Soil Types and Characteristics Within the Region of Interest

The diversity of the soil types within the study area is limited. To come to this

conclusion, two soil maps have been studied in terms of initially identifying the

variation. The older version has been produced by the Soil Division of the Tunisian

Ministry of Agriculture in year 1973. The scale of the map is 1:500,000. The second

map, produced in year 1986, has been prepared by the ACSAD service of mapping

in scale 1:1,000,000 (Figure A1- Appendix 1.1). The types of soils as well as their

codes are explained below. The description of the soil maps is provided by the Soil

Survey Staff (1999), and it is done in numerical order per class, taking into account

the units used on the soil map from 1986.

Aridisols are soils with limited moisture content and holding capacity. Due to runoff

or a very low storage capacity the soil moisture regime is aridic, despite the fact that

during periods of heat, the soil water is held at potentials less than the permanent

wilting point or has a great content of soluble salts caused by the extreme imbalance

between evapotranspiration and precipitation. Tunisia’s main soil group is the


11

Aridisols, covering 75% of the Southern part of the country, and having 21

subclasses in total. Within the area of interest, covered by the ASTER scenes (60 x

60 km), there are 5 soil sub-classes (see Figure A1-Appendix 1.1). These include

typic Salids (codes 4 and 5-Figure A1b), typic Paleorthids (code 8), which type is

also associated with the sampling study area, typic and petrogypsic Gypsids, or also

known as Gypsiothords (code 10 and 14 respectively), typic Calcids (code16 and

18). The Salids (also referred to as Halomorphs in different classifications) are most

commonly found in depressions or closed basins in the wetter areas bordering the

desserts. These are usually associated with accumulation of excessive amount of

salts, more soluble than gypsum, with typical electroconductivity of 30 µS/cm. If this

type of soil is used for agriculture, the salts are usually leached out. Paleorthids are a

class removed from the revised legend produced by USDA; however the main

characteristics of this type are stony soils, prominent to Calcium Magnesium

encrusting (Ca/Mg) with relatively low salts content. Gypsids are soils that have

gypsic or petrogypsic horizon with its upper boundary within 100cm of the soil

surface. These are characterised by accumulation of gypsum that takes place initially

as crystals aggregates in the voids of the soil. These aggregates build up, which

process results in displacement of the enclosed soil material. Some of the Gypsids

are characterised by a calcic horizon which overlays the gypsic horizon. The last

subclass of the Aridisols found in the area of interest is the Calcids. These are

developed from Calcium Carbonate as a parent material. Precipitation tends to leach

or move the carbonates to great depth. The upper boundary of the calcic horizon is

usually within 50cm of the soil surface. If the soils are irrigated or cultivated,

micronutrient deficiencies are normal.

The second main group of soils is the Entisols (also referred to as Lithosols). These

have little or no evidence of the development of pedogenic horizons, mainly

explained by the fact that in many landscapes the soil material is not in place long

enough to form these horizons. These soils are present either on steep slopes which

are actively eroded, or on flood planes, receiving new deposits of alluvium at

frequent intervals. The only features common to all soils under the order of Entisols

are the virtual absence of diagnostic horizons and the mineral nature of the soils. It is

possible, due to the specific location and different composition, these soils to be

mixed with other soil types resulting in sub-classes such as Brown Lithosols,

Lithosols/Regosols, etc. These, however, are essentially Lithosols, with small

number of additional constituents. The most clearly defined two subclasses

representing this diverse soil group in the region of interest are Torriorthents (code

33) and typic Torrifluvents (code 34), referred to as ‘fluvial soil’ further in the


12

discussion. The typic Torriorthents are shallow to weakly cemented rocks. Some are

moderately deep or deep to hard rock and are extremely dry. These are mainly

present on gently slopes where the sediments are recent and have little organic

carbon. On the other hand, the Torrifluvents are the Fluvents of the arid climates.

Characterized by aridic moisture regime, these soils have higher pH and are mainly

calcareous; however some could be salty depending on their location. The typic

Torrifluvents are the driest Fluvents. These are defined by big cracks and

slickensides, or wedge-shaped aggregates. In their upper 75cm, they do not have a

deposit of pyroclastic minerals (Soil Survey Staff, 1999).

2.3. Field Data Collection

The fieldwork was spread over 2 weeks between the 15th

and 30th

of September

2006, when the first was associated with visual inspection and selection of the study

area. Information about particular soil features and processes such as soil encrusting

formations, erosion rate and debris formation, vegetation characteristics and

seasonality (if any), was also obtained. The study area was selected according to the

following criteria: Areas with no mixture of soil types, availability of bare soil (no

vegetation and no bare rock), and easy access.

The sampling procedure is clustered random sampling. Thirty five soil sampling

units were collected. Six duplicate samples for accuracy assessment of the chemical

analysis were also located at every fifth location. The locations of the sampling units

were selected according to randomly generated coordinate pairs within the study area

(defined by the watershed and the Matmata Mountain)- see Figure A3- Appendix

1.1. Around 200g of soil material was collected at each location and placed in paper

and plastic bags for storage.

The measurements in the field included: location the sampling units (using GPS),

measurements of reflectance using ASD FIELDSPEC spectrometer (not included in

the analysis due to the missing measurements in the SWIR region), pH, and

identification of soil colour using Munsell colour chart. A record of these

characteristics as well as GPS accuracy and projection was kept and it is attached in

Appendix 2. The grain size was not measured in the field due to the many lumps of

soil that have occurred because of the high temperatures. A soil map was not

available at the time of sampling, and therefore soil types were identified after the

field work. Any clear remarks that were considered critical for the analysis (e.g.

vegetation, weather conditions) were noted and are also included in Appendix 2.


13

2.4. Data Processing

The methodology of the data processing is displayed on Figure 2. Each one of the

stages is described in more details further in the text.

Figure 2: Data Processing flowchart. The numbers correspond to the appropriate

section in the text

2.4.1. Laboratory Samples Analyses

Out of the 35 soil samples, the geochemical laboratory analyses were performed on

only 9 (plus one duplicate) of them transported to the Netherlands, due to financial

and time constraints. The analysis of the soil in the laboratory included analysis of

pH, electroconductivity, moisture content, organic matter, as well as chemical

concentration detection of Calcium (Ca), Magnesium (Mg), Manganese (Mn),

Sodium (Na), Potassium (K) and Iron (Fe). The carbonate concentrations have also

been estimated.

A factor, which strongly affects the spectral response of soil, is the soil structure and

grain size. In the laboratory this has been established, where the larger than 2mm

fractions have been placed in dishes and observed. The large lumps, originally


14

identified in the field, have not been identified due to the fact that they have broken

down during the transportation and/or sieving. The large particles found in the

laboratory had a diameter of around 1 cm. The procedures applied on the samples

can be found in Appendix 4 or referring to ISRIC Technical paper 9, (Van Reeuwijk,

2002). The concentrations of Ca, Mg, Mn, Na, K and Fe have been detected using

Induced Coupled Plasma- Optical Emission Spectrometer (ICP-OES). The actual

procedures followed during the laboratory analysis are included in Appendix 4,

while the results are summarised in Table 1. Iron concentrations are not included in

the table due to the fact that they have been found to be lower than the detection limit

of the apparatus. Records of the results have been kept and these have then been

implemented into the following stages of the analysis.

2.4.2. Spectral analysis

2.4.2.1. Laboratory spectra

Spectral analyses of the soil have been performed in laboratory conditions using

ASD FIELDSPEC spectrometer. Albedo and soil colour analysis have also been

done using special extension to ENVI software and a device-independent colour

space, originally developed in 1931 by the Commision Internationale de l’Eclairage

(CIE). It is based on the tristimulus colour theory of the human vision (Van der

Werff, 2006) and it has been accepted internationally as a standard for colour

measurements (De Jong & Epema, 2001). The relationships between the bands and

the soil properties have been studied using simple statistical correlations. Partial least

squared regression models (PLSR) using each of the produced spectral libraries

produced, have been run in terms of linking the laboratory spectra to the ASTER

images.

2.4.2.2. CIE diagram

CIE diagram and special extension to the ENVI software have been used. The

program separates the colour form the albedo providing coordinates for referencing

with the CIE diagram and at the same time calculating the percentage albedo. As

described by Mainam (1999), the system uses a set of imaginary XYZ primaries, also

known as tristimulus values that allow most colours, to be defined by a triplet of

these primaries. According to Van der Werff (2006), the CIE standard observer

shows how much of each primary would be used by an average observer to match

each wavelength to light. For the computation of the XYZ tristimulus values, the

tristimulus curves (Figure A5a) are integrated over a spectrum. To plot the colours

on a two-dimensional graph (such as a chromaticity diagram- Figure A5b),


15

chromaticity coordinates can be used. These are colour definitions independent of

the luminance (Y) and are calculated by a normalization of the tristimulus values

(XYZ), Van der Werff (2006). More detailed description of the procedure is given

by Brown & Feringa (2003). In this research, the CIE coordinates used are x, y

(chromaticity coordinates) and Y (luminance). The main purpose of these analyses

was to estimate the variation of albedo and soil colour between the samples, which

then determines the pre-processing techniques applied on the spectra prior to Partial

Least Squared Analysis (see next section).

2.4.2.3. PLSR

PLSR (Partial Least Squares Regression) was originally developed by Wold (1981)

and it provides quantitative multivariate modelling methods, with inferential

possibilities similar to multiple regression, t-test and ANOVA. It can analyse data

with strongly correlated x variables, and at the same time model several response

variables. PLSR is a way to estimate parameters in a scientific model, which

basically is linear. This model, like any other scientific models, consists of several

parts: the philosophical, conceptual, the technical, the numerical, the statistical and

so on. It is important to note that before the analysis, the X and Y variables are often

transformed to make their distributions be fairly symmetrical. Furthermore, the result

from the method depends highly on the scale. In this respect, with the appropriate

scaling, the focus of the model could be on more important Y-variables and use

experience to increase the weights of more informative X variables. However, in the

absence of knowledge about the relative importance of the variable, the standard

multivariate approach is to, firstly, scale each variable to unit variance by dividing

them by their standard deviation, and secondly, centre them by subtracting their

averages (a process also called auto-scaling). This corresponds to giving each

variable the same weight or the same prior importance in the analysis (Wold et al.

2001). The latter is also strongly recommended for ease of interpretation and for

numerical stability. The PLSR data arrangement procedure is displayed in Figure A2.

There are a number of assumptions that are made related to PLSR: the investigated

system or process is influenced by just a few underlying variables (latent variables).

Both X and Y variables are assumed to be realisations of these latent variables and

are not therefore considered independent. In spectroscopy, it is clear that the

spectrum of a sample is the sum of the spectra of constituents multiplied by their

concentrations on the sample. Furthermore, any data analysis is based on the

assumption of homogeneity. This means that the investigated system or process must

be in a similar state throughout all the investigation, and the mechanism of influence


16

of X and Y must be the same, which limits the diversity of the variables. X should

contain a quantitative description of the variation in chemical structure between the

investigated compounds. The objective is to understand the variation of Y.

There are some limitations related to PLSR models. Udelhoven et al. (2003) point

out that when the spectra is normalised, there is a clear reduction in the spectral

albedo variations due to grain size differences between the soil samples. Typically,

the small soil surface variations and micro shadows have a large impact on the

measurements which cannot be eliminated by simple spectra pre-treatment. Another

reason for different PLS results could be unequal distribution of nutrients and water

content (Udelhoven et al., 2003). It has been found that on no account the achieved

results can be compared to measurements of the soil surface taken by remote

operating system. If up-scaling is planned, factors such as the atmospheric

transmission path as well as larger spectrally mixed footprints are taken into account.

PLSR has been performed on the measured variables and wavelength range between

500 and 2500nm, using the 10 samples analysed in the laboratory. The software

used for the analyses is ParLeS version 2.1a (Viscarra-Rossel, 2005). The produced

prediction model, as stated by Scholte (2005), is a set of equations (or PLSR

factors), computed as linear combinations of the spectral amplitudes, by using a

regression coefficient for each wavelength position. Therefore it is clear that the

quality and accuracy of the prediction model strongly depends on the pre-processing

techniques, such as scaling or auto-scaling of the data, detection of outliers and also

the number of factors used in the model. In more detail the importance of the pre-

processing is discussed in Scholte (2005). The best training set has been achieved

when five factors are used and the spectra is normalised. Here the normalisation has

been applied in terms of removing the albedo effect, due to the fact that from the CIE

analysis, the albedo has shown little variation between the different samples. A

number of techniques for accurate determination of the optimal number of factors

have been discussed in the literature (Davies, 2001; Scholte, 2005). The smoothing

technique applied to the library derived under laboratory conditions is Savitzky-

Golay filter, second polynomial. This function performs a local polynomial

regression on a distribution to determine the smoothed value for each point (Savitzky

& Golay, 1964). The main advantage of this filter is the fact that the features of the

distribution (relative maxima, minima, width) are preserved. The smoothing

technique proven to be more effective for the libraries derived from the two ASTER

scenes is Median Filter. This is a non-linear digital filtering used for noise removal,

by examining a sample and assessing if it is representative or not.


17

The results from the PLSR analyses are a set of calculated values (summarised in the

results section). These include the root mean square error (RMSE), ratio of

prediction to deviation (RPD) and mean and relative mean absolute errors (MAE),

where only the first two are used for the assessment of the models performance.

Additionally, the R2, or also known as coefficient of determination, has been

estimated, showing the strength of statistical correlations between the measured in

the laboratory properties concentrations and the models predictions. The RPD

values together with the R2 have been used in terms of assessing the accuracy of the

predicted Y variables. According to Williams (2001), to indicate good prediction of

the model the RPD value should be higher than 2.5 while the R2 should be estimated

0.91. Viscarra Rossel (2005) provides a classification which defines the quality of

the predicted value with the PLSR software. This is stated under each of the tables

containing the results from these analyses. The procedure has been applied on the

laboratory derived spectral library, the resampled to ASTER spectral library, as well

as on the spectra measured from the ASTER scenes after processing, in terms of

identifying the effect of the reduction of spectral resolution. The produced PLS

regression coefficients were then plotted for each property of interest in terms of

identifying the most significant wavelength regions for the prediction of the soil

constituents. The results from these analyses have been combined with those from

the following image manipulations (see section 2.4.3 and Figure 2), in terms of

identifying and predicting accurately the soil properties of interest (see section

2.4.4).

2.4.3. Image analysis

Two ASTER scenes have been acquired, same season (winter) but different years

(2003, 2005), covering the same area. The raw 2A type images have been

georeferenced, and the Digital Elevation Models extracted from them. The softwares

used for the analysis are Arc GIS 9.1 and ENVI 4.2. A number of analytical

techniques have been used in terms of identifying soil types and properties, as well as

variation between and within soil classes. The purpose of using two ASTER scenes

is not to detect changes in soil composition or land cover changes, but rather to

minimise effects that were not identified by the pre-processing techniques. This is

done by comparing and combining the information from the two.

2.4.3.1. Atmospheric correction

Atmospheric correction was required in terms of converting the radiance values into

reflectance. The procedure originally chosen for this was ENVI FLAASH (Fast Line-

of-sight Atmospheric Analysis of Spectral Hypercubes). This is known as the first-


18

principles atmospheric correction modelling tool for retrieving spectral reflectance

from radiance images. It corrects wavelengths in the visible through near infrared

and short wave infrared range. In terms of achieving faster and more efficient

performance of the operations, originally the image was planned to be resampled to a

smaller area that covers only the study area and some surrounding regions. This

procedure was applied to both scenes; however, due to the extensive cloud cover, not

removed after the atmospheric correction, over the scene from year 2005, it did not

result in useful output and therefore all analyses were performed on full scene

coverage (wherever possible). Unfortunately, the results produced were not

satisfactory and therefore other techniques were examined.

Log Residuals as well as Internal Average Relative Reflectance (IARR) calibration

tools were then applied in terms of obtaining the optimal atmospheric correction

technique. The Log Residuals is used when there is a need of solar irradiance,

atmospheric transmittance, instrument gain, topographic effects and albedo effects

removal from radiance data. The result is a pseudo reflectance image, based on in-

scene statistics that could be used in absorption features identification. In terms of

defining the logarithmic residuals the input spectrum is divided by the spectral

geometric mean, and then divided by the spatial geometric mean. The spectral

geometric mean is the mean of all bands for each pixel and it is used to remove the

topographic effects while the spatial geometric mean is used because the

transmittance and other effects are considered multiplicative. The spatial geometric

mean is the mean of all pixels for each band and accounts for the solar irradiance,

atmospheric transmittance and instrument gain (ENVI 4.2, 2007). On the other hand,

the IARR is used to normalise images to a scene average spectrum. It is found to be

effective for reducing image data to relative reflectance in an area where no ground

measurements exist and little is known about the scene. It has proven to work best in

arid areas with little or no vegetation. The algorithm is designed to calculate an

average spectrum from the entire scene and use it as a reference spectrum, which is

then divided into the spectrum at each pixel of the image. The method found to be

the most useful and give the most accurate (similar to the resampled library) results

is the Log Residuals algorithm which was chosen for the analysis.

2.4.3.2. Histogram matching

In terms of equalising (or make it as close as possible) the brightness of both scenes,

histogram matching was performed. The function automatically matches the

histogram of one image to another, resulting in a histogram of the display, where the

function has started, matching the source histogram (ENVI Help, 2006).


19

2.4.3.3. Minimum Noise Fraction

Minimum Noise Fraction (MNF) has been applied to exclude the bands from the

ASTER scenes that contain noise. This is essentially a sequence of two principal

component transformations. It is assumed that the data for each pixel consists of

signal and noise, where the adjacent pixels contain the same signal but different

noise (Zhou et al. 2005). The resulting MNF bands represent linear combination of

the original spectral bands. The first MNF band contains the largest percentage of

data variance and the highest spatial coherence, the second contains the second

largest eigenvalue and coherence and so on until reaching the last band, which is

essentially noise-dominated and has the least variance. The MNF bands included in

this study are bands 1 to 4, which are most coherent and provide noise-filtered

datasets.

2.4.3.4. Spectral profiles per sample

The spectral signatures have been measured per sampling point, from the ASTER

scenes. These, then have been examined in terms of recognising main regions of

variation between the samples as well as common absorption features that coincide

with the peaks and sinks outlined by the PLSR analyses (as stated earlier).

2.4.3.5. Image classifications

A number of classifications have also been performed on both images. The purpose

of this is to define the distribution of the different soil types across the area of

interest. The results are then implemented into the following stages of the method

(see figure 2) in terms of identifying and predicting the soil properties. ISODATA

(Iterative Self-Organising Data Analysis Technique) unsupervised classification was

chosen in terms of identifying areas suitable for training sites, while Maximum

Likelihood supervised classifications have been done using ASTER bands 1-9 and

MNF bands 1-4. The results from the supervised classifications were then compared

in terms of assessing the accuracy.

ISODATA is an unsupervised classification scheme, developed with empirical

knowledge gained through experimentation, which requires relatively little

information. Because of the nature of this approach, many problems may emerge.

One of them, typical for all unsupervised image classification techniques, is the fact

that certain proportion of the generated clusters cannot be labelled as an

informational class and therefore the pixels should be excluded from the image or

reclassified (Klein, 2006). Another problem is that, unless the number of desired

classes is well-known, the choice is usually a guess that does not necessary lead to


20

the desired outcome. Nevertheless, the classifier was used so that a general idea

about the area is obtained. Applying unsupervised classification method before the

supervised allowed the identification of the most common and the most insignificant

classes. The result served as a base for the definition of the training sites. The most

homogeneous areas were compared against the topographic map. The ones identified

as bare soil have been checked with the available soil map from year 1973 (scale

1:500000) and have been used as the so-called ‘Regions of Interest’ in the maximum

likelihood classification.

Maximum Likelihood supervised classification algorithm has been chosen for this

stage of the analysis. It considers the cluster centre as well as its size, shape and

orientation, by calculating a statistical distance based on the mean variance and

covariance matrix of clusters. The main assumption behind this classification

technique is that the statistics of the clusters have a Gaussian distribution. This

method for image classification has been used by many and it is known to give the

most accurate results (Gomez et al., 2005). Another reason for the choice of this

classifier over others is the lack of soil variability within the study area. It allows

more accurate classification of the mixed pixels typical for areas located on the

borders between two different soil classes.

Two band selections have been used, chosen according to previous studies on soil

image classifications. The band combination includes all visible and infrared (one to

nine) ASTER bands, since not only it is identified by Apan et al (2002) that all

ASTER bands contain information about soil attributes, but also due to the fact that

different band combinations and ratios were tested and produced poorer results due

to the nature of the soil types in the area. The second method uses the first four

Minimum Noise Fraction (MNF) bands, found to contain 98% of the de-noised

information. To avoid confusion between the results from the two approaches in

ASTER image classification, the former will be referred to as Maximum Likelihood

(MaxLik) and the latter as MNF ASTER, although both procedures are Maximum

likelihood classifications.

The accuracy assessment of the classifications has been performed applying three

different techniques: assessment of the accuracy according to the regions of interest

(training sites for each class), assessment of accuracy according to the soil map from

year 1973 and examination of the rule images produced prior to the classifications.

Realistically, the first two procedures are relatively unrepresentative due to the fact

that the regions of interest (training and testing sites) are selected according to the

same soil map which is later used for the accuracy assessment. Furthermore, this soil


21

map is produced 30 years prior to the date of production of the older ASTER scene

(2003). Therefore, differences outlined from the classifications, and considered

inaccuracies, could be simply detected changes. In this sense, the third accuracy

assessment procedure was performed, implementing the use of Rule images. Each

pixel in a rule image represents the similarity between the corresponding pixel in the

image to a reference spectrum (Debba et al. 2005), which in this case are the regions

of interest selected to represent the soil types. The digital number (DN) value

represents the angular distance in radius between each pixel spectrum and the

reference. The user defines a threshold, according to a probability level, and if the

angular distance is smaller than the defined threshold, the pixel is assigned to the

category of the respective reference material (Debba et al. 2005).

Ultimately, to assess what is the probability a particular pixel to be assigned to the

correct soil type class, the classification methods were tested against different

probability levels at 75%, 85% as well as 95% degree of confidence. The limitation

here is also related to the selection of the training and testing sites, which ultimately

leads to the conclusion that the separability and detailed investigation prior to the

selection of the regions of interest are crucial. In ENVI 4.2, this procedure is done,

plotting the DN values on a histogram and calculating the threshold value for each

class. This is followed by calculating the basic statistical parameters such as mean,

median, mode, standard deviation as well as the percentage area covered by the

classes taking into account the probability level.

Due to the limited number of validation points, the rule images have been resized to

the region covered by the selected study area and only the performance of the soil

types known to be found in this area (from the soil map) have been tested. Figure 7

is produced in terms of comparing the results from the classification techniques as

well as the performance of each ASTER scene. It is important to note that the

percentages allocated to each soil type do not add up to 100%, which means that the

rest of the area should be covered by a different soil type, or the pixel simply could

not be allocated to any class due to spectral mixing.

Furthermore, the 9 validation points were tested against the results. For this purpose,

a validation matrix was created (see Table 5). Each point is tested against this matrix.

If the point is classified as one soil type on both images by both classification

techniques (receiving 4 validation units), the soil type would be correct for the

confidence level at which the point is tested. Respectively, if one of the classification

techniques performed differently for only one of the scenes, therefore the point will

be assigned with a validation unit of 3, and will fall into class ‘probably classified


22

correctly’. The procedure continues by assigning 2 units (‘possibly classified

correctly’) or 1 ‘not classified correctly or belongs to a different class’. The points

with assigned values of 2 and below are considered to fall on mixed pixels, being

defined by a spectral mixture which prevents the classifier to perform accurately.

The validation plot represents not only the difference between the two ASTER

scenes and the performance of the classifications, but also the number of classes, per

validation point, which are supposed to be found within the study area. The

assessment of this has been done so that if a point falls completely outside any of the

predicted classes, a validation unit of 4 is not assigned to it, but rather it receives a 0.

The accuracy assessments of the classifications play a crucial role when it comes to

soil types/properties identification. The results from these procedures have then been

combined with those from the previous analyses in terms of predicting the presence

of the properties of interest (see Figure 2). Accurate soil type identification allows

better determination of the expected soil properties and therefore contributes to the

explanation of any anomalies that may occur during the process.

2.4.4. Soil types identification per sample

The soil types per sampling unit have been extracted not only from the soil maps but

also from the classifications in terms of identifying the performance capabilities of

the techniques. To evaluate the use of ASTER imagery in identifying soil types and

respectively the soil properties discussed earlier (organic matter, moisture content,

electroconductivity, carbonates, calcium, potassium, magnesium, manganese, and

sodium), the classifications and the reference soil maps have been used. This process

involved outlining the study area onto the classification images and the soil maps

followed by comparison of the results for each of the sampling points.

A number of limitations with this technique should be mentioned before any

conclusion is drawn. These are: only 9 soil samples analysed in a laboratory (plus

one duplicate at location A30), the scale of the map used for validation is 1:500000,

while the resolution of ASTER imagery in the VINR is 15m, and 30m in the SWIR.

Here it is important to note that although two soil maps are available, the one with

finer scale was chosen. Furthermore, the accuracy of the soil map has not been

recorded and consequently, this may result in misinterpretation. Therefore, any

points that fall on, or are located near the border between soil types on the map, may

be misclassified. If there is such case, the laboratory results, the results from the

PLSR models as well as the characteristics of the soil types will be the primary factor

in terms of drawing the appropriate conclusion. Another drawback is the cloud


23

formation present on the scene from 2005, because of which the accuracy of the

classifications in the region where the clouds are, is poor. The same procedure has

been applied for the rest of the samples, which have not been analysed in the

laboratory, in terms of predicting to what extent the technique can be applied on

unknown locations. If there is mismatch between the classification results and the

soil map, instead of laboratory results, the PLSR results have been used, relating the

spectral response to the soil properties of interest.


25

3. Results

3.1. Laboratory analysis results

The results from the laboratory analysis are summarised in Table 1, while the

procedures followed are shown in Appendix 4. It is important to note that the

analysis of only 10 out of 32 samples has been completed. The remaining samples

are currently being analysed, and therefore not included in this study.

Table 1: a) Results from laboratory analysis. Electroconductivity (EC) µS/cm, Moisture

Content (MC) %, Organic Matter (OM) %, Carbonate, Calcium (Ca), Potassium (K),

Magnesium (Mg), Manganese (Mn), Sodium (Na)- mg Kg-1; b) Precision at 95% confidence

ID pH EC MC OM Carb Ca K Mg Mn Na

A28 7.9 167 0.89 1.16 6.03 25.3 9.9 4.3 0.08 4.7

A32 7.9 131 0.84 1.02 5.68 11.7 9.1 5.1 0.08 3.5

A30A 7.9 155 0.71 1.35 9.39 23.0 6.3 2.7 0.31 2.5

A30B 7.7 140 0.85 1.63 6.37 36.1 * 5.3 0.31 *

A25 8.0 135 1.19 2.51 5.78 17.6 6.5 3.9 0.00 3.5

A22 7.9 130 0.86 0.89 9.26 21.6 8.5 3.2 0.29 3.1

A16 8.1 125 1.17 2.38 6.58 20.0 7.6 3.6 0.00 5.4

A18 8.1 121 0.82 1.62 6.29 13.6 9.6 5.6 0.00 3.0

A23 8.0 150 0.59 0.89 9.72 19.0 7.9 3.0 0.22 3.8

A15 8.0 126 0.07 2.05 7.42 18.7 6.5 3.4 0.00 2.5

*Not detected due to bottle contamination

pH EC MC OM Carb Ca Mg Mn

Precision% 3.63 14.38 25.38 26.58 54.20 62.69 91.92 0

The precision of the measurements has been estimated using the duplicate sample

collected at location A30, following the formula: Precision (%)= 200*SD/mean (with

95% confidence). It can be seen that the worst precision has been found for

Magnesium (91.92%) and therefore the estimations for this property will be taken

into consideration only if necessary. Except Magnesium, unsatisfying results are

obtained for nearly all properties except pH and electroconductivity (accepted

precision maximum 15%). This does not necessary state that the results are


26

unreliable since a detection of external contamination at sample A30B have been

reported and therefore the results from the analysis of this sample should not be

considered as determining. Larger dataset would have overcome this problem.

3.2. Spectral and image analysis

3.2.1. Laboratory spectra and CIE diagram

Table 2: Albedo and soil colour analysis

x y Albedo %

A23 0.42 0.38 35.01

A22 0.43 0.38 29.48

A18 0.43 0.38 32.28

A16 0.43 0.38 30.05

A32 0.43 0.38 30.55

A15 0.42 0.38 29.57

A25 0.43 0.38 30.34

A28 0.42 0.38 31.67

A30A 0.43 0.38 31.6

All ten samples, analysed in the laboratory, have been found to be pale brown

according to the standard Munsell colour chart used in the field. Furthermore, as it

can be seen from Figure A7- Appendix 1.1, the variation between the samples is

minimal. All absorption features expected due to moisture seen in 1000nm, 1416nm,

1919nm and 2217nm are present. Slight absorption is also noted at 841nm, which is

a wavelength associated with presence of Iron. The spectral library has also been

converted into image format in terms of performing albedo and soil colour analysis.

The original idea behind this is to remove the soil albedo from the measurements so

that soil colour variation could be noted. When the results from these analyses,

summarised in table 2, are plotted on the same diagram (see Figure A6), it can be

seen that there is limited variation and indeed the identified colour is the same as the

one observed in the field. The albedo varies from 29.48% (at location A22) to

35.02% (at A23).


27

3.2.2. PLSR on laboratory spectral library

The results from the PLS performed on the spectral library derived under laboratory

conditions are summarised in Table A1- Appendix 1.2. The PLSR prediction model

could not provide reliable results for Magnesium (RPD of 1.71), Manganese (RPD

of 1.96) and Carbonates (1.97). The poor prediction for Mg and Mn could be

explained by the lack of spectral significance of these three elements/ properties, as

well as by the limited number of samples analysed in the laboratory. The low

prediction values for the Carbonates can be explained by the fact that these are

usually bound to other elements (Calcium in this case). The best results emerged for

Moisture Content (RPD of 5.05), Organic Matter (RPD of 2.6), Sodium (RPD of

3.86) and Potassium (RPD of 2.49). Here it is important to note that due to the

limited number of samples (nine plus one duplicate at location A30) some of the

correlations as well as quality of the models may not be as high as expected. Using

the calculated regression coefficients for each wavelength region, plots are produced

(see Figure 3) so that the spectrally active constituents can be identified. Looking at

these plots, the most significant to the prediction model wavelength regions can be

seen, according to the peaks and valleys. These are summarised in Table 3, outlining

the regions typical for each one of the examined properties, and compared to the

existing literature.

Table 3: Spectral wavelength regions important for the PLSR prediction

Wavelengths (nm) Property/Variable

Most Significant Other Literature

Electroconductivity 730, 2031 1001, 1439, 2297 7393, 1952

5

Moisture Content 1478, 1901 1005, 1100, 2200 14001, 1900

1

Organic Matter 1415, 1911 949, 1806, 2468 14002, 1900

2

Carbonate 2333 949,1910, 2210 23331,6

Calcium 1768, 2333 1582, 1768, 2030 23334,6

Manganese 1906 1094, 1408, 2201 19006, 2300

6

Magnesium 2331 1424, 1805, 1921 23312

Potassium 1005 1765, 1917, 2286 10034

Sodium 2022 1435, 1571, 2286 19525

References: 1Ben-Dor et al. 2003, 2Palacios-Orueta & Ustin 1998, 3Ben-Dor et al.2002, 4Sholte 2005, 5Farifteh et al. 2006, 6Udelhoven et al. 2003

The spectral library has been resampled to ASTER and has been input into the PLSR

model in terms of achieving results similar to those derived from the sensor. Table


28

A2-Appendix 1.2, contains the values used for the assessment of the prediction

models ran on the resampled library. It has been proven to be very good only for

electroconductivity, with highest RPD of 3.79. Good results are obtained for organic

matter, Calcium and Sodium, while in the RPD range between 1.5 and 2 fall

Moisture Content, Potassium, Magnesium and Manganese. The worst results have

been observed for Carbonates, which could be driven by the same factors that affect

this property in the previous PLS analysis. The regression coefficients from these

PLSR analyses were plotted against those from the first set and the results are

displayed on Figure 4. This has been done in terms of outlining peaks and sinks

commonly present on the graphs from the laboratory derived spectra and the

resampled library. The important wavelengths are: moisture content: 2264nm and

2333nm; Carbonates: 1655, and 2165 to 2208; electroconductivity: 805 and

2333nm; organic matter: 2264nm; Manganese: 805 and 2208nm; Calcium: 2208 and

2264nm; Magnesium: 2333nm; Potassium: range between 2165 and 2208nm;

Sodium: 2208 and 2333nm.

3.2.3. Image analysis

3.2.3.1. PLSR for image library

The spectral signatures have been derived per sample, from the ASTER scenes.

These have then been examined in terms of recognising main regions of variation

between the samples as well as common peaks and sinks that coincide with the peaks

and sinks outlined by the previous PLSR analyses (see Figure 5). The PLSR

prediction models have been performed separately for the two images. The results

are summarised in Tables 6 and 7 (Appendix 1.2) for images from 2003 and 2005

respectively. The regression coefficients have been plotted against each other for

identifying the common wavelengths explaining the properties of interest between

the two scenes (Figure 5), and then compared with already identified wavelength

regions from the previous PLS analyses. The model have been proven suitable (very

good) for electroconductivity (RPD of 3.03), while good results obtained for

moisture content, organic matter, and Sodium, result in RPD values greater than 2.

Lower accuracy results are those for Carbonates concentration, Calcium, Potassium,

Manganese and especially Magnesium (RPD of 1.40).

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Figure 3: PLSR coefficients for Moisture Content, Carbonates, Electroconductivity, Organic Matter, Manganese,

Calcium, Magnesium, Potassium, and Sodium derived from analyses on laboratory spectral library

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Figure 4: PLSR coefficients (resampled to ASTER spectral library vs Library derived under laboratory conditions) for Moisture

Content, Carbonates, Electroconductivity, Organic Matter, Manganese, Calcium, Magnesium, Potassium, and Sodium

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Figure 5: PLSR coefficients: ASTER 2003 vs ASTER 2005 for Moisture Content, Carbonates, Electroconductivity,

Organic Matter, Manganese, Calcium, Magnesium, Potassium, and Sodium


32

When PLSR was run on the library extracted from the ASTER scene (year 2005), the

best results were produced for Magnesium (RPD of 15.37), Calcium (RPD of 4.29),

electroconductivity (RPD of 3.18) and Carbonates concentration (RPD of 2.81). The

prediction models for Manganese and Sodium had RPD of 2.35 and 2.02

respectively, while unsuitable prediction has been done in terms of identifying

Potassium, Organic matter and Moisture Content (RPD<2.0). Figure 5 displays the

common peaks and valleys derived from the PLSR coefficients for the two scenes.

Common wavelengths are: carbonates –1656nm and 2262nm, electroconductivity-

2209nm, Calcium- 1656nm, valley at 2336nm, Magnesium at 2209nm, Potassium-

1656nm and Sodium- 1656nm and 2262nm. The rest of the properties do not

indicate clear common features and therefore the model with more accurate result

has been considered. For Moisture Content and Organic Matter, the model taken into

account is the one developed for spectra extracted from ASTER scene (year 2003),

where the valley at 2336nm have been most clearly defined for both properties. The

same wavelength has been associated with Manganese concentration, however here

the model from ASTER scene (year 2005) has been considered.

3.2.3.2. Classifications

Minimum noise fraction (MNF) is a modified version of the procedure Principle

Component Analysis. It is used to identify and isolate noise (and bands containing it)

in an image. It results in eigenvalues, where the larger the eigenvalue is, the higher

the variance of the data in the transformed band is. If the value is 1, this indicates

that the noise is left in the transformed band (Ramsey, 2006). Ideally, this procedure

should indicate potential end-members in the images. The plots on Figure A4-

Appendix 1.1 show the eigenvalues for each MNF transformed band, for each of the

two ASTER scenes. It can be seen that the MNF bands containing the most noise are

6, 7, 8 and 9 for both images. ISODATA was performed on both ASTER scenes,

selecting 5-10 initial classes, in terms of identifying main homogeneous areas on the

images. The scene dating from year 2003 resulted in 7 classes while after running

ISODATA on the one from 2005, the classes were 9. This could be explained with

the extensive presence of clouds and cloud shadows in the latter scene. An important

issue to note here is that the ISODATA results do not necessarily represent soil types

but most likely represent landcover. The results are displayed on Figure 6a.

The image dating from 2003 has been classified into 9 main soil classes and 1 water

class/no data, while the image from 2005 has two additional classes: clouds and

cloud shadows. The first set of Maximum Likelihood supervised classification

performed included the first nine ASTER bands, and the results are illustrated on


33

Figure 6b. In terms of improving the accuracy of the classification and at the same

time removing the noise from the scenes, Maximum Likelihood classification has

been applied on the first four Minimum Noise Fraction bands (MNF-ASTER). The

results are displayed in Figure 6c. The full reports of the accuracy assessments,

according to the regions of interest and the reference soil map, as well as the

confusion matrices are included in Appendix 5, while summary of the overall

accuracies of the classifications as well as a list of the most successfully classified

soil types are shown in Table 4.

Table 4: Image classifications overall accuracy, kappa coefficients and most

successfully classified soil types.

Image

classification

Overall

accuracy

Kappa

coefficient

Classes

Maximum

Likelihood-

ASTER 2003

88.05% 0.86 Halomorphic soils, Lithosols,

Water

Maximum

Likelihood-

ASTER 2005

71.34% 0.67 Halomorphic soils, Lithosols,

Fluvial soil, Cloud

MNF- ASTER

2003*

76.12% 0.72 Halomorphic soils, Lithosols

MNF- ASTER

2005*

64.96% 0.59 Halomorphic soils, Fluvial

soils, Clouds, Cloud shadows

*MNF ASTER stands for Maximum Likelihood classification applied on MNF bands 1-4

Another procedure for accuracy assessment has been applied according to the rule

images produced prior to the classifications, and the results for the study area have

been plotted (see Table 5). The results from the two ASTER scenes classified using

all ASTER bands and the first four MNF bands have been combined in terms of

estimating the reliability of the classifications with certain level of confidence. In

general, the overall accuracy of the classifications performed on the image from year

2003 is better than this on the 2005 scene. Explanation behind this is the cloud and

cloud shadow classes, which on its own are classified with good accuracy, however,

they cause problems in terms of the performance of other classes.


34

Figure 6:ASTER classifications: scene 2003 (left), 2005 (right)a)

ISODATA, b) Maximum Likelihood-all bands; c) MNF bands

a

b

c


35

Some interesting patterns emerge, when the plots are studied. According to the

previous accuracy assessment, the overall performance of the classification methods

applied on the ASTER scene from year 2005 have proven less successful than those

performed on the scene from 2003 mainly associated with the presence of cloud

cover. Therefore, it is expected the outcome to show decrease in area covered by a

certain soil type as the confidence level increases. Furthermore, it is also assumed

that the classifications performed on the scene from 2003 should result in higher

number if pixels classified correctly, than this estimated from the scene dating from

2005. This, however, is not always the case and therefore each class should be

discussed separately, taking into account the quality and performance of the two

ASTER images, in terms of soil type and respectively properties identification. The

results from the analysis of the rule images are displayed and summarised in Figure 7

(a and b), while the threshold matrix for each probability level and each sampling

point is included in Table 5.

Table 5: Validation matrix for sampling points against soil types from the two

ASTER scenes, 2 classification methods and different probability levels

3.3. Soil types and properties identification per sample

The results from these analyses, for the samples measured under laboratory

conditions are summarised in Table A5-Appendix 1.2, while the extracted from the

image classifications and soil map soil types and consequently properties of the

samples not tested in laboratory are shown in Table A6- Appendix 1.2 and discussed

later on in the report. The accuracy assessment of the procedure is done according to

the classification accuracy per class. Figure A8 displays the spectra derived from the

two ASTER scenes for the two groups of samples, while figures 8 and 9 visualise the

points of extraction as well as the classes assigned to them by the classification

techniques.


36

Figure 7: Soil types coverage defined by classification techniques at

confidence levels 75%, 85% and 95%


37

Figure 8: Soil types according to classifications for sampling points

Figure 9: Soil Types according to classifications for selected Test points


39

4. Discussion

4.1. Spectral library analysis

In this part of the discussion, attention has been paid on the soil colour and albedo

estimates. As it has been determined so far, the difference between the analysed

samples in respect to these two soil characteristics is minimal. However, some

correlations are identified, between these two and the rest of the measured properties.

Correlation analyses have been done so that the variation in albedo, derived from the

CIE diagram and the soil colour analysis, is explained. The results from this clearly

showed that the most significant factor in this case is the electroconductivity (R2

0.78). Even though the electroconductivity has not been found to be the main force

behind any differences in soil albedo, the observations can be explained by the high

salts content associated with the soil types, when compared with the soil maps

described earlier. Correlations of 0.42 and 0.47 have been estimated between the

albedo and organic matter and pH respectively. It has been noted by Post et al.

(2000), that this sort of relationship between soil colour (soil reflectance) and

organic matter is obtained when soils are collected from an extensive geographic

area or the soils have a wide range of soil properties. The small study area as well as

the limited number of sampling units, explain the weak correlation, however, it is

important to acknowledge the fact that the number of tested properties is relatively

small and there might be other, significant ones, not considered in the study.

Interestingly, moisture content, the factor identified in the literature (Post et al.,

2000) as one of the most important when it comes to albedo estimates, has not been

found to be correlated or significant (R2 0.27). This can be explained mainly by the

limited presence of moisture in the soil, due to the geographic location of the study

area as well as the season of sampling.

Overall, it can be seen that the soil colour and albedo can play a significant role if

there is wider dataset and greater variation between the estimated values. However,

what was established so far serves as a base for choice of normalisation technique in

the following PLSR analysis. The lack of significant difference between the albedo

estimates determined the removal of this characteristic from further analysis


40

4.2. Partial Least Squared Regression models

PLSR modelling has been applied extensively by many (Viscarra Rossel, 2006;

Scholte, 2005, Lee et al., 2003, Udelhoven et al., 2003), in terms of identifying the

significance of some bands/wavelengths in soil properties identification. Diffuse

reflectance has proven itself as a successful screening method to characterise soil

chemical properties (Ben-Dor & Banin, 1995). It has been made clear that the PLSR

prediction model identifies more significant wavelengths than what can be seen from

the soil spectral signatures alone. It is important to note, however, that many

chemical compounds show overlapping bands in the SWIR region. According to

Scholte (2005), the regions between 1115-1150 nm, 1345- 1417 and 1820-1932nm

should be left out of the discussion because they are associated with major water

vapour absorption areas. Furthermore, a great limitation is the fact that soil chemical

properties, as outlined by Udelhoven et al. (2003), are not independent from each

other. Variables, not studied and therefore not identified, play a major role in

outlining correlation, causing indirect effects on the results. Another limitation is the

bound compounds such as Calcium Carbonate (CaCO3). The PLSR model indirectly

estimates Calcium since in most cases the Carbonate group is stronger in terms of

estimating the values for the model, or in other words, the form of the compounds is

crucial. A constraint, which should not be excluded, is the limited number of

laboratory measurements integrated into the model.

Udelhoven et al. (2003) tested the feasibility of the analysis of certain chemical

properties using PLSR and diffuse reflectance spectrometry. They indicated that the

indirect correlations are those that prevent PLSR models from being applied to other

areas where the correlation structure among the soil constituents significantly differs.

In theory, a possible up-scaling requires the consideration of factors such as the

atmospheric transmission path and larger spectrally mixed footprints. Therefore, the

correlations of certain properties with the spectra, found in the present study, have

been compared with those estimated by Udelhoven et al.( 2003), Farifteh et al.

(2006), and Lee et al. (2003), in terms of testing the overall applicability of the

PLSR technique. Conclusions can be drawn only about the correlations regarding the

spectral library derived in the laboratory due to the fact that no PLSR analyses have

been done on the spectra from satellite imagery in the above three studies. A

correlation of 0.8 between the spectra and electroconductivity (EC) has been found

by Farifteh et al. (2006), which concurs with the R2 estimated for EC by the PLSR

model for the soil spectral library produced in the laboratory. Much better estimates

have been found when the library is resampled to ASTER (R2 of 0.92) and the


41

ASTER derived spectral library for the same locations (R2 of 0.88). This could be

explained by the fact that due to the poorer spectral resolution of the scene, the

spectral variations at the image level are much smaller (Farifteh et al. 2006).

Furthermore, within an area of one ASTER pixel, soil moisture, soil type and

reflectance of other scene components affect the spectral variation and therefore

result in an error in the prediction model, even though the RPD and RMSE values do

not indicate this. Lee et al. (2003), performed PLSR analyses on Ca, OM, Mg, K and

P in terms of finding more accurate and advance solution to farm land management.

Generally, however, their findings in terms of accuracy of the PLSR models are

limited. The strongest correlation found between spectra and soil properties is for

Calcium (0.88). The rest performed poorly, which the authors explain by the

complex interaction between soil colour and soil properties.

The correlation found between the spectra and moisture content, has been estimated

by all, and this coincides with the reported results from this study for the laboratory

spectral library analyses (R2 of 0.95). Much weaker correlation have been estimated

for the resampled library as well as for the library derived from ASTER (R2 of 0.63

and 0.73 respectively). The study by Udelhoven et al. (2003) looked at the

correlations between Calcium, Organic Carbon, Manganese and Potassium. The

Calcium correlation with the spectra established in their work (R2 of 0.94) has been

found to be the same as what was estimated in the current report; however, this value

is derived from the analyses of the library from the ASTER scenes. When it comes to

the correlation between the spectra from the laboratory analyses and the Calcium

concentration, the R2 estimate is 0.74. There could be many reasons behind this

including the small sample size, relatively different concentrations of Ca found

within the two studies, climate and atmospheric condition differences. Another

disagreement has been recognised in terms of Potassium even though strong

correlation was found only between the property and the spectra from the library

produced from the spectroscopy measurements obtained in the laboratory (R2 of

0.82). Although, Udelhoven et al. (2003) state that lower value than R2

of 0.5 has

been produced from the PLSR analysis for Potassium, which is the case of the

analysis on the resampled to ASTER library and the derived from ASTER scenes

library. Considering the Manganese concentrations, perfect match between the

derived R2 values in the current study from the laboratory spectral library and the

Udelhoven et al. (2003) team, has been found (R2 of 0.71). Lower correlations were

observed between this property and the spectra included in the resampled and

ASTER spectral libraries. Overall, the highest correlations (R2 higher than 0.9)

derived from the laboratory spectra as outlined in earlier chapters, are those of


42

Moisture Content and Sodium (found at 1100nm and 1760nm respectively), from the

resampled library is only the Electroconductivity (at 2333nm), and from ASTER

scenes spectra- Magnesium, Electroconductivity and Calcium (at 2209nm for the

first two and 1656nm for Ca). In terms of assessing if these correlations correspond

to the spectral regions typical for the properties, five sampling locations were used

(see Figure A9- Appendix 1.2). These locations were associated with the highest

concentrations estimated in the laboratory for Moisture Content, Electroconductivity,

Sodium, Calcium and Magnesium- A16, A18, A25, A28 and A30.

The specific wavelengths associated with the properties giving highest correlation

are illustrated on the Figure A9. The first issue that emerges is the missing

absorption feature at 1100nm associated with moisture content. It can be seen

slightly on the spectral response from sample A25, which is also the one with the

highest laboratory estimate. This weak absorption band, according to Baumgardner

et al. (1985) corresponds to the absorption bands observed in transmission spectra of

water of a few millimetres thickness. It is assigned in terms of water vibrations, with

additional combination of librations of the water molecule in the 1770 nm band,

which is also not present. The most reasonable explanation behind this as well as the

determination of the strong correlation by the PLSR model is not only the small

variation between all observations, but also the limited presence of moisture content

(highest of 1.19%) for all soil samples. The absorption feature expected to explain

the Sodium concentrations is present and distinctive for the samples with the highest

correlation estimated by the PLSR model. Obviously, if the spectral library is

considered on its own, without combining the results from all PLSR models, there

are other important wavebands associated with Moisture Content, and picked by the

PLSR model including 1450nm and 1950nm.

When the created spectral library is resampled, as mentioned earlier, only the

electroconductivity showed coefficient of determination higher than 0.9. It was

determined by the model that this property corresponds to 2333nm wavelength and

the absorption feature is present for all samples having high levels of it. On the other

hand, interesting observation is the matching wavelength typical for Magnesium and

Electroconductivity (2209nm) identified by the PLSR model performed on the

library extracted from the ASTER scenes. According to Farifteh et al., 2006, the

accurate prediction of electroconductivity could be used to predict Mg containing

salts. The lack of correlation observed from the laboratory analysis (R2 of 0.33),

between the two properties, suggests that there are other factors that affect the

spectra and the prediction model for ASTER imagery. Mainly, however, the


43

insufficient number of samples prevents from determining whether the error occurs

at correlation estimation level, or ASTER scene level. In the case of Calcium, the

corresponding waveband, determined by the PLSR model to be 1656nm, has been

explored in terms of identifying the reliability of the prediction. The deepest

absorption feature has been observed for sample A30, even though the location has

been identified with having the second highest Ca concentration. However, as

clarified earlier, the PLSR model takes into account the Carbonate group that acts as

the stronger property influencing the spectra at the same spectral region, despite the

fact that strong correlation was not identified by the model for this group.

The results from the PLSR analysis in this report illustrate how from a large number

of significant for the prediction model wavelengths per property, extracted from the

laboratory spectral library, the spectral regions of importance have been reduced to

maximum of two or in some cases none common wavelengths, when ASTER

spectral library is studied. The results also show that there are many factors that

should be taken into account when soil samples analysed in laboratory are linked to

satellite images. The most important of all is the change in spectral resolution

ultimately resulting in the removal of the weaker absorption features. Furthermore,

the wavelengths at 2208 and 2333nm have been observed to be the most important

for the PLS prediction modelling, including most of the properties (Moisture

Content, Electroconductivity, Organic Matter, Manganese, Calcium, Potassium and

Sodium). The wavebands identified as the most important for the determination of

the soil properties by the PLSR models can be explained by the occurrence of certain

absorption features or general observations that determine the soil reflectance

spectra. These include the decrease of soil reflectance between 400 and 2500nm as

the organic matter content increases, the two very strong absorptions at 1400 and

2200 nm associated with typical kaolinite reflectance with lack of appreciable bound

water, resulting in only a weak band at 1900nm (Baumgardner et al. 1985). The

presence of kaolinite is also associated with a small amount of ferrous ion resulting

in the slight absorption at 1011nm. Furthermore, the Aridisols, generally high in

soluble salts, determine the high average reflectance observed between 520 and

900nm, which is also confirmed by Baumgardner et al. (1985).

A more accurate results could be observed if the area of sampling is classified

according to main geological features and the PLS models are calibrated with these

results instead of with a global model. To do this, however, as shown with this study

and further proven by Udelhoven et al. (2003), not only a larger data set is required

in terms of producing a stable model, but also water accumulation rate as well as


44

other properties influencing the measured, should be taken into consideration.

Further work can also look into a way of sampling one pixel (30 by 30 meters in the

SWIR region) and exclude noise caused by properties that are not of interest.

Generally, the PLSR procedure was applied to the spectra in terms of identifying the

most important for the properties prediction and identification wavelength regions.

Furthermore, the results have been used as a link between the laboratory spectra and

the spectra derived from the two ASTER scenes (discussed in the next section). The

purpose if this is to estimate or predict, as accurately as possible, the level of

properties concentration.

4.3. ASTER scenes analysis

4.3.1. Spectral profiles per sample

According to Figure A6, the feature at 2167nm observed at sampling locations A30

and A32 in year 2003 and at all profiles derived from the scene from year 2005,

could be associated with Potassium concentrations. It is interesting to examine the

behaviour of the spectra at 1656nm observed for all location in year 2003 and deep

absorption at the same wavelength found for all locations in year 2005. This

wavelength is also associated with abundance of carbonate groups, which could be

explained by the limestone presence (CaCO3), Calcium and higher moisture content.

Furthermore, it can be seen that variation between locations is mainly found between

2209 and 2336nm for ASTER scene from 2003 and 2167 and 2262nm for ASTER

scene from 2005. It can also be observed that there are three wavelengths common

for both scenes: 660nm, 2209nm and 2262nm. Referring to the PLS analysis these

three features are associated with Calcium and Magnesium content characteristic for

the type of soil in the region. The variation within the above wavelength regions has

been compared with the actual concentrations of the properties, measured in the

laboratory.

The negative correlation observed between Calcium and Magnesium concentrations

(R2 of -0.67), can be also noted looking at the derived spectral profiles. Interestingly,

however, the fluvial soils with Ca/Mg encrusting do not have defined ratio, typical

for all sampling points. This varies from 8.5:1 at A30 associated with Magnesium

deficiency (Department of Natural Resources, 2007) to 2.3:1 at A32. According to

the soil map of the area, the soil samples at locations A32, A18 and A15 are the three

not associated with Ca/Mg encrusting, which could explain the small ratio between

the elements. Some unexpected findings have been related to 2203nm wavelength,

typical for Calcium rich soils. The pattern that should emerge in terms of absorption


45

values does not follow the pattern established from the laboratory results. In other

words, the soil samples showing the highest concentrations of Calcium do not appear

on the spectral response as having the strongest absorption on any of the ASTER

scenes. This could be explained by the undefined form of Calcium in the samples. In

terms of Magnesium concentrations, the spectra at 661nm behave as expected, with

some exceptions at location A23 and A30. It is also very important to note that the

characteristics of the soil types, especially when it comes to soil properties, apply to

the soil horizon, while the samples have been collected from the top 10cm in terms

of appropriate comparison with the results from the satellite imagery.

The spectra derived from the ASTER scenes was not only included in the PLSR

analyses, but also it was used as a determining for the soil composition factor. The

observed differences between the laboratory spectra and these measured on the

images are caused by the decrease in spectral resolution (as discussed earlier). The

strong absorption features identified by the PLSR analyses were studied and

implemented later in the soil properties identification per sampling point.

4.3.2. Classifications

Looking at the Table 4 it can be seen that the soil types most successfully classified

by all classification techniques are the Halomorphic soils and the Lithosols. As

mentioned earlier in the report both types of soils are typically very pale brownish

soils rich in soluble salts. The former are typical for regions close to saline water

bodies while the latter are one of the most commonly found soils in Tunisia and most

of the arid and semi-arid regions. Moreover, the region South-West of the study area

is known as the largest salt marsh in Sahara desert- Chott el Djerid. The problem

associated with this is the high reflectance occurring due to the nature of the salt

marsh. On ASTER imagery this region appears completely white causing problems

in identifying soils or any other characteristics of the surrounding areas. A main

limitation of all classifications is the fact that the regions of interest (training and

testing sites) have been selected according to a soil map dating from 1973. Knowing

the high rate of erosion and desertification in the area, as well as the lack of

sufficient field data for validation, the main challenge behind the image classification

description is to identify whether the difference that appears is due to change, image

noise or inaccurate classification. Furthermore, due to the cloud formation over some

parts of the image from year 2005, the information is limited for some of the soil

classes. Nevertheless, similarities in the classifications as well as patterns do emerge.

In terms of the Halomorphic soils, their location and extent corresponds to what is

expected as an output from the classifications. There is a problem occurring due to


46

the extensive cloud cover present on the scene from 2005, however this did not affect

the classification of the Halomorphic soils to a great extent. The Lithosols, Lithosolic

brown and the mixed Lithosols/Regosols classes are more problematic, although the

estimated accuracies for these classes are found to be over 87%. Here, the need of

validation field data is essential, since the rate with which the salt marsh close to the

study area increases and varies over the years, driven by many complex climatic

conditions. However, it could be expected that the change appearing between the two

classifications is not entirely due to noise and faults in the second scene, but also due

to actual change. Further contribution to this statement has the fact that the three

classes are similar in nature (all of them belonging to the group of Lithosols) and

therefore more difficult to differentiate. In terms of the class that includes bare rock,

the change could be explained by increase in the soil erosion rate. The fluvial soils,

on the other hand, characterised by their specific location (near rivers) and the

presence of relatively large stones and sediment deposits, are more difficult to

identified with simple classification technique such as Maximum Likelihood, even

when the noise is removed from the scenes. Moreover, the irregular precipitation and

the extensive evaporation result in the formation of gypsum. The fact that the

gypsum is in sandy form can be an indicator for easy wind transportation of particles.

However, the poor quality of the ASTER scene from 2005, especially in the regions

where gypsum is present, prevents from drawing any conclusions about change or

pattern. To identify the regions on the ASTER scenes, which have been misclassified

due to noise, accuracy assessment have been performed comparing the classification

on the nine ASTER bands with the ones done on the MNF bands 1- 4. The

misclassification mainly applies to the bare rock class, which phenomenon could be

explained by possible difference in rock types not identified due the unavailability of

geological maps for the region. The error, discussed earlier, explaining the

classification of the Lithosols, Brown Lithosols and Lithosols/Regosols classes is

illustrated in Figure A10-Appendix 1.2. The least error has been indicated for the

Halomorphic soil type, which was noted after the performance of accuracy

assessment according to the regions of interest earlier in the report.

From the analyses of the Rule images, it can be seen that there are two dominating

classes in the area and these are the ‘Brown Lithosols’ and ‘Fluvial soil’. Maximum

Likelihood classification including all ASTER bands and the same classification on

the first four Minimum Noise Fraction bands, performed on the ASTER scene from

year 2005 resulted in higher number of accurately classified pixels for these

dominant classes, despite the fact that the overall accuracy for this scene is lower.

The classifications performed on the scene from 2003, on the other hand, resulted in


47

more pixels classified correctly at 95% level of confidence for the ‘Bare rock’ and

‘Gypsiorthids’ classes (see Figure A1-Appendix 1.2). There are many reasons behind

this, one of them being the equalisation of the image brightness between the two

scenes (by histogram matching) prior to the classifications, which consequently

resulted in removing the finer detail caused by the albedo from the scene from year

2005, and therefore affecting the classes less present at the scene. Another

explanation is the atmospheric conditions differing for both scenes. Since the

FLAASH algorithm that takes into account the time of acquisition as well as

geographic location of the image did not show satisfactory results, factors such as

atmospheric transmittance, combined with the spectral mixing, could cause reduction

of the accuracy and performance of the scenes.

The expected phenomenon related to the reduction of the number of pixels classified

correctly with the increase in confidence level, can be seen for all classes on both

scenes and classifications. A main limitation is the fact that the four soil types

included in this discussion, tend to have similar spectral response due to the issues

emerging from particle size, soil crusting formation, smaller variation in soil colour

and properties, all discussed earlier. Thus, the results mainly show high spectral

mixing creating a problem in terms of accurately assigning the pixel to the

appropriate class. To overcome this, the results from the two scenes and the

classification of the different ASTER and MNF bands were compared and combined.

Three probability levels were selected (75%, 85% and 95% confidence), and the 9

validation points were tested against the pixel probability. The procedure resulted in

the creation of a validation matrix (Table 5), illustrating the assigning of classes to

the pixels containing the points at each probability level. The results are then

compared with the actual concentrations of the properties of interest measured in the

laboratory, and a conclusion is drawn for each point. A great drawback of the

procedure is the limited number of validation points, which prevents from

interpolating the accuracy results beyond the tested pixels.

As it can be expected, at the lowest confidence level of 75%, more pixels are

assigned to the same class on both scenes by the different classifications, than at

higher level. Referring to Table 5, it can be seen that three of the validation pixels

were assigned the correct soil class not only with 75 but also 85% confidence- A15,

A16 and A18: fluvial soil. With 95% confidence, however, A15 is assigned to a

different from these 4 classes, or it is not classified. The classification of the first

four MNF bands of the ASTER image from 2003 did not allocate the pixel

containing A16 to any of the four classes of interest, while as ‘Fluvial soil’ it was


48

recognised by the rest of the classification outputs. A18 was classified as ‘Fluvial

soil’ by the two ASTER scenes, but only when Maximum Likelihood algorithm was

applied on all ASTER bands. The results from this analysis in regards to the rest of

the validation samples were accepted. Evidently moving closer to the border

between two soil types, the number of mixed pixels increases. This is especially the

case with the locations A22, A25 and A32, where the mixing of soil properties

causes the problem associated with the classification of these pixels. Therefore, these

are allocated to class ‘possibly correctly classified’ on Table 5. At 95% confidence,

two locations have not been allocated to any of the classes of interest by any of the

classification techniques- A28 and A30. Reasonable explanations for this could be

many including the slightly higher percentage albedo characteristic for the two

locations (as well as for location A25), preventing the classification algorithm to

assign accurately (or at all) these pixels to a soil class typical for the other locations.

The removal of the albedo from the laboratory spectra prior to the PLSR modelling

seemed to have overcome the problem. Consequently removing it from the image

spectra could improve the accuracy of the technique. A combination of the results

from the classifications performed using different ASTER and MNF bands on both

scenes, provides a reasonable validation for area where not much is known, and not

many validation points are tested. To further improve the accuracy assessment from

Rule images, the correctly classified pixels per class from each of the performed

analyses, can be overlaid and only the ones that coincide on both scenes and both

classification techniques, for particular confidence level, should be taken into

account. In this way, the limited number of validation points can be overcome by

comparing spectral responses from correctly classified pixels with the rest of the

pixels.

Interesting discussion emerges from the issue related to band selection and expected

spectral response for the soil types and consequently the soil properties. As already

established, a number of properties have been associated with the same waveband

from the SWIR region of ASTER by the PLSR models interest (MC, EC, Mg, Na- at

2333nm, Carbonates, EC, Mn, K at 2208nm, etc.). Although this is the case, as

discussed earlier, properties such as EC, OM, salts and sand content affect the whole

spectrum by decreasing or increasing the overall spectral reflectance. Therefore, the

selection of specific ASTER bands prior to the classifications in terms of reducing

the noise from outside factors, is a challenge. A further work can emphasise on

certain bands or band ratios typical for only one of the properties and repeat the

procedure accordingly for the rest of the properties of interest. Unfortunately due to


49

limited time available for the completion of the current study, this approach could

not be implemented.

The image classifications were performed so that the soil types within the area of

interest are identified. Due to the limited dataset, sufficient information about the soil

properties was not acquired and therefore the determination of the soil types by the

classifications was used as a reference to the expected soil properties. The results

from the classification techniques are referred to in more detailed in the next section

of the discussion, where soil properties identification has been done per sampling

point taking into account the results from the geochemical laboratory analysis, PLSR

models as well as the derived spectra in the laboratory and from the ASTER scenes.

4.4. Soil types identification within the study area

Looking at Table A5-Appendix 1.2, it can be seen that for some points the

classification results coincide with what is displayed on the soil map, while for other

points this is not the case (illustrated on Figure 8). To identify the correct soil type

of the samples the properties organic matter (OM), electroconductivity (EC) and

Calcium (Ca), Magnesium (Mg), Sodium (Na) and Potassium (K) concentrations are

considered. In this respect, literature has been consulted in terms of identifying the

characteristics of the soil types falling within the study area according to the

classifications as well as the reference soil map-Ministry of Agriculture, 1973. The

same procedure is applied on the 25 samples which have not been analysed in the

laboratory. It is important to note that all the arguments and assumptions used in this

section apply only to the soil surface (0-10cm depth). The characteristics of the soil

profile related to different soil types, have not been studied and therefore may result

in misinterpretation.

With the CIE analysis and the field observations (see Appendix 2) it was shown that

the soil colour at all of these locations is the same as the 9 previously tested.

Considering the small study area and the small variation between the tested samples,

it can be assumed that the additional set have similar spectra and properties and

therefore the PLSR models could be used in terms of interpreting the spectra derived

from the images, if required.

The findings show that the large sediment particles, distinctive for fluvial soils, have

a strong influence on the spectral response. This, therefore, explains the fact that at

locations A16 and A28, the soil type (Fluvial soil) has been allocated most

accurately by all classifications. Moreover, locations A16, as well as A22, are a clear

illustration of the fact that clear borders between soil types do not exist, and the scale


50

of the reference soil map does influence the accuracy of the results. The grain size

can cause confusion not only in the case of fluvial soils identification. A good

example for this is location A25, where the classifications of the image from year

2005 recognise the soil type as fluvial soil (match with soil map), while the scenes

from 2003 indicate bare rock. These could not only be related to the limited quality

of the image from year 2005, but also to the stage of formation of the Calci-

Magnesium crust, typical for the region. To identify the main reason behind this,

laboratory data for the actual concentrations of Calcium and Magnesium, from both

years, could have played an important role. Furthermore, it is generally known that

the region is rich on limestone (CaCO3); however, more detailed investigation of the

geology of the region is useful in terms of identifying the properties of the bare

rocks.

When the laboratory analyses are taken into account, the values for Calcium and

Magnesium observed at this sampling point are almost identical to those observed at

A16 (fluvial soil with Ca/Mg encrusting). The spectral curve also follows the same

pattern with difference of 0.01 in reflectance (SWIR region). Looking at the spectra

derived under laboratory conditions (Figure A7), a feature can be observed at

2215nm, which does not appear for any of the other sampling locations. This

wavelength is associated with moisture content according to the PLSR analysis and

the finding corresponds to the laboratory measurements (A16 has the highest

percentage moisture content). Interestingly, referring to the spectra derived from the

ASTER scenes (see figure A8), location A16 is not characterised by a feature at

661nm, which, according to the PLSR analyses, should indicate small Magnesium

concentrations. This could indicate that due to the specific location of this point (on

the border between fluvial soils with Ca/Mg encrusting and Brown lithosols), mixing

of some properties may have occurred. The same problem arises at location A32,

however here the ASTER scene from year 2005 also indicates different class- bare

rock. An explanation behind this is the fact that due to erosion and relatively thin

layer of soil, larger sediment material may have been present. Not as clear is the fact

that the classification performed on the first four MNF bands launches different from

the reference map results. Both scenes identify the soil class as brown lithosols,

while the map indicates fluvial soils with Calcium-Magnesium encrusting. Here,

comparison between the laboratory results at location A16 (fluvial soil), A22 (brown

lithosols) and A32 has been done. A great difference is observed between the values

estimated for Calcium and Magnesium between A32 and the other two reference

locations, most probably due to the mixed nature of soils at location A22, as

explained earlier. Similarities have been observed between A22 and A32 in terms of


51

electroconductivity, moisture content, organic matter, potassium and sodium

determined in the laboratory. Moreover, the spectra derived from the ASTER scenes

are for the two locations, are characterised by much higher overall reflectance

values. The reason behind this could be higher sand content or presence of different

salts, not included in the present study. Therefore, it could be concluded that the

difference between the predicted by the MNF classifications and the reference soil

map occurs due to the scale of the map, inaccuracy of the map, problems with

georeferencing or undefined soil constituents. Referring to the probability levels

associated with the classifications accuracies, with 95% confidence only location

A16 falls into ‘probably’ classified correctly group, while A18 and A32 are the

‘possibly’ classified correctly locations. Therefore, a larger dataset is crucial in terms

of accurate prediction of the soil types and consequently soil properties associated

with them.

Nevertheless, the possibility of observing landcover change between the two years is

not excluded, considering the high erosion rates as well as the lack of validation data.

At location A23 bare rock is identified by ASTER scene from 2003, while image

from 2005 recognises the soil type as Brown Lithosols. The problem here, however,

is mainly the fact that the soil reference map shows fluvial soil as characteristic for

the location. To find out whether fluvial soils can be mistaken for bare rock on

ASTER imagery, or this misclassification is simply a result of a limitation of the

technique; the properties estimated in the laboratory, as well as the results from the

PLSR analyses and the derived spectra have been compared for the two locations

recognised as fluvial soils on the soil map, with location A16 correctly classified on

all scenes. Limited difference, mainly associated with brightness, has been observed

in the SWIR region of the spectra, for sample locations A16 and A25. In contrast,

location A23 is defined not only by the highest reflectance values, but also by much

more defined feature at 2262nm related to Calcium carbonate presence (PLSR

analyses section 3.2.3.1.). Furthermore, great similarities can be observed between

the estimated laboratory concentrations at location A25 and those at A16 in terms of

electroconductivity, moisture content, organic matter, carbonates, Calcium,

Magnesium and Manganese, while between A16 and A23 resemblance occurs only

for Calcium, Potassium and Magnesium (refer to table 1, section 3.1). Therefore it

could be concluded that the soil types at A16 and A25 are more likely to be the

same, but to defer from the one at location A23. It is possible, however, that the

presence of Calci-Magnesium encrusting has been very strong in year 2003, however

no validation data is available from this year. Moreover, the possibility of an error on

soil map level is also considered.


52

Looking at locations characterised by Brown Lithosols soil type and considering that

this soils are mainly found at steep slopes areas with risk of severe erosion (FAO,

2006), the effect from elevation difference should be considered. Referring to the

digital elevation model extracted from the ASTER scenes, interesting observations

can be seen for locations A22 and A23. The uncertainty that is noted in terms of

identifying the soil type from the ASTER scene (year 2003), by supervised

classification on ASTER bands 1-9, can be related to the shadow due to elevation.

Before the noise is removed with Minimum Noise Fraction (MNF), the soil is

recognised as Gypsiorthids, characterised by high gypsum content and appearing

dark on satellite imagery. PLSR and laboratory analyses of gypsum could overcome

the problem. If this is not considered, the soil type at location A22 is classified

correctly according to the soil map.

Before moving on to the testing sites not measured in the laboratory, selected in

terms of assessing performance and applicability of the technique, it is important to

refer to the probability levels associated with the correctly classified pixels. Taking

into account the small number of validation points, as well as the relatively small

amount of correctly classified pixels with 95% confidence, the properties prediction

done on the samples not tested in the laboratory is only hypothetical and cannot be

considered statistically accurate. Soil material collection and laboratory analysis

should be performed prior to the study in terms of drawing statistically significant

conclusions. Figure 9 illustrates the position of the test locations and the soil types

corresponding to each of them, while Table A6 includes the identified from the

classifications and the soil map soil types. It can be seen that for some of the samples

the extensive cloud cover does not allow identification of the soil type from the

ASTER scene (2005). These include B1, B2, B3, B5 and B6. The classification

methods however show similarities between the soil type shown on the soil map and

the one identified by the images, considering that the class “recent soil” represents

soil carried by the river flow included in the class ‘Fluvial soil’. Due to the fact

stated earlier, that the ratio between the Calcium and Magnesium is not stable, no

conclusion can be drawn. Correctly classified, according to the soil map, are also

locations B7, B8, B20, B24 and B38.

On the other hand, the areas classified as “bare rock” have one of the lowest

accuracies after the classifications are performed. As discussed earlier, this is either

due to the larger size deposits or thick Calcium/Magnesium crust, which can explain

the misclassification at locations B9, B21, B27, B37 and B39. It was made clear

from the PLSR models (section 4.2) that most of the variation between the properties


53

of interest was noted in the SWIR region and therefore this part of the spectrum was

studied from the extracted spectra in more detail (see Figure A8). The presence of

Carbonates (absorption feature at 2165nm) can be noted from the spectral response

curves at all of the testing locations, and therefore did not play a major role in the

identification process of the soil types. Looking at the spectra derived from ASTER

scene (year 2003), location B29 has been characterised by the highest reflectance

values, while B19 with the lowest. In contrast, the highest brightness values on

ASTER scene from 2005 are associated with location B21, while the lowest with

location B37. Even though the locations differ between the two scenes, the highest

reflectance is associated with points falling within Fluvial soils, while the lowest with

points characterised by Brown Lithosols, on both images. Furthermore, both B29 (on

ASTER 2003) and B21 (ASTER 2005), have different from the rest spectral

behaviour at 2209nm. This wavelength is related to higher salt content and

electroconductivity (according to the PLSR analyses). Another general observation

in regards to spectra is noted at locations B19 (ASTER 2003) and B21 and B26

(ASTER 2006). These three locations have the same behaviour as location A16,

lacking absorption at 661nm on the spectra derived from the ASTER scenes,

defining lower Magnesium content according to the PLSR analyses on the laboratory

spectra.

In addition locations B31 to B39 have fallen on the border between two soil types. In

this respect, the allocation of soil type should be determined not only by the

classification techniques and accuracy but also by the actual concentrations of the

properties. These two locations are characterised by defined peak at 2264nm, which

corresponds to Calcium concentration and very slight absorption feature at 2333nm,

which defines the Magnesium presence. Therefore it could be stated that there is a

presence of Calcium- Magnesium encrusting and the classifications performed on the

scene from 2003 (for location B31) and those on the ASTER scene from 2005 (for

location B39) have identified the class correctly (see table A6).

Similar situation occurs at locations B21, B26 and B27, where the absorption

features associated with Calcium and Magnesium are reported and the correct soil

class has been identified as Fluvial Soil. The bare rock identified by some of the

classifications (see table A6- Appendix 8.1.2) in these cases could be again due to

larger size stones or sediment deposits. Location B34, on the other hand, has been

classified by the Maximum Likelihood on the first nine ASTER bands as well as

MNF classification of the image from year 2003 as “bare rock”, by the 9 ASTER

band classification on the scene from year 2005 as Brown Lithosols, by MNF


54

classification of the image from 2005 as Fluvial soil while the map indicates border

between Fluvial and Brown Lithosolic soil. Referring to the spectral response,

however, it is noted that the signature matches perfectly the one at B39 on the image

from 2003, and differs greatly on the image from 2005. The reasons behind this can

be many. The most obvious one is the specific location of both sampling locations on

the border between Fluvial soil and Brown Lithosols. Furthermore, the difference in

the spectra is noted greatly at 807nm, associated with electroconductivity and

magnesium concentration, which consequently may indicate change in soil

composition over the two year period.

According to the reference soil map, sample B35 has been classified by all

classification techniques as “Bare Rock” instead of fluvial soil (as indicated on the

soil amp). The spectral curve indicates deep absorption at 2264nm related to high

Calcium concentration and considering the relatively high Carbonate concentration

defined earlier for all locations from the spectra, it could be assumed that the pixel

containing the location could be comprised of bare rock (probably limestone) or

thick Calcium crusting. To improve the prediction at this, as well as all the other

locations, more detailed geological investigation should be carried out.


55

5. Conclusions

Overall, ten soil properties were identified as important in relation to the main soil

types typical for the location of the study area- in South-East Tunisia. These include

electroconductivity (EC), Sodium (Na), Calcium (Ca), Carbonates, and Magnesium

(Mg), distinctive for the dominant soil group- Aridisols. In terms of the Entisols, Iron

(Fe), Potassium (K) and organic matter (OM) were studied. In addition, the moisture

content and pH of the soil were selected due to their strong influence on the soil

reflectance in general. Applying the standard techniques for analysis in the

laboratory, the concentrations of most of these properties were estimated as trace,

and for Fe these were even lower than the detection limit of the analytical apparatus,

for nine (plus one duplicate) samples.

Although a limited amount of material was available for laboratory analysis, the

concentration estimation, together with spectral information obtained under

laboratory conditions, was enough for the development of Partial Least Squared

Regression (PLSR) prediction models. The albedo was estimated (by CIE

chromaticity diagram) and since small variation was observed, it has been removed

from the PLS analysis. The ratio of prediction to deviation (RPD), in combination

with the coefficient of determination (R2), was used for the assessment of the

prediction model. These have indicated satisfying results for MC, OM, K and Na. A

number of significant wavelengths for the prediction model were also identified by

plotting the regression coefficients produced by the PLSR and examining the highest

peaks and the deepest sinks on the plot, most of them related to the shortwave

infrared region (2000-2500nm).

Two ASTER scenes (year 2003 and 2005) were pre-processed applying Log

Residual Atmospheric correction, Histogram matching and Minimum Noise

Fraction. ISODATA unsupervised classification was performed in terms of

identifying homogeneous areas and selecting appropriate regions of interest in

combination with soil maps from year 1975 (1:500,000). Maximum Likelihood

algorithm was chosen for the supervised classification. Generally, the classification

techniques applied on the ASTER scene from year 2003 showed better overall

accuracies than those applied on the scene from 2005, applying three different

accuracy assessment techniques: according to regions of interest, according to the

reference soil map and exploring the Rule images created in addition to the


56

classifications. It has been established that the selection and separability of the

regions of interest are crucial for achieving accurate results.

To overcome the gap between the laboratory measurements and the information

derived from the ASTER scenes, the spectral library obtained under laboratory

conditions was resampled to ASTER, so that the spectral resolution is reduced. This

was input into the PLSR model. The libraries derived from the scenes were also

tested against the measured properties. The result indicated stronger relationships

between the properties and the spectra as well as reduction in the number of

important wavelengths associated with the properties of interest.

PLSR coefficients were plotted against each other resulting in good predictions and

strong relationships established between spectra and electroconductivity, Calcium

and Magnesium (R2>0.9 and RPD>3.18). The important wavelengths for most of the

properties have been recognised in the shortwave infrared region of the spectrum,

and the absorption features corresponding to these wavelengths have been studied, in

terms of explaining the bands that overlap for different properties.

The link between the classification techniques and the PLSR analyses has been

established by determining the soil types, and the related soil properties assigned to

the sampling locations. The emerging problems are mainly related to the old soil map

used as reference; the scale of 1:500,000 compared to the 30m spatial resolution of

ASTER (in the SWIR region); the similarity between the soil types and general lack

of validation and analytical data. These prevented the study from drawing significant

conclusions. With 95% confidence, only small amount of pixels that belong to the

main soil types present in the study area can be considered as correctly classified,

resulting in one out of ten samples ‘probably’ assigned accurately. With 85%

confidence, the number of samples classified correctly is three. The applicability

and performance of the method have been tested with additional 25 sampling points.

The use of ASTER imagery in soil properties variation detection has indeed a great

potential related to the many applications such study can have in agriculture

management, soil erosion monitoring, soil salinisation, soil pollution detection, etc..

Many drawbacks, mainly associated with the quality of the available data, limit the

scope of this study, however the findings suggest that more detailed study

implementing this analytical methodology could greatly widen the prospective of

using readily available multispectral remotely sensed data.


57

6. Recommendations

• Further study should have a larger dataset available for analysis and

validation

• More recent soil maps as well as geological maps should be made available

• A larger and more heterogeneous study area or a combination of a number

of homogeneous but different in terms of soil types and properties soil

clusters should be selected, so that the class separability computation could

launch useful results.

• The image classifications should be performed according to soil properties

rather than soil types.

• Future study can also emphasise on the potential of PLSR coefficients in

terms of gaining information about actual concentrations of certain

elements

• More accurate results could be obtained if terrain parameters such as digital

elevation model, slope and aspect are included in the analysis of the

ASTER scenes.

• The potential of different band combinations associated with the properties

of interest could be studied in terms of improving the accuracy of the

prediction.

• The possibilities of better accuracy assessment related to Rule images

analyses are many. Further study could emphasise on analyses related to

overlapping of correctly classified pixels derived from a number of different

classifications.

• Field spectrometer measurements, areal photographs and hyperspectral

imagery could be applied in terms of minimising the gap between the fine

spectral resolution of the laboratory measurements and the coarse resolution

of the ASTER images.


59

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11/2006]

• Viscarra Rossel, R.A., Walvoort, D.J, McBratney, A.B, Janik, L.J. &

Skiemstad, J.O. 2006, Visible, Near infrared or combined diffuse reflectance

spectroscopy for simultaneous assessment of various soil properties, Geoderma

131:59-75

• Williams, P. 2001, Implementation of Near Infrared technology, In: Williams,

P. & Norris, K., 2001, Near Infrared technology in the agricultural and food

industries, American Association of Cereal Chemists, Minnesota

• Wold, S., Sjostrom, M. & Eriksson, L., 2001, PLS-regression: a basic tool of

chemometrics, Chemometrics and Intelligent Laboratory Systems, 58: 109-130

• Zhou, L., Dickenson, R. & Tian, J., 2005, Derivation of a soil albedo dataset

from MODIS using principle component analysis: Northern Africa and The

Arabian Peninsula, Geophysical research letters, 32, L21407


63

8. Appendices

8.1. Appendix 1: Selected figures and tables

8.1.1. Figures

Figure A1:a) Soil map 1:500000- ASTER coverage; b) Soil map 1:1000000- ASTER

coverage; c) Soil map 1:500000- Study area; d) Soil map 1:1000000- Study area

Sources of maps: Ministry of Agriculture, 1973; ACSAD, 1986


64

Figure A2: Flowchart of PLSR data arrangement procedure (Wold et al., 2001).


65

Figure A3: Distribution of sampling points

Figure A4: Eigen values for MNF transformed bands: a) ASTER image 2003; b)

ASTER image 2005


66

Figure A6: CIE diagram and soil samples colour distribution showing no difference

in soil colour

Figure A5: a) CIE tristimulus

curves (Van der Werff, 2006);

b) CIE chromaticity diagram


67

Figure A8: Spectral Profile extraction from ASTER at each sampling location:

a)Spectral library for sampling points; b) Spectral library for test point

Figure A7: a) spectra of samples A22

and A23-showing highest brightness

variation; b) Duplicate samples at

location A30-no variation; c) spectral

library of all samples displaying limited

variation between the samples


69

Figure A9: Analysis of the three spectral libraries (continuum removed) for selected

sampling points.

Figure A10: Misclassified pixels associated with Lithosols [green], Brown Lithosols

[blue] and Lithosols/Regosols [red]; a) ASTER 2003; b) ASTER 2005


70

8.1.2. Tables

Table A1: PLSR prediction model for Electroconductivity (EC), Moisture Content

(MC), Organic Matter (OM), Carbonate, Calcium (Ca), Potassium (K), Magnesium

(Mg), Manganese (Mn), Sodium (Na), using ASD FIELDSPEC spectrometer

measurements under laboratory conditions. SD (standard deviation) RMSE (root

mean square error), RPD (relative performance determinant).



(Mg), Manganese (Mn), Sodium (Na), using ASD FIELDSPEC spectrometer

measurements under laboratory conditions. Resampled to ASTER library. SD

(standard deviation) RMSE (root mean square error), RPD (relative performance

determinant)

Element Factors Variance SD R² RMSE RPD*

EC 5 193.35 13.91 0.80 6.64 2.38

MC 5 0.1 0.32 0.95 0.06 5.05

OM 5 0.4 0.63 0.83 0.27 2.60

Carbonate 5 1.6 1.27 0.71 0.79 1.97

Ca 5 13.74 3.71 0.74 2.05 2.09

K 5 1.62 1.27 0.82 0.55 2.49

Mg 5 0.59 0.77 0.62 0.57 1.71

Mn 5 0.01 0.11 0.71 0.07 1.96

Na 5 0.86 0.93 0.93 0.25 3.86

Element Factors Variance SD R2

RMSE RPD*

EC 5 226.11 15.04 0.92 4.17 3.79

MC 5 0.07 0.27 0.63 0.19 1.74

OM 5 0.35 0.59 0.73 0.34 2.04

Carbonate 5 0.87 0.93 0.41 1.13 1.37

Ca 5 14.26 3.78 0.77 1.94 2.22

K 5 1.17 1.08 0.49 0.93 1.48

Mg 5 0.23 0.48 0.16 0.84 1.51

Mn 5 0.01 0.1 0.56 0.08 1.6

Na 5 0.76 0.87 0.75 0.46 2.12


71



(Mg), Manganese (Mn), Sodium (Na): Spectral library derived from ASTER image

2003. SD (standard deviation) RMSE (root mean square error), RPD (relative

performance determinant)

*RPD classification for indicating the model prediction abilities (<1.5v bad; 1.5-2 bad; 2-2.5 good;

>2.5v good)



(Mg), Manganese (Mn), Sodium (Na): Spectral library derived from ASTER image

2005. SD (standard deviation) RMSE (root mean square error), RPD (relative

performance determinant)


RMSE RPD*

EC 5 238.75 15.15 0.88 5.22 3.03

MC 5 0.08 0.29 0.73 0.16 2.05

OM 5 0.38 0.61 0.80 0.29 2.35

Carbonate 5 1.63 1.28 0.66 0.85 1.82

Ca 5 13.79 3.71 0.68 2.30 1.87

K 5 1.00 1.00 0.59 0.84 1.65

Mg 5 0.37 0.61 0.43 0.69 1.40

Mn 5 0.01 0.11 0.69 0.07 1.90

Na 5 0.75 0.87 0.74 0.47 2.07


RMSE RPD*

EC 5 203.82 14.28 0.89 4.97 3.18

MC 5 0.05 0.22 0.43 0.24 1.40

OM 5 0.24 0.49 0.46 0.48 1.44

Carbonate 5 1.95 1.40 0.86 0.55 2.81

Ca 5 16.64 4.08 0.94 1.00 4.29

K 5 0.99 0.99 0.66 0.77 1.79

Mg 5 0.94 0.07 0.99 0.06 15.37

Mn 5 0.01 0.11 0.80 0.06 2.35

Na 5 0.68 0.82 0.73 0.48 2.02


73

Table A5: Extraction of soil types for each classification per sample point

Point MAXLIK

2003

MAXLIK 2005 MNF ASTER 2003 MNF ASTER 2005 Map scale 1:500000

A15 Fluvial soil Fluvial soil Fluvial soil Brown Lithosols Brown Lithosols

A16 Fluvial soil Fluvial soil Fluvial soil Fluvial soil Brown Lithosols

border with Fluvial

A18 Fluvial soil Fluvial soil Fluvial soil Fluvial soil Brown Lithosols

A22 Gypsiorthids Brown

Lithosols

Lithosols Brown Lithosols Fluvial soil/border

with Brown Lithosols

A23 Bare Rock Brown

Lithosols

Bare Rock Brown Lithosols Fluvial soil/Calci-

Magnesium

encrusting

A25 Bare Rock Fluvial soil Bare Rock Fluvial soil Fluvial soil/Calci-

Magnesium

encrusting

A28 Fluvial soil Fluvial soil Fluvial soil Brown Lithosols Fluvial soil/Calci-

Magnesium

encrusting

A30 Bare Rock Brown

Lithosols

Brown Lithosols Brown Lithosols Fluvial soil/Calci-

Magnesium

encrusting

A32 Gypsiorthids Bare Rock Brown Lithosols Brown Lithosols Fluvial soil/Calci-

Magnesium

encrusting


74

Table A6: Extraction of soil types for each classification per test sample point

Point

MAXLIK

2003

MAXLIK

2005 MNF 2003 MNF 2005 Soil Map

B1 Recent soil Cloud Recent soil Cloud Fluvial soil/Ca-Mg

B2 Fluvial Soil Cloud Fluvial Soil Cloud Fluvial soil/Ca-Mg

B3 Recent soil Cloud Recent soil Cloud Fluvial soil/Ca-Mg

B4 Fluvial Soil Fluvial Soil Fluvial Soil Fluvial Soil Fluvial soil/Ca-Mg





B9 Fluvial Soil Fluvial Soil Bare Rock Fluvial Soil Fluvial soil/Ca-Mg

B11 Fluvial Soil

Brown

Lithosols Recent soil Brown Lithosols Fluvial soil/Ca-Mg

B17 Bare Rock Bare Rock Bare Rock Bare Rock Brown Lithosols

B19 Fluvial Soil

Brown

Lithosols Fluvial Soil Brown Lithosols Brown Lithosols


B21 Recent soil Bare Rock Recent soil Bare Rock Fluvial soil/Ca-Mg


B26 Bare Rock Fluvial Soil Gypsiorthids Fluvial Soil Fluvial soil/Ca-Mg

B27 Bare Rock Fluvial Soil Bare Rock Fluvial Soil Fluvial soil/Ca-Mg


75

B29 Fluvial Soil Bare Rock Recent soil Bare Rock Fluvial soil/Ca

B31 Fluvial Soil

Brown

Lithosols Fluvial Soil Brown Lithosols

Fluvial soil/Ca

B33 Bare Rock

Brown

Lithosols Bare Rock Brown Lithosols

Fluvial border


B34 Bare Rock

Brown

Lithosols Bare Rock Fluvial Soil

Fluvial border


B35 Bare Rock Bare Rock Bare Rock Bare Rock

Fluvial border


B36 Fluvial Soil

Brown

Lithosols Fluvial Soil Brown Lithosols

Fluvial border


B37 Bare Rock Fluvial Soil Recent soil Bare Rock

Fluvial border


B38 Fluvial Soil Fluvial Soil Fluvial Soil Fluvial Soil

Fluvial border


B39 Bare Rock Fluvial Soil Bare Rock Fluvial Soil

Fluvial border



76

8.2. Appendix 2: Field work report

Sample

ID Date X Y Grain size pH

Soil

colour

Visible

contamination/Other

comments

Projectio

n

GPS

accuracy

A15 28/09/2006 600891 3724263

Fine:

<2mm, dry

lumps

NOT IN

FIELD

VERY

PALE

BROWN

Land prepared for

agriculture, some dry

small grass, dry lumps WGS 84 6m

A16 28/09/2006 600550 3723646

Fine:

<2mm, dry

lumps

NOT IN

FIELD

VERY

PALE

BROWN

Land prepared for

agriculture, some

stones WGS 84 7m

A18 28/09/2006 601003 3723984

Fine:

<2mm, dry

lumps

NOT IN

FIELD

VERY

PALE

BROWN

Land prepared for

agriculture, some

stones, dry grass WGS 84 6m

A22 28/09/2006 600483 3723119

Fine:

<2mm, dry

lumps

NOT IN

FIELD

VERY

PALE

BROWN

Land prepared for

agriculture, some

stones, small bushes,

stones WGS 84 6m

A23 28/09/2006 599203 3722903

Fine:

<2mm, dry

lumps

NOT IN

FIELD

VERY

PALE

BROWN

Land prepared for

agriculture (olive

trees), lumps of dry

soil WGS 84 8m

A25 28/09/2006 599386 3722868

Fine:

<2mm, dry

lumps

NOT IN

FIELD

VERY

PALE

BROWN

Dry small bushes,

lumps of dry soil and

stones WGS 84 7m


77

A28 28/09/2006 600121 3722139

Fine:

<2mm, dry

lumps

NOT IN

FIELD

VERY

PALE

BROWN

Land prepared for

agriculture, lumps of

dry soil, trees close by WGS 84 6m

A30 27/09/2006 598675 3721309

Fine:

<2mm, dry

lumps

NOT IN

FIELD

VERY

PALE

BROWN

Land prepared for

agriculture, some

palm trees near by,

crust WGS 84 7m

A32 27/09/2006 600159 3721074

Fine:

<2mm, dry

lumps

NOT IN

FIELD

VERY

PALE

BROWN

Some dry vegetation,

stones and crust WGS 84 6m

B1 26/09/2006 601576 3728960

Fine:

<2mm

NOT IN

FIELD

VERY

PALE

BROWN

Small bushes, dry

crust, no stones WGS 84 6m

B2 26/09/2006 601558 3728815

Fine:

<2mm

NOT IN

FIELD

VERY

PALE

BROWN

Small bushes, dry

crust, stones are

present WGS 84 6m

B3 26/09/2006 601916 3728174

Fine:

<2mm, dry

lumps

NOT IN

FIELD

VERY

PALE

BROWN

Land prepared for

agriculture, some

stones and dry lumps

of soil WGS 84 6m


78

B4 26/09/2006 602259 3727279

Fine:

<2mm,

stones

NOT IN

FIELD

VERY

PALE

BROWN

Big stones, dry

bushes, crust WGS 84 10m

B5 26/09/2006 601346 3726698

Fine:

<2mm, dry

lumps

NOT IN

FIELD

VERY

PALE

BROWN

Some dry lichen, crust

and big lumps of dry

soil WGS 84 6m

B6 26/09/2006 600969 3726616

Fine:

<2mm

NOT IN

FIELD

VERY

PALE

BROWN

Some dry vegetation,

stones and crust WGS 84 6m

B7 26/09/2006 601473 3726399

Fine:

<2mm, dry

lumps

NOT IN

FIELD

VERY

PALE

BROWN

Crust and a lot of dry

vegetation and roots WGS 84 10m

B8 26/09/2006 601574 3726443

Fine:

<2mm, dry

lumps

NOT IN

FIELD

VERY

PALE

BROWN

Crust and dry

vegetation WGS 84 10m

B9 26/09/2006 600998 3726168

Fine:

<2mm, dry

lumps

NOT IN

FIELD

VERY

PALE

BROWN

Land prepared for

agriculture, some

crust, small trees and

some stones WGS 84 6m

B11 26/09/2006 600807 3725665

Fine:

<2mm, dry

lumps

NOT IN

FIELD

VERY

PALE

BROWN Crust, no vegetation WGS 84 6m


79

B17 28/09/2006 600622 3723922

Fine:

<2mm, dry

lumps

NOT IN

FIELD

VERY

PALE

BROWN

Land prepared for

agriculture, some

stones, some small

bushes WGS 84 6m

B19 28/09/2006 600684 3723274

Fine:

<2mm, dry

lumps

NOT IN

FIELD

VERY

PALE

BROWN

Land prepared for

agriculture, some

grass and small

bushes WGS 84 7m

B20 28/09/2006 600316 3723169

Fine:

<2mm, dry

lumps

NOT IN

FIELD

VERY

PALE

BROWN

Some grass and small

bushes, and trees WGS 84 6m

B24 28/09/2006 599372 3722846

Fine:

<2mm, dry

lumps

NOT IN

FIELD

VERY

PALE

BROWN

Dry small bushes,

lumps of dry soil and

stones WGS 84 8m

B26 28/09/2006 599676 3722231

Fine:

<2mm, dry

lumps

NOT IN

FIELD

VERY

PALE

BROWN

Land prepared for

agriculture, dry grass

and trees near by WGS 84 8m

B27 28/09/2006 598970 3722035

Fine:

<2mm, dry

lumps

NOT IN

FIELD

VERY

PALE

BROWN

Land prepared for

agriculture, some

trees and buildings WGS 84 7m


80

B29 28/09/2006 598250 3721413

Fine:

<2mm, dry

lumps

NOT IN

FIELD

VERY

PALE

BROWN

Land prepared for

agriculture, some

small bushes, crust,

lumps WGS 84 6m

B31 27/09/2006 599045 3720967

Fine:

<2mm, dry

lumps

NOT IN

FIELD

VERY

PALE

BROWN

Lumps and crust,

some green and dry

vegetation, buildings

near by WGS 84 6m

B33 27/09/2006 598385 3720822

Fine:

<2mm, dry

lumps

NOT IN

FIELD

VERY

PALE

BROWN

Land prepared for

agriculture, lumps of

dry soil, stones WGS 84 7m

B34 27/09/2006 598775 3720577

Fine:

<2mm, dry

lumps

NOT IN

FIELD

VERY

PALE

BROWN

Some dry and green

vegetation, lumps of

dry soil, land prepared

for agriculture WGS 84 10m

B35 27/09/2006 599343 3720737

Fine:

<2mm, dry

lumps

NOT IN

FIELD

VERY

PALE

BROWN

Land prepared for

agriculture, crust and

lumps of dry soil,

some trees around WGS 84 7m

B36 27/09/2006 599814 3720654

Fine:

<2mm, dry

lumps

NOT IN

FIELD

VERY

PALE

BROWN

Some dry bushes and

stones, some trees and

buildings near by WGS 84 7m


81

B37 27/09/2006 600087 3720676

Fine:

<2mm, dry

lumps

NOT IN

FIELD

VERY

PALE

BROWN

Some dry bushes,

trees and buildings

near by, dry lichen WGS 84 6m

B38 27/09/2006 599374 3720059

Fine:

<2mm, dry

lumps

NOT IN

FIELD

VERY

PALE

BROWN

Some dry bushes,

trees and buildings

near by, dry lichen WGS 84 7m

B39 27/09/2006 598003 3720844

Fine:

<2mm, dry

lumps

NOT IN

FIELD

VERY

PALE

BROWN Dry vegetation WGS 84 6m


83

8.3. Appendix 3: Samples photographs


84

8.4. Appendix 4: Procedures for soil analysis

Reference: Van Reeuwijk, L.P. (2002)

pH

1. Weigh 20g fine soil into a 100ml polythene wide-mouth type bottle. Include

a blank

2. Add 50ml water and cap the bottle

3. Shake for 2 hours

4. Before opening the bottle for measurement, shake by hand once more

5. Immerse pH electrode in the upper part of suspension

6. Read pH when reading has stabilised

Electroconductivity

1. Add about 30ml standard 0.01M KCl solution to a 50ml beaker and

measure temperature

2. Rinse and fill pipette cell with the standard KCl solution or insert dip cell in

this solution

3. Set temperature compensation deal at measured temperature and adjust

reading of the meter

4. Measure the temperature of the extract and set temperature compensation

dial at this temperature

5. Fill pipette cell with extract or insert dip cell into extract and read

condictivity

Moisture Content

1. Transfer approximately 5g fine material to a tared moisture tin and weigh

2. Dry overnight at 105 degrees

3. Remove tin from the oven, close with a lid, cool in desiccators and weigh

4. Calculate moisture content (water loss)


85

Soil Organic Carbon/Organic Matter

1. 1g of soil material is places into a 500ml flask

2. Add 10ml dichloromate solution

3. Add 20ml sulphuric acid, swirl the flask and allow to stand on a pad for 30

minutes

4. Add 200 ml water and 10ml phosphoric acid

5. Add 1ml indicator solution and titrate with ferrous sulphate solution. Stir

until the brown colour turns purple or violet-blue. Slow down the titration

6. At the end point the colour changes to green

7. Use the formula to convert to Organic Matter:

%Organic Matter= 2x % Organic Carbon

Carbonate

1. Weigh 5g fine material into a shaking bottle

2. Add 100ml 0.2M HCL by pipette and swirl

3. Loosely screw on lid and swirl occasionally during the next hour. Let then

stand over night

4. The next day, indent the bottle by hand, tighten the lid and shake for 2 hours

in reciprocating shaker.

5. Let the suspension settle, pipette 10ml supernatant solution into 100ml

Erlenmeyer flask and add about 25ml water

6. Add a few drops phenolphthalein indicator and titrate with 0.1M NaOH

Calcium, Potassium, Magnesium, Manganese, Sodium by ICP-OES


86

1. For the digestion of solid samples, a representative 1 gram (wet weight)

sample is digested with repeated additions of nitric acid (HNO3) and

hydrogen peroxide (H2O2).

2. For ICP-OES analysis, hydrochloric acid (HCl) is added to the initial

digested solution and the sample is refluxed and then diluted to a final

volume of 200 mL. The digested sample is analyzed using the ICP-OES


87

8.5. Appendix 5: Classification Accuracies

MAXLIK 2003

Overall accuracy: 88.0482%; Kappa coefficient: 0.86

Ground Truth

(pixels)

Class Eroded rocky Lithosols Lithosols/Rig Brown Lithosols Gypsum/gley

Unclassified 0 0 0 0 0

Eroded rocky 62946 2 2713 754 25362

Lithosols [Gr 0 116602 44 5029 5

Lithosols/Rig 2090 127 105969 297 12702

Brown Lithosols 243 4004 1250 107432 1411

Gypsum/gley [ 17318 38 14225 2459 79141

Halomorphs/ u 0 0 0 0 0

Young soil 0 0 0 0 3

Fluvial soil 6160 0 23 4 805

Sols bruns je 778 1 15 41 81

Water/no data 0 0 0 0 0

Total 89535 120774 124239 116016 119510

Ground Truth

Class Halomorphs Young soil Fluvial soil Sols bruns je

Water/no

data


Eroded rocky 0 0 5041 370 0

Lithosols [Gr 0 0 0 0 0

Lithosols/Rig 0 0 71 0 68

Brown Lithosols 0 0 5 20 0

Gypsum/gley [ 0 0 548 28 6


Young soil 0 4239 41 136 0

Fluvial soil 0 0 80668 394 0

Sols bruns je 0 10 2174 39546 0

Water/no data 0 0 0 0 182257

Total 8702 4250 88548 40494 182331


88

Ground Truth

Class Total

Unclassified 0

Eroded rocky 97188

Lithosols [Gr 121680

Lithosols/Rig 121324

Brown Lithosols 114365

Gypsum/gley [ 113763

Halomorphs/ u 8703

Young soil 4419

Fluvial soil 88054

Sols bruns je 42646

Water/no data 182257

Total 894399

Ground Truth%



Eroded rocky 70.3 0 2.18 0.65 21.22

Lithosols [Gr 0 96.55 0.04 4.33 0

Lithosols/Rig 2.33 0.11 85.29 0.26 10.63

Brown Lithosols 0.27 3.32 1.01 92.6 1.18

Gypsum/gley [ 19.34 0.03 11.45 2.12 66.22



Fluvial soil 6.88 0 0.02 0 0.67

Sols bruns je 0.87 0 0.01 0.04 0.07


Total 100 100 100 100 100

Ground Truth%

Class Halomorphs/ uS ols peu evol Fluvial soil Sols bruns je

Water/no

data


Eroded rocky 0 0 5.69 0.91 0


89


Lithosols/Rig 0 0 0.08 0 0.04

Brown Lithosols 0 0 0.01 0.05 0

Gypsum/gley [ 0 0 0.62 0.07 0

Halomorphs/ u 100 0.02 0 0 0

Young soil 0 99.74 0.05 0.34 0

Fluvial soil 0 0 91.1 0.97 0

Sols bruns je 0 0.24 2.46 97.66 0

Water/no data 0 0 0 0 99.96

Total 100 100 100 100 100

Ground Thruth %

Class Total

Unclassified 0

Eroded rocky 10.87

Lithosols [Gr 13.6

Lithosols/Rig 13.56

Brown Lithosols 12.79

Gypsum/gley [ 12.72

Halomorphs/ u 0.97

Young soil 0.49

Fluvial soil 9.85

Sols bruns je 4.77

Water/no data 20.38

Total 100

Class Commission Omission Commission Omission

(Percent) (Percent) (Pixels) (Pixels)

Eroded rocky 35.23 29.7 34242/97188 26589/89535

Lithosols [Gr 4.17 3.45 5078/121680 4172/120774

Lithosols/Rig 12.66 14.71 15355/121324 18270/124239

Brown Lithosols 6.06 7.4 6933/114365 8584/116016

Gypsum/gley [ 30.43 33.78 34622/113763 40369/119510

Halomorphs/ u 0.01 0 01 8703 0/8702


90

Young soil 4.07 0.26 180/4419 11 4250

Fluvial soil 8.39 8.9 7386/88054 7880/88548

Sols bruns je 7.27 2.34 3100/42646 948/40494

Water/no data 0 0.04 0/182257 74/182331

Class Prod. Acc. User Acc. Prod. Acc. User Acc.


Eroded rocky 70.3 64.77 62946/89535 62946/97188

Lithosols [Gr 96.55 95.83 116602/120774 116602/121680

Lithosols/Rig 85.29 87.34 105969/124239 105969/121324

Brown Lithosols 92.6 93.94 107432/116016 107432/114365

Gypsum/gley [ 66.22 69.57 79141/119510 79141/113763

Halomorphs/ u 100 99.99 8702/8702 8702/8703

Young soil 99.74 95.93 4239/4250 4239/4419

Fluvial soil 91.1 91.61 80668/88548 80668/88054

Sols bruns je 97.66 92.73 39546/40494 39546/42646

Water/no data 99.96 100 182257/182331 182257/182257

ASTER 2005

Ground Truth pixel



Eroded rocky 53568 1187 2493 8397 19963

Lithosols [Gr 998 106466 5622 16865 11637

Lithosols/Rig 1433 3478 111642 2976 32508

Brown Lithosols 11200 9225 2500 82225 30673

Gypsum/gley [ 2910 362 1847 2304 18851

Cloud [White] 1194 0 0 185 2280

Cloud shadow 255 0 0 0 58


Young soil 285 0 0 16 26

Sols bruns je 3354 0 8 170 622

Fluvial soil 14338 56 127 2878 2892


91

Total 89535 120774 124239 116016 119510

Ground Truth-

pixel

Class Cloud Cl

oud

shadow

Halomorphs/

u Young soil Sols bruns je


Eroded rocky 156 128 0 35 220




Gypsum/gley [ 9 73 0 9 30

Cloud [White] 22575 41 24 118 673



Young soil 171 1 0 5479 2114

Sols bruns je 345 347 0 352 15208

Fluvial soil 12 1 0 3 184

Total 23277 23694 8702 5997 18485

Ground Truth-

pixel

Class Fluvial soil Total

Unclassified 0 0

Eroded rocky 6154 92301

Lithosols [Gr 11 141599

Lithosols/Rig 64 152101

Brown Lithosols 1563 137409

Gypsum/gley [ 160 26555

Cloud [White] 57 27147

Cloud shadow 0 23459

Halomorphs/ u 0 8678

Young soil 76 8168

Sols bruns je 1222 21628

Fluvial soil 79241 99732


92

Total 88548 738777

Ground Truth%



Eroded rocky 59.83 0.98 2.01 7.24 16.7

Lithosols [Gr 1.11 88.15 4.53 14.54 9.74

Lithosols/Rig 1.6 2.88 89.86 2.57 27.2

Brown Lithosols 12.51 7.64 2.01 70.87 25.67

Gypsum/gley [ 3.25 0.3 1.49 1.99 15.77

Cloud [White] 1.33 0 0 0.16 1.91

Cloud shadow 0.28 0 0 0 0.05


Young soil 0.32 0 0 0.01 0.02

Sols bruns je 3.75 0 0.01 0.15 0.52

Fluvial soil 16.01 0.05 0.1 2.48 2.42

Total 100 100 100 100 100

Ground Truth%

Class Cloud Cl

oud

shadow

Halomorphs/



Eroded rocky 0.67 0.54 0 0.58 1.19



Brown Lithosols 0.04 0 0 0.02 0.07

Gypsum/gley [ 0.04 0.31 0 0.15 0.16

Cloud [White] 96.98 0.17 0.28 1.97 3.64

Cloud shadow 0 97.51 0 0 0.23

Halomorphs/ u 0 0 99.72 0 0

Young soil 0.73 0 0 91.36 11.44

Sols bruns je 1.48 1.46 0 5.87 82.27

Fluvial soil 0.05 0 0 0.05 1


93

Total 100 100 100 100 100

Ground Truth (Percent)


Unclassified 0 0

Eroded rocky 6.95 12.49

Lithosols [Gr 0.01 19.17

Lithosols/Rig 0.07 20.59

Brown Lithosols 1.77 18.6

Gypsum/gley [ 0.18 3.59

Cloud [White] 0.06 3.67

Cloud shadow 0 3.18

Halomorphs/ u 0 1.17

Young soil 0.09 1.11

Sols bruns je 1.38 2.93

Fluvial soil 89.49 13.5

Total 100 100



Eroded rocky 41.96 40.17 38733/92301 35967/89535

Lithosols [Gr 24.81 11.85 35133/141599 14308/120774

Lithosols/Rig 26.6 10.14 40459/152101 12597/124239

Brown Lithosols 40.16 29.13 55184/137409 33791/116016

Gypsum/gley [ 29.01 84.23 7704/26555 100659/119510

Cloud [White] 16.84 3.02 4572/27147 702/23277

Cloud shadow 1.52 2.49 356/23459 591/23694

Halomorphs/ u 0 0.28 0/8678 24/8702

Young soil 32.92 8.64 2689/8168 518/5997

Sols bruns je 29.68 17.73 6420/21628 3277/18485

Fluvial soil 20.55 10.51 20491/99732 9307/88548



94


Eroded rocky 59.83 58.04 53568/89535 53568/92301

Lithosols [Gr 88.15 75.19 106466/120774 106466/141599

Lithosols/Rig 89.86 73.4 111642/124239 111642/152101

Brown Lithosols 70.87 59.84 82225/116016 82225/137409

Gypsum/gley [ 15.77 70.99 18851/119510 18851/26555

Cloud [White] 96.98 83.16 22575/23277 22575/27147

Cloud shadow 97.51 98.48 23103/23694 23103/23459

Halomorphs/ u 99.72 100 8678/8702 8678/8678

Young soil 91.36 67.08 5479/5997 5479/8168

Sols bruns je 82.27 70.32 15208/18485 15208/21628

Fluvial soil 89.49 79.45 79241/88548 79241/99732

MNF classification- ASTER 2003

Ground Truth-

pixel



Eroded rocky 36125 4 14481 1461 25182

Lithosols [Gr 1 112491 15 7770 2

Lithosols/Rig 21257 130 79731 950 30538

Brown Lithosols 1842 7899 1940 100709 6780

Gypsum/gley [ 20805 250 18009 4823 52051



Fluvial soil 7569 0 10044 193 4673

Sols bruns je 1887 0 19 109 278


Total 89535 120774 124239 116016 119510

Ground Truth-

pixel

Class Halomorphs/ u Young soil Fluvial soil Sols bruns je

Water/no

data


Eroded rocky 0 0 7706 543 0


95




Gypsum/gley [ 0 0 645 13 0


Young soil 0 4221 216 357 0

Fluvial soil 0 1 66656 1734 0

Sols bruns je 0 28 5665 37799 0

Water/no data 0 0 0 0 182331

Total 8702 4250 88548 40494 182331

Ground Truth-

pixel

Class Total

Unclassified 0

Eroded rocky 85502

Lithosols [Gr 120279

Lithosols/Rig 140246

Brown Lithosols 119238

Gypsum/gley [ 96596

Halomorphs/ u 8702

Young soil 4850

Fluvial soil 90870

Sols bruns je 45785

Water/no data 182331

Total 894399

Ground Truth %



Eroded rocky 40.35 0 11.66 1.26 21.07

Lithosols [Gr 0 93.14 0.01 6.7 0

Lithosols/Rig 23.74 0.11 64.18 0.82 25.55


96

Brown Lithosols 2.06 6.54 1.56 86.81 5.67

Gypsum/gley [ 23.24 0.21 14.5 4.16 43.55


Young soil 0.05 0 0 0 0.01

Fluvial soil 8.45 0 8.08 0.17 3.91

Sols bruns je 2.11 0 0.02 0.09 0.23


Total 100 100 100 100 100

Ground Truth %

Class Halomorphs/ u Young soil Fluvial soil Sols bruns je

Water/no

data


Eroded rocky 0 0 8.7 1.34 0


Lithosols/Rig 0 0 8.59 0.09 0

Brown Lithosols 0 0 0.06 0.03 0

Gypsum/gley [ 0 0 0.73 0.03 0


Young soil 0 99.32 0.24 0.88 0

Fluvial soil 0 0.02 75.28 4.28 0

Sols bruns je 0 0.66 6.4 93.34 0


Total 100 100 100 100 100

Ground Truth %

Class Total

Unclassified 0

Eroded rocky 9.56

Lithosols [Gr 13.45

Lithosols/Rig 15.68

Brown Lithosols 13.33

Gypsum/gley [ 10.8


97

Halomorphs/ u 0.97

Young soil 0.54

Fluvial soil 10.16

Sols bruns je 5.12

Water/no data 20.39

Total 100



Eroded rocky 57.75 59.65 49377/85502 53410/89535

Lithosols [Gr 6.47 6.86 7788/120279 8283/120774

Lithosols/Rig 43.15 35.82 60515/140246 44508/124239

Brown Lithosols 15.54 13.19 18529/119238 15307/116016

Gypsum/gley [ 46.11 56.45 44545/96596 67459/119510

Halomorphs/ u 0 0 0/8702 0/8702

Young soil 12.97 0.68 629/4850 29/4250

Fluvial soil 26.65 24.72 24214/90870 21892/88548

Sols bruns je 17.44 6.66 7986/45785 2695/40494

Water/no data 0 0 0/182331 0/182331



Eroded rocky 40.35 42.25 36125/89535 36125/85502

Lithosols [Gr 93.14 93.53 112491/120774 112491/120279

Lithosols/Rig 64.18 56.85 79731/124239 79731/140246

Brown Lithosols 86.81 84.46 100709/116016 100709/119238

Gypsum/gley [ 43.55 53.89 52051/119510 52051/96596

Halomorphs/ u 100 100 8702/8702 8702/8702

Young soil 99.32 87.03 4221/4250 4221/4850

Fluvial soil 75.28 73.35 66656/88548 66656/90870

Sols bruns je 93.34 82.56 37799/40494 37799/45785

Water/no data 100 100 182331/182331 182331/182331


98

MNF classification- ASTER 2005

Ground Truth-

pixel



Eroded rocky 31142 705 1705 9096 12613

Lithosols [Gr 2034 100725 7440 16837 17013

Lithosols/Rig 1856 5004 107358 6034 33765

Brown Lithosols 15899 12650 3682 75479 32656

Gypsum/gley [ 6240 1483 3268 3640 17456

Cloud [White] 602 0 0 116 1769



Young soil 1179 0 0 69 59

Sols bruns je 3508 0 8 161 454

Fluvial soil 26249 207 771 4584 3446

Total 89535 120774 124239 116016 119510

Ground Truth-

pixel

Class Cloud C

loud

shadow

Halomorphs/



Eroded rocky 74 103 0 68 514




Gypsum/gley [ 27 57 0 6 217

Cloud [White] 22223 0 9 177 567

Cloud shadow 0 23176 0 0 169


Young soil 421 0 15 5160 3587

Sols bruns je 470 352 2 548 13305


99

Fluvial soil 18 3 0 36 126

Total 23277 23694 8702 5997 18485

Ground Truth-

pixel


Unclassified 0 0

Eroded rocky 8658 64678

Lithosols [Gr 23 144074

Lithosols/Rig 299 154319

Brown Lithosols 2869 143279

Gypsum/gley [ 128 32522

Cloud [White] 0 25463

Cloud shadow 2 24459

Halomorphs/ u 0 8676

Young soil 643 11133

Sols bruns je 726 19534

Fluvial soil 75200 110640

Total 88548 738777

Ground Trutth %



Eroded rocky 34.78 0.58 1.37 7.84 10.55

Lithosols [Gr 2.27 83.4 5.99 14.51 14.24

Lithosols/Rig 2.07 4.14 86.41 5.2 28.25

Brown Lithosols 17.76 10.47 2.96 65.06 27.32

Gypsum/gley [ 6.97 1.23 2.63 3.14 14.61

Cloud [White] 0.67 0 0 0.1 1.48

Cloud shadow 0.92 0 0.01 0 0.23


Young soil 1.32 0 0 0.06 0.05

Sols bruns je 3.92 0 0.01 0.14 0.38


100

Fluvial soil 29.32 0.17 0.62 3.95 2.88

Total 100 100 100 100 100

Ground Truth %

Class Cloud C

loud

shadow

Halomorphs/



Eroded rocky 0.32 0.43 0 1.13 2.78

Lithosols [Gr 0.01 0 0 0 0

Lithosols/Rig 0 0.01 0 0 0

Brown Lithosols 0.18 0 0 0.03 0

Gypsum/gley [ 0.12 0.24 0 0.1 1.17

Cloud [White] 95.47 0 0.1 2.95 3.07

Cloud shadow 0 97.81 0 0 0.91

Halomorphs/ u 0 0 99.7 0 0

Young soil 1.81 0 0.17 86.04 19.4

Sols bruns je 2.02 1.49 0.02 9.14 71.98

Fluvial soil 0.08 0.01 0 0.6 0.68

Total 100 100 100 100 100

Ground Truth %


Unclassified 0 0

Eroded rocky 9.78 8.75

Lithosols [Gr 0.03 19.5

Lithosols/Rig 0.34 20.89

Brown Lithosols 3.24 19.39

Gypsum/gley [ 0.14 4.4

Cloud [White] 0 3.45

Cloud shadow 0 3.31

Halomorphs/ u 0 1.17

Young soil 0.73 1.51

Sols bruns je 0.82 2.64


101

Fluvial soil 84.93 14.98

Total 100 100



Eroded rocky 51.85 65.22 33536/64678 58393/89535

Lithosols [Gr 30.09 16.6 43349/144074 20049/120774

Lithosols/Rig 30.43 13.59 46961/154319 16881/124239

Brown Lithosols 47.32 34.94 67800/143279 40537/116016

Gypsum/gley [ 46.33 85.39 15066/32522 102054/119510

Cloud [White] 12.72 4.53 3240/25463 1054/23277

Cloud shadow 5.25 2.19 1283/24459 518/23694

Halomorphs/ u 0 0.3 0/8676 26/8702

Young soil 53.65 13.96 5973/11133 837/5997

Sols bruns je 31.89 28.02 6229/19534 5180/18485

Fluvial soil 32.03 15.07 35440/110640 13348/88548



Eroded rocky 34.78 48.15 31142/89535 31142/64678

Lithosols [Gr 83.4 69.91 100725/120774 100725/144074

Lithosols/Rig 86.41 69.57 107358/124239 107358/154319

Brown Lithosols 65.06 52.68 75479/116016 75479/143279

Gypsum/gley [ 14.61 53.67 17456/119510 17456/32522

Cloud [White] 95.47 87.28 22223/23277 22223/25463

Cloud shadow 97.81 94.75 23176/23694 23176/24459

Halomorphs/ u 99.7 100 8676/8702 8676/8676

Young soil 86.04 46.35 5160/5997 5160/11133

Sols bruns je 71.98 68.11 13305/18485 13305/19534

Fluvial soil 84.93 67.97 75200/88548 75200/110640

Identification of Soil Property Variation Using Spectral ... Output/Mila Luleva.pdf · Identifying...

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