An overview of the use of Sentinel 2 in the CwRS Spanish ... · in Murcia (Southeast of Spain) F E...

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An overview of the use of Sentinel 2 in the CwRS Spanish

program – LPIS update

martes, 6 de junio de 2017

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Index

I. Current use of Sentinel - 2 in the CwRS in Spain.

II. Use of Sentinel - 2 for classification of land cover uses and crop

types. Case study in Zaragoza (Northwest of Spain)

2016. Classification of arable crops in irrigation.

2017. Evaluation of Sentinel 2 potential capabilities in the

control of diversification (greening payment)

III. Use of Sentinel 2 for updating LPIS land cover uses. Case study

in Murcia (Southeast of Spain)

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I. Current use of Sentinel - 2 in the CwRS in Spain

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Control with Remote Sensing in Spain

10 11 12 1 2 3 4 5 6 7 8 9

10

XS1 XS2 XS3

Performance of winter crops

Performance of summer crops

XS0

XS0 XS1 XS3XS2_VHR

XSa XSa

Satellite image selection’s dates are scheduled

according to the phenologic cycle of the crops

•Sowing of winter crops (XS0)

•Beginning of the growth of winter crops

(XS1)

•Maximum development of winter crops

(XS2)

•Maximum development of summer

crops (XS3)

•Since 2016, an additional image has

been included in order to control

diversification (XSa).XSa XSa

2016 CwRS zones

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CwRS 2016 – Images selected in one of the control zones (Zaragoza) and derived products used in photointerpretation

XS0 DMC 21/01/2016XS1 Spot 6

13/03/201613 XS2 WV2 29/04/2016XS3 Spot 6

02/07/2016

NDVI composite

2F (X1, X2, X3)Crop group

classification

Crop type

classification

Derived RS

products

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Photo interpretation processThe photo interpretation process comprises all

the activities carried out until the right

assignment of areas and uses to the declared

parcels.Aid application

Declared land use

Imagery

Photo interpretation procedure

Photo interpretation

of the Parcel

Assignment of

Photo interpretation

codes

Photo interpreted area

LPIS data

TR

TR

IM

Remote sensing products used for CAPI

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Integration of Sentinel 2 in the 2017 CwRS (Spain)

• Autumn imagery (XS0) acquired for all the 23 zones have been Sentinel

2A images with a scheduled window of acquisition from 15/11/2016 to

31/01/2017 65% success rate.

• However, because of the 10 day revisit period of Sentinel 2A, a window

extension was needed for some sites and the autumn images were

actually taken in the period 03/11/2016 - 19/02/2017 Period of

extension: 2-5 days, with a maximum of 12 days, in one case.

• Validated images (full cover & minimum cloud cover): 23 (100%). In one

site was necessary to make a compound of two different dates in order

to have a free cloud image over the site.

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II. Use of Sentinel 2 for classification of land cover

uses and crop types. Case study in Zaragoza

(Northwest of Spain):

- 2016. Classification of arable crops in irrigation.

- 2017. Evaluation of Sentinel 2 potential

capabilities in the control of diversification

(greening payment)

This work is supported by proDataMarket project (H2020 644497).

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2016. Case study in Zaragoza (Northwest of Spain)

Use of Sentinel 2 for classification of arable

crops in irrigation


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2016 - Case study in Zaragoza. Use of Sentinel 2 for identification of arable crops in irrigation

• Case study area TAEJ, 2016

CwRS site (1.600 Km2), province

of Zaragoza.

• It is located in the River Ebro

basin, one of the most important

irrigated areas in Spain.

• The main crops are wheat, barley

and alfalfa.

• Evaluation/ Validation using

ground truth survey.

Goal: Generation of an irrigated crop layer based on Sentinel 2 temporal series of

images and reference ancillary data from the Ministry (National Irrigation Plan)

and FEGA (LPIS).

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LPIS

Ground

Truth survey

Herb. Spring 81.75 %

Herb. Summer 89.57 %

Herb. Spring-Summer 90.69 %

Monthly NDVI

series

11

Validation

User Accuracy

HA= High Activity

LA = Low Activity

Automated

Classfication

based on

Decision

Tree

Geoprocessing

Geoprocessing

2016 - Case study in Zaragoza. Use of Sentinel 2 for identification of arable crops in irrigation

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2017. Evaluation of Sentinel 2 potential capabilities in the

control of diversification (greening payment)


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Campaign Number of

zones

Declared

reference

parcels

Number of

Rapid Field

Visits

Percentage

of Rapid

Field Visits

2011 18 401.735 31.903 7.94%

2012 22 400.266 42.516 10.62%

2013 17 357.006 40.287 11.28%

2014 18 359.462 36.312 10.10%

2015 18 325.043 51.846 15.95%

2016 18 336.128 60.069 17.87%

2017. Evaluation of Sentinel 2 potential capabilities in the control of diversification (greening payment)

CwRS - Increase of RFV due to the diversification control

Goal: Evaluate how Sentinel 2 spectral bands can contribute to improve

photointerpretation´s products and vegetation´s indices in the control of diversification

and, consequently reduce the number of necessary Rapid Field Visits

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Scenario 1: The same number of

images used in the 2016 CwRS are

kept, but:

• All the HR and HHR images are

replaced by Sentinel images,

maintaining only the VHR image.

• New band combinations for

photointerpretation are considered,

testing Se2 high spectral resolution

for crop classification.

Sentinel2 TAEJ (Zaragoza) 04/05/2016 RGB composites:

RGB: 11,5,2

SWIR, RE1, B

RGB: 8,4,3

NIR,R,G

(CwRS PHI)

RGB: 8a,6,5

NIR,RE2,RE1

RGB: 4,3,2

(Real color)

2016 CwRS TEST

XS0 (HR) Sentinel 2

XS1 (HHR) Sentinel 2

XS2 (VHR) XS2 (VHR)

Xsa (HHR)

XS3 (HHR)

Sentinel 2

Sentinel 2


Aids

applications

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Scenario 2:

The discrimination of some crops, such as wheat and

corn, is evaluated by testing different spectral bands of

Sentinel 2.

More Sentinel 2 dates and bands are added to the

classification to analyze the time series capabilities for

crop diversification control.

In particular, the spectral evolution of wheat and barley

for several band combinations within a Se2A time series

is tested in the site of TAEJ – Zaragoza (CwRS2 2016)


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This graph shows the NDVI evolution of the two crops (index

produced using the Red (4) and NIR (8) bands, which are

common in several earth observation satellites.

Notice that both crops follow a similar pattern, showing a very

similar spectral response with not significant differences.

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.


This chart shows the reflectivity values of wheat and barley in the

Red Edge Se2 band 6 (exclusive band of Se2).

A certain spectral reflectivity difference is appreciated, especially in

the first half of May.

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This chart depicts the evolution of the two crops, wheat and barley,

in the NIR Edge Se2 band 7 (exclusive band of Se2).

Reflectivity values for wheat remain high between April and late

May, while barley values begin to fall from early April.

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III. Use of Sentinel 2 for updating LPIS land cover uses.

Case study in Murcia (Southeast of Spain)

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2016. Potential of Sentinel2 for identification of LPIS land cover uses & change detection layer: Case study in Murcia

LPISLPIS group of

uses

Surface

(ha)%

Woody crops 172,046 29

Herbaceous crops 98,753 17

Forest 81,660 14

Grassland 178,573 30

Non-productive 51,415 9

Water 8,698 1

Total 591,147

Goal: Generation of a layer with the main LPIS uses (land cover), using machine learning

classification, and producing a LPIS changing layer.

Location. Sentinel tile (100 km x 100 km) in Murcia.

Murcia has a Mediterranean climate of semi-arid type. The average annual

temperature is 18ºC and the precipitation is abut 300-350 mm per year.

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Methodology for LPIS Main Uses Layer and LPIS Change Map

LPIS data

Sentinel2 8-band composites

from 6 dates of 2016

Vegetation Height Map from

LiDAR

Slope map (from DEM)

Agroclimatic classification map

Main LPIS uses layer

(2016)

Farmers aid applications

National Forest Map

Sample data

Classification data

LPIS Change Map

(2015-2016)

LPIS before updating

(2015)

Decision

Tree Algorithm

Classification

model

Criteria for class

identification:Global Accy: >80%

Kappa>75%

Machine Learning

Classification Process

Change 7%

Uncertainty 13%

Not change 80%

Geoprocessing

Support decision making

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Validation of LPIS Change MapLPIS 2015

Main LPIS uses

layer (2016)

Difference LPIS

2015-2016

(Validation Layer)

Ge

op

rocessin

g

Geoprocessing

LP = low probability

LPIS change

map

2015-2016

Layer of discrepancies

Available

Ortophoto

Based on

2016 fligh

Changes between

groups of uses

Omission Err : 0,1 %

Commission Err: 7%

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Detailed agricultural land use classification map

Agricultural area

in the Main LPIS

uses layer (2016)

User Accuracy:

Decision

Tree Algorithm

Classification

model

Criteria for class

identification:Global Accy: >80%

Kappa>70%

Detailed land use

classification map (2016)

Machine Learning

Classification ProcessCereal 85.5%

Citrus 94.7%

Forest 89.3.%

Fruit trees 91.6%

Grassland 86.2%

Nuts 82.6%

Other woody crops 76.9%

Urban 94.9%

Water 100%

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STRENGTHS

• Higher spatial, spectral and radiometric resolution than other HR/MR imagery

• Open (free) data policy

• Useful as back-up imagery 2017 RS zone in PADU (Granada) as XS1

WEAKNESSES

• Less spatial resolution than other HR imagery (Spot 6/7)

• Revisit time of 10 days is insufficient for the requirements of the CwRS. Once

Sentinel 2B is operative, it will be reduced to 5 days.

• Orthorectification of imagery, though valid, in some areas has worse accuracy

than desired. The situation is being improved by ESA.

• The growing number of images available makes the processing and

exploitation of the data for photointerpretation more complex.

Strengths and weaknesses of Se2 for the CwRS

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Conclusions

The potential use of Se2 images in relation to CwRS and LPIS updating

has been studied through the development of three case studies. They

must only be considered as pre-opeartional technical assessments. More

studies are necessary prior to operational deployment.

As a general outcome, Se2 images can be usefull for the identification and

mapping of land use covers and crop types.

The degree of contribution of SE2 images at improving crop diversification

control (greening) is still under evaluation, although the first impressions

are promising.

Sentinel2 provides a big amount of high quality RS data with a great

potential and many different applications in IACS. However, it is necessary

to do further computing developments and carry out some more pilot

projects.

More powerful computing resources are required to process and manage

the large volume of data involved in the Sentinel2 time series.

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An overview of the use of Sentinel 2 in the CwRS Spanish ... · in Murcia (Southeast of Spain) F E...

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