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AN ANALYSIS USING LISS III DATA FOR ESTIMATING WATER DEMAND FOR RICE

CROPPING IN PARTS OF HIRAKUD COMMAND AREA, ORISSA, INDIA

Ambuja Ballav Nayak January, 2006

AN ANALYSIS USING LISS III DATA FOR ESTIMATING WATER DEMAND FOR RICE CROPPING IN PARTS OF

HIRAKUD COMMAND AREA, ORISSA, INDIA

by

Ambuja Ballav Nayak 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 Geoinformatics. Thesis Assessment Board Thesis Supervisors Chairman: Prof. Dr. Ir. M.G.Vosselman, ITC Dr. V. Hari Prasad, IIRS External Examiner: Dr. S.K.Jain, NIH, Roorkee Prof.Dr.Ir. A. (Alfred) Stein, ITC IIRS Member : Dr. S.P. Aggarwal Mr. P.V.Raju, NRSA IIRS Member : Mr. C. JeganathanIIRS Guide : Dr. V. Hari Prasad

iirs

INDIAN INSTITUTE OF REMOTE SENSING NATIONAL REMOTE SENSING AGENCY, DEPARTMENT OF SPACE, GOVERNMENT OF INDIA

DEHRADUN, INDIA

&

INTERNATIONAL INSTITUTE FOR GEO-INFORMATION SCIENCE AND EARTH OBSERVATION ENSCHEDE, THE NETHERLANDS

I certify that although I may have conferred with others in preparing for this assignment, and drawn upon a range of sources cited in this work, the content of this thesis is my original work. Signed …………………..

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.

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Abstract

Rice is the single most important food crop in India that occupies 44.0 million hectares of agricultural land, which is the largest rice area in the world. It is grown in almost all states of India and in the state of Orissa rice cultivation practices in 4.4 million hectares. Orissa is a predominantly agrarian state as more than two third of the state’s population depend on agriculture. Irrigation is the paramount importance for development of agriculture. Crop water requirement of the crops are met by irrigation besides natural rainfall. Irrigation projects are built up to support crops with adequate water supply during the growing period. Dams are built to store large volumes of monsoon water which were earlier being drained into rivers and sea. Hirakud Dam over Mahanadi River in Orissa is one of such scheme which built up in early days of independence (1957) having live storage capacity of 4823 x 106 m3 and it provide irrigation potential of 159106 ha during kharif and 108385 ha during rabi season. In the central part of the command two distributaries namely Babebira and Bugbuga distributary with command area of 1662 ha and 1211 ha respectively have been taken up for this study. In this study area rice is the dominant crop covering 81 % of the total crop area. Since it is an old command area an attempt has been made for estimating water demand for ric e cropping using the latest technology such as satellite remote sensing. Since crop growing phenomenon is dynamic, using multi-temporal IRS 1C/ 1D Linear Imaging and Self Scanning (LISS)-III satellite data acquired on five dates (16th February 2002, 21st March 2002, 7th April 2002, 14th April 2002 and 2nd May 2002) an attempt has been made to understand the crop phenology and also identify crop growth stages spatially.

Using the temporal Normalized Difference Vegetation Index (NDVI) rice map of the study area has been generated and also aerial extent of different rice growth stages such as early, normal and late transplanted have been generated. The aerial extend of agriculture area of water resources department and agriculture department are 2873 ha and 3214 ha respectively. And using remote sensing technology the reported aerial extent is 3208 ha. And total crop acreage extraction from satellite for rice crop is 2624 ha against the agriculture department data of 2604 ha. This shows the relevance of use of space technology for understanding the irrigation command system. The areas under early, normal and late transplanted rice for Babebira distributary are 408 ha, 889 ha, 127 ha and for Bugbuga distributary are 231 ha, 807 ha, 162 ha respectively.

Rice crop water requirement vs. water supply was analysed with the help of meteorological data and irrigation data. The crop water requirement of rice crop was computed with the help of reference evapotranspiration (pan evaporation method) and crop coefficients. It was found that the water demand for rice crop only exceeds the irrigation supply. Water requirement of Babebira distributary is 1278 ha-m for rice crop only against the total water supply of 1054 ha -m with a deficit of 224 ha -m (17.5 %). And water requir ement of Bugbuga distributary is 1085 ha-m for rice crop only against the total water supply of 581 ha-m with a deficit of 504 ha-m (46.4 %).

The canal network was extracted from the Resourcesat1 (P6) LISS IV with 6-m spatial resolution images It was found that the deviation of canal extract from LISS IV image in Babebira distributary is (+) 9.10 % and in Bugbuga distributary is (-) 2.36 % when compared with the extend provided by the command area authorities.

Key words: IRS 1C/1D, LISS III, LISS IV, Rice Crop, Hirakud command area

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Acknowledgements

I am thankful to the Department of Water Resources, Government of Orissa for giving me the opportunity to undergo the M.Sc. course in Geoinformatics, a joint educational program between Indian Institute of Remote Sensing (National Remote sensing Agency), Dehradun and International Institute for Geo-Information Science and Earth Observation (ITC), The Netherlands. My foremost thanks are due to my thesis supervisor Dr. V. Hari Prasad, In-charge, Water Resources Division, whose encouragement and stimulating support helped me to shape my research skills. I thank him for his endurance, creative thoughts and energetic working mode that influenced me highly. I also thank my other supervisor Mr. P. V. Raju, Scientist, Water Resources Division, NRSA, who advised me in various aspects of research. I am deeply indebted to my supervisor Prof. Dr. Ir. Alfred Stein, for scientific advice and encouragement for this research. His valuable feedback, illuminating guidance and support especially for the conceptualization of the research helped me to improve the research in many ways. I am delighted to express my gratitude to Dr. V.K. Dadhwal, Dean, IIRS, for his critical comments and suggestions to fulfil research objectives. I am also thankful to Mr. P. L. N. Raju, In-charge, Geoinformatics division for his valuable guidance and suggestion during the research period. My sincere thanks to Mr. C. Jeganathan, Programme coordinator Geoinformatics courses, and all staff of IIRS for their kind support. I profess my thanks and regards to Dr. G.C. (Gerrit) Huurneman, Prof. Dr. M.J. (Menno-Jan) Kraak, Dr. A. (Andreas) Wytzisk, Ms. Dr. J.E. (Jantien) Stoter, Dr. Ir. R.A. (Rolf) de By, Dr. V.A. (Valentyn) Tolpekin and Dr. Cees van Westen for their guidance and encouragement at ITC. I thank Dr. N.R.Patel, Ms. Shefali Aggarwal and Mr. Praveen Thakur for discussion and suggestions. I acknowledge the data supplied by the Water Resources Department, Agriculture Department, Government of Oris sa, National Remote Sensing Agency, Department of Space, Hyderabad, Regional Meteorological Center, Bhubaneswar, and Regional Research Station (Orissa University of Agriculture and Technology), Chipilima for this study. Thanks are also due to my family and friends for their encouragement and support during this study. Ambuja Ballav Nayak Dehradun, India January, 2006

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Table of contents

1. Introduction...........................................................................................................................7

1.1. Background................................ ................................ ................................ ..................7 1.2. Problem statement........................................................................................................8

1.3. Objectives....................................................................................................................9

1.4. Research Questions ................................ ................................ ................................ ......9 1.5. Hypothesis ...................................................................................................................9

1.6. Assumptions and limitations......................................................................................... 10 1.7. Chapter scheme ......................................................................................................... 10 1.8. Data .......................................................................................................................... 10

1.9. Study Area:................................ ................................ ................................ ................ 11 2. Literature Review ............................................................................................................... 13

2.1. Rice crop ................................................................................................................... 13

2.2. Water demand of Rice:............................................................................................... 14 2.3. Remote Sensing to extract rice crop growth stages ...................................................... 16

2.4. Irrigation water demand .............................................................................................. 18 3. Study Area ......................................................................................................................... 20

3.1. Location..................................................................................................................... 20

3.2. Climate ...................................................................................................................... 21 3.3. Soils........................................................................................................................... 21

3.4. Geology ..................................................................................................................... 21

3.5. Agriculture................................................................................................................. 21 4. Materials and Methods ................................ ................................ ................................ ........ 23

4.1. Materials ................................ ................................ ................................ .................... 23 4.2. Methods..................................................................................................................... 28

4.2.1. Determination of NDVI values and the rice map ...................................................... 29

4.2.2. Computation of reference evapotranspiration................................ ............................ 37 4.2.3. Computation of water demand................................................................................. 38

4.2.4. Canal Network Extraction ....................................................................................... 38

5. Analysis.............................................................................................................................. 40 5.1.1. Determination of threshold values to generate rice map............................................. 40

5.1.2. Rice stage classification derived from NDVI:........................................................... 49 5.1.3. Rice stage classification derived from SAVI............................................................. 49 5.1.4. Rice crop coefficients............................................................................................. 52

5.1.5. Reference crop evapotranspiration........................................................................... 54 5.1.6. Validation of results................................ ................................ ................................ 55

6. Results and Discussions ....................................................................................................... 57

6.1. Results....................................................................................................................... 57 6.1.1. Rice map:............................................................................................................... 57

6.1.2. Canal Network ....................................................................................................... 59

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6.1.3. Reference Crop evapotranspiratio n.......................................................................... 61 6.1.4. Crop evapotranspiration .......................................................................................... 63

6.1.5. Crop water requirement .......................................................................................... 63 6.1.6. Water demand vs. supply for rice crop..................................................................... 66

6.2. Discussion:................................................................................................................. 76

6.2.1. Geometric correction and radiometric normalization .................................................. 76 6.2.2. Generation of Rice crop map................................................................................... 76

6.2.3. Crop growth stages.................................................................................................77 6.2.4. Reference crop evapotranspiration........................................................................... 77 6.2.5. Irrigation Water Demand:................................ ................................ ........................ 77

6.2.6. Canal network extraction from the IRS P6 LISS IV .................................................. 78 6.2.7. The Canal network extraction from the cadastral level map....................................... 78

6.2.8. Satellite Data.......................................................................................................... 78

6.2.9. Role of Remote Sensing:......................................................................................... 78 6.2.10. Limitation on Data:............................................................................................. 79

7. Conclusions and Recommendations ...................................................................................... 80 7.1. Conclusions ................................ ................................ ................................ ................ 80 7.2. Recommendations ...................................................................................................... 83

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List of figures

Figure 2.1: Rice growth stage 13

Figure 2.2: Depth of water layer during the growing season 15 Figure 2.3: Crop coefficient of rice suggested by different authors 16

Figure 3.1: Study Area 20

Figure 4.1: Temperature trend in the study area during Rabi season 2001-02 24 Figure 4.2: Humidity in the study area during Rabi season 2001-02 24

Figure 4.3: Pan Evaporation and rainfall in the study area during Rabi season 2001-02 25 Figure 4.4: Irrigation water supply during Rabi season 2001-02 27 Figure 4.5 : Methodology 29

Figure 4.6: Plots generated for PIFs (Urban, Water and Dry sand) on Band-1 of LISS III image 30 Figure 4.7: Plots generated for PIFs (Urban, Water and Dry sand) on Band-2 of LISS III image 31

Figure 4.8: Plots generated for PIFs (Urban, Water and Dry sand) on Band-3 of LISS III image 31

Figure 4.9: Histogram showing DN values of 21st March 02 images before and after normalisation 33 Figure 4.10 : Flow chart for extraction of Rice pixels from NDVI image 36

Figure 4.11: Flow chart for classification and generation of Rice map 37 Figure 4.12: Reference evapotranspiration 38 Figure 5.1: Crop Growth Stage vs. crop area as derived from NDVI 41

Figure 5.2: Scatte r Plots of temporal NDVI images (Scatter Plot before applying threshold) 42 Figure 5.3: Modified Scatter Plot of temporal NDVI image after eliminating non-rice crop 43

Figure 5.4: Temporal variation of NDVI in Rice Crop Area 46

Figure 5.5: Temporal variation of NDVI vs. Cumulative Rice Crop Area 47 Figure 5.6: Temporal variation of NDVI of rice crop 48

Figure 5.7: Trend of NDVI for different crop stages 49 Figure 5.8: Crop coefficient of Rice 52 Figure 6.1: Spatial distribution of Rice Crop in the study Area 57

Figure 6.2: Canal network extracted from LISS IV image 59 Figure 6.3: The Canal network extracted from the cadastral level map 60

Figure 6.4: The Canal network extracted from the IRS P6 LISS IV and cadastral map 61

Figure 6.5: Water demand vs. supply 63

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List of tables

Table 1.1 : Details of satellite images used in the study..................................................................10 Table 1.2 : Specification of IRS Optical Sensors: LISS III and LISS IV .......................................... 10

Table 3.1: Details of Main Canals of Hirakud Command Area ....................................................... 22

Table 4.1: Village wise agricultural data ....................................................................................... 26 Table 4.2: Rice growth stages and duration in days…………………………………………………...24 Table 4.3: Distributary-wise Command Area................................ ................................ ................ 28 Table 4.4: Village-wise Command Area under each distributary..................................................... 28

Table 4.5: Regression equations between satellite data of 5 acquisitions for Pseudo Invariant Features.......................................................................................................................................... 32

Table 4.6: Limits of Radiance values from the header file of the satellite data .................................34

Table 5.1: NDVI of the temporal satellite images.......................................................................... 40 Table 5.2: Minimum and maximum NDVI values of final rice map............................................... 45

Table 5.3 : Rice crop acreage in different NDVI range .................................................................45 Table 5.4: Values of average NDVI for various rice crops ................................ ............................ 49 Table 5.5: Minimum and maximum SAVI values for rice crops ...................................................... 50

Table 5.6 Values of average SAVI for various rice crops.............................................................. 50 Table 5.7: Rice growth in days as on image acquisition dates......................................................... 51

Table 5.8: Crop coefficients as on day of image acquisition................................ ............................ 52

Table 5.9: Crop coefficients for Early transplanted rice .................................................................53 Table 5.10: Crop coefficients for Normal transplanted rice ................................ ............................ 53

Table 5.11 : Crop coefficients for Late transplanted rice................................ ................................ 54 Table 5.12: Reference Crop Evapotranspiration................................ ................................ ............ 54 Table 5.13: Village Wise Gross Command Area & Culturable Command Area ............................... 55

Table 6.1: NDVI threshold for various rice ................................................................................... 58 Table 6.2: Spatial distribution of Rice crop…………………………………………………………...55 Table 6.3: Comparison of distributary length extracted by different method: ................................ .... 61

Table 6.4: Computation of ET0 (10-day average reference evapotranspiration) ............................. 62 Table 6.5: Crop water requirement of Early Transplanted Rice..................................................... 67

Table 6.6 : Crop water requirement of Normal Transplanted Rice.................................................. 68 Table 6.7 : Crop water requirement of Late Transplanted Rice ...................................................... 70 Table 6.8 : Crop water requirement for Babebira Distributary................................ ........................ 71

Table 6.9 : Crop water requirement for Bugbuga Distributary or ................................ .................... 73 Table 6.10 : Supply and demand analysis of Irrigation water for Babebira distributary...................... 75

Table 6.11 : Supply and demand analysis of Irrigation water for Bugbuga distributary...................... 75

Table 7.1: Crop phenological stages of Rice crop as on day of image acquisition............................. 81

AN ANALYSIS USING LISS III DATA FOR ESTIMATING WATER DEMAND FOR RICE CROPPING IN PARTS OF HIRAKUD COMMAND AREA, ORISSA, INDIA

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1. Introduction

1.1. Background

Rice is the single most important food crop in India that occupies 44.0 million hectares of agricultural land, which is the largest rice area in the world. It is grown in almost all states of India and the state of Orissa contributes 4.4 million hectares to rice cultivation practice (IRRI, 2005). Rice is grown in three seasons in India, autumn and winter or Kharif season from June to October and summer (or Rabi) from December to May. The Kharif season accounts for 88 percent, and Rabi season accounts for 12 percent of total production. In India rice cropping is highly dependent on the southwest monsoon, which occurs over the subcontinent from June through September. The green revolution in India (1967-1978) brought substantial increase in production of cereals, particularly wheat and rice. Among the cereals, rice and wheat continue to dominate among various crops. These crops are grown in very vast regions in the country due to its adaptability to wider range of agro-climatic conditions. Thus, rice is the principal food grain of future and management of rice crop production can emerge as the key area of management in agriculture. Double cropping on existing farmland is one of three basic elements of green revolution. This encompassed to have two crop seasons per year instead of one that depend on the monsoon. So, Irrigation projects were built up to support crops with adequate water supply during the growing period. Dams were built to store large volumes of monsoon water, which were earlier being drained into rivers and sea. Irrigated agricultural land comprises less than a fifth of all cropped area but produces 40–45% of the world’s food (Doll and Siebert, 2002). In Asia, irrigated rice accounts for about 50% of the total amount of water diverted for irrigation, which in itself accounts for 80% of the amount of fresh water diverted (Guerra et al., 1998). In India, irrigation facilities cover about 43 percent of the rice growing area, where state -wise distribution of irrigation is highly variable. In Andhra Pradesh, Haryana, Punjab, and Tamil Nadu, over 95 percent of the area under rice is irrigated. In Bihar, Orissa, and Uttar Pradesh, only 30 to 45 percent of the rice-cultivated area is irrigated. To cater irrigation to the crops a canal network (conveyance system) is scattered in the command. A command is the area bounded within the irrigation boundary of a project, which can be economically irrigated without considering the limitation of the quantity of available water. Irrigation is providing water to meet the crop water requirement besides natural rainfall. The canals are fed from the reservoirs or from the weirs, the structures meant to collect and store water in rainy days. Hydraulic designs for canals are based on the peak flow rate required to meet the crop water requirement. For


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the design of a water conveyance system, it is necessary to assess the water requirement of the crop intended to be grown. The irrigation demand of a command addressed in this study is assessed by the crop calendar, cropping pattern, cropping intensity. The irrigation schedule is prepared which suit the irrigation demand of the command. But in due course, the cropping pattern changes, which is subjective and depends on the choice of the farmer. The introduction of a high yielding variety of crops, influence of market demand, salinity and waterlogging are causes of change of cropping pattern. This leads to analysis of crop water requirements based on the changes in the cropping system and market demand etc from time to time. Irrigation system water allocations are, most often, based on assumptions about the irrigated area, crop types, and the near-surface meteorological conditions that determine crop water requirements. The real time water demand requires a spatial analysis of water use. Remote sensing is promising in monitoring agricultural and water management activities as both the spatial and temporal characteristics of a region can be easily accounted for by satellite imageries. Remote sensing, with varying degrees of accuracy, has been able to provide information on land use, irrigated area, crop type, biomass development, crop yield, crop water requirements, crop evapotranspiration, salinity and water logging (Bastiaanssen et al., 2000). Water demand by the crop depends upon the phenological stages. It is possible to extract crop phenological stages from satellite image(Ray and Dadhwal, 2001; Ray et al., 2002). Also it is possible to estimate evapotranspiration form meteorological data and crop data. NOAA AVHRR satellite images have been used to generate daily evaporation maps for the Naivasha basin, Kenya (Farah et al., 2001). Further, an established model for rice cropping ORIZA2000 allows simulation of crop management options such as irrigation and nitrogen fertilizer management(Bouman et al., 2001). Studies have been done to establish correlation between Leaf Area Index (LAI) and crop coefficient (Kar and Verma , 2005a). Remote sensing determinants like actual evapotranspiration soil water content, crop growth are in use to compute overall water utilization at a range of scale upto field level (Bastiaanssen and Bos, 1999). These all are related to irrigation performance and predicting crop yield. No work has been done so far to encompass conveyance and distribution system of the irrigation. An attempt is proposed in this study to analyse the irrigation conveyance system with the real time water demand. Water demand for paddy rice depends upon growth stages, the so-called phenological stages. Crop transpiration rate is low at early stages of growth and increases almost linearly (Tomar and O'Toole , 1980). Four phenological stages of crops can be distinguished: initial, crop development, mid season, late season (Farmwest.com, 2004). Wetland rice has two more stages: nursery and land preparation. In the present study, it is proposed to develop a model to estimate field level water demand from LISS III satellite images and meteorological data, distinguishing the various growth stages of the crop on the field.

1.2. Problem statement

All irrigation engineers can compute total water volume given the canal network and irrigation demand issue and to match the water supply to the spatially and temporally varying crop/irrigation water requirement.


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1.3. Objectives

The main objective of this study is to extract information on water demand for rice plants at the distributary level from LISS III and LISS IV data and from meteorological data. More specifically, the aim is

• to use multi-temporal satellite data to estimate rice acreage and extract rice phenology during growth period

• to estimate crop wate r requirement and supply and the demand of irrigation water, also using meteorological data.

• to do a spatial analysis of water use at the cadastral level using IRS P6 LISS-IV data and cadastral level maps.

1.4. Research Questions

The research objective leads to the following research questions.

I. Can multi-temporal LISS III satellite image derive rice crop phenology?

1. Which phenology stage of rice crop is best derived from the LISS III images?

2. Which vegetative index is suitable for extraction of rice crop phenology?

3. What is the accuracy of rice crop phenological stage extraction from the image?

4. What is the accuracy of crop acreage estimation of different phenological stages of

the rice?

II. Is it possible to extract water distribution system using high-resolution satellite data (LISS-

IV)?

1. Which method of extraction gives best result?

Ø Visual interpretation

Ø Object/segment based

Ø Edge detection method

2. Upto what level is it possible to extract the canal network?

Ø Upto Distributary level

Ø Upto Minor level

Ø Upto Sub-minor level

Ø Upto Field channel level

1.5. Hypothesis

The main hypothesis of this study is that water use by crop depends on crop type, crop growth stage. Both can be derived from LISS III image during the growing season.


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1.6. Assumptions and limitations

In order to be able to carry out this research, we have to make several assumptions from the start. The main assumptions are that crops in the field at the time of study are free from stress, and disease free, and the crop coefficients obtained from literature can be used effectively without much error. The present study is done for paddy crop only. In multi-crop command crop coefficients are to be modified to suit the situation.

1.7. Chapter scheme

Chapter two discusses about literature review, chapter three about study area, chapter four about materials and methodology, chapter five about analysis, chapter six about results and discussions , chapter seven about conclusions and recommendations.

1.8. Data

Satellite images The following satellite images are used in the study: Table 1.1 : Details of satellite images used in the study

Satellite Date of acquisition of image Cloud cover IRS 1D LISS-III 16 Feb. 2002 No cloud cover

IRS 1C LISS-III 21 March 2002 No cloud cover IRS 1D LISS-III 07 April 2002 No cloud cover

IRS 1C LISS-III 14 April 2002 No cloud cover

IRS 1D LISS-III 02 May 2002 No cloud cover P6 LISS-IV (MX) 30 May 2005 No cloud cover

The technical specification of IRS sensor is given in the Table -1.2. Table 1.2 : Specification of IRS Optical Sensors: LISS III and LISS IV

Sensor Spectral Bands

(µm)

Spatial resolution (m)

Swath (km)

Quantization (bits)

SNR* SWR# @ Nyquist frequency

Green : 0.52-0.59

23.5 141 7 >128 > 0.40

Red: 0.62-0.68

23.5 141 7 >128 > 0.40

NIR: 0.77-0.86

23.5 141 7 >128 > 0.35

LISS-III

SWIR: 1.55-1.70

70.0 148 7 >128 > 0.30

LISS IV Green : 0.52-0.59

5.8 23 10


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Red: 0.62-0.68

5.8 23 10

NIR: 0.77-0.86

5.8 23 10

*SNR (signal to noise ratio) is the ratio between a signal (meaningful information) and the background noise

#SWR (standing wave ratio) is the ratio of the amplitude of a partial standing wave at an antinode (maximum) to the amplitude at an adjacent node (minimum).

Meteorological data Meteorological data has been collected from IMD station Sambalpur and Chipilima observatory of Orissa University of Agriculture and Technology. (on Daily basis for the study period during December 2001 to May 2002).

Irrigation data Irrigation data has been collected from Water resources department Government of Orissa for the study period (December 2001 to May 2002).

Agriculture data Agriculture data has been collected from Agriculture department Government of Orissa for the study period (December 2001 to May 2002).

1.9. Study Area:

For this research, parts of Hirakud command, Orissa, India has been chosen as study area. It extends

from 210 05'N to 210 55'N latitude and from 830 55'E to 84005'E longitude A part of command area of

6 x 6 km has been selected for study. It comes under agro climatic zone no. 12 i.e. eastern plateau

(Chhotanagpur) and Eastern Ghats, hot sub humid eco-region with red and laterite soils and Length of

Growing Period 150-180days (Mandal et al., 1999). In the entire Hirakud command area paddy is the

predominant crop covering 95 % of the total crop area (NRSA, 2004). Hirakud command area has a

culturable command area of 159106 ha. Source of irrigation of the command is Hirakud reservoir. A

dam over Mahanadi river in Orissa was built up in 1957 having live storage capacity of 4823 x 106 m3

and it provide irrigation potential of 159106 ha during kharif and 108385 ha during rabi season. In the

central part of the command two distributaries namely Babebira and Bugbuga distributary with

command area of 1662 ha and 1211 ha respectively have been taken up for this study. In this study

area rice is the dominant crop covering 81 % of the total crop area. Since it is an old command area an

attempt has been made for estimating water demand for rice cropping using the latest technology such

as satellite remote sensing. For this study area LISS III satellite images are available for the rabi crop

period 2001-2002. Rabi season was selected for the study, as during this period the images are


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generally cloud free. The study area is nearer to one of the meteorological station situated in the

command.


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2. Literature Review

The purpose of the research is to extract rice phenological stages from the satellite imagery and use it to compute water demand of rice crop with meteorological data. So, the review of literature is divided into sections as i) review the phenological stages of rice crop ii) water demand of rice iii) role of remote sensing in rice crop growth stage extraction iv) the irrigation water demand.

2.1. Rice crop

Rice (Oryza sativa L) is one of the main grain crops next to wheat. Rice is grown both as rabi (winter crop) and kharif (monsoon crop) crop under three conditions: upland rice, medium land rice and lowland rice in India. Growth Stages of the Rice Plant Two growth stages are distinguished in rice plant development -- vegetative and reproductive.

Figure 2.1: Rice growth stage [Source: http://www.fao.org/docrep/T7202E/t7202e0e.jpg, Accessed Date 14.07.2005]


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Nursery: The period from sowing to transplanting, duration approximately 25 to 30 days; Vegetative stage: the period from transplant to panicle initiation duration varies from 45 to 90 days; Mid season stage: the period from panicle initiation to flowering, duration approximately 30 days. This stage includes stem elongation, panicle extension and flowering. Late season or ripening stage: the period from flowering to full maturity; duration approximately 30 days. Counce et al. (2000) introduced the cumulative leave number (CLN) to express rice growth. In this method the rice growth stage has been divided into three phases: seedling, vegetative, and reproductive. Seedling development consists of four growth stages: unimbibed seed, radicle and coleoptile emergence from the seed, and prophyll emergence from the coleoptile, vegetative development stage according to the number of leaves with collars on the main stem, reproductive stage development consist of 10 growth stage based on discrete morphological criteria : panicle initiation, panicle differentiation, flag leaf collar formation, panicle exertion, anthesis, grain length and width expansion, grain depth expansion, grain dry down, single grain maturity, and complete panicle maturity. Goswami et al. (2003) expressed the growth stage of ric e and wheat in growing degree days for Ludhiana region, India. The growing degree days are calculated by summing mean temperature above base temperature (for rice the base temperature is 100C).

2.2. Water demand of Rice:

Water demand for rice varies from nursery to the harvesting. Water demand for entire growth period varies from 950 mm to 1050 mm for 3 month duration rice crop and 1120 to1250 mm for 4 month duration rice crop. It depends on crop growth stage, climatic condition and soil characteristics. For different conditions it varies from 1000-1500 mm for heavy soils high water table, short duration variety, Kharif season; 1500-2000 mm for medium soils Kharif or early spring season and 2000-2500 mm for light soils, long duration varieties during Kharif, medium duration varieties during summer (Indiaagronet, 2005). Kar and Verma (2005b) computed the crop water requirement of rice using CROPWAT 4.0 model as 450- 550 mm, 600-720 mm, 775-875 mm for autumn rice, winter rice and summer rice respectively in different agro-ecological sub-region of 12. Based on soil physiography, bio -climate and length of growing period India is divided into 20 agro-ecological regions and 60 agro-ecological sub regions (Mandal et al., 1999).


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Figure 2.2: Depth of water layer during the growing season [Source: http://www.fao.org/docrep/T7202E/t7202e07.htm, accessed date 14.07.2005] Crop water requirement: Crop water requirement is defined as the depth of water needed to meet the water loss through evapotranspiration of a disease-free crop, growing in large field under non-restricting soil conditions including soil water and fertility and achieving full production potential under given growing environment (Doorenbos and Pruitt, 1984).

Crop coefficient: The crop coefficient KC is the ratio of potential evapotranspiration for a given crop to the evapotranspiration of a reference crop. It represents an integration of effects of four primary characteristics that adjusts the crop from reference grass (i) Crop height, (ii) Albedo, (iii) Canopy resistance, (iv) Evaporation from soil; especially exposed soil. Factors determining the crop coefficient are crop type, climate, soil evaporation, crop growth stage (Allen et al., 1998).

Crop coefficient of rice: Most of the attempts have been made to extract crop coefficient for rice for wet season (July to October). Tyagi et al,(2000) found that the crop coefficient for Karnal, India as 1.15, 1.23, 1.14 and 1.02 for four crop growth stages of initial, crop development, reproductive (mid stage) and maturity (late stage), respectively. Tripathy (2004) calculated it for Tarai region of Uttarancahl, India as 0.39,1.0,1.7, 1.7, and 0.39 at transplantation, 24 days, 48 days, 66 days and at maturity of the crop, respectively. Shah et al (1986) derived the crop coefficient of rice at vegetative, reproductive and maturation stages as 0.96, 1.20 and 1.17 respectively for central plain of Thailand. Tomar and O’Toole (1980) found these values as 1.0, 1.15, 1.3, at transplanting, maximum tiller stage and flowering stages for wetland rice. Doorenbos and Pruitt (1984) suggested these values for both wet and dry season (December to mid May) for different geographical locations and seasons. According to him these values for wet season are 1.10, 1.05, and 0.95 and for dry season are 1.25,


16

1.10, 1.0 for 1st & 2nd month, mid season and last 4 weeks respectively for humid Asia with light to moderate wind.

Crop Coefficients of Rice1.

00

0.96

1.15

0.39

1.10 1.

25

1.23

1.001.

15 1.20

1.14

1.70

1.05 1.10

1.30

1.17

1.02

0.39

0.95 1.00

00.20.40.60.8

11.21.41.61.8

Tomor

Shah

Tyag

i

Tripath

y

Doore

nbos(w

s)

Doore

nbos(d

s)

Initial

Development

Reproductive

Maturity

Figure 2.3: Crop coefficient of rice suggested by different authors

Evapotranspiration: The combination of two separate process whereby, water is lost on the one hand from the soil surface by the evaporation and on the other hand from the crop by transpiration is referred as evapotranspiration (Allen et al., 1998). We distinguish reference crop evapotranspiration and crop evapotranspiration.

Reference crop evapotranspiration (ET0 ): Reference crop evapotranspiration represents the rate of evapotranspiration from an extensive surface of 8 to 15 cm tall, green grass cover of uniform height, actively growing, completely shading the ground and not short of water (Doorenbos and Pruitt, 1984). The methodology to compute ET0 is suggested by Allen et al. (1990). Lee et al.(2004) found that computation of monthly average evapotranspiration with eight evapotranspiration estimation methods (Penman, Penman-Monteith, Pan Evaporation, Kimberly-Penman, Priestley-Taylor, Hargreaves, Samani-Hargreaves and Blaney-Criddle have the same trend throughout the year.

Crop evapotranspiration, ETC : Crop evapotranspiration is the evapotranspiration from disease-free, well-fertilized crops, grown in large fields, under optimum soil water conditions and achieving full production under the given climatic conditions.

2.3. Remote Sensing to extract rice crop growth stages

Sakamoto et al. (2005) used Moderate Resolution Imaging Spectro-radiometer (MODIS/Terra) data to determine the planting date, heading date, harvesting date, and growing period in 30 paddy fields in


17

Japan in 2002 with root mean square error (RMSE) of phenological dates as 12.1 days for planting days, 9.0 days for heading date, 10.6 days for harvesting date and 11 days for growing period. Sakthivadivel et al. (1999) used multi-date satellite data of IRS-1B Linear Imaging and Self Scanning-II (LISS II) to generate spatially distributed information in total cropped area, area under major crop of Bhakra irrigation system in Haryana, India. Thiruvengadachari et al. (1996) performed remote sensing based assessment of cultivated areas, area under paddy and crop yields of the Bhadra irrigation project in Karnatak, India. In his study he used IRS LISS I data of 72.5 m spatial resolution and Landsat multi-spectral Scanner (MSS) data of 80 m resolution and Thematic Mapper data of 30 m resolution. Xiao et al (2005) found that MODIS-based paddy rice mapping have good agreement in area estimation of paddy field in southern China. Oguro et al (2003) found that Normalized Difference Vegetation Index (NDVI) increases corresponding to the growth of rice plant until flowering stage while Enhanced Vegetation Index (EVI) further continues to increase until the frutification stage. Mahi Right Bank Canal (MHRC) command in Gujarat in western-central India covers a culturable command area (CCA) of 212694 ha has six branches with 38 distributaries. Ray and Dadhwal (2002) used IRS-1C LISS-III and Wide Field Sensor (WiFS) multi-temporal data to generate crop inventory, vegetation spectral index profiles and crop evapotranspiration estimation over the Mahi Right Bank Canal (MHRC) command.

Vegetation Indices

Vegetation Indices (VI) has been suggested by various authors for various applications. The VI that commonly used for agricultural application are Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI) and Leaf Area Index (LAI).

The NDVI gives the information on vegetation cover defined as the ratio of difference in red and near infrared reflectance to their sum. Index values can range from -1.0 to 1.0, but vegetation values typically range between 0.1 and 0.7. Higher index values are associated with higher levels of healthy vegetation cover, whereas clouds and snow will cause index values near zero, making it appear that the vegetation is less green (Tucker, 1979).

The SAVI has been introduced by Huete (1988) to minimize the effects of soil background on the quantification of greenness by incorporating a soil adjustment factor (L) in the basic NDVI form. The value of L is taken as 0.5 for annual field crops.

Leaf Area Index (LAI): it is the cumulative area of leaves per unit area of land. It represents the total biomass and is indicative of crop yield, canopy resistance, and heat fluxes (Bastiaanssen, 1998).

Some research have been done relating the NDVI to the rice crop for the different growth stages (Kiyoshi, 2003; Mandal et al., 2003). Kiyosi (2003) establishes a relation between age of rice crop and NDVI of Landsat TM having a regression value of 0.93. He takes NDVI as dependent variable(y) and days after transplantation as independent variable(x): y = - 0.0002 x2 + 0.0252 x - 0.4508. Mandal et al. (2003) found that NDVI attained peak values at 62 days after transplanting of rice. Vegetation Indices derived from LISS III images:

The LISS III has 4 spectral bands (Table 1.2) from which various vegetation indices can be derived like Simple Ratio (SR), Normalised Differential Vegetation Index (NDVI), Transformed Vegetation


18

Index (TVI), Soil Adjusted Vegetation Index (SAVI), and Weighted Difference Vegetation Index (WDVI).

2.4. Irrigation water demand

The irrigation water demand varies according to the crop water requirement, which is in turn varying according to the crop growth stages. Irrigation scheduling of paddy is based on three questions: (i) When to, (ii) How often and (iii) How much. Irrigate when the crop need water to meet its evapotranspiration demand, and often enough to prevent the plants suffering from drought. Irrigate as much as the plants’ demand. Evapotranspiration is low at early stages of crop and maximum at heading stage that demands more frequency of irrigation towards flowering . In a water distribution system water allocation is made according to the designed crop water requirement, which is based on crop season, crop calendar, cropping pattern.

There are three types of irrigation supplies: (i) Continuous supply (ii) rotational supply and (iii) demand based supply. In continuous supply the supply is adjusted according to the requirements over the season. In rotational supply the requirements are met with by adjusting the duration and interval of supply and the user adjust their crop water requirement according to the supply. In demand based irrigation supply the users take the irrigation water as per demand (Doorenbos and Pruitt, 1984). Rotational irrigation supply, locally named as Warabandi, is practised in the states of Haryana and Uttar Pradesh in India. Bhakra Irrigation (Sakthivadivel et al., 1999) system is an example of this system. The demand is practised in the state of Maharastra and Shejapali irrigation system is an example of this system. Most of the irrigation systems in southern part of India aim at both of these objectives, namely: equity and adequacy. These canal systems were designed as continuous water supply systems. The increase in cropping area and changes in cropping pattern in course of time increased the demand in these systems. So, the main canal capacity is inadequate to run all the distributaries canals simultaneously. Rotational water distribution has been introduced in some of the systems to manage the shortage of water.

The models, that helps in irrigation scheduling are CROPWAT for windows (Clarke, 1998), ORYZA2000 (Bouman et al. , 2001), GISAREG (Fortes et al., 2005) and Surface Energy Balance Algorithm For Land (SEBAL) (Waterwatch, 1998). All models are aiming at meeting the crop demand with the available water to get maximum production. The model is able to generate irrigation scheduling alternatives that are evaluated from the relative yield loss produced when crop evapotranspiration is below its potential level [Oweis et al.,2003 and Zairi et al., 2003 Liu et al., 2000 and Campos et al,2003 cited in (Fortes et al., 2005)]. The CROPWAT model was originally developed by the FAO in 1990 to calculate crop water requirements and for planning and managing irrigation projects. The input data of the CROPWAT model include crop, meteorology, and soil. The meteorology data include: (1) maximum and minimum temperature; (2) wind speed; (3) sunshine hours; (4) relative humidity; (5) rainfall. Kuo et al. (2005) found, the irrigation water requirements in the paddy fields of Taiwan are 962 mm and 1114 mm for the rice crop planted on dated 15 January and 15 June respectively. Jehangir et al. (2004) tested the irrigation requirement for different rice establishment technologies and found that the direct seeding on flat need the least irrigation water (865 mm) followed by direct seeding on beds


19

(924 mm) and transplanting on beds (999 mm) compared to 1130 mm needed in case of conventional rice cultivation.


20

HIRAKUD

RESERVOIR

3. Study Area

3.1. Location

The study area, also called the command, lies in the central part of the Orissa on the eastern coast of

India. It extends from 210 05' N to 210 55' N latitude and from 830 55' E to 84005' E longitude. A part of

command area of 6 km x 6 km has been selected for study.

Figure 3.1: Study Area The motivation behind the study is that the Hirakud command is an old command nearly 50 years old. The command is dominated by rice cropping covering 81 % of the total crop area. Since it is an old command area an attempt has been made for estimating water demand for rice cropping using the latest technology such as satellite remote sensing. The irrigation potential of Hirakud command is 159106 ha during kharif and 108385 ha during rabi season. In the central part of the command two distributaries namely Babebira and Bugbuga distributary with command area of 1662 ha and 1211 ha respectively have been taken up for this study. For this study area LISS III satellite images are


21

available for the rabi crop period 2001-2002. Rabi season was selected for the study as during this period the images are generally cloud free. The study area is nearer to one of the meteorological station situated in the command.

3.2. Climate

The climate of the command is tropical monsoon with four distinct seasons: summer- March to May, monsoon- June to September, post-monsoon- October to November, and winter- December to February. The command gets rain by the southwest monsoon season. The annual average rainfall is 1038 mm and 75% dependable annual rainfall is 816 mm. The mean maximum and mean minimum temperature are 42 0C and 13 0C respectively. The humidity varies from 94 % in summer to 24 % in winter.

3.3. Soils

The soil type is a mixture of sand and gravel as well as of clay. The surface texture varies from loamy sand to sandy loam abruptly underlined by heavy surface and in some parts it varies from sandy clay to clay loam and the clay content increases with depth. The water capacity varies from 100 to 125 mm/m (Kar and Verma, 2005a).

3.4. Geology

The command is made of garnetiferous sillimanite schist, predominant rock. The schist shows regular veins and knots of feldspar and quartz along foliation planes. It exhibits minor evidence of sulphide mineralization. The next rock type Gondwana rocks Cuddppahs. Schistose rocks occur as lenses and pockets of considerable dimensions within the granitic rocks.

3.5. Agriculture

There are two cropping season namely Kharif from June to December and Rabi from Dec-Jan to May in practice. Culturable Command Area of Hirakud command area during Kharif is 159106 ha and during Rabi is 108385 ha. The major crops are Rice, Wheat, Pulses like Arhar, Mung and Biri, Oil-seeds like Groundnuts, Til and Mustard, and Sugarcane. Rice is the most dominant crop. There are three verities of rice namely early, normal and late. The crop period of rice varies according to varieties. It is 75 days for early rice paddy and 150 days for late rice paddy. The transplantation days are also spread over a month. For rabi paddy it spreads from January 10 to February 10. January 20 is being the peak period of transplanting.

Agricultural practices: Paddy is the dominant crop in both Rabi and Kharif season. Nearly 95% of the CCA is under paddy cultivation (NRSA, 2004).

Crop calendar: The agriculture year of the command begins from July and ends in next June. The crop calendar provides information about cultivation of various crops in a year. Two principal cropping seasons Rabi and Kharif are prevailed in most of the command. Rabi crops also known as winter crops are grown from December to May. Kharif crops also known as summer crops are grown from July to December.


22

Crop period: The period from the instant of sowing to the instant of its harvesting is called crop period. Crop period of Rabi paddy varies from 110 days to 130 days in Hirakud command.

Cropping pattern: The cropping pattern practised in the study area is Rice-Rice-Rice during kharif; Rice- Mung-Rice during winter. Other crops grows in the command are pulses, vegetables, oilseeds and sugarcane.

Irrigation: The main source of irrigation is surface irrigation from the Hirakud reservoir. The canal network encompasses the command: main canal, branch canal, distributaries, minors, and sub-minors. The source of irrigation is Hirakud reservoir, which has a live storage capacity of 4823 x 106 m3. The Canals scattered in the command area are Bargarh Main Canal, Sasan Main Canal and Sambalpur Distributary. Table3.1 below shows the most important features of the canals:

Table 3.1: Details of Main Canals of Hirakud Command Area

S. No. Name of Canal Length (Km.) Full supply discharge (Cumec)

Bed width ( m)

Full supply depth (m)

1 Bargarh Main Canal 84.28 107.60 45.7 2.68 2 Sasan Main Canal 21.79 17.80 16.67 1.49

3 Sambalpur Distributary 18.08 3.40 4.57 1.06 The irrigation potentials equal 159106 ha and 108385 ha during kharif and rabi, respectively. The distributaries under study get water from Attabira Branch Canal of Bargarh Main canal. The Bargarh Main canal is fed from a reservoir. The Irrigation practice in the command is a demand-based system.

The details of the two distributaries being investigated in this study are presented in Table -4.3 (page 25)


23

4. Materials and Methods

In this chapter, the materials used for the study and the methodologies adopted to attain the objectives are discussed.

4.1. Materials

The materials used in this study are satellite imagery for extraction of crop acreage and canal network extraction, meteorological data for computation of reference crop evapotranspiration, crop coefficient of rice crop for computation of crop evapotranspiration of rice crop, agricultural data for statistical computation and validation of results, irrigation data for analysis of water demand and supply of rice crop. Satellite Imagery: IRS 1C/1D Linear Imaging and Self Scanning-III (LISS III) images acquired on 5 days (16 th Feb 2002, 21st March 2002, 7th April 2002, 14th April 2002 and 2nd May 2002) are used to derive rice crop phenology and one IRS P6 LISS IV (30th May 2005) image is used for canal network extraction. The specifications of IRS sensor are given in the Table- 1.2.

Meteorological data: The meteorological data like maximum and minimum temperature, maximum and minimum relative humidity, wind speed, sunshine hour, solar radiation, pan evaporation, rainfall on daily basis of Sambalpur. an Indian Meteorological Department (IMD), station and Chipilima observatory, which are situated in the command area and near to the study area are used for the study. The maximum and minimum temperature, maximum and minimum relative humidity at the Chipilima observatory are given in the Figures 4.1 and 4.2. Pan evaporation and rainfall are given in Figure 4.3. The temperature varies from 7.5 o C during January 2002 to 44 o C during May 2002. The relative humidity varies from 19.0 % during December to 95 % during February.


24

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

40.0

45.0

50.0

1-D

ec-0

1

11-D

ec-0

1

21-D

ec-0

1

31-D

ec-0

1

10-J

an-0

2

20-J

an-0

2

30-J

an-0

2

9-Fe

b-02

19-F

eb-0

2

1-M

ar-0

2

11-M

ar-0

2

21-M

ar-0

2

31-M

ar-0

2

10-A

pr-0

2

20-A

pr-0

2

30-A

pr-0

2

10-M

ay-0

2

20-M

ay-0

2

30-M

ay-0

2

Dates

Tem

per

atu

re in

0 C

enti

gra

de

Minimum Temperature Maximum Temperature

Figure 4.1: Temperature trend in the study area during Rabi season 2001-02

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

90.0

100.0

1-D

ec-0

1

11-D

ec-0

1

21-D

ec-0

1

31-D

ec-0

1

10-J

an-0

2

20-J

an-0

2

30-J

an-0

2

9-F

eb-0

2

19-F

eb-0

2

1-M

ar-0

2

11-M

ar-0

2

21-M

ar-0

2

31-M

ar-0

2

10-A

pr-0

2

20-A

pr-0

2

30-A

pr-0

2

10-M

ay-0

2

20-M

ay-0

2

30-M

ay-0

2

Dates

% o

f Hu

mu

dit

y

Minimum Maximum

Figure 4.2: Humidity in the study area during Rabi season 2001-02


25

14.0

0

8.00

3.20

4.20

1.60

3.80

4.40

2.00 2.

40

0.20

1.20

2.60

2.40

7.20

5.40

14.8

07.

0014

.40

0.40

4.00

0

2

4

6

8

10

12

14

16

1-D

ec-0

1

11-D

ec-0

1

21-D

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1

31-D

ec-0

1

10-J

an-0

2

20-J

an-0

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30-J

an-0

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9-Fe

b-02

19-F

eb-0

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1-M

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ar-0

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ar-0

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pr-0

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ay-0

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ay-0

2

30-M

ay-0

2

Dates

Eva

pora

tion

and

Rai

nfal

l in

mm

Pan Evaporation Rainfall

Figure 4.3: Pan Evaporation and rainfall in the study area during Rabi season 2001-02

The Figure 4.3 shows the trend of evaporation recorded by Class-A pan at Chipilima observatory. The evaporation is high during the summer. The minimum pan evaporation recorded was 1.3 mm on 30th January and maximum 10.0 mm on 24th May. There were 21 rainy days during the crop period, maximum being 14.8 mm on 26th may and total rainfall was 47.6 mm. The contribution of rainfall to crop growth was negligible as the effective rainfall was zero.

Crop coefficient: As crop coefficients from the nearby agricultural research stations were not available, hence crop coefficients were collected from the literature. The crop coefficients as suggested by Tyagi et al. (2000) were used in the computation of crop water requirement. He suggested 1.15, 1.23, 1.14 and 1.02 for four crop growth stages of initial, crop development, reproductive (mid stage) and maturity (late stage), respectively.

Agricultural data: The data maintained by the agricultural department and water resources department of Government of Orissa are used. The following table shows the agricultural data of the study area during rabi season (December 2001 to May 2002). Table 4.1 shows the village-wise crop grown during the rabi season. Table 4.2 shows the rice growth stages and its duration during rabi season.


26

Table 4.1: Village wise agricultural data

Source: Agriculture Department, Government of Orissa

Table 4.3 and 4.4 show the distributary-wise and village-wise command area respectively. It may be pointed out that under one distributary many villages are falling, and also the command of one village may fall under more than one distributary. Hence to compute the statistics of command area the interpolation method has been adopted. In the study area rice being the dominant crop covers 81 % of the total crop area during rabi season. The rice crop grown is the medium variety having crop period of 120 days. The table below shows the rice crop growth stages. Table 4.2: Rice growth stages and duration in days

Source: Agriculture Department, Government of Orissa

Village wise report on Agricultural data of Hirakud command, Attabira block of Bargarh district For Rabi season 2001-02

Area of Different types of Crops

S.No. Village Name Paddy Pulses Oilseed Vege-tables Sugar-cane

Other crops Total Area

Sowing/ Transplanting Dates

Harvesting dates

(ha) (ha) (ha) (ha) (ha) (ha) (ha) 1 Attabira 561.0 27.7 53.0 20.0 1.0 13.3 676.0 2 Rengalipali 104.0 10.5 12.0 16.0 1.0 12.5 156.0

3 Kandpalli 91.0 7.0 13.0 11.0 0.5 5.5 128.0 4 Ladarpali 102.0 9.3 21.0 14.0 0.5 18.8 165.6

5 Kulunda 800.0 35.0 125.0 60.0 10.0 25.0 1055.0

20-Dec-01

to

8-Feb-02

15-Apr-02

to

10-May-02

6 Bhursipali 308.0 15.0 20.0 22.0 13.0 378.0 7 Babebira 280.0 25.0 40.0 15.0 15.0 375.0

8 Birakhakata 6.0 8.0 8.0 1.0 2.0 25.0

9 Khandgali 20.0 10.0 10.0 1.0 3.0 44.0 10 Bugbuga 1220.0 30.0 40.0 15.0 3.0 1308.0

Total 3492.0 177.5 342.0 175.0 13.0 111.1 4310.6

Cropped area

(%) 81.0 4.1 7.9 4.1 0.3 2.6 100.0

20-Dec-01

to 10-Feb-02

15-Apr-02

to 10-May-02

Growth Stages For 120 days crop period Nursery Period 25 Initial Stage 19 Development Stage 20 Mid Stage 37 Late Stage 19 Rice crop period 120


27

Irrigation data: The water resources department, Government of Orissa, maintains irrigation scheduling data like time duration, frequency and quantity of irrigation supply. These data has been collected and used in this study (see Figure 4.4).

Water Supply

0

0.2

0.4

0.6

0.8

1

1.2

25-Nov-01

15-Dec-01

4-Jan-02

24-Jan-02

13-Feb-02

5-Mar-02

25-Mar-02

14-Apr-02

4-May-02

24-May-02

Dates

Dis

char

ge

in C

um

ec

Babebira Bugbuga

Figure 4.4: Irrigation water supply during Rabi season 2001-02 Source: Department of water Resources, Government of Orissa

Canal network: The canal network encompasses the command are main canal, branch canal, distributaries, minors, sub-minors. The source of irrigation water is Hirakud reservoir, which has a gross storage capacity of 7189 x 106 m3 and live storage capacity of 4823 x 106 m3 . The irrigation demand is 2670 x 106 m3 . It is 55.36 % of live storage. The distributaries under study get water from Attabira Branch Canal of Bargarh Main canal. The Bargarh Main canal is fed from reservoir. The Irrigation practice in the command is demand based system.

i. Irrigation method: In the irrigation command, the water supply in the channel is on continuous basis, and the farmers irrigate their lands according to the demand. They regulate the supply as per their requirement by closing and allowing the water.

ii. Irrigation frequency and interval: for the rabi season the irrigation starts from mid December to mid May.

iii. Irrigation application depth / discharge / duration: depth.


28

Table 4.3: Distributary-wise Command Area

S. No. Name of canal Off-taking R.D. in km of the branch canal

Length (km) Full Supply Discharge (Cumec)

CCA (ha)

1 Babebira Distributary 14.295 6.706 1.092 1662.00

2 Bugbuga Distributary 15.666 5.944 0.764 1211.00

Source: Department of water Resources, Government of Orissa

Table 4.4: Village-wise Command Area under each distributary

CCA (ha.)

S. No. Village

Babebira Distributary

Bugbuga Distributary

1 Ladarpali 38.368 2 Attabira 163.720 3 Kulunda 601.110 290.873 4 Bhuinpura 301.872 5 Birakhakata 25.374 6 Khandagali 34.912 7 Babebira 342.815 8 Bugbuga 153.506 920.077

Total 1661.678 1210.950

Source: Department of water Resources, Government of Orissa

4.2. Methods

Methods are divided into four sections (i) Extraction of rice crop phenology and rice crop acreage estimation from the satellite images. (ii) Computation of potential evapotranspiration from meteorological data. (iii) Computation of water demand. (iv) Extraction of canal network from LISS IV image The methodology followed to estimate distributary level water demand by rice (and also irrigation water demand) is sketched in Figure 4.5.

The methodology consists of four parts: collection of data, image processing to extract spatial data, estimation of evapotranspiration, and estimation of water demand at field level.


29

Figure 4.5 : Methodology

4.2.1. Determination of NDVI values and the rice map

The procedures followed in this section are geographic registration, radiometric normalisation, conversion of DN value of pixel to radiance value, and generation of NDVI maps. For the image processing the software “ERDAS Imagine (ERDAS, 2003)” has been used.

Geographic registration

Geometric correction of images has been done with the help of Ground Control Points (GCPs) from the topo map. The errors of geo-reference are in order of 0.069, 0.120, 0.119, 0.099, and 0.072 pixels for images of dated 16th February 2002, 21st March 2002, 7th April 2002 , 14th April 2002 and 2nd May 2002 respectively. The projection system adopted in this study is Polyconic with Modified Everest as Datum.

Radiometric normalization

Radiometric normalization: Multiple temporal images of the same area taken under different conditions have the reflectance values which are biased with non-scene dependent parameters like illumination, atmospheric propagation and sensor response during the time of acquisition. It needs some form of normalisation to interpret true changes between the scenes. In normalisation one of the images is transformed, band by band, to appear (to first order) as though they were acquired under the same conditions as the reference image. Schott et al. (1988) suggests pseudoinvariant feature (PIF) approach to address radiometric scene normalisation and in-scene man-made elements (e.g., roads, urban area, and industrial areas) are taken as PIF. In this study urban, clear water and dry sand are considered as PIF. Taking urban [(average of 3x3 pixels matrix) of 4 training sites (cyan colour in FCC)], water [(average of 3x3 pixels matrix) of 3 training sites (black colour in FCC)] and dry sand [(average of 3x3 pixels matrix) of 4 training sites (white colour in FCC)] as Pseudo Invariant Features (PIF) regression equations are derived between satellite data of various dates having high r² (0.9214 – 0.9927). The image having minimum DN value in Near Infra Red (NIR) band was chosen as

Geometric correction (LISS III)

Crop Acreage estimation For diff. phenological stages

Water Demand Analysis Crop Water Requirement

ET0

Crop coefficient KC ETC

Reliable Irrigation

Water at field

Radiometric normalisation (Using Regression equations of PIFs)

Generation of Rice map


30

reference image as it was considered that that image might have contain less atmospheric noise. From the image information window in the ERDAS IMAGINE it was found that the minimum DN values were 16, 23, 36, 26, 42 for 16th February 2002, 21st March 2002, 7th April 2002, 14th April 2002 and 2nd May 2002 image respectively. Since the minimum DN value of 16 was for 16th February 2002 image, hence the same image was taken as reference image.

Regression plots generated for the band 1, band 2 and band 3 for PIFs are shown in the Figures 4.6, 4.7, 4.8 respectively. Summery of the regression generated for the PIFs is shown in Table 4.5.

Figure 4.6: Plots generated for PIFs (Urban, Water and Dry sand) on Band-1 of LISS III image Note:

D1: Day 1 (16th Feb 2002); D2:Day2 (21st March 2002); D3: Day 3 (7th April 2002); D4: Day 4 (14th April 2002);

D5: Day 5 (2nd May 2002), B1: Band1; B2: Band2; B3: Band3 for all the dates.

y = 0.9673x - 18.153R2 = 0.984

0

20

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0 20 40 60 80 100 120 140

D2B1

D1B

1

y = 1.1162x - 47.717R2 = 0.9322

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D3B1

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1

y = 0.7508x - 6.1698R2 = 0.9714

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1

y = 1.2491x - 82.106R2 = 0.9214

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120

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D5B1

D1B

1


31

Figure 4.7: Plots generated for PIFs (Urban, Water and Dry sand) on Band-2 of LISS III image

Figure 4.8: Plots generated for PIFs (Urban, Water and Dry sand) on Band-3 of LISS III image

y = 0.9805x - 7.1993R2 = 0.9903

0

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D1B

3

y = 0.9869x - 20.921R2 = 0.9686

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120

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D3B3

D1B

3

y = 0.8759x - 7.9151

R2 = 0.9927

0

20

40

60

80

100

120

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D4B3

D1B

3

y = 1.0434x - 35.375R

2 = 0.9682

0

20

40

60

80

100

120

0 20 40 60 80 100 120 140 160

D5B3

D1B

3

y = 1.0414x - 13.672R2 = 0.9886

0

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40

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120

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D1B

2

y = 1.0008x - 29.547R2 = 0.9546

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120

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2

y = 0.8456x - 9.3225R

2 = 0.9839

0

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60

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120

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D4B2

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2

y = 1.0636x - 50.206R2 = 0.9426

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D5B2D

1B2


32

Table 4.5: Regression equations between satellite data of 5 acquisitions for Pseudo Invariant Features

Between Ist and 2nd date acquisition image Regression

r²

D1B1 = 0.9673 x D2B1 - 18.153 0.9840

D1B2 = 1.0414 x D2B2 - 13.672 0.9886

D1B3 = 0.9805 x D2B3 - 7.1993 0.9903

Between Ist and 3rd date acquisition image

D1B1 = 1.1162 x D3B1 - 47.717 0.9322

D1B2 = 1.0008 x D3B2 - 29.547 0.9546

D1B3 = 0.9869 x D3B3 - 20.921 0.9686

Between Ist and 4th date acquisition image D1B1 = 0.7508 x D4B1 - 6.1698 0.9714

D1B2 = 0.8456 x D4B2 - 9.3225 0.9839

D1B3 = 0.8759 x D4B3 - 7.9151 0.9927

Between Ist and 5th date acquisition image D1B1 = 1.2491 x D5B1 - 82.106 0.9214

D1B2 = 1.0636 x D5B2 - 50.206 0.9426

D1B3 = 1.0434 x D5B3 - 35.375 0.9682

Note: D1 : 16-Feb-02 ; D2 : 21-Mar-02 ; D3 :7-Apr-02 D4 : 14-Apr-02 ; D5 : 2-May-02

B1 : Green ; B2 : Red ; B3 : NIR

From the Figures 4.6, 4.7, 4.8 it is seen that test sites of pseudo invariant features plays an important roll. To overcome this bias it was decided to consider 3x3 pixels for one feature class and the average value was taken to generate the plot. From the plot it is seen that the points at lower end have influences on the regression line. Actually they are the pixels representing the water body, which has minimum DN values than other features in the image. Similarly the dry-sand have higher values. The urban area considered as other pseudo invariant features have the intermediate values. As the image has high reflectance values as well as low reflectance values, to have better control over the regression equation derived for normalisation both the features area included. The choice of test sites for pseudo invariant features are subjective, but there will be no much difference.

The images have been normalized with the help of above equations in Model Maker of ERDAS IMAGINE software. Figure 4.9 shows the histogram before and after the normalisation of 21st March 2002 image. It reflects the change in Digital Number (DN) values of pixels , which are free from atmospheric influence like aerosol, sun illumination. A and C shows the histogram of 21st March before and after normalisation for band 2 and B and D shows the histogram of 21st March before and after normalisation for band 3 respectively. It is seen from the Figure 4.9 (C) that the histogram contains some gaps. This reflects that there is no pixel having that DN values after normalisation. With normalisation the DN values having these values in the original image has been changed to new values that free from atmospheric and other influences. Similarly it is seen from the Figure 4.9 (D) due to atmospheric normalisation more pixels have one value which reflects a high peak. The scales in y-axis are different for A, B, C and D as the ERDAS Imagine software generates these by default. The


33

maximum values in the y-axis in the histograms are 11329236, 11329524, 358320 and 1132176 for A, B, C and D respectively. With normalisation all the images became free from atmospheric influence and sun illumination.

Figure 4.9: Histogram showing DN values of 21st March 02 images before and after normalisation Note: A: Band2 of 21st March before normalisation, B: Band3 of 21st March before normalisation,

C: Band2 of 21st March after normalisation, D: Band3 of 21st March after normalisation,

Conversion of DN values to radiance:

The sensor recorded the reflectance value converting it to DN values. To interpret the reflectance of the same object recorded by different sensors and on different times, it need to be convert back the DN values to its original reflectance. We need the reflectance values of the crop to interpret the growth stage with the help of vegetation indices. It needs the maximum and minimum radiance value for each band which is unique for each sensor. This information is provided with the header file of the image. For this study these parameters were considered. Table 4. 6 gives the maximum and minimum radiance for LISS-III sensor of IRS-1C/1D image of study area.

The radiance of the images has been computed with the equation: Lrad = (DN / MaxGray) * (Lmax – Lmin) + Lmin

Lrad : Radiance for a given DN value DN : Digital count MaxGray : 255 Lmin / Lmax: Minimum/ Maximum radiance value for a given brand available in the header file of the image

A B

D C


34

Table 4.6: Limits of Radiance values from the header file of the satellite data

Satellite Image Date Band Lmin Lmax

band1 0.00 14.8005 band2 0.00 15.6644

IRS – 1D 16th Feb 2002 07 April 2002 02nd May 2002 band3 0.00 16.4523

band1 1.76 14.4500 21st March 2002 14th April 2002 band2 1.54 17.0300

IRS – 1C

band3 1.09 17.1900

Source: National Remote Sensing Agency (NRSA)

DN values have been converted to radiance images with the help of Model Maker of ERDAS IMAGINE software.

Computation of NDVI for all the images:

Normalised difference vegetation index (NDVI) suggested by Tucker (1979) used for estimate vegetation cover. Its value ranges from -1.0 to 1.0. More information on this is explained in the literature review page-16.

RNIRRNIR

NDVI+−

= ….. …. (1)

where R and NIR are reflectance in red and near-infrared wave length regions.

All the NDVI images of full scene were Stacked into a single file for further use.

Classification of NDVI image:

Unsupervised classification has been done from stacked NDVI image with 50 no of classes. Those classes match with the ground truth of agriculture were considered. Out of 50 classes 18 were identified as agriculture. Unsupervised classification is based on Iterative Self-Organising Data Analysis Technique (ISODATA) clustering method. It is an iterative method. The number of clustering is based on the number of classes. The more number of classes helps in post classification stage to interpret the features more visually in feature space image. Samples: The ground truths taken by National remote Sensing Agency (NRSA) for another study have been used in this study. The samples for rice and non-rice have been used in this study are as follows: Rice: 80 samples; area of samples ranges from 2757 m2 to 34809 m2. Total area sample for rice crop is 1053083 m2. Non-rice: 30 samples; area of samples ranges from 97 m2 to 9067 m2. Total area sample for non-rice crop is 87601 m2.


35

These samples cover the entire scene of LISS III image.

Post-classification: Recoded the classified image into two classes; 1 for agriculture 0 for others. Mask the area in the stacked NDVI image by intersecting it with the recoded classified map. Unsupervised classification (ISODATA) is a multi-stage approach. So, The agriculture map again classified into 20 classes. From these 14 classes were identified as agriculture. The image has been recoded . The stacked NDVI image has been masked with it.

Again the image classified into 10 classes . Three classes were identified as paddy. Recoded it 1 for Paddy 0 for others. Thus, Rice map of the study area was generated. At this level of classification in post classification stage the ground truth used for the classification are satisfied so no more iteration has been attempted.

Masked area other than rice in the NDVI stacked image by intersecting it with the rice map (recoded classified) image. The rice map was generated.

Since the NDVI approach has been followed to generate rice map, some non-rice pixels having same NDVI value as of rice pixels might have been miss-classified as rice pixels. These non-rice pixels need to be delineated from the final rice map.

Non-rice pixels have been delineated from the rice map by trial and error method using the following rules.

Rule 1: During the period from initial stage to reproductive stage the greenness of the rice crop increases. Hence during this period the NDVI value of two consecutive images have been considered to derive a threshold. On 21st March, the rice crop is in mid (reproductive) stage of crop growth stage while on 16th February the rice crop is in development stage of crop growth stage . So the rice on 21st March has more greenness than 16th February. The rule 1 has been considered as:

If NDVI of 21st March > NDVI of 16th February , then rice else non-rice

Rule 2: During the period from reproductive stage to maturity stage the greenness of the rice crop decreases. Hence during this period the NDVI value and the crop area of two consecutive images have been considered to derive a threshold. With a threshold, the area above the threshold of 2nd May should be less than the area of the 14 th April. The threshold is derived by trial and error.

If NDVI of 2nd May < threshold, then rice else non-rice NDVI of previous image,


36

Generation of Agriculture Map

Rice Map

Threshold to delineate area Other than Rice

Generation of Final Rice Map

Unsupervised Classification With 10 classes (3 rd Iteration)

Stacked NDVI Image

Unsupervised Classification With 50 classes (1st Iteration)/ With 20 classes (2nd Iteration)

Post Classification

Mask the area other than agriculture (Intersecting Stacked NDVI image with recoded

Recoded Image (1 for agriculture and 0 for others)

Post Classification

Recoded Image

(1 for rice and 0 for others)

Generation of NDVI images (For 5 acquisitions)

Figure 4.10 : Flow chart for extraction of Rice pixels from NDVI image


37

Figure 4.11: Flow chart for classification and generation of Rice map Distributary level crop area statistics were extracted by digitally overlaying the base maps of the command area on geometrically rectified crop classification map using GIS software.ArcGIS was used to generate the GIS database and also analysis. Figure 4.11 shows the flow chart for generation of final rice map.

4.2.2. Computation of reference evapotranspiration

It is reported that pan evaporation is a more satisfactory method of estimating reference crop evapotranspiration than other methods for rice [(Azhar et al., 1992, Sriboonlue and Pechrasksa, 1992) in (Lee et al., 2004)]. The pan evaporation method was used to compute reference crop evapotranspiration of the study area. It needs only the depth of daily evaporation together with wind speed and relative humidity to calculate the pan coefficient. The meteorological data of Chipilima was used to compute evapotranspiration. It is nearest to the study area among three meteorological stations situated in the entire command area. Allen et al. (1998) recommends where observations of wind speed and relative humidity, required for the computation of Kp, are not available at the site, estimates of the weather variables from nearby station have to be utilized. Hence, the non-availability of wind data of Chipilima observatory is overcome by using the data of other nearer observatory, Sambalpur. The variation of reference evapotranspiration of study area is shown in the Figure 4.12. The computed maximum ET0 was 6.47 mm/day during first decade of May and minimum was 2.06 mm/day during 3rd decade of December. ET0 has been computed by the equation (2). The pan coefficient, Kp, has been computed with equation (3) as suggested by Allen et al. (1998).

Reference Crop Evapotranspiration

0.0

1.0

2.0

3.0

4.0

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7.0

1-D

ec-0

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ec-0

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ec-0

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an-0

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b-02

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ay-0

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ay-0

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ay-0

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Dates

ET 0

in m

m/d

ay

10 days average

Validation


38

Figure 4.12: Reference evapotranspiration Pan Evaporation method:

panp EKET =0 …. (2)

Where ETo is the reference crop evapotranspiration (mm / day) Kp the pan coefficient: it depends on relative humidity, wind speed and upwind buffer zone fetch. Epan the pan evaporation (mm / day). Kp is computed with the equation for Class A pan with green fetch on 10 day basis (Allen et al., 1998).

)ln()][ln(000631.0)ln(1434.0)ln(0422.00286.0108.0 22 meanmeanpan RHFETRHEFETUK −++−=

… (3) Here, U2 = average daily wind speed at 2 m height (ms -1)

RH mean = average daily relative humidity (%) FET = fetch, or distance of the identified surface type (grass or short green agricultural crop

for case A, dry crop or bare soil for case B upwind of the evaporation pan)

0ETKET ccrop = … (4)

Where ETcrop is the crop evapotranspiration (mm / day) and Kc is the crop coefficient.

Extraction of information from satellite images

Crop phenological stage extraction of the study area for each image has been done. This is explained more in Chapter 5.

4.2.3. Computation of water demand

The computation of water demand at distributary level has been done. The interval duration has been considered as 10 days. From the study it is found that it is possible to extract the spatially distribution of rice crop for three phase as early transplanted rice, normal transplanted rice and late transplanted rice. Crop water requirement of rice crop are computed for early, normal and late transplanted rice has been computed 10 days basis.

4.2.4. Canal Network Extraction

IRS P6 LISS IV image of 30th May 2005 was used to extract canal network of the study area. Two approaches (i) Multi-resolution segmentation approach, (ii) Edge detection method.


39

Multi-resolution segmentation approach of eCognition software is used for this purpose. This approach uses different parameters like Layer Weight, Scale Parameter, Shape Factor, and Compactness. As this approach a trial and error approach different combinations of parameters are adopted. Those are as follows:

1. Layer weights:

i. All layers weights are taken 1.0

ii. Layer of green band : 0.5, Layer of red band : 1.0, Layer of NIR band : 1.5

iii. Layer of green band : 1.5, Layer of red band : 1.0, Layer of NIR band : 0.5

2. Scale parameters : 50, 35, 25, 20

3. Shape factor : 0.5, 0.4, 0.3, 0.2, 0.1

4. Compactness: 0.5, 0.4, 0.3, 0.2, 0.1

In most of the trial the main canal is highlighted only.

Edge detection method: ERDAS Imagine software is used. Different type of filter available on raster option and also Radar | Radar Interpreter | Edge Enhancement are attempted. These are Robinson 3-level, Multi level, Prewitt gradient, Kirsch, Unweighted line, Weighted line, edge detect, edge enhance and Laplacian edge detection with kernel of 3 x3 and 5 x 5. In all the trial it is seen that the canal network in original image is more prominent than the images generated applying the filter.


40

5. Analysis

In this section the threshold to derive rice map, interpretation of crop phonological stages with vegetation indices like Normalised Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI), rice crop coefficients, reference crop evapotranspiration and validation of results are discussed.

The Normalised Difference Vegetation Index (NDVI) as suggested by Tucker (1979) is generated for each image. NDVI represents the greenness of the vegetation. From attribute table of images the NDVI values of each image are extracted and the values are shown in Table 5.1.

Table 5.1: NDVI of the temporal satellite images

Image acquisition date Minimum NDVI Maximum NDVI 16th February 2002 (-) 0.54795 0.60694

21st March 2002 (-) 0.60000 0.70558 7th April 2002 (-) 0.52795 0.75887

14th April 2002 (-) 0.49315 0.66460 2nd May 2002 (-) 0.56757 0.76389

All the images have negative and positive values. This reflects the image has the water body that have negative value as well as greenness that have positive value.

Crop Phenological stage extraction: From the known crop growth period from December to mid May an attempt was made to establish crop phenological relationship between multi-temporal images. As the image covers the crop period, the image of one date has the advance stage of growth than previous date image. During the crop growth period, crop has increasing greenness up to flowering stage. From flowering and grain filling stage onwards there is a decline in the greenness. Based on this the crop growth stages were derived with the help of NDVI.

5.1.1. Determination of threshold values to generate rice map

The threshold values to delineate rice pixels are derived from the NDVI values of the pixels and also with the help of 2D-scatter plot.

With the help of temporal NDVI plots and iterative process the thresholds have been arrived as explained bellow.


41

NDVI vs Rice Crop Area

0

100

200

300

400

500

600

700

-0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

NDVI values

Ric

e C

rop

Are

a in

ha.

16-Feb-02 21-Mar-02 7-Apr-02 14-Apr-02 2-May-02

Figure 5.1: Crop Growth Stage vs. crop area as derived from NDVI

Trial 1: With NDVI Curve

The above curves are derived from the rice map of the study area. The areas for various ranges of NDVI with interval of 0.025 are plotted. The ranges below zero represents the water body, others are the vegetative cover. The area under rice crop is 3034 ha. Interpretation: (i) Areas under each date are equal and it represents the rice area of 3034 ha. (ii) NDVI peak for 16th Feb. image is at 0.1. This shows that most of the rice crop area was in initial growth stage. (iii) The peak NDVI value (0.5) on 14th April and 2nd May are coinciding with each other but area of the histogram differs. (iv) The peak NDVI (0.5) on 14th April is narrow and area under peak NDVI value increased from 21st March, which shows the crop was passing from less green to more green during that period. (v) and almost all the crops had uniform greenness on 14th April. (vi)


42

Similarly 2nd May image shows that most of the crops reached the maturity stage. In histogram area covered from minimum to peak NDVI (0.5) is more than peak NDVI to maximum NDVI. (vii) The NDVI curve of 2nd May and 14th April intercept each other at NDVI 0.575. From the above curve it is seen that the NDVI curve of 2nd May and 14th April intercept each other at NDVI 0.575 and the area above this value of 2nd May is greater than the area of 14th April. As the harvesting period of rice crop is from 15th April to 10th May the area having high NDVI above 0.5075 on 14th April image should be decreased on 2nd May image. So, in the 1st trial to delineate the rice crop the NDVI value at this interception point (0.575) has been considered as a threshold. If NDVI on 2nd May 02 is less than 0.575 then the pixel belongs to paddy. Two more iteration with threshold value of 0.560 and 0.550 has been tried. With threshold value of “NDVI on 2nd May less than 0.550”, the NDVI curve of 2nd May in descending segment comes before the NDVI curve of 14th April curve , (see Figure 5.4). This shows the greenness area of pixels of 2nd May has reduced from the area of pixels of 14th April. This satisfies the phonological stages of rice crop as the rice is nearer to the ripening stage on 2nd May image than 14th April image and has declining trend in greenness. Hence, one threshold is derived as “ if NDVI on 2nd May 02 is less than 0.550, then the pixel belongs

to paddy.”

Trial 2: With 2-D Scatter Plot

The NDVI trend between layers of NDVI stacked images have been plotted as 2D-scatter plot. Each 5 layers represent the NDVI of 5 acquisition dates.

Figure 5.2: Scatter Plots of temporal NDVI images (Scatter Plot before applying threshold) Interpretation: (i) From the scatter plot of 1st (16-Feb-02) and 2nd (21-March-02) layers, top-left scatter plot, it was noticed that the NDVI value of some pixels in the Day1 have been reduced on the Day2 image. The rice crop has more greenness on 21-March-02 than 16-Feb-02, hence the pixels that have less NDVI values in Day 2 may represent the non-rice crops. (ii) This is one criterion to delineate non-


43

rice pixels and those pixels were masked in the final rice map. “If NDVI of 21st March 02 is more

than NDVI of 16th February 02, then the pixel belong s to paddy.”

Figure 5.3: Modified Scatter Plot of temporal NDVI image after eliminating non-rice crop

Note: Layer1: NDVI values of 16th Feb 02 image; Layer2: NDVI values of 21st March 02 image; Layer3: NDVI values of 7th April 02 image;

Layer4: NDVI values of 14th April 02 image; Layer5: NDVI values of 2nd May 02 ima ge;

Interpretation: Figures 5.2 and 5.3 shows the scatter plots of temporal NDVI images. From the top-left scatter plot it is noticed that the NDVI values of 16 th February increases on 21st March image. And top-right scatter plot of 21st march vs. 7th April shows the trend of increase NDVI and for some pixels its values was as high as 0.75. The bottom-left plot of 7th April vs. 14th April shows that most of the pixel have decline trend of NDVI. It implies that the crop crosses the greenness stage and heads towards maturity. From the bottom-right scatter plots of 14th April vs. 2nd May it is noticed that the NDVI value was declining further, it implies that the crop heads towards maturity. The thresholds put on for the generation of final rice map as follows:

NDVI of 21st March image is greater than the NDVI of 16th Feb image. NDVI of 2nd May image is less than a threshold value of 0.550

The total rice crop acreage extracted from the attribute table of final rice map is 2624 ha. From attribute table of final rice map the maximum, minimum NDVI of each satellite image is shown in Table 5.2. The rice crop acreage for different NDVI ranges is given in Table 5.3. Figure 5.4 shows the


44

temporal variation of NDVI in Rice Crop Area generated from the data on Table5.3 and Figure 5.5 shows the temporal variation NDVI vs. Cumulative Rice Crop Area


45

Table 5.2: Minimum and maximum NDVI values of final rice map

Image acquisition Minimum NDVI Maximum NDVI

16th February 2002 (-) 0.3222 0.4500

21st March 2002 (-) 0.0126 0.6730

7th April 2002 0.0652 0.7518

14th April 2002 0.0508 0.6241

2nd May 2002 (-) 0.0061 0.5500 Table 5.3 : Rice crop acreage in different NDVI range

Rice crop area extracted form different NDVI images NDVI Ranges 16th Feb 02 21st March 7th April 02 14th April 02 2nd May 02

area below value (-) 0.025 0.0729 0.00 0.00 0.00 0.00

area below value (-) 0.100 27.3375 0.00 0.00 0.00 0.00

area between ndvi value of (-)0.100 to (-)0.050 80.919 0.00 0.00 0.00 0.00

area between ndvi value of (-)0.100 to 0.000 181.16 0.07 0.00 0.00 0.07

area between ndvi value of 0.0 to 0.025 234.67 0.00 0.00 0.00 0.00































46

Histogram of NDVI vs. Rice Crop Area

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16-Feb-02


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Histogarm of NDVI vs. Rice Crop Area

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ha.


Figure 5.4: Temporal variation of NDVI in Rice Crop Area

It is clearly evident from the figure 5.4 that on 16th February 02 majority of crop is in early stage and the early paddy is reflected towards the end of the histogram, late paddy is reflected in the beginning of the histogram. In between it is normal paddy. On 21st of March, the early paddy has reached its peak NDVI about 0.575 and for the normal and late paddy there is an increase in NDVI. On 7th April, the range of NDVI has reduced due to further crop growth. On 14th April, the peak NDVI area has increased. On 2nd May, there is a declining trend of NDVI as crop is nearing its maturity.

From the NDVI trend curve it is seen that peak NDVI has shifted from 16th February to 21st March and to 7th April, and then it declines to 14th April and 2nd May has reached its peak NDVI. It reflects that the crop is passing greenness of 16th February to more greenness upto 7th April and then greenness declines.


47

NDVI vs. Cummulative Rice Crop Area

0

500

1000

1500

2000

2500

3000

-0.2

00

-0.1

00

0.00

0

0.10

0

0.20

0

0.30

0

0.40

0

0.50

0

0.60

0

0.70

0

0.80

0

NDVI values

Cum

mul

ativ

e R

ice

Cro

p A

rea

in h

a.


Figure 5.5: Temporal variation of NDVI vs. Cumulative Rice Crop Area

From this cumulative area curve, it is possible to decide the thresholds for different stages of paddy using image of 16th February than others as it has the gentle slope. If other image are used for differentiating the crop stages the error will be more.

Figure 5.6 shows the temporal variation of NDVI for rice crop. From this figure it is seen that the peak greenness of crop increases from 16 th February to 7th April and then declines. It ha s the good correlation with crop growth stages with respect to growth days. The image acquisition dates are 16-Feb-02, 21-March-02, 7-April-02, 14-April-02 and 2-May-02. It is seen that the pixels that has high NDVI values on 16th February 02 has low NDVI values on 2nd May 02. These pixels represent the early transplanted rice. As the first harvesting date of rice crop was on 15th April 02, the pixels representing early transplanted rice had no crop on 2nd May 02. Hence the NDVI values on 2nd May 02 for these pixels have the NDVI values of background soil.

Figure 5.7 shows the NDVI trend of late, normal and early transplanted rice.


48

Figure 5.6: Temporal variation of NDVI of rice crop

NDVI trend over days

-0.0467

0.4726

0.5753

0.4675 0.4582

-0.1000

0.0000

0.1000

0.2000

0.3000

0.4000

0.5000

0.6000

0.7000

0 1 2 3 4 5 6

Acquisition no. of image

ND

VI v

alu

es

For NDVI value of 16 Feb < 0.0


0.0942

0.4855

0.5694

0.4678 0.4426

0.0000

0.1000

0.2000

0.3000

0.4000

0.5000

0.6000

0 1 2 3 4 5 6

Acquisition no of image

ND

VI v

alue

s

0.000 < NDVI value of 16th Feb. < 0.200


0.2872

0.53660.5857

0.4744

0.3801

0.0000

0.1000

0.2000

0.3000

0.4000

0.5000

0.6000

0.7000

0 1 2 3 4 5 6

Acquisition no of image

ND

VI v

alue

s

0.200 < NDVI value of 16th Feb. < 0.450

NDVI Trend of Early,Normal and Late planted rice crop

-0.10

0.10.20.30.40.50.60.7

3-Fe

b-02

13-F

eb-0

2

23-F

eb-0

2

5-M

ar-0

2

15-M

ar-0

2

25-M

ar-0

2

4-A

pr-0

2

14-A

pr-0

2

24-A

pr-0

2

4-M

ay-0

2

14-M

ay-0

2

Time

ND

VI V

alue

s

Late Plantation

Normal Plantation

Early Plantation


49

Figure 5.7: Trend of NDVI for different crop stages

5.1.2. Rice stage classification derived from NDVI:

From the Figure 5.5, taking the curve of 16th February 02 an attempt has been made to classify rice stages. Form the curve of 16th February 02 the rice stages has been derived considering the change of slope of the curve. From this it is derived that (i) if the NDVI value of 16th Feb. 2002 was below 0.00, the pixel belonged to late transplanted rice. (ii)If it was between 0.00 and 0.20, the pixel belonged to normal transplanted rice, and (iii) if it was more than 0.20, the pixel belonged to early transplanted rice. Table 5.4: Values of average NDVI for various rice crops

Early Transplanted Rice

Normal Transplanted Rice

Late Transplanted Rice Image Date

Growth (days)

NDVI

Growth (days)

NDVI

Growth (days)

NDVI

16th February 2002 34 0.2872 24 0.0942 13 (-)0.0467

21st March 2002 67 0.5366 57 0.4855 46 0.4726

7th April 2002 84 0.5857 74 0.5694 63 0.5753

14th April 2002 91 0.4744 81 0.4678 70 0.4675

2nd May 2002 Harvested 0.3801 92 0.4426 88 0.4582

Table 5.4 show the growth days and corresponding NDVI values. It is observed that NDVI is high on 7th April irrespective of transplantation dates and declines after this date. The NDVI value increases according to growth days i.e., from 13 to 24, 24 to 34, 34 to 46, 46 to 57, and 57 to 63. After 63 days , NDVI value of different rice stages has no relation with growth days .

5.1.3. Rice stage classification derived from SAVI

An attempt also made to derive rice crop stages from Soil Adjusted Vegetation Index (SAVI), as defined by Huete (1988) to determine the crop phenology stages. SAVI was computed for each image. SAVI is defined by:

)1( LLRNIR

RNIRSAVI +

++−= …. …. … (5)

where R and NIR are reflectance in red and near-infrared wave length regions and L an adjustment factor to minimize soil brightness influences. For annual crop Huete suggested the value of L as 0.5, this values was used for the present study. The SAVI was computed for all the images. The SAVI values are given in Table 5.5.


50

Table 5.5: Minimum and maximum SAVI values for rice crops

Image acquisition date Minimum SAVI Maximum SAVI 16th February 2002 (-) 0.42697 0.6383

21st March 2002 (-) 0.07806 0.72878 7th April 2002 0.090226 1.06740

14th April 2002 (-) 0.04278 0.63443

2nd May 2002 (-) 0.00868 0.77420

Table 5.5 shows the temporal SAVI values. The trend is same as discussed for NDVI in Table 5.4. The SAVI values for different rice stages derived from the NDVI values in 5.1.2 have been analysed. It was found that, if the SAVI value of 16th Feb. 2002 was between (-) 0.42697 and 0.00, the pixel belonged to late transplanted rice. If it was between 0.00 and 0.2780, the pixel belonged to normal transplanted rice, and if it was between 0.2780 and 0.6383, the pixel belonged to early transplanted rice. Table 5.6 Values of average SAVI for various rice crops

Early Transplanted Rice

Normal Transplanted Rice

Late Transplanted Rice Image acquisition

Date

Growth (days)

SAVI

Growth (days)

SAVI

Growth (days)

SAVI

16th February 2002 34 0.405 24 0.133 13 -0.065 21st March 2002 67 0.548 57 0.489 46 0.472 7th April 2002 84 0.825 74 0.802 63 0.810 14th April 2002 91 0.459 81 0.448 70 0.443 2nd May 2002 Harvested 0.534 92 0.621 88 0.641

Table 5.6 shows the growth days and SAVI values. The SAVI value increases according to growth days i.e., from 13 to 24, 24 to 34, 34 to 46, 46 to 57, and 57 to 63. After 63 days, SAVI value of different rice stages has no relation with growth days.

Analysis of crop growth stages: The showing/transplanting date was varies from 20th December 2001 to 8th Feb. 2002 (Table 4.1, page -24). Considering the seedling of 21 to 30 days old were being transplanted, the transplanted duration spans over a period of 30 days from 10th January 2002 to 8th February 2002 and harvesting duration spans over a period of 26 days from 15th April to 10th May. The transplanted period and harvested period are divided into three stages as early, normal and late transplanted rice crop. For analysis purpose the period from 10th January to 17th January (8 days) is


51

considered as transplanting period for early transplanted rice. Similarly the transplanting period from 18th January to 31st January (14 days) and 1st February to 8th February (8 days) are considered for normal transplanted rice and late transplanted rice respectively. The period from 15th April to 22nd April (8 days), 23rd April to 05th May (13 days) and 06 th May to 10th May (5 days) are considered as harvesting period for early ,normal and late transplanted ric e. The growth days of early, normal and late transplanted rice as on image acquisition dates are given below: Table 5.7: Rice growth in days as on image acquisition dates

Image Acquisition dates 16th Feb. 2002

21st March 2002

7th April 2002

14th April 2002

2nd May 2002

Transplanting period

Growth on days as on image acquisition dates from the date of transplantation

Harvesting period

10-Jan-2002 to

17-Jan- 2002

38 to 31

71 to 64

88 to 81

95 to 88

harvested 15-April-2002

to 22-Apr-2002

18-Jan-2002 to

31-Jan- 2002

30 to 17

63 to 50

80 to 67

87 to 74

harvested to 92

23-April-2002 to

05-May-2002 01-Feb-2002

to 08-Feb- 2002

16 to 9

49 to 42

66 to 59

73 to 66

91 to 84

06-May-2002 to

10-May-2002


52

5.1.4. Rice crop coefficients

Values of rice crop coefficients for different growth stages are collected from the literature. The crop coefficients suggested by Tyagi et al. (2000) as 1.15, 1.23, 1.14 and 1.02 for four crop growth stages of initial, crop development, reproductive (mid stage) and maturity (late stage), are taken into consideration as follows:

Figure 5.8: Crop coefficient of Rice Source: Tyagi et al. ( 2000) Table 5.8: Crop coefficients as on day of image acquisition


53

Table 5.8 gives the crop coefficient values for various rice crop verities with respect to crop growth period. During rabi crop (December 2001 to May 2002) the sowing of rice was started from 20th December 2001 and transplantation continued upto 8th February 2002. Considering the seedling of 21-30 days old are transplanted the transplanted date starts from 10 th January (21 day from the sowing date). As the Transplantation duration varies from 10th January to 8th February and 20th January being the peak transplanting date the duration of transplanting and harvesting dates for early, normal and late transplanted rice are considered different. For early transplanted rice the transplanting dates and harvesting dates are considered as 10 th January and 15th April (Table 5.9). For normal transplanted rice these dates are 18th January and 23rd April (Table 5.10) and for late transplanted rice, these dates are 1st February and 6th May respectively (Table 5.11). With this, the crop coefficient are computed for different crop growth stages. Details of crop coefficient are given in the Table s 5.9 to 5.11 for early, normal and late transplanted rice. Table 5.9: Crop coefficients for Early transplanted rice Crop : Early Paddy

Planting date:

10- Jan-02 to 17- Jan-02 : Mid date 14-Jan-02

Harvesting date:

15- April-02 to 22- April-02 :Mid date 19-April-02

Stage Days Cumulative days after

transplantation

Calendar days Kc

Nursery 25 1.20 0 0 14-Jan-02 1.15 Initial stage

19 19 02-Feb-02 1.15

Crop development stage 20 39 22-Feb-02 1.23

Reproductive (Mid stage) 37 76 31-Mar-02 1.14 Maturity (Late stage) 19 95 19-Apr-02 1.02

Crop Period 120

Table 5.10: Crop coefficients for Normal transplanted rice

Crop stages Early Transplanted rice (10-Jan-02 to 17-Jan-02)

Mid-date: 14-Jan-02

Normal Transplanted rice (18-Jan-02 to 31-Jan-02) Mid -date:

24-Jan-02

Late Transplanted rice (01-Feb-02 to 08-Feb-02)

Mid-date: 04-Feb-02

Image acquisition

Dates

Crop growth stages in days

Crop Coefficient,

Kc


Crop Coefficient,

Kc


Crop Coefficient

Kc

16-Feb-02 34 1.210 24 1.170 13 1.150 21-Mar-02 67 1.162 57 1.186 46 1.213 7-Apr-02 84 1.089 74 1.145 63 1.170 14-Apr-02 91 1.045 81 1.108 70 1.153 2-May-02 Harvested - 92 1.039 88 1.053


54

Crop : Normal Paddy

Planting date: 18-Jan-02 to 31-Jan-02 : Mid day 24-Jan-02

Harvesting date: 23- April-02 to 05-May-02 : Mid day 29-April-02

Stage Days Cumulative days after transplantation

Calendar days

Kc

Nursery 25 0 0 24-Jan-02 1.15 Initial stage 19 19 12-Feb-02 1.15

Crop development stage 20 39 04-Mar-02 1.23

Reproductive (Mid stage) 37 76 10-April-02 1.14 Maturity (Late stage) 19 95 29-Apr-02 1.02

Crop Period 120 Table 5.11 : Crop coefficients for Late transplanted rice

Crop : Late Paddy

Planting date: 01-Feb-02 to 08-Feb-02 : Mid date 04-Feb-02

Harvesting date: 06-May to 10- May-02 : Mid date 08-May-02

Stage Days

Cumulative days after

transplantation Calendar

days Kc Nursery 25

0 0 04-Feb-02 1.15 Initial stage 19 19 23-Feb-02 1.15

Crop development stage 20 39 15-Mar-02 1.23 Reproductive (Mid stage) 36 75 20-Apr-02 1.14

Maturity (Late stage) 18 93 8-May-02 1.02 Crop Period 118

5.1.5. Reference crop evapotranspiration

ET0 computed from meteorological data with 10 days average Pan Evaporation method and Penman-Montieth method are as follows: Table 5.12: Reference Crop Evapotranspiration

Month 10 days Reference crop evapotranspiration ET0 Pan Evaporation Penman-Montieth

December 2001 1 2.24 2.42


55

2 2.18 2.34

3 2.06 2.17 1 2.09 2.25

2 2.38 2.77

January 2002

3 2.44 2.76 1 2.70 2.90

2 2.75 3.47

February 2002

3 3.66 3.42

1 3.95 3.72 2 4.45 3.98

March 2002

3 3.91 4.09

1 4.56 4.34 2 5.29 5.22

April 2002

3 5.71 5.48 1 6.47 5.62

2 6.21 5.38

May 2002

3 6.23 5.14

Table 5.12 gives the reference evapotranspiration of the study area estimated from pan evaporation and Penman-Montieth method. It is seen that there is no much difference in both the method. For use of this study the reference evapotranspiration computed from pan evaporation method was used as it take less missing parameters (wind speed) from the nearby meteorological station.

5.1.6. Validation of results

Rice crop acreages were computed both from satellite imagery and field data. The field data was available in village-wise and statistical approach was adopted to compute the rice crop acreage on distributary-wise. The table below shows the gross command area and culturable command area in the study area. Table 5.13: Village Wise Gross Command Area & Culturable Command Area

Distributary wise CCA in ha.

S.

No.

Name of the

Village

GCA

in ha.

CCA

in ha. Babebira Bugbuga

Babebira +

Bugbuga

GCA of both distributary

(Col.3 x Col.7 /

Col.4)

Rabi crop in ha. From

field(Agriculture

Department)

Rabi Crop Area

of both

distributary

(Col.7 x Col.9 /

Col.4)

1 2 3 4 5 6 7 8 9 10

1 Ladarpali 268.75 245.68 38.37 38.37 41.97 165.6 25.86

2 Attabira 1031.31 864.73 163.72 163.72 195.26 960.0 181.76

3 Kulunda 1128.63 1050.84 601.11 290.87 891.98 958.01 1055.0 895.51

4 Bhuinpura 343.74 301.87 301.87 301.87 343.74 378.0 378.00

5 Birakhakata 30.85 25.37 25.37 25.37 30.85 25.0 25.00

6 Khandagali 46.66 34.91 34.91 34.91 46.66 44.0 44.00


56

7 Babebira 433.10 342.82 342.82 342.82 433.10 375.0 375.00

8 Bugbuga 1776.44 1089.12 153.51 920.08 1073.58 1751.11 1308.0 1289.35

Total 5059.48 3955.34 1661.68 1210.95 2872.63 3800.70 4310.60 3214.48

GCA: Gross Command Area; CCA: Culturable Command Area Source: Water resources department, Government of Orissa

Table 5.13 show the village wise gross command area (GCA) and culturable command area (CCA). Data on column (1) to (7) are collected from the field, water resources department. The total area of the study area in column (8) has been computed from the data in column (3), (4) and (7) and found 3801 ha. Similarly total cropped area in column (10) has been computed from the data in column (4), (7) and (9) and found 3214 ha. Data in column (9) has been taken from Table 4.1, page 24.

Rabi crop acreage as per water resources department is 2873 ha; 1662 ha for Babebira Distributary and 1211 ha for Bugbuga Distributary. These are figures as per design statement adopted at the construction of the project. The land use pattern has been changed. This is reflected in the data of Agriculture department. As per their record the total crop area during Rabi 2001-02 was 4310.6 ha in Attabira block (Table 4.1). This shows that the culturable command area has increased from 3955 ha to 4310 ha in the study area. Taking these figures into account the rice crop area of the command has been computed: Total cropped area during Rabi 2001-02: 4310.6 ha (Table 5.15. Col. 8) Out of these the Area under paddy : 3492.0 ha (Table 4.1) Percentage of Paddy crop = 3492.0/ 4310.6 = 81 %. Hence the Paddy area under the study = 3214 x 81% = 2604 ha. The rice area computed from satellite image is 2624 ha (page 41).


57

6. Results and Discussions

In this section, the results of the study like spatial distribution of rice crop, canal network extraction from LISS IV image, computation of crop evapotranspiration, and water demand vs. supply for rice crop are discussed.

6.1. Results

This section shows the spatially distribution of rice crop and rice crop acreage , canal network extraction from LISS IV image.

6.1.1. Rice map:

The rice map generated from the multi-temporal satellite image

Figure 6.1: Spatial distribution of Rice Crop in the study Area


58

1. Rice crop grown during rabi 2001-02 was medium verity having crop growth period of 115 to 120 days including the nursery period of 21 to 30days.

a. The Rice map have classified in to three verities i. Early transplanted Rice ( in yellow colour) ii. Normal transplanted Rice ( in Green colour) iii. Late transplanted Rice ( in magenta colour)

Figure 6.1 shows the spatial distribution of early, normal and late transplanted rice. It was found that in the head reach the early transplanted rice were grown while the late transplanted rice was grown in the tail end of the distributary and also far end of the command boundary. It reflects the water abundance in head reaches and scarcity at tail reaches.

2. Classification has been done with slicing the NDVI values of 16th Feb. image. Table 6.1 shows the NDVI values of 16 th February 2002 image used for differentiate the early, normal and late transplanted rice. Table 6.1: NDVI threshold for various rice

Threshold Type of crop NDVI < 0.0 Late transplanted rice

0.0 < NDVI < 0.2 Normal transplanted rice NDVI > 0.2 Early transplanted rice

3. The area under each class area as follows:

Early transplanted Rice : 639 ha. Normal transplanted Rice : 1696 ha. Late transplanted Rice : 289 ha.

Total : 2624 ha. 4. Distributary wise Area

a. Babebira distributary Early transplanted Rice : 408 ha. Normal transplanted Rice : 889 ha. Late transplanted Rice : 127 ha.

Total : 1424 ha. b. Bugbuga distributary

Early transplanted Rice : 231 ha. Normal transplanted Rice : 807 ha. Late transplanted Rice : 162 ha.

Total : 1200 ha. Table 6.2: Spatial distribution of Rice crop

Total Study area (%)

Babebira Distributary (%)

Bugbuga Distributary (%)

Early transplanted rice 24.4 28.7 19.3 Normal transplanted rice 64.6 62.4 67.2 Late transplanted rice 11.0 8.9 13.5


59

The field officers and farmers reported that nearly 60 % rice crop is normal transplanted.

6.1.2. Canal Network

The Canal network extraction from the IRS P6 LISS IV:

Figure 6.2: Canal network extracted from LISS IV image

Canal network has been extracted by digitisation. It is possible to do the visual interpretation of canal network up to distributary level. Hence, it is possible to extract the main canal, branch canal and distributary from the LISS IV image by digitisation. The width of the main canal, branch canal and distributary are 45.7 m. 16.76 m., 2.29 m. respectively. Figure 6.2 shows the canal network extracted from the LISS IV image. Figure 6.3 shows the canal network extracted from the cadastral level map.


60

Figure 6.3: The Canal network extracted from the cadastral level map


61

Figure 6.4: The Canal network extracted from the IRS P6 LISS IV and cadastral map

In Figure 6.4 the canal networks extracted from both methods were overlaid. It was found that the alignment of canal slightly differs. The reason may be the error in geo-referencing the cadastral map. Table 6.3: Comparison of distributary length extracted by different method:

Length

Name of Distributary From field

record (m)

From LISS IV image

(m)

From Cadastral Map (m)

From Topo map

Babebira Distributary 6706 7316 7527 6884 Bugbuga Distributary 5944 5804 5926 5998

Babebira Distributary 610 821 178 Difference from field data

(m) Bugbuga Distributary -140 -18 54 Difference from field data

(%) Babebira Distributary 9.10 12.24 2.65

Bugbuga Distributary -2.36 -0.3 0.91

Table 6.3 shows the comparison between the canal networks extracted with different approaches. It was noticed that Bugbuga distributary the deviation between the data extracted from the image and ground is less than the Babebira distributary. The Bugbuga distributary has straight reaches than Babebira distributary. That may be the reason of having less deviation from the field in case of Bugbuga distributary. Where the canal inventory is not available this method of canal extraction can be implemented with an error of (-) 2.36 to 12.24 %.

6.1.3. Reference Crop evapotranspiration

It was computed with the equation panp EKET =0 …. (2).

It was computed on 10 days basis. The computation is shown in tabulation form in the Table 6.4.


62

Table 6.4: Computation of ET 0 (10-day average reference evapotranspiration)

December 2001 January 2002 February 2002

Decade 1 2 3 1 2 3 1 2 3

u2 0.03 0.06 0.10 0.45 0.20 0.67 0.47 0.58 0.45

(ms -1)

RH mean 60.00 64.00 61.00 70.00 70.00 73.00 71.00 70.00 59.00

(%)

Kpan 0.86 0.87 0.86 0.87 0.88 0.87 0.87 0.86 0.85

Epan 2.60 2.50 2.40 2.40 2.70 2.80 3.10 3.20 4.30

(mm/day)

ET0 2.24 2.18 2.06 2.09 2.38 2.44 2.70 2.75 3.66

(mm/day)

March 2002 April 2002 May 2002

Decade 1 2 3 1 2 3 1 2 3

u2 0.39 0.42 0.53 0.42 0.39 0.70 0.56 0.45 0.56

(ms -1)

RH mean 53.00 54.00 59.00 63.00 54.00 57.00 54.00 59.00 53.00

(%)

Kpan 0.84 0.84 0.85 0.86 0.84 0.84 0.84 0.85 0.83

Epan 4.70 5.30 4.60 5.30 6.30 6.80 7.70 7.30 7.50

(mm/day)

ET0 3.95 4.45 3.91 4.56 5.29 5.71 6.47 6.21 6.23

(mm/day)

U 2 = average daily wind speed at 2 m height (ms -1) RH mean = average daily relative humidity [%] Kpan = Pan Coefficient

Epan = Pan Evaporation ET0 = Reference Evapo-transpiration

Table 6.4 shows the computation of reference crop evapotranspiration (ET0) in tabular format. The reference crop evapotranspiration is computed on 10 days basis. The pan evaporation method was followed which requires the climatic data such as pan evaporation, relative humidity, and wind speed. Pan coefficient has been derived from the equation (Allen et al. , 1998):

)ln()][ln(000631.0)ln(1434.0)ln(0422.00286.0108.0 22 meanmeanpan RHFETRHEFETUK −++−=

… (3)

here,


63

Kpan is the pan coefficient computed from the meteorological data..

U2 is the average daily wind speed at 2 m height (ms-1). As the wind speed of Chipilima observatory was not available, the wind speed of nearby station Sambalpur observatory has been considered.

RHmean is the average daily relative humidity (%) is computed from the RHmax and RHmin of Chipilima observatory = (RHmax + RHmin) / 2

FET is the fetch, or the distance of the identified surface type (grass or short green agricultural crop). As the Class A pan is situated in the agriculture research firm the wind ward fetch of green crop of 1000 m is considered. Epan is the measured pan evaporation of Class A pan. The data of Chipilima observatory is used. ET0 is computed from Kp and Epan with equation (2).

6.1.4. Crop evapotranspiration

ETcrop has been computed for each variety of rice crops using equation

0ETKET ccrop = … (4)

The Kc values were derived with respect to growth days by interpolation method from the curve shown in Figure 5.8 (page 49) Kc values for early, normal and late rice crop have been computed and shown in column (6) of the Tables 6.5 to 6.7. ETcrop has been computed for early, normal and late rice crop and shown in column (7) of the table 6. 5 to 6.7.

6.1.5. Crop water requirement

Tables 6.5 to 6.7 show the computation of crop water requirement for early, normal and late transplanted rice. The computations have been made for 100 ha on 10 days basis. The nursery area has been considered as 10 % of the crop area. The land preparation for transplantation has been considered in three phases: 33 % in first phase, 57 % in second phase and rest 10 % which was under nursery in third phase. Provision of water for land saturation has been kept as 200 mm before transplantation and sowing. This value has been taken as 0 for the 3rd phase of land preparation for the area that was under nursery as it is in saturated stage due to watering during nursery. The provision of water layer has been kept as 100 mm during initial stage. As the provision for water requirement to meet the crop evapotranspiration and percolation are also kept, this water layer will maintain as 100 mm throughout the initial stage. During vegetative stage the water layer needs to be reduced and maintained as 20-50 mm. During starting of vegetative stage to draw down the water layer, it has been considered to meet the crop water requirement from this water layer. So, during this 10 days period the water demand has been taken as 0 in column (16) of Table s 6.5-6.7. The balance water depth left after the crop requirement (evapotranspiration) has been considered the water layer maintained in the field during this period. Again, during mid stage water layer is to be maintained as 100 mm. So in the 1st decade of mid stage the provision has been kept for that. It is the value same as the value during drop down case. As during harvesting the land is to be dry. The water layer is to be reduced to 0. This has


64

been achieved by using this for crop water requirement towards the last part. The notations used in the table are as follows:

In Table 6.5, column (1) shows the month under study. Column (2) shows the decades, 1 means 1 to 10 days of the month, 2 means 11 to 20 days of the month and 3 means 21 to 30/31 days of the month. Column (3) shows the potential crop evapotranspiration (ET0). The ET0 values are taken from Table 6.4. Column (4) shows the crop growth stages comprised of nursery, initial stage, development stage, reproductive stage (mid stage) and late stage. It is derived with considering crop period of 120 days including 21-30 days nursery period from Table-4.2 (page 24). Column (5) shows growth days from the day of transplantation to the mid of the decade considered. Column (6) gives the crop coefficient. Crop coefficient Kc is taken from the value suggested by Tyagi (2000) for rice crop. It is interpolated according to the crop growth days given in column (5). Column (7) shows the crop evapotranspiration per day for rice crop. ETcrop is the product of ET0 and Kc. Column (8) shows the crop evapotranspiration for rice crop for 10 days period. It is the product of column (7) and 10. Column (9) shows the saturation (SAT), the water needed to bring the soil upto field capacity. It is applied before sowing or transplanting. The amount of water needed depends on the soil type and rooting depth. Its value has been taken as 200 mm. This value is suggested by Mandal et al. (1999) for Sambalpur. SAT for 2nd decade of January is taken as 0 because this 10 % land used for nursery and already in saturation stage. Column (10) shows the water loss through pe rcolation. The percolation and seepage losses depend on the type of soil. As the soil type in the command is a mixture of sand and gravel as well as of clay, the value (PERC) is considered as 1.5 mm per day or 15 mm per decade. Column (11) shows the depth of water layer to be maintained. A water layer (WL) is established after transplanting. The depth of water layer are considered as 100 mm , 20-50 mm, 100 mm and 0 mm during initial stage, development stage, mid stage and late stage respectively as shown in Figure2.2. In the initial period, provision has been kept for 100 mm. In the mid stage it varies from 20-50 mm. During mid stage growth period the water requirement of crop is likely to meet from this difference. In this case 48.61 mm is the crop water requirement. For this period the total water demand is computed as 0. Again in the late stage water layer to be maintained is 100. So, provision of 48.61 mm that consumed by the crop during mid stage has been kept. Again the Column (12) shows the precipitation (P) during each 10 day period. These values have been taken from the meteorological data shown in Figure 4.3 page-23. Column (13) shows the effective rainfall (Pe). It is computed with the formulae: (i) Pe = 0.8 P – 25 if P > 75 mm/month or (ii) Pe = 0.6 P – 10 if P < 75 mm/month (Brouwer, 1986). In the present case as precipitation per month is less than 75 mm the effective precipitation was computed with the equation Pe = P * 0.6 – 10. Column (14) shows the crop water requirement of rice cropping for 10 days duration. IN is the irrigation requirement to meet the crop water requirement in field. It is computed as IN = ETcrop + SAT + PERC + WL – Pe. It is the sum of water loss through evapotranspiration, water required to bring the field into saturation condition before sowing, water loss through percolation and seepage and water layer to be maintained during crop growth stage and minus the contribution of effective precipitation. Column (15) shows the area under each growth stage for the 10 days period, considering 100 ha area under early transplanted rice. The 100 ha is selected as a unit area, later actual crop water requirement is calculated using the actual crop area. Column (16) shows


65

the water requirement for 100 ha area for 10 days duration in ha-m. Its value computed from Column (14) and Column (15).

Column (16) = Column (14) * Column (15) * 100 / 1000

As the water layer to be maintained in the field varies, in the mid stage it is reduced to 20-50 mm form 100 mm in the initial period, the crop water requirement during this period is met from this water layer. So the crop water requirement for the period February 2nd decade is considered 0 in column (16). Again as the field should be dry during harvesting, the water layer during this period is to be maintained to 0. So the crop water requirement from the last period is likely to be met from this water layer. As such, the water requirement during April 2nd decade is kept 0 though the requirement in column 14 shows 68.96. That means this amount of water is to be met from the water layer of 100 mm maintained during mid stage. The balance (100 – 68.96) mm water layer is cons idered to fulfil the crop water requirement of rice crop during April 1st decade. So in the column (16) it is mentioned as 3.334 ha-m though in column (14) it is mentioned 68.96). This value comes from water required during this period 64.38 mm from column (14) and water available from the water layer as (100 – 68.96).

Column (16) during April 1st decade = (64.38 – (100- 68.96)) * 100 / 1000 = 33.34 / 10 = 3.334 ha-m.

Tables 6.6 and 6.7 shows the computation for normal and late transplanted rice same as Table 6.5. Here, the rows are different from the Table 6.5 and adjusted according to the crop period. These tables also give the crop water requirement for 100 ha area.

Table 6.8 show the crop water requirement and irrigation requirement for the Babebira distributary under study. In this table column (1) and (2) shows the month and 10 days duration. The column (3) and (4) shows the water requirement for early transplanted rice. Column (3) shows the water requirement for 100 ha area. Its value is taken from the Table 6.5 column (16) 10 days basis. Column (4) computes the crop water requirement for early transplanted rice of 408 ha. Column (5) and (6) compute the water requirement for normal transplanted rice and column (7) and (8) compute for late transplanted rice. The values of Column (5) are taken from the Table 6.6 column (16) and values of (7) are taken from Table 6.7 column (16). The values in column (6) and (8) are computed for the area of 889 ha for normal transplanted rice and 127 for late transplanted rice respectively. Column (9) shows the total net irrigation requirement for early, normal and late transplanted rice for 10 days period. It is the sum of column (4), (6) and (8).

The total water requirement for rice cropping in Babebira distributary comes to sum of column (9) i.e. 1086.45 ha-m. When we express it in depth, it is 763 mm (1086.45 /1424 * 100).

Similarly Table 6.9 shows the crop water requirement for Bugbuga distributary. The values for column (3), (5) and (7) are same as the respective column of Table 6.8 and these values are taken from the column (16) of Tables 6.5, 6.6 and 6.7 respectively. In this table computation are made for 231 ha of early transplanted rice, 807 ha of normal transplanted rice and 162 ha of late transplanted rice of Bugbuga distributary in column (4), (6) and (8) respectively. Column (9) shows the total net irrigation requirement for early, normal and late transplanted rice for 10 days period. It is the sum of column (4), (6) and (8).


66

The total water requirement for rice cropping in Bugbuga distributary comes to sum of column (9) i.e. 922.22 ha-m. When we express it in depth, it is 769 mm (922.22 /1424 * 100).

6.1.6. Water demand vs. supply for rice crop

Gross irrigation requirement is computed by dividing net irrigation requirement with the irrigation efficiency. Irrigation efficiency is taking care of losses through conveyance and field application. In this study the irrigation efficiency was considered as 0.85. Then the water balance study has been done with demand vs. supply. Tables 6.10 and 6.11 show the supply and demand analysis of irrigation water of Babebira and Bugbuga distributary respectively. Column (1) shows the month and column (2) shows the decade of the month. Column (3) shows the net irrigation requirement. Its value comes from the Table 6.8 column (9). Column (4) shows the gross water requirement. It is computed from column (2) divided by the irrigation efficiency of 0.85. Column (5) shows the water supply during the period under study. It is field data collected from the water resources department during field visit. Column (6) shows the surplus or deficit of water supply. Column (7) shows the figure in percent wise. From this table it is seen that the water deficit is 223.9 ha (17.52 %) for Babebira distributary.

Similarly the Table 6.11 shows the supply and demand analysis of irrigation water for Bugbuga distributary. In this table column (3) values comes from the Table 6.9 column (9). The water deficit is 503.49 ha-m (46.41 %).

From water balance study, it is seen that water demand for rice crop only exceeds the irrigation supply. Figure 6.5 shows the irrigation water demand vs. water supply of rice crop during rabi season (December 2001 to May 2002) for two distributaries under this study graphically.

Figure 6.5: Water demand vs. supply

050

100150200250

Dec

_1

Dec

_2

Dec

_3

Jan_

1

Jan_

2

Jan_

3

Feb

_1

Feb

_2

Feb

_3

Mar

ch_1

Mar

ch_2

Mar

ch_3

Apr

il_1

Apr

il_2

Apr

il_3

May

_1

May

_2

Time (10 days)

Irr. water demand of Babebira distributary Irr. water supply of Babebira distributary

Irr. water demand of Bugbuga distributary Irr. water supply of Bugbuga distributary


67

Table 6.5: Crop water requirement of Early Transplanted Rice Crop :Early Transplanted Rice

Sowing date: 20-Dec-01; Planting date: 10-Jan-02 to 17-Jan-02; Mid transplanted date: 14-Jan-02 ; Harvesting date:15-Apr-02 to 22-Apr-02; Mid transplanted date: 19-Apr-02

For 100 ha Area

Month

Decade

(10

days)

ET0

(mm/

day)

Growth stage

Growth days from transplan

tation

Kc per

decade

ETcrop

(mm/day)

(3) x (6)

ETcrop (mm/decade) (7) x 10

SAT

(mm)

PERC

(mm/

decade)

WL

(mm)

P (mm/

decade)

Pe

(mm/

decade)

IN

(mm/decade)

(8) +(9)+(10)+ (11)-(13) Area (ha)

IN (ha-m/decade)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

1 2.24 0

2 2.18

Land preparation for

Nursery (10 %) 200 15 215 10 2.150

2.06 Nursery (10 %) 0.39 0.803 8.03 15 23.03 10 0.230 Dec-01

3


transplantation (33 %) 200 100 300 33 9.900

2.09 Nursery (10 %) 1.20 2.508 25.08 15 14 0 40.08 10 0.401

1



2.38 Nursery (10 %) 6 1.17 2.773 27.73 15 42.73 10 0.427

2



Jan-02

3 2.44 Initial stage 16 1.15 2.806 28.06 15 15.4 0 43.06 100 4.306

1 2.70 Initial stage 27 1.182 3.191 31.91 15 46.91 100 4.691

2 2.75 Development stage 37 1.222 3.361 33.61 15 48.61 100 0.000 Feb-02

3 3.66 Development stage 47 1.211 4.432 44.32 15 59.32 100 5.932

1 3.95 Mid Stage 55 1.191 4.704 47.04 15 48.61 110.65 100 11.065

2 4.45 Mid Stage 65 1.167 5.193 51.93 15 66.93 100 6.693 Mar-02

3 3.91 Mid Stage 75 1.142 4.465 44.65 15 9.8 0 59.65 100 5.965

1 4.56 Late stage 85 1.083 4.938 49.38 15 2 0 64.38 100 3.334

2 5.29 Late stage 95 1.02 5.396 53.96 15 2.4 0 68.96 100 0.000 Apr-02

3 1.4 0 0 100 0.000

May-02 1 2.6 0 0 100 0.000

ET0 = Reference Crop Evapotranspiration SAT = Saturation P = Precipitation Kc = Crop Coefficient PERC = Percolation Pe = Effective Precipitation ETcrop = Crop Evapotranspiration WL = Water Layer IN = Net Irrigation Requirement * indicates the water demand met from the water layer from the field.


68

Table 6.6 : Crop water requirement of Normal Transplanted Rice

Crop :Normal Transplanted Rice

Sowing date: 28-Dec-01; Planting date: 18-Jan-02 to 31-Jan-02; Mid transplanted date: 24-Jan-02 ; Harvesting date:23-Apr-02 to 5-May -02; Mid harvested date: 29-Apr-02

For 100 ha Area

Month

Decade

(10

days)

ET0

(mm/

day)

Growth stage

Growth

days

from

transpl

antatio

n

Kc per

decade

ETcrop

(mm/day)

(3) x (6)

ETcrop

(mm/deca

de)

(7) x 10

SAT

(mm)

PERC

(mm/

decade)

WL

(mm)

P

(mm/

decad

e)

Pe

(mm/

decade)

IN (mm/decade)

(8) +(9)+(10)+

(11)-(13) Area (ha)

IN (ha-

m/decade)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Dec-01 3 2.06


Nursery (10 %) 200 15 215 10 2.150

2.09 Nursery (10 %) 0.39 0.815 8.15 15 14 0 23.15 10 0.232

1



2.38 Nursery (10 %) 1.2 2.856 28.56 15 43.56 10 0.436

2



2.44 Nursery (10 %) 6 1.15 2.806 28.06 15 15.4 0 43.06 10 0.431

Jan-02

3



1 2.7 Initial stage 17 1.15 3.105 31.05 15 46.05 100 4.605

2 2.75 Initial stage 27 1.182 3.251 32.51 15 47.51 100 4.751 Feb-02

3 3.66 Development stage 37 1.222 4.473 44.73 15 59.73 100 0.000*

1 3.95 Development stage 45 1.215 4.799 47.99 15 62.99 100 6.299

2 4.45 Mid Stage 55 1.191 5.300 53.00 15 59.73 127.73 100 12.773 Mar-02

3 3.91 Mid Stage 65 1.167 4.563 45.63 15 9.8 0 60.63 100 6.063

1 4.56 Mid Stage 75 1.142 5.208 52.08 15 2 0 67.08 100 6.708

2 5.29 Late stage 85 1.083 5.729 57.29 15 2.4 0 72.29 100 4.553* Apr-02

3 5.71 Late stage 95 1.020 5.824 58.24 15 1.4 0 73.24 100 0.000*

May -02 1 2.6 0 0 100 0.000

ET0 = Reference Crop Evapotranspiration Kc = Crop Coefficient

ETcrop = Crop Evapotranspiration SAT = Saturation

PERC = Percolation WL = Water Layer


69

P = Precipitation P e = Effective Precipitation IN = Net Irrigation Requirement* indicates the water demand met from the water layer from the field


70

Table 6.7 : Crop water requirement of Late Transplanted Rice Crop :Late Transplanted Rice

Sowing date: 11-Jan-02; Planting date: 1-Feb-02 to 8-Feb-02; Mid transplanted date: 4 -Feb-02 ; Harvesting date:6-May-02 to 10-May-02; Mid harvested date: 8-May-02

For 100 ha Area

Month

Decade

(10

days)

ET0

(mm/

day)

Growth stage

Growth

days

from

transpl

antatio

n

Kc per

decade

ETcrop

(mm/day)

(3) x (6)

ETcrop

(mm/deca

de)

(7) x 10

SAT

(mm)

PERC

(mm/

decade)

WL

(mm)

P

(mm/

decad

e)

Pe

(mm/

decade)

IN (mm/decade)

(8) +(9)+(10)+

(11)-(13) Area (ha)

IN (ha-

m/decade)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Dec-01 3 2.06

1 2.09


Nursery (10 %) 200 15 14 0 215 10 2.150

2.38 Nursery (10 %) 0.39 0.928 9.28 15 24.28 10 0.243

2



2.44 Nursery (10 %) 1.20 2.928 29.28 15 15.4 0 44.28 10 0.443

Jan-02

3



2.7 Nursery (10 %) 5 1.15 3.105 31.05 15 46.05 10 0.461 1



2 2.75 Initial stage 15 1.150 3.163 31.63 15 46.63 100 4.663

Feb-02

3 3.66 Initial stage 25 1.174 4.297 42.97 15 57.97 100 5.797

1 3.95 Development stage 33 1.206 4.764 47.64 15 62.64 100 0.000*

2 4.45 Development stage 43 1.220 5.429 54.29 15 69.29 100 6.929 Mar-02

3 3.91 Mid Stage 53 1.195 4.672 46.72 15 62.64 9.8 0 124.36 100 12.436

1 4.56 Mid Stage 63 1.170 5.335 53.35 15 2 0 68.35 100 6.835

2 5.29 Mid Stage 73 1.145 6.057 60.57 15 2.4 0 75.57 100 7.557 Apr-02

3 5.71 Late stage 83 1.087 6.207 62.07 15 1.4 0 77.07 100 5.806*

May -02 1 6.47 Late stage 93 1.02 6.599 65.99 15 2.6 0 80.99 100 0.000*

ET0 = Reference Crop Evapotranspiration Kc = Crop Coefficient ETcrop = Crop Evapotranspiration

SAT = Saturation PERC = Percolation WL = Water Layer

P = Precipitation Pe = Effective Precipitation IN = Net Irrigation Requirement

* indicates the water demand met from the water layer from the field


71

Table 6.8 : Crop water requirement for Babebira Distributary

Babebira Distributary:

Early Rice : 408 ha

Normal Rice : 889 ha

Late Rice : 127 ha

Total : 1424 ha

Month Decade (10 days) Net rrigation required for Early Rice Net Irrigation required for Normal rice Net Irrigation required for Late Rice

Net irrigation requirement

For 100 ha. Area (ha-mm/ decade)

For 408 ha area (ha-m /decade)

For 100 ha. Area (mm/day

For 889 ha area (ha-m /day)

For 100 ha. Area (mm/day)

For 127 ha area (ha-m /day) (ha-m / decade)

1 2 3 4 5 6 7 8 9

1 0.00 0.000 0.00 0.000 0.00 0.000 0.00

2 2.15 8.772 0.00 0.000 0.00 0.000 8.77 Dec-01

3 10.13 41.330 2.15 19.114 0.00 0.000 60.44

1 17.50 71.404 10.13 90.073 2.15 2.731 164.21

2 1.43 5.822 17.54 155.895 10.14 12.882 174.60 Jan-02

3 4.31 17.568 1.43 12.722 17.54 22.280 52.57

1 4.69 19.139 4.61 40.938 1.46 1.855 61.93

2 0.00 0.000 4.75 42.236 4.66 5.922 48.16 Feb-02

3 5.93 24.203 0.00 0.000 5.80 7.362 31.57

1 11.07 45.145 6.30 55.998 0.00 0.000 101.14

2 6.69 27.307 12.77 113.552 6.93 8.800 149.66 Mar-02

3 5.97 24.337 6.06 53.900 12.44 15.794 94.03

1 3.33 13.603 6.71 59.634 6.84 8.680 81.92

2 0.00 0.000 4.55 40.476 7.56 9.597 50.07 Apr-02

3 0.00 0.000 0.00 0.000 5.81 7.374 7.37

1 0.00 0.000 0.00 0.000 0.00 0.000 0.00

2 0.00 0.000 0.00 0.000 0.00 0.000 0.00 May-02

3 0.00 0.000 0.00 0.000 0.00 0.000 0.00

Total 73.19 298.63 77.00 684.538 81.32 103.277 1086.445


72

T otal Water requirement during Rabi 2001-02 for Babebira Distributary : 1086 ha-m or (1086 / 1424) * 100 = 763 mm


73

Table 6.9 : Crop water requirement for Bugbuga Distributary or Babebira Distributary:

Early Rice : 231 ha

Normal Rice : 807 ha

Late Rice : 162 ha

Total : 1200 ha

Month Decade (10 days) Net Irrigation required for Early Rice Net Irrigation required for Normal rice Net Irrigation required for Late Rice

Net Irrigation requirement

For 100 ha. Area (ha-mm/ decade)

For 231 ha area (ha-m /decade)

For 100 ha. Area (mm/day

For 807 ha area (ha-m /day)

For 100 ha. Area (mm/day)

For 162 ha area (ha-m /day) (ha-m / decade)

1 2 3 4 5 6 7 8 9

1 0.00 0.000 0.00 0.000 0.00 0.000 0.00

2 2.15 4.967 0.00 0.000 0.00 0.000 4.97 Dec-01

3 10.13 23.400 2.15 17.351 0.00 0.000 40.75

1 17.50 40.427 10.13 81.765 2.15 3.483 125.68

2 1.43 3.296 17.54 141.516 10.14 16.432 161.24 Jan-02

3 4.31 9.947 1.43 11.548 17.54 28.420 49.92

1 4.69 10.836 4.61 37.162 1.46 2.367 50.37

2 0.00 0.000 4.75 38.341 4.66 7.554 45.90 Feb-02

3 5.93 13.703 0.00 0.000 5.80 9.391 23.09

1 11.07 25.560 6.30 50.833 0.00 0.000 76.39

2 6.69 15.461 12.77 103.078 6.93 11.225 129.76 Mar-02

3 5.97 13.779 6.06 48.928 12.44 20.146 82.85

1 3.33 7.702 6.71 54.134 6.84 11.073 72.91

2 0.00 0.000 4.55 36.743 7.56 12.242 48.99 Apr-02

3 0.00 0.000 0.00 0.000 5.81 9.406 9.41

1 0.00 0.000 0.00 0.000 0.00 0.000 0.00

2 0.00 0.000 0.00 0.000 0.00 0.000 0.00 May-02

3 0.00 0.000 0.00 0.000 0.00 0.000 0.00

Total 73.19 169.078 77.00 621.399 81.32 131.739 922.22


74

Total Water requirement during Rabi 2001-02 for Bugbuga Distributary : 922 ha-m or (922 / 1200) * 100 = 769 mm


75

Table 6.10 : Supply and demand analysis of Irrigation water for Babebira distributary

Note: Column (3) is column (9) of Table 6.7 Table 6.11 : Supply and demand analysis of Irrigation water for Bugbuga distributary

Babebira Distributary Considering irrigation efficiency of 0.85

Surplus / Deficit

Month Decade (10

days) Net Irrigation requirement

Gross irrigation requirement

Water supply

%=(6)/(4) x100

( ha-m ) ha-m (ha-m) (ha-m) (%)

(1) (2) (3) (4) (5) (6) (7)

1 0.00 0.000 0.000

2 8.77 10.320 49.25 38.930 377.229 Dec-01

3 60.44 71.110 70.25 -0.860 -1.209

1 164.21 193.190 61.90 -131.290 -67.959

2 174.60 205.410 66.49 -138.920 -67.631 Jan-02

3 52.57 61.850 81.34 19.490 31.512

1 61.93 72.860 74.80 1.940 2.663

2 48.16 56.660 79.60 22.940 40.487 Feb-02

3 31.57 37.140 57.03 19.890 53.554

1 101.14 118.990 77.20 -41.790 -35.121

2 149.66 176.070 80.56 -95.510 -54.245 Mar-02

3 94.03 110.620 92.36 -18.260 -16.507

1 81.92 96.370 84.40 -11.970 -12.421

2 50.07 58.910 75.51 16.600 28.179 Apr-02

3 7.37 8.680 82.56 73.880 851.152

1 0.00 0.000 21.03 21.030

2 0.00 0.000 0.000 May -02

3 0.00 0.000 0.000

Total 1086.45 1278.180 1054.28 -223.900 -17.517

Bugbuga Distributary Considering irrigation efficiency of 0.85

Surplus / Deficit

Month Decade (10 days) Net Irrigation requirement

Gross irrigation requirement

Water supply

%=(6)/(4) x 100

( ha-m ) ha-m (ha-m) (ha-m) (%)

(1) (2) (3) (4) (5) (6) (7)

1 0.00 0.000 0.000

2 4.97 5.840 29.34 23.500 402.397 Dec-01

3 40.75 47.940 45.98 -1.960 -4.088

1 125.68 147.850 38.86 -108.990 -73.717

2 161.24 189.700 46.08 -143.620 -75.709 Jan-02

3 49.92 58.720 33.88 -24.840 -42.302

1 50.37 59.250 38.00 -21.250 -35.865

2 45.90 53.990 41.42 -12.570 -23.282 Feb-02

3 23.09 27.170 33.06 5.890 21.678

1 76.39 89.870 41.80 -48.070 -53.488

2 129.76 152.660 44.88 -107.780 -70.601 Mar-02

3 82.85 97.470 52.11 -45.360 -46.537

1 72.91 85.780 45.60 -40.180 -46.841

2 48.99 57.630 39.90 -17.730 -30.765 Apr-02

3 9.41 11.070 42.94 31.870 287.895

1 0.00 0.000 7.60 7.600

2 0.00 0.000 0.000 May -02

3 0.00 0.000 0.000

Total 922.22 1084.940 581.45 -503.490 -46.407


76

Note: Column (3) is column (9) of Table 6.8

6.2. Discussion:

In this section the topics discussed are purpose and methods for geometric correction and radiometric normalisation, method of generation of rice map, crop growth stages of rice, reference crop evapotranspiration, irrigation water demand, canal network extraction from satellite data and cadastral level map have been discussed. In the last satellite data used in this study, role of remote sensing to understand crop phonological stages spatially, whether the satellite image represent the crop phenology in better way by sensing it at far above and the limitation of data have been discussed.

6.2.1. Geometric correction and radiometric normalization

The satellite images bear some distortions and degradation. These are removed by geometric corrections. The radiance measured by the sensor over a given object is influenced by scene illumination, atmospheric condition, viewing geometry and sensor characteristics. Radiometric normalization helps to correct the atmospheric degradation, illumination effects and sensor differences in multi-temporal, multi-spectral images. The radiometric normalization is based on the reflectance of manmade in-scene elements such as roof top, dry sand, concrete, asphalt, parking lots. Difference in the gray-level distributions of this Pseudo Invaria nt Features (PIF) is assumed to be a linear function and is corrected statistically to perform normalization. The empirical regression equations between satellite data of various dates for PIFs were found to be having high r² (0.9214 – 0.9927) values by Table 4.5 (page 29). These equations were used for normalisation.

6.2.2. Generation of Rice crop map

The Study Area, a part of Hirakud command was created by sub-setting the IRS-1C/1D LISS-III image. From the attribute table of the image total area of the study area found 3845 ha against the ground truth of 3801. As there was no information about the total area of the study area, the figure 3801 ha has been computed from the information of Village-wise Gross Command Area (GCA) & Culturable Command Area (CCA) in Table- 5.13 column (8), page-52. The area extracted from the image under agriculture is 3208 ha against the ground truth of 3214 ha (column (10) of Table 5.13). The ground truth figure is derived from village wise culturable command area. But as per water resources department, the rabi crop acreage is 2873 ha (1662 ha for Babebira distributary and 1211 ha for Bugbuga distributary) (Table 4.4, page-26). The area extracted from the satellite image and the area collected from agriculture department seems to have good correlation while the field data of water resources department differed. The reason of that is the water resources department maintains the same area as it was in the time of inception of the project during 1957. The data of agriculture department reflects the change in land use pattern with an increase of agricultural land from 2873 to 3214 ha which demands the need of re-computation of the crop water requirement. From the attribute table of the agricultural map the distributary wise area under agriculture for Babebira and Bugbuga distributary are 1620 ha and 1588 ha respectively. From the attribute table of


77

rice map the areas under rice are 1424 ha and 1200 ha in Babebira and Bugbuga respectively, total 2624 ha against the ground truth of 2604ha (page-53).

6.2.3. Crop growth stages

From Table-5.4 it is seen that it is possible to establish relationship between NDVI and crop growth stage of individual rice (early, normal or late transplanted rice). But it fails to establish a general relationship with the rice crop growth during a crop season. It is observed that NDVI is high on 7th April irrespective of transplantation dates and declines after this date. While considering all the variety of rice it is observed that the NDVI value increases according to growth days i.e., from 13 to 24, 24 to 34, 34 to 46, 46 to 57, and 57 to 63. After 63 days it has no relation.

Similarly from Table5.6 it is seen that it is possible to establish relationship between SAVI and crop growth stage of individual rice (early, normal or late transplanted rice). But it fails to establish a general relationship with the rice crop growth during a crop season. It is observed that SAVI is high on 7th April irrespective of transplantation dates and decline s after this date. While considering all the variety of rice it is observed that the SAVI value increases according to growth days i.e., from 13 to 24, 24 to 34, 34 to 46, 46 to 57, and 57 to 63. After 63 days it has no relation.

6.2.4. Reference crop evapotranspiration

The evapotranspiration have been computed from the Pan Evaporation data of the Chipilima observatory which is nearest to the study area in the command. The estimation of reference crop evapotranspiration by Penman-Montieth method was also attempted. The non-availability of parameters of Chipilima observatory like wind speed and sunshine hours were overcome by using the data of Sambalpur and Jharsuguda observatory. The wind speed of Sambalpur observatory and sunshine hours of Jharsuguda observatory were used with other parameters of Chipilima observatory in Penman-Montieth method. The results of two methods are presented in the Table 5. 14. As there was not much difference in both the approaches the results of pan evaporation was used which has taken only wind speed data from other nearby station.

6.2.5. Irrigation Water Demand:

The distributary-wise water demand has been computed. The irrigation demand is 1278 ha-m (Table 6.10) and 1085 ha-m (Table 6.11) for Babebira and Bugbuga distributary respectively. The irrigation supplies during that period were 1054 ha-m and 581 ha-m for Babebira and Bugbuga distributary respectively. There is a high difference in the demand and supply of irrigation water in the Bugbuga distributary. The demands might have been met from the other sources like tanks. The water requirement for land preparation was considered as 200 mm during 20 days before transplantation. The percolation rates were considered as 1.5 mm/day as the soil type is clay mixed. Water layer maintained is 100 mm at initial stage and 40 mm at vegetative stage and 100 mm at mid stage. Provision for this


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has been kept in the computation of irrigation water requirement. Crops other than rice were also grown in the command during the period under study. The present study is confined to water requirement of rice crop only. The water requirement for other crops is not considered here. The water loss by seepage and percolation might be available for use in the lowland field as the command experienced an elevation difference of 15 m (at head the Reduced Level (RL) is 170 m and at the tail it is 145 m) in 5.0 km.

6.2.6. Canal network extraction from the IRS P6 LISS IV

The P6 LISS IV (MX) image of 30th May 2005 is used for extraction of canal network. It has 3 spectral bands in visible range and 5.8 m spatial resolution (more details on Table 1.2, page 10).The image covers a part of Bargarh Main Canal ( width 45.7 m), Attabira Branch canal ( width 22.86 m) and distributaries of various width ranging from 2.29 m and 1.52 m. Three methods (i) visual, (ii) multi-resolution segmentation and (iii) Edge detection approach are attempted.

Visual Approach: In visual approach the image is displayed on the computer screen in FCC. Manual digitisation is performed. It is possible to digitise the water distribution system upto distributary level. Water courses are not clearly visible.

6.2.7. The Canal network extraction from the cadastral level map

The study has found that the canal network can be extracted up to water course level from the cadastral level map. But there is a constraint in geo-referencing the cadastral map. Therefore the rice crop acreage estimation at water course level could not be possible in this study.

6.2.8. Satellite Data

In this study, an attempt is made by using only 5 LISS III images during crop growth period, how far the crop phenological stages can be extracted. In this study it is shown that using the NDVI, it is possible to derive 3 crop growth stages and also 3 crop stages.

In this study the 1st image is available after 38 days of transplantation. Hence, it is not possible to correlate the early stage crop phenology with NDVI. From multi-temporal images rice crop acreage has been computed. The crop acerage computed is very close to the acreage reported by the agriculture department, which takes into account, current ground conditions. Where as this acreage is more than 11.67 % , of the design acreage at the beginning of Hirakud command area as reported by the water resources department.

6.2.9. Role of Remote Sensing:

Role of remote sensing is to get the spatial crop acreages map and understand the crop phenological stages spatially.

In a conventional method, spatial maps of crop growth stages are not available and it is difficult to prepare them by conventional survey technique. Using satellite image in this study it is shown that it is possible to make spatial crop growth map. In this study each distributary is considered as one unit for irrigation water supply/demand analysis.


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In future with much high resolution satellite like CARTOSAT 1 (2.5 m spatial resolution), CARTOSAT 2 (1.0 m spatial resolution) it will be possible to map water courses also using which spatial supply/demand analysis is possible.

6.2.10. Limitation on Data:

The objective of this study is to use the existing 5 LISS III images to extract the crop phenological stages and also compute supply/demand analysis of irrigation water. In this study use of multi-resolution images was not an objective, so our study was confined to only LISS III images. However, an attempt has been made to use LISS IV (5.8 m) image to extract the canal network upto distributary level. Much higher resolution such as IKONOS, QUICK BIRD would have made it possible to map upto water course level. However the cost of the satellite data would have increased many folds.


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7. Conclusions and Recommendations

7.1. Conclusions

R.Q. 1: Crop Phenological stage extraction from the image

Which phenology stage of rice crop is best derived from the LISS III images?

Answer: Beside nursery stage the rice crop has four phenological stages. They are initial stage, crop development stage, mid stage and late stage. The duration of these four growth stages are different. In this study area, rice crop grown is medium variety crop. Its growth period is of 120 days including nursery. The growth duration are 19, 20, 37 and 19 days for initial stage, crop development stage, mid stage and late stage respectively (Table 4.2, page 24). Table 7.1 shows the rice crop phenological stages as on image acquisition date. From the histogram of NDVI images in Figure 5.4 (page 43) it is seen each image has one peak. The crop phenological stages can be extracted by NDVI values. From the Table7.1 it is seen that satellite image acquired on 16th February, 02 has two crop growth stages, initial stage for late transplanted rice and development stage for normal and early transplanted rice. The NDVI ranges from (-) 0.322 to 0.450 on 16th February, 02 (Table 5.2). Using these NDVI values, three crop stages i.e. late transplanted rice (-0.322 to 0.00), normal transplanted rice (0.00 to 0.20) and early transplanted rice (0.20 to 0.45) is derived (more details on Figure 5.5 page 44 and para 1 page 46). The average NDVI values of late, normal and early transplanted rice from 16th February, 02 image are (-) 0.0467, 0.0942 and 0.2872 respectively (Table 5.4). Satellite image acquired on 21st March, 02 has one crop growth stage , mid stage, for late, normal and late transplanted rice. The NDVI ranges from (-) 0.013 to 0.673 on 21st March, 02 (Table 5.2) and the average NDVI values of late, normal and early transplanted rice are 0.4726, 0.4855 and 0.5366 respectively (Table 5.4). Satellite image acquired on 7th April, 02 has two crop growth stages, mid stage for late and normal transplanted rice and late stage for early transplanted rice. The NDVI ranges from 0.065 to 0.752 (Table 5.2) and the average NDVI values of late, normal and early transplanted rice are 0.5753, 0.5694 and 0.5857 respectively (Table 5.4). Satellite image acquired on 14th April, 02 has two crop growth stages, mid stage for late transplanted rice and late stage for normal and early transplanted rice. The NDVI ranges from 0. 051 to 0.624 (Table 5.2). The average NDVI values of late, normal and early transplanted rice are 0.4675, 0.4678 and 0.4744 respectively (Table 5.4). Satellite image acquired on 2nd May, 02 has one crop growth stage, late stage, for late, normal transplanted rice. During this period early transplanted rice and part of normal transplanted rice has been harvested. The NDVI ranges from (-) 0.006 to 0.550


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(Table 5.2) and the average NDVI values of late, normal transplanted rice are 0.4582, and 0.4426 respectively (table 5.4). The crop phenological stages as on image acquisition dates are given below: Table 7.1: Crop phenological stages of Rice crop as on day of image acquisition

Note: The rice crop is classified into 3 classes i.e. early transplanted rice, normal transplanted rice and late transplanted rice. The transplanting and harvesting dates of different stages are explained in analysis of crop growth stages in para 3. (page 47).

Which vegetative index is suitable for extraction of rice crop phenology?

Answer: From the literature it is seen that both NDVI and SAVI indices are mostly used in crop growth studies. NDVI is used to have the greenness of the crop. SAVI is used to minimise the soil background on the quantification of greenness. In the present study, vegetation indices like NDVI and SAVI have been used. Form the NDVI images from Figure 5.4 (page 43), it is seen that NDVI of 16th February has wide range of value s from (-) 0.322 to 0.450, while NDVI of 21st March ranges from (-) 0.013 to 0.673, NDVI of 7th April ranges from 0.065 to 0.752, NDVI of 14th April ranges from 0.051 to 0.624 and NDVI of 2nd May ranges from (-) 0.006 to 0.550. Due to wide range of NDVI value of 16th February from (-) 0.322 to 0.450, it is suitable for extraction of different crop growth stages. From Table 5.5 (page 47) it is seen that SAVI of 16th February has a wide range of values from (-) 0.427 to 0.638, while SAVI of 21st March ranges from (-) 0.078 to 0.729, SAVI of 7th April ranges from 0.090

Crop stages Early Transplanted rice (10-Jan-02 to 17-Jan-02)

Mid-date: 14-Jan-02

Normal Transplanted rice (18-Jan-02 to 31-Jan-02) Mid -date:

24-Jan-02

Late Transplanted rice (01-Feb-02 to 08-Feb-02)

Mid-date: 04-Feb-02

Image acquisition

Dates


Crop Phenological

stage


Crop Phenological

stage


Crop Phenological

stage

16-Feb-02 34 Development stage

24 Development stage

13 Initial Stage

21-Mar-02 67 Mid stage 57 Mid stage 46 Mid stage 7-Apr-02 84 Late stage 74 Mid stage 63 Mid stage 14-Apr-02 91 Late stage 81 Late stage 70 Mid stage 2-May-02 Harvested - Partly

harvested/ 92

Late stage 88

Late stage


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to 1.067, SAVI of 14th April ranges from (-) 0.043 to 0.634 and SAVI of 2nd May ranges from (-) 0.008 to 0.774. Due to wide range of SAVI value of the 16th February image it is also suitable for extraction of rice crop phenology.

What is the accuracy of rice crop phenological stage extraction from the image?

Answer: Accuracy of crop phenological stages is possible in real time mode only. In this study, period of study and period of analysis differ by 3 years. In this study crop phenological stages are extracted from multi-temporal satellite image. Similar map from the field authorities is not available.

What is the accuracy of crop acreage estimation of different phenological stages of the rice?

Answer: In the present study the rice crop acreage for early, normal and late transplanted rice has been estimated from the 16th February NDVI with slicing approach. The image of 16th February image has been chosen because it has wide range of NDVI value and has gentle slope in NDVI vs. Cumulative rice crop area curve (Figure 5.5, page 44). In combination of NDVI values of all multi-date images of the rabi crop period it is possible to generate rice map which spatially shows the early, normal and late transplanted rice crop (Figure 6.1, page 54). The total rice crop acreage estimation from the image is 2624 ha against ground truth of 2604 ha (more details in para 5 page 52). The field data on crop phenological stage -wise acreage was not available, but using NDVI data of 16th February crop growth stages have been extracted. The spatial distribution of these rice stages gives an idea of different period of transplantation. The head reaches encompassed with the early transplanted rice while the tail reaches with late paddy. It may be concluded that the water scarcity at tail reaches forced the farmers to have late transplantation of rice crop. R.Q. 2: Is it possible to extract water distribution system using high resolution satellite data (LISS-IV)?

Which method of extraction gives be st result?

Ø Visual interpretation Ø Object/segment based Ø Edge detection method

Answer: LISS IV image has 3 spectral bands in visible range with 5.8 m spatial resolution (more details on Table 1.2, page 10). The IRS P6 LISS IV (MX) image of 30th May 2005 is used for extraction of canal network. The image covers a part of Bargarh Main Canal (width 45.7 m), Attabira Branch canal (width 22.86 m) and distributaries. Babebira and Bugbuga distributaies under study area has width of 2.29 m and 1.52 m respectively. Three methods visual interpretation, multi-resolution segmentation and Edge detection approach are attempted From the study it is found that the Visual interpretation is the best method of canal network extraction. Object/ segment based and edge detection methods were attempted, which could not yield satisfactory results.


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Upto what level is it possible to extract the canal network?

Ø Upto Distributary level Ø Upto Minor level Ø Upto Sub-minor level Ø Upto Field channel level

Answer: It is found that upto distributary level canal network can be extracted. In the present study, the minimum width of the distributary extracted from LISS IV image is 1.52 m. The water courses are not clearly visible in the IRS P6 LISS IV image. In the study area, the existing canal network is upto distributary level only. There is no minor and sub-minor canal network. The water courses are taken directly from the distributary.

Present study: From the study it is found that the crop phonological stages can be extracted from the multi-temporal satellite images.

In this ever changing world we observe that one system designed for a time needs renovation to suit future requirements. In the present study analysis has been made to estimate water demand vs. supply for rice crop in parts of Hirakud command, Orissa using multi-temporal IRS LISS III images. From this study it is found that the agricultural area has been increased by 8.96 % form 3955 ha to 4310 ha (Table 5.13, page 52) during the period from beginning of the project in the year 1957 to the present study year 2002. Rice being the dominant crop covering 81 % of the crop area during rabi season demands more water than supply. The water demand is 1278 ha -m against supply of 1054 ha-m for Babebira distributary (Table 6.10, page 69). And the demand is 1085 ha-m against supply of 581 ha-m for Bugbuga distributary (Table 6.11, page 69). To meet this demand water conveyance system needs to be renovated. Also an attempt was made to extract canal network from the Resourcesat1 (P6) LISS IV image, which has 5.8 m spatial resolution. The error of canal network extracted from LISS IV image is varying from -2.36 % to 9.10 % (Table 6.3).

7.2. Recommendations

The present study has utilised only multi-spectral, multi-temporal LISS III images and associated NDVI for the crop phenological extraction and rice classification. The crop water requirement is computed from the pan evaporation. The study can be improved with more no of multi-temporal and multi-sensor images like Landsat ETM, ASTER. The latest satellite like CARTOSAT 1 (2.5 m spatial resolution) and future satellites like CARTOSAT 2 (1.0 m spatial resolution) can be used upto water course level spatial supply demand analysis. Using multi-temporal and multi-sensor images, it will be possible to extend this study to other crops also.


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Appendix:

Figure A.1: Model for normalization

Figure A.2: Model for conversation of DN values of pixel to radiance values


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