AN ANALYSIS USING LISS III DATA FOR ESTIMATING WATER DEMAND … · · 2012-03-31old command area an attempt has been made for estimating water demand for rice cropping using

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.

i

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 5375 MCum and it provide irrigation potential of 159106 ha during kharif and 108385 ha during rabi season.

Among this, 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. 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 requirement 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

ii

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 Orissa, 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

iii

Table of contents 1. Introduction ..................................................................................................................................... 6

1.1. Background............................................................................................................................ 6 1.2. Problem statement.................................................................................................................. 7 1.3. Objectives .............................................................................................................................. 7 1.4. Research Questions................................................................................................................ 8 1.5. Hypothesis ............................................................................................................................. 8 1.6. Assumptions........................................................................................................................... 8 1.7. Limitations ............................................................................................................................. 8 1.8. Chapter scheme...................................................................................................................... 9 1.9. Data........................................................................................................................................ 9 1.10. Study Area: ............................................................................................................................ 9

2. Literature Review.......................................................................................................................... 10 2.1. Rice crop .............................................................................................................................. 10 2.2. Water demand of Rice: ........................................................................................................ 11 2.3. Remote Sensing to extract rice crop growth stages ............................................................ 13 2.4. Irrigation water demand....................................................................................................... 14

3. Study Area..................................................................................................................................... 16 3.1. Location ............................................................................................................................... 16 3.2. Climate................................................................................................................................. 16 3.3. Soils ..................................................................................................................................... 17 3.4. Geology................................................................................................................................ 17 3.5. Agriculture ........................................................................................................................... 17

4. Materials and Methods .................................................................................................................. 19 4.1. Materials .............................................................................................................................. 19 4.2. Methods ............................................................................................................................... 23

5. Analysis......................................................................................................................................... 32 6. Results and Discussions ................................................................................................................ 45

6.1. Results.................................................................................................................................. 45 6.2. Discussion:........................................................................................................................... 59

7. Conclusions and Recommendations.............................................................................................. 62 7.1. Conclusions.......................................................................................................................... 62 7.2. Recommendations................................................................................................................ 63

iv

List of figures Figure 2.1: Rice growth stage 10 Figure 2.2: Depth of water layer during the growing season 11 Figure 3.1: Study Area 16 Figure 4.1: Temperature trend in the study area during Rabi season 2001-02 20 Figure 4.2: Humidity in the study area during Rabi season 2001-02 20 Figure 4.3: Pan Evaporation and rainfall in the study area during Rabi season 2001-02 21 Figure 4.4 : Methodology 23 Figure 4.5: Plots generated for PIFs (Urban, Water and Dry sand) on Band-1 of LISS III image 25 Figure 4.6: Plots generated for PIFs (Urban, Water and Dry sand) on Band-2 of LISS III image 26 Figure 4.7: Plots generated for PIFs (Urban, Water and Dry sand) on Band-3 of LISS III image 26 Figure 4.8: Histogram showing DN values of 21st March 02 images before and after normalisation 27 Figure 4.9: Reference evapotranspiration 30 Figure 5.1: Crop Growth Stage vs. crop area as derived from NDVI 33 Figure 5.2: Scatter Plots of temporal NDVI images (Scatter Plot before applying threshold) 34 Figure 5.3: Modified Scatter Plot of temporal NDVI image after eliminating non-rice crop 35 Figure 5.4: Crop coefficient of Rice 41 Figure 6.1: Spatial distribution of Rice Crop in the study Area 45 Figure 6.2: Canal network extracted from LISS IV image 46 Figure 6.3: The Canal network extracted from the cadastral level map 47 Figure 6.4: The Canal network extracted from the IRS P6 LISS IV and cadastral map 47

v

List of tables Table 3.1: Details of Main Canals of Hirakud Command Area ............................................................ 18 Table 4.1: Specification of IRS Optical Sensors: LISS III and LISS IV .............................................. 19 Table 4.2: Village wise agricultural data............................................................................................... 21 Table 4.3: Distributary-wise Command Area ....................................................................................... 22 Table 4.4: Village-wise Command Area under each distributary ......................................................... 22 Table 4.5: Regression equations between satellite data of 5 acquisitions for Pseudo Invariant Features

....................................................................................................................................................... 27 Table 4.6: Limits of Radiance values from the header file of the satellite data .................................... 28 Table 5.1: NDVI of the temporal satellite images................................................................................. 32 Table 5.2: Rice growth days as on day of image acquisition ................................................................ 34 Table 5.3: Values of average NDVI for various rice crops................................................................... 38 Table 5.4: Minimum and maximum SAVI values for rice crops .......................................................... 39 Table 5.5 Values of average SAVI for various rice crops..................................................................... 39 Table 5.6: Rice growth stages and Duration in days:............................................................................ 39 Table 5.7: Rice growth in days as on image acquisition dates.............................................................. 40 Table 5.8: Crop coefficients as on day of image acquisition ................................................................ 41 Table 5.9: Crop coefficients for Early transplanted rice ....................................................................... 42 Table 5.10: Crop coefficients for Normal transplanted rice.................................................................. 42 Table 5.11 : Crop coefficients for Late transplanted rice...................................................................... 43 Table 5.12: Reference Crop Evapotranspiration ................................................................................... 43 Table 5.13: Village Wise Gross Command Area & Culturable Command Area.................................. 44 Table 6.1: NDVI threshold for various rice .......................................................................................... 46 Table 6.2: Comparison of distributary length extracted by different method: ...................................... 48 Table 6.3: Computation of ET0 (10-day average reference evapotranspiration)................................. 48 Table 6.4: Crop water requirement of Early Transplanted Rice .......................................................... 53 Table 6.5 : Crop water requirement of Normal Transplanted Rice ....................................................... 54 Table 6.6 : Crop water requirement of Late Transplanted Rice ............................................................ 55 Table 6.7 : Crop water requirement for Babebira Distributary ............................................................. 56 Table 6.8 : Crop water requirement for Bugbuga Distributary ............................................................. 57 Table 6.9 : Water balance study for Babebira distributary.................................................................... 58 Table 6.10 : Water balance study for Bugbuga distributary.................................................................. 58

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

6

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 the rice crop is highly dependent on the southwest monsoon, which occurs over the subcontinent from June through September. 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 in 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, 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, 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. 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 the design of water conveyance systems, it is necessary to assess the water requirement of the crop intended to be grown. The irrigation demand of a command under the project 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 induction of high yielding


7

verity of crops, influence of market demand, salinity and water-logging are causes of change of cropping pattern. This leads the review of irrigation demand for the command. 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 leads to spatial analysis of water use. Remote sensing (RS) is very 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, water logging (Bastiaanssen, 2000). Water demand by the crop depends on the phenological stages. It is possible to extract crop phenological stages from satellite image (Ray, 2001; Ray, 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, 2001). The model for rice cropping ORIZA2000 allows simulation of crop management options such as irrigation and nitrogen fertilizer management (Bouman and Laar, 2001). Studies have been done to establish correlation between Leaf Area Index (LAI) and crop coefficient (Kar, 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 up to field level (Bastiaanssen, 1999). These all are related to irrigation performance and predicting crop yield. No works has been done encompassing conveyance and distribution system of the irrigation. An attempt is proposed to analyse the irrigation conveyance system with the real time water demand. Water demand for paddy rice depends on growth stages, phenological stages. Crop transpiration rate is low at early stages of growth and increases almost linearly (Tomar, 1980). There are four phenological stages of crops: initial, crop development, mid season, late season (Farmwest.com, 2004) and wetland rice has two more stages: nursery and land preparation. So, irrigation water demand varies according to the crop growth stages. 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.

1.2. Problem statement

Conventional irrigation water supply leads to over irrigation on some parts while water deficit on other parts. Prevailing cropping pattern and crop acreage changed from the designed one that needs analysis of water demand versus water supply.

1.3. Objectives

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 water requirement and supply and demand of irrigation water using remote sensing data and meteorological data.


8

• 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

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?

Visual

Object/segment based

Edge detection method

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

Upto Distributary level

Upto Minor level

Upto Sub-minor level

Upto Field channel level

1.5. Hypothesis

Water use by crop depends on crop type, crop growth stage. Both can be derived from LISS III image during the growing season.

1.6. Assumptions

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.

1.7. Limitations

The present study is done for paddy crop only. In multi-crop command area crop coefficients are to be modified to suit the ground situation.


9

1.8. 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.

1.9. Data

Satellite images The following satellite images are used in the study:

• IRS 1D LISS-III ( 16 Feb. 2002) • IRS 1C LISS-III ( 21 March 2002) • IRS 1D LISS-III ( 07 April 2002) • IRS 1C LISS-III ( 14 April 2002) • IRS 1D LISS-III ( 02 May 2002) • P6 LISS-IV (MX) (30 May 2005)

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

1.10. 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 5 x 5 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 150-180 days (Mandal, 1999). In the entire Hirakud command area paddy is the

predominant crop covering 95 % of the total crop area (NRSA, 2004).


10

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 crop 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] 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


11

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 rice 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, 1999).

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]


12

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, 1984).

Crop coefficient

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, 1998). Crop coefficient of rice

Most of the attempts have been made to extract crop coefficient for rice for wet season (July to October) (Shah, 1986; Tomar, 1980; Tripathy, 2004; Tyagi, 2000). Tyagi (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 Toole (1980) found these values as 1.0, 1.15, 1.3, at transplanting, maximum tiller stage and flowering stages for wetland rice. Doorenbos (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, 1.10, 1.0 for 1st & 2nd month, mid season and last 4 weeks respectively for humid Asia with light to moderate wind. 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, 1998). Reference crop evapotranspiration (ET0 )

It 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, 1984). The methodology to compute ET0 is suggested by Allen (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.


13

Crop evapotranspiration, ETC

It 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 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 using 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. 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 in Gujarat, India. 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 to its growth stages (Kiyoshi, 2003; Mandal, 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).According to him: y = - 0.0002 x2 + 0.0252 x - 0.4508. Mandal (2003) found that NDVI attained peak values at 62 days after transplanting of rice.


14

Vegetation Indices derived from LISS III images:

The LISS III has 4 spectral bands (Table 4.1) from which various vegetation indices can be derived like Simple Ratio (SR), Normalised Differential Vegetation Index (NDVI), Transformed Vegetation 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 also varying according to the crop growth stages. Irrigation scheduling of paddy is based on three questions, they being: (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 (IRRI, 2005). 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. 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, 1984). Rotational irrigation supply, locally named as Warabandi, is practised in the states of Haryana and Uttar Pradesh in India. Bhakra Irrigation (Sakthivadivel, 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 and Laar, 2001), GISAREG (Fortes et al., 2005), Surface Energy Balance Algorithm For Land (SEBAL) (Waterwatch, 1998). 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 (2004) tested the irrigation requirement for different rice establishment technologies and found that the direct seeding on flat need the least irrigation water (865


15

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


16

HIRAKUD RESERVOIR

3. Study Area

3.1. Location

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 5 km x 5

km has been selected for study.

Figure 3.1: Study Area

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 south-west 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.


17

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, 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.

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. Where three crops seasons are prevailed, the Crops grown during July to October are known as autumn crop; during November to February are known as winter crops and during March to June are known as summer crops. 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

Rice-Rice-Rice; Rice- Mung-Rice are the crops grown in the command in rotation during kharif, winter and summer. Other crops grows in the command are pulses, vegetables, oilseeds and sugarcane.


18

Irrigation

The main source of irrigation is surface irrigation from the Hirakud reservoir. The command is encompassed by the canal network: main canal, branch canal, distributaries, minors, sub-minors. The source of irrigation is Hirakud reservoir which has a storage capacity of 7189 MCum. The Canals scattered in the command area are Bargarh Main Canal, Sasan Main Canal and Sambalpur Distributary. Table3.1 below shows the salient features of the canals: Table 3.1: Details of Main Canals of Hirakud Command Area S. No. Name of Canal Length of

canal (Km.)

Full supply discharge (Cumec)

Bed width of Canal ( m.)

Full Supply Depth of canal (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 potential are 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 reservoir. The Irrigation practice in the command is demand based system.

The details of the two distributaries being investigated in this study are presented in Table 4.3: Distributary-wise Command Area.


19

4. Materials and Methods

4.1. Materials

Satellite Imagery: IRS 1C/1D Linear Imaging and Self Scanning-III (LISS III) images acquired on 5 days (16th Feb 2002, 21st March 2002, 7th April 2002, 14th April 2002 and 2nd May 2002), one IRS P6 LISS IV (30th May,2005) image are used. Table 4.1: Specification of IRS Optical Sensors: LISS III and LISS IV Sensor Spectral

Bands (µm)

Spatial resolution (Meter)

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

Green : 0.52-0.59

5.8 23 10

Red: 0.62-0.68

5.8 23 10

LISS IV

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: 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 of Chipilima observatory are given in the figure 4.1 and 4.2. Pan evaporation and rainfall are given in figure 4.3.


20

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

40.0

45.0

50.0

335

340

345

350

355

360

365 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100

105

110

115

120

125

130

135

140

145

150

Julian Day

Tem

pera

ture

in 0 C

entig

rade

Minimum Temperature Maximum Temperature

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

335

340

345

350

355

360

365 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100

105

110

115

120

125

130

135

140

145

150

Julian Day

% o

f Hum

udity

Minimum Maximum


21

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

335

340

345

350

355

360

365 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100

105

110

115

120

125

130

135

140

145

150

Julian Day

Evap

orat

ion

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 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 4.2: Village wise agricultural data

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 OilseedVege-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


22

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 (2000) were used in the computation of crop water requirement.

Irrigation data: Irrigation scheduling data like time duration and frequency and quantity of irrigation supply are maintained by the water resources department, Government of Orissa. These data are collected and used in this study.

Canal network: The command is encompassed by the canal network: main canal, branch canal, distributaries, minors, sub-minors. The source of irrigation water is Hirakud reservoir which has a gross storage capacity of 7189 MCum and live storage capacity of 5375 MCum. 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 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


23

4.2. Methods

Methods are divided into three sections (i) Extraction of required information from the satellite images. (ii) Computation of potential evapotranspiration from meteorological data. (iii) Computation of water demand. The methodology followed to estimate distributary level water demand by rice (and also irrigation water demand) is sketched in Figure 4.4 : Methodology

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.

Figure 4.4 : Methodology

Multi-temporal Satellite Data

Geometric correction (LISS III)

Canal network Extraction

From LISS IV

Crop Acreage estimation For diff. phenological stages

Water Demand Analysis

Meteorological Data

Crop Water Requirement

ET0

Crop coefficient KCETC

Irrigation Data

Reliable Irrigation

Radiometric correction (Using Regression equations of PIFs)

Generation of Rice map


24

Image Processing: Software used: ERDAS Imagine (ERDAS, 2003) was used for all satellite image processing ArcGIS was used to generate the GIS database and also analysis. eCognition was used to do automatic extraction of canal network from LISS IV image.

Geographic registration

Geometric correction of images has been done with the help of Ground Control Points (GCPs) from the topo map. The error of geo-reference are in order of 0.0692, 0.120, 0.1187, 0.0986, and 0.0717 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 here 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 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 4.5, 4.6, 4.7 respectively. Summery of the regression generated for the PIFs is shown in table 4.5.


25

Figure 4.5: 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

40

60

80

100

120

0 20 40 60 80 100 120 140

D2B1

D1B1

y = 1.1162x - 47.717R2 = 0.9322

0

20

40

60

80

100

120

0 20 40 60 80 100 120 140 160

D3B1

D1B1

y = 0.7508x - 6.1698R2 = 0.9714

0

20

40

60

80

100

120

0 20 40 60 80 100 120 140 160

D4B1

D1B1

y = 1.2491x - 82.106R2 = 0.9214

0

20

40

60

80

100

120

0 50 100 150 200

D5B1

D1B

1


26

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

Figure 4.7: Plots generated for PIFs (Urban, Water and Dry sand) on Band-3 of LISS III image

y = 0.9805x - 7.1993R2 = 0.9903

0

20

40

60

80

100

120

0 20 40 60 80 100 120

D2B3

D1B3

y = 0.9869x - 20.921R2 = 0.9686

0

20

40

60

80

100

120

0 20 40 60 80 100 120 140

D3B3

D1B

3

y = 0.8759x - 7.9151R2 = 0.9927

0

20

40

60

80

100

120

0 20 40 60 80 100 120 140

D4B3

D1B

3

y = 1.0434x - 35.375R2 = 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

20

40

60

80

100

120

0 20 40 60 80 100 120 140

D2B2

D1B2

y = 1.0008x - 29.547R2 = 0.9546

0

20

40

60

80

100

120

140

0 20 40 60 80 100 120 140 160

D3B2

D1B2

y = 0.8456x - 9.3225R2 = 0.9839

0

20

40

60

80

100

120

0 20 40 60 80 100 120 140 160

D4B2

D1B

2

y = 1.0636x - 50.206R2 = 0.9426

0

20

40

60

80

100

120

140

0 50 100 150 200

D5B2

D1B

2


27

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

Between Ist and 2nd date acquisition image Regressio

n r² D1B1 = 0.9673 x D2B1 - 18.153 0.9840D1B2 = 1.0414 x D2B2 - 13.672 0.9886D1B3 = 0.9805 x D2B3 - 7.1993 0.9903

Between Ist and 3rd date acquisition image D1B1 = 1.1162 x D3B1 - 47.717 0.9322D1B2 = 1.0008 x D3B2 - 29.547 0.9546D1B3 = 0.9869 x D3B3 - 20.921 0.9686

Between Ist and 4th date acquisition image D1B1 = 0.7508 x D4B1 - 6.1698 0.9714D1B2 = 0.8456 x D4B2 - 9.3225 0.9839D1B3 = 0.8759 x D4B3 - 7.9151 0.9927

Between Ist and 5th date acquisition image D1B1 = 1.2491 x D5B1 - 82.106 0.9214D1B2 = 1.0636 x D5B2 - 50.206 0.9426D1B3 = 1.0434 x D5B3 - 35.375 0.9682Note:

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

The images have been normalized with the help of above equations in Model Maker of ERDAS IMAGINE software. Figure 4.8 shows the effect of normalisation.

Figure 4.8: 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,

A B

D C


28

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 taken over same place 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.

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

Satellite Image Date Band Lmin Lmax

band1 0.00 14.8005band2 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 200214th 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.

RNIRRNIRNDVI

+−

= …. …. …. (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 (Level 1 classification). 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.


29

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. 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.

As unsupervised classification is a multi-stage approach. The agriculture map again classified into 20 classes. From these 14 classes were identified as agriculture. The image has been recoded (Level 2 classification). The stacked NDVI image has been masked with it.

Again the image classified into 10 classes (Level 3 classification). Three classes were identified as paddy. Recoded it 1 for Paddy 0 for others. Thus, Rice map of the study area was generated.

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.

In the rice map some area may have other crops due to same NDVI values that need to be delineated.

Non-rice area from the rice is map generated by trial and error method using the following rules. With Rule 1: NDVI of 1st image < threshold, then paddy else non-paddy Rule 2: NDVI of last image < threshold, then paddy else non-paddy Rule 3: NDVI of Maximum vegetative stage > threshold, then paddy Rule 4: NDVI of maximum vegetative stage image > NDVI of last image, then paddy Rule 5: NDVI of maximum vegetative stage image >NDVI of last image AND maximum vegetative stage NDVI > threshold, then paddy 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.

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, 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


30

Reference Crop Evapotranspiration

0.01.0

2.03.0

4.05.0

6.07.0

335

345

355

365 10 20 30 40 50 60 70 80 90 100

110

120

130

140

150

Julian Day

ET 0 i

n m

m/d

ay

10 days average

entire command area. 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 4.9. Figure 4.9: 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 both weekly and 10 day basis (Allen, 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 cc = … (4) Where ETc is the crop evapotranspiration (mm / day)


31

and Kc is the crop coefficient.

Preprocessing of Satellite data:

IRS 1C/1D LISS III images are used. Images have been geo-referenced; atmospherically corrected. Rice Map of the study area has been generated.

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.


32

5. Analysis

Satellite Image: The satellite image of IRS 1C and 1D LISS III are geometrically corrected and radiometrically rectified. The DN values of the pixels are converted to the radiance values with the parameters given in the header file of corresponding images. The Normalised Difference Vegetation Index (NDVI) is generated for each image. NDVI represents the greenness of the vegetation. The NDVI values of temporal satellite data used in this study are as follows: 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.

The NDVI images have been stacked. From this stacked NDVI images classification has been done using ISODATA clustering approach i.e. unsupervised classification. As it is a multistage approach the classification have been done in three iterations. In post classification with the ground truth the classes are assigned. The agriculture map and rice have been generated.

The study area has been extracted by subset of the image covering Babebira and Bugbuga distributaries of Hirakud Command Area. This image is used in further analysis.

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 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.

As the rice map of the study area has been generated from the stacked NDVI images, it is likely to have other area other than rice having same NDVI responses; thresholding approach is applied to delineate the area other than rice.

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


33

NDVI vs Rice Crop Area

0

200

400

600

800

1000

1200

1400

1600

1800

-0.2 -0.1 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 are plotted. NDVI ranges considered for this plot are (i)<0.0; (ii) between 0.0 to 0.50 with interval of 0.10 and (iii) between 0.5 to 0.75 with interval of 0.025 . The ranges below zero represents the water body, others are the vegetative cover. The area under rice crop is 3034 ha out of total study area of 3843 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) Plots of 21st March and 7th April shows two peaks of NDVI, which reflects that two growth stages of rice were prominent on that day of acquisition. (iv) The peak NDVI value (0.5) on 21st March, 14th April and 2nd May are coinciding with each other but area of the histogram differs. (v) 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. (vi) and almost all the crops had uniform greenness on 14th April. (vii) 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. (viii) The NDVI curve of 2nd May and intercept each other at NDVI 0.575. (ix) 1st trial to delineate the rice crop considering the NDVI value at this interception point (0.575) as a threshold. If (NDVI on 2nd May 02 < 0.575) then paddy. (x) the rice area has reduced to 2808 ha. (xi) Two more iteration with threshold value of 0.560 and 0.550 are tried that reduced the rice area to 2698 ha.


34

Table 5.2: Rice growth days as on day of image acquisition

Days after plantation as on the day of image

acquisition

Rice crop growth stages in days

Transplantation date 21st March 02

7th April 02

Growth stage Days

Remarks

Rice planted on 10th January 2002

71 days 88 days

Rice planted on 8th Feb 02

42 days 59 days

Peak day of transplantation 20th January 02

61 days 78 days

Nursery Initial stage Crop Development stages Productive stage Maturity stage Total

25 19 20 37 19 120

Transplantation dates: 10th January 2002 to 8th February 2002;

Harvesting dates: 15th April 2002 to 10th May 2002

Trial 2: With 2-D Scatter Plot

The NDVI trend between layers of NDVI stacked images have been plotted as 2D-scatter plot. The each 5 layer represents 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 and 2nd layers it was noticed that the NDVI value of some pixels in the Day1 have been reduced on the Day2 image. Those pixels may be represented the non-rice crops. (ii) Those pixels were masked in the map.


35

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 image;

Trial 3: With thresholds derived from NDVI Curve and 2-D Scatter Plot

The thresholds put on for the generation of final rice map as follows: (i) NDVI of 21st March image is greater than the NDVI of 16th Feb image. (ii) NDVI of 16th Feb image is less than a threshold value of 0.450 (iii) NDVI of 2nd May image is less than a threshold value of 0.550

The final rice crop acreage comes to 2624 ha against ground truth of 2604 ha.

Some more graphs: NDVI vs. rice crop acreage.

Graph 1: Temporal variation of NDVI in Rice Crop Area Graph 2: Variation NDVI vs. Cumulative Rice Crop Area


36

Histogram of NDVI vs. Rice Crop Area

0

50

100

150

200

250

300

-0.2

50

0.00

0

0.07

5

0.15

0

0.22

5

0.30

0

0.37

5

0.45

0

0.52

5

0.60

0

0.67

5

0.75

0

NDVI values

Rice

Cro

p Ar

ea in

ha.

16-Feb-02


050

100150200250300350400450

-0.2

50

0.00

0

0.07

5

0.15

0

0.22

5

0.30

0

0.37

5

0.45

0

0.52

5

0.60

0

0.67

5

0.75

0

NDVI values

Rice

CRo

p Ar

ea in

ha.

21-Mar-02


050

100150200250300350400450500

-0.2

50

0.00

0

0.07

5

0.15

0

0.22

5

0.30

0

0.37

5

0.45

0

0.52

5

0.60

0

0.67

5

0.75

0

NDVI values

Ric

e C

Rop

Are

a in

ha.

7-Apr-02


0

100

200

300

400

500

600

-0.2

50

0.00

0

0.07

5

0.15

0

0.22

5

0.30

0

0.37

5

0.45

0

0.52

5

0.60

0

0.67

5

0.75

0

NDVI values

Ric

e C

Rop

Are

a in

ha.

14-Apr-02


0

100

200

300

400

-0.2

50

0.00

0

0.07

5

0.15

0

0.22

5

0.30

0

0.37

5

0.45

0

0.52

5

0.60

0

0.67

5

0.75

0

NDVI values

Ric

e C

Rop

Are

a in

ha.

2-May-02

Histogarm of NDVI vs. Rice Crop Area

0

100

200

300

400

500

600

-0.3

00

-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

Ric

e C

rop

Area

in h

a.


Graph 1: Temporal variation of NDVI in Rice Crop Area

It is clearly evident from the above graphs 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.


37

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 Ri

ce C

rop

Area

in h

a.


Graph 2: Variation of NDVI vs. Cumulative Rice Crop

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 area used for differentiating the crop stages the error will be more.

Temporal variation of NDVI for Rice Crop

-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

24-Jan-02

13-Feb-02

5-Mar-02

25-Mar-02

14-Apr-02

4-May-02

24-May-02

Time

NDV

I val

ues

Series1

Series2

Series3

Series4

Series5

Series6

Series7

Series8

Series9

Series10

Series11

Series12

Series13

Series14

Series15

Series16

Series17

Series18

Series19

Graph 3: Temporal variation of NDVI at randomly selected points Note: 1st acquisition date: 16th Feb, 2nd acquisition date: 21st March,

3rd acquisition date: 7th April, 4th acquisition date: 14th April, 5th acquisition date: 2nd May


38

Trend of NDVI for different crop stages

Graph 4: Trend of NDVI for different crop stages Rice stage classification derived from NDVI: If the NDVI value of 16th Feb. 2002 was below 0.00, the pixel belonged to late transplanted rice. If it was between 0.00and 0.20, the pixel belonged to normal transplanted rice, and if it was more than 0.20, the pixel belonged to early transplanted rice. Table 5.3: 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

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

NDV

I val

ues

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

NDVI

val

ues

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

NDV

I val

ues

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


39

Rice stage classification parameters were derived from SAVI Soil Adjusted Vegetation Index (SAVI), as defined by Huete (1988) 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. Table 5.4: 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

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 5.5 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: Rice growth stages and Duration in days:

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


40

Analysis of crop growth stages:

The showing/transplanting date was varies from 20th December 2001 to 8th Feb. 2002 (Table 4.2, page-21). 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 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 06th May to 10th May (5 days) are considered as harvesting period for early ,normal and late transplanted rice. 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


41

Value 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 5.4: Crop coefficient of Rice Source: Tyagi ( 2000) Table 5.8: Crop coefficients as on day of image acquisition

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


42

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 : Normal Paddy

Planting date: 18-Jan-02 to 31-Jan-02 : Mid day 14-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


43

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

ET0 computed from meteorological data with 10 days average Pan Evaporation method and Penman-Montieth method area as follows:

Table 5.12: Reference Crop Evapotranspiration

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

1 2.24 2.42 2 2.18 2.34

December 2001

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


44

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

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

The total area of the study area is 3801 ha computed from the field data of Attabira Block. Total cropped area computed from the field data is 3214 ha.

Rabi crop acreage as per water resources department is 2873 ha; 1662 ha for Babebira Distributary and 1211 ha for Bugbuga Distributary. These are figure 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. This shows that the culturable command area has increased from 3955 ha to 4310 ha. Taking these figures into consideration the rice crop area of the command computed as: Total cropped area during Rabi 2001-02: 4310.6 ha (Table 5.13. Col. 8) Out of these the Area under paddy : 3492.0 ha (Table 4.2) 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.


45

6. Results and Discussions

6.1. Results

The rice map generated from the multi-temporal satellite image

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

1. Rice crop grown during rabi 2001-02 were medium verity having crop growth period of 115 to 120 days including the nursery period of 25 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)


46

2. Classification has been done with slicing the NDVI values of 16th Feb. image as follows: 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. 5. The Canal network extraction from the IRS P6 LISS IV:

Figure 6.2: Canal network extracted from LISS IV image


47

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


48

Table 6.2: 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

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 6.3.

Table 6.3: Computation of ET0 (10-day average reference evapotranspiration) Julian Day of 2001 Julian day of 2002 335-344 345-354 355-365 1-10 11-20 21-30 31-40 41-50 51-60

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)

Julian Day of 2002 61-70 71-80 81-90 91-100 101-110 111-120 121-130 131-140 141-151

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


49

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

0ETKET cc = … (4)

The Kc values were derived with respect to growth days by interpolation method from the curve shown in figures 5.4.The computation and results are shown in the Table 6.4 to 6.6. Distributary-wise water demand and supply are shown in table 6.7 and 6.8. 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. Those are shown in the Table 6.9 to 6.10. Explanation of Tables and figures:

Figure 2.1 shows the growth stages of rice crop.

Figure 2.2 shows the water layer in the field during different growth stages of rice crop.

Figure 3.21 shows the study area India/Orissa/ LISS III/ Study area.

Table 3.1 gives the salient features canal network of the Hirakud command.

Figure 4.1 reflects the minimum and maximum temperature time series for crop period 2001-2002. The minimum temperature recorded 7.5o C on 3rd January 2002 and maximum temperature of 44o C on may 2002.

Figure 4.2 reflects the minimum and maximum relative humidity time series for crop period 2001-2002. The minimum humidity recoded of 19.0 % on 5th December and maximum of 95 % on 21st February.

Figure 4.3 reflects the pan evaporation and rainfall for time series for crop period 2001-2002. The minimum pan evaporation recorded 1.3 mm on 30th January and maximum 10.0 mm on 24th May. There are 21 rainy days during the crop period, maximum being 14.8 mm on 26th may and total rainfall is 47.6 mm. Its contribution to water balance study is negligible. The effective rainfall was zero.

Table 4.2 reflects the village-wise crop grown during the rabi season crop period 2001-2002. Table 4.3 and 4.4 gives the distributary-wise and village-wise command area respectively. It may be pointed out that under one distributary many villages are falling 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.

Figure 4.4 gives the flow chart of the workflow in the study.

Plots shown in the Figures 4.5, 4.6, 4.7 shows regression plots generated for the band 1, band 2 and band 3 for Pseudo Invariant Features (PIF). 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 same way 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


50

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.

Table 4.5 shows the summery of the regression generated for the PIFs.

Figure 4.8 shows the histogram before and after the normalisation of 21st March 2002 image. It reflects the change in Digital Number (DN) values which are free from atmospheric influence like aerosol, sun illumination. With normalisation all the images were free from atmospheric influence and sun illumination.

Table 4.6 gives the maximum and minimum radiance for LISS-3 sensor of IRS-1C/1D image of study area. The sensors record the reflectance value in the form of DN values. To convert back to the original object reflectance values the DN values are processed. 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.

Figure 4.9 gives the trend of reference crop evapotranspiration (ET0) for the study period. The computed maximum ET0 was 6.47 mm/day during first week of May and minimum was 2.06 mm/day during December. ET0 has been computed by the equation (2). The pan coefficient, Kp, has been computed with equation (3) as suggested by Allen (1998).

Table 5.1 gives the NDVI range of temporal images. The NDVI gives the change in greenness over the period.

Figure 5.1 shows the NDVI curve of rice crop. It was used to derive thresholds to delineate non-rice crops. The details have been discussed in the analysis part.

Table 5.2 gives the peak rice growth stages, which were used to derive threshold to generate rice map.

Figure 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 16th 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.

Graph-1 shows the temporal variation of NDVI in rice crop area and its interpretation has been described in analysis section.

Graph 2 shows the variation of NDVI vs. cumulative rice crop. The curve was used to derive threshold for late, normal and early transplanted rice.

Graph 3 shows the temporal variation of NDVI for rice crop. From this graph it is seen that the peak greenness of crop increases from 16th February to 7th April and then declines. It has the good correlation with crop growth stages with respect to growth days.

Graph 4 shows the NDVI trend of late, normal and early transplanted rice.

Table 5.3 shows 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 it has no relation.


51

Table 5.4 shows the temporal SAVI values. The same trend as discussed for NDVI in table 5.3 is shown for the SAVI values.

Table 5.5 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 it has no relation.

Table 5.6 gives the duration of rice corp growth stages.

Table 5.7 shows the rice growth days as on image acquisition dates.

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 10th 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 10th 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 theses 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 no 5.9 to 5.11 for early, normal and late transplanted rice.

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.

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.

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.

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 6.1 shows the NDVI values of 16th February 2002 image used for differentiate the early, normal and late transplanted rice.

Table 6.2 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. Where the canal inventory is not available this method of canal extraction can be implemented with an error of (-) 2.36 to 12.24.

Table 6.3 shows the computation of reference crop evapotranspiration (ET0) in tabular format. 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-3 (page-30).


52

Table 6.4 to 6.6 show the computation of crop water requirement for early, normal and late transplanted rice. The notation s used in the table as follows. ET0 is the potential crop evapotranspiration. The ET0 values are taken from the Table 5.12. 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 21-30days nursery period. Crop coefficient Kc is taken from the value suggested by Tyagi (2000). It is interpolated according to the crop growth days. ETcrop is the product of ET0 and Kc . the saturation ( SAT) is the water needed to bring the soil upto field capacity. Its value have been taken as 200 mm. This values is suggested by Mandal (1999). Percolation value (PERC) is considered as 1.5 mm per day. Water Layer (WL) maintained in the field. 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.P is the precipitation during study period. Pe is the effective rainfall. It is computed with the equation Pe = P * 0.6 – 10. IN is the irrigation requirement to meet the requirement in field. It is computed as IN = ETcrop + SAT + PERC + WL – Pe. The nursery area has been considered as 10 % of the crop area. The land preparation has been considered in three phase 33 % in first phase, 57 % in second phase and rest 10 % which was under nursery in third phase.

Table 6.7 and 6.8 show the crop water requirement and irrigation requirement for the distributaries under study.

Table 6.9 and 6.10 show the water balance study of the Babebira and Bugbuga distributary respectively.


53

Table 6.4: 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

transplantation

Kc per decade

ETcrop

(mm/day) (3) x (6)

ETcrop (mm/decade) (8) 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

Land preparation for 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 0 0 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


54

Table 6.5 : 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

transplantatio

n

Kc per decade

ETcrop

(mm/day) (3) x (6)

ETcrop (mm/deca

de) (8) 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

Land preparation for 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

Land preparation for

transplantation (10 %) 0 100 100 10 1.000

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

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

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


55

Table 6.6 : 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

transplantatio

n

Kc per decade

ETcrop

(mm/day) (3) x (6)

ETcrop (mm/deca

de) (8) 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 Land preparation for

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


56

Table 6.7 : 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) Irrigation required for Early Rice Irrigation required for Normal rice Irrigation required for Late Rice Total

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) (ha-m /day) (cumec)

1 0.00 0.000 0.00 0.000 0.00 0.000 0.00 0.000 0.000

2 2.15 8.772 0.00 0.000 0.00 0.000 8.77 0.877 0.102 Dec-01

3 10.13 41.330 2.15 19.114 0.00 0.000 60.44 6.044 0.700

1 17.50 71.404 10.13 90.073 2.15 2.731 164.21 16.421 1.901

2 1.43 5.822 17.54 155.895 10.14 12.882 174.60 17.460 2.021 Jan-02

3 4.31 17.568 1.43 12.722 17.54 22.280 52.57 5.257 0.608

1 4.69 19.139 4.61 40.938 1.46 1.855 61.93 6.193 0.717

2 0.00 0.000 4.75 42.236 4.66 5.922 48.16 4.816 0.557 Feb-02

3 5.93 24.203 0.00 0.000 5.80 7.362 31.57 3.157 0.365

1 11.07 45.145 6.30 55.998 0.00 0.000 101.14 10.114 1.171

2 6.69 27.307 12.77 113.552 6.93 8.800 149.66 14.966 1.732 Mar-02

3 5.97 24.337 6.06 53.900 12.44 15.794 94.03 9.403 1.088

1 3.33 13.603 6.71 59.634 6.84 8.680 81.92 8.192 0.948

2 0.00 0.000 4.55 40.476 7.56 9.597 50.07 5.007 0.580 Apr-02

3 0.00 0.000 0.00 0.000 5.81 7.374 7.37 0.737 0.085

1 0.00 0.000 0.00 0.000 0.00 0.000 0.00 0.000 0.000

2 0.00 0.000 0.00 0.000 0.00 0.000 0.00 0.000 0.000 May-02

3 0.00 0.000 0.00 0.000 0.00 0.000 0.00 0.000 0.000

Total 73.19 298.63 77.00 684.538 81.32 103.277 1086.445 108.6445 Total Water requirement during Rabi 2001-02 for Babebira Distributary : 1086 ha-m (763 mm)


57

Table 6.8 : Crop water requirement for Bugbuga Distributary Babebira Distributary:

Early Rice : 231 ha

Normal Rice : 807 ha

Late Rice : 162 ha

Total : 1200 ha

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

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) (ha-m /day) (cumec)

1 0.00 0.000 0.00 0.000 0.00 0.000 0.00 0.000 0.000

2 2.15 4.967 0.00 0.000 0.00 0.000 4.97 0.497 0.057 Dec-01

3 10.13 23.400 2.15 17.351 0.00 0.000 40.75 4.075 0.472

1 17.50 40.427 10.13 81.765 2.15 3.483 125.68 12.568 1.455

2 1.43 3.296 17.54 141.516 10.14 16.432 161.24 16.124 1.866 Jan-02

3 4.31 9.947 1.43 11.548 17.54 28.420 49.92 4.992 0.578

1 4.69 10.836 4.61 37.162 1.46 2.367 50.37 5.037 0.583

2 0.00 0.000 4.75 38.341 4.66 7.554 45.90 4.590 0.531 Feb-02

3 5.93 13.703 0.00 0.000 5.80 9.391 23.09 2.309 0.267

1 11.07 25.560 6.30 50.833 0.00 0.000 76.39 7.639 0.884

2 6.69 15.461 12.77 103.078 6.93 11.225 129.76 12.976 1.502 Mar-02

3 5.97 13.779 6.06 48.928 12.44 20.146 82.85 8.285 0.959

1 3.33 7.702 6.71 54.134 6.84 11.073 72.91 7.291 0.844

2 0.00 0.000 4.55 36.743 7.56 12.242 48.99 4.899 0.567 Apr-02

3 0.00 0.000 0.00 0.000 5.81 9.406 9.41 0.941 0.109

1 0.00 0.000 0.00 0.000 0.00 0.000 0.00 0.000 0.000

2 0.00 0.000 0.00 0.000 0.00 0.000 0.00 0.000 0.000 May-02

3 0.00 0.000 0.00 0.000 0.00 0.000 0.00 0.000 0.000

Total 73.19 169.078 77.00 621.399 81.32 131.739 922.22 92.222

Total Water requirement during Rabi 2001-02 for Bugbuga Distributary : 922 ha-m (769 mm)


58

Table 6.9 : Water balance study for Babebira distributary

Table 6.10 : Water balance study 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


59

6.2. Discussion:

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 Invariant 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: Regression equations between satellite data of 5 acquisitions for Pseudo Invariant Features. These equations were used for normalisation.

DN value to radiance

The DN values of the image have been converted to the radiance with the help of sensor parameters.

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. The total area of the study area is 3845 ha form the image 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 derived by interpolation from the information of Village-wise Gross Command Area (GCA) & Culturable Command Area (CCA) by Table 4.4: Village-wise Command Area under each distributary.

The area extracted from the image under agriculture is 3208 ha against the ground truth of 3214 ha. 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). 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. The distributary wise area under agriculture for Babebira and Bugbuga distributary are 1620 ha and 1588 ha respectively. The areas under rice are 1424 ha and 1200 ha in Babebira and Bugbuga respectively.

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 5.12: Reference


60

Crop Evapotranspiration. 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.

Crop coefficient:From the literature the crop coefficients are considered. The coefficients suggested by the Tyagi et al. (2000) for Karnal, India was used in computation of Crop evapotranspiration as the command area has similar climatic conditions of Karnal, India.

Rice Crop: The rice cultivated in the command during rabi season is high yielding verity having crop growth period of 120 days including nursery period of 30 days.

Crop water requirement: The water requirement for rice was computed from the crop evapotranspiration (ETcrop). ETcrop was computed from ET0 and crop coefficient Kc. The area under nursery was assumed to 10% of the crop area under that variety. The crop coefficient for that period was taken as 1.20. It was found that the crop water requirement for rice crop in rabi season for Babebira distributary was 763 mm (Table 6.7) and for Bugbuga distributary was 769 mm (Table 6.8).

Irrigation Water Demand:

The distributary-wise water demand has been computed. The irrigation demand is 1278 ha-m (Table 6.9) and 1085 ha-m (Table 6.10) 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 during standing water in the field and less in other period. Water layer maintained as 100 mm at initial stage and 40 mm at mid stage and the provision for this 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 is study was 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.

Spatial distribution of Rice crop in the Command:It has been noticed in the result part of the image that the early transplanted rice are grown in the head reach of the canal, while late transplanted rice are grown in the tail reach of the canal, where water scarcity prevails in that region. In the other hand most of the command is covered by the normal transplanted rice.

Canal network extraction from the IRS P6 LISS IV:

From the visual interpretation of the IRS P6 LISS IV image it has observed that main canal, branch canal and distributaries are clearly visible. But water course are not traced out. Hence it has been concluded that from the LISS IV image, it is possible to extract canal network up to distributary level. Here it can be mention that the LISS IV has the spatial resolution of 5.8 m and the canal width varies from 45.7 m to 2.29m for main canal and distributary respectively.


61

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.

Satellite Data:In this study only 5 LISS III images has been used. In a crop period we have 4 to 5 images from a satellite. The frequency of LISS III images has been reduced due to termination of satellite life. In near future new satellites will fill up the gaps. Though the study is confined to the LISS III images only, it can be used in combination of other satellite images having nearly similar sensor parameters like Landsat ETM, Landsat ALI, and ASTER.

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 crop acreage has been computed for various rice crops. As the area extracted from image is significantly high as compared to field records of water resources department, the cent percent ground truth can help to draw a conclusion. But the figure of Agriculture department is nearly equal to the area extracted from the satellite image. So, we can conclude the crop acreage estimation from satellite image is very promising. It can be extended to the entire command and also for other command with other crops.

Crop Model:The crop model is meant for water balance study to analyse water demand vs. supply. The important parameters used in this model are reference crop evapotranspiration, crop coefficient, percolation losses and rainfall. To have more accurate results it needs field percolation losses which vary with soil characteristics. As soil differs from place to place in a command it is not possible to use common value. At disaggregating level it is possible to use appropriate values. These values can be used without much error at distributary level. Crop coefficient derived for local crop season will give better results in crop model.

Limitation on Data:

The data extracted from the satellite image has limitations against both spatial and temporal. In this study as rice is the dominant crop, the result gives the ground values. For real time water demand vs. supply it needs images of more frequency. As the LISS III image has the repetivity of 24 days it is difficult to derive this information. In future we may have more satellites in service. That will give us images with less repetivity time.


62

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: It was found from the histogram of the NDVI image that each day image has single peak. Putting the threshold of each phenological stage it is possible to extract phenological stages.

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

Answer: NDVI is suitable for extraction of rice crop phenology. The SAVI with L factor 0.5 is also suitable.

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

Answer: It is not possible to compute accuracy assessment of rice crop phenological stage extraction due to absence of ground truth. The study is carried out for 2001-2002 rabi season in 2005. For a near real time studies it is possible.

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

Answer: 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. The total rice crop acreage estimation from the image is 2624 ha against ground truth of 2604 ha. The field data on crop phenological stage wise acreage was not available, but from NDVI data of 16th February an attempt was made to extract crop acreage. 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 late transplantation R.Q. 2: Is it possible to extract water distribution system using high resolution satellite data (LISS-IV)?

Which method of extraction gives best result?

Visual Object/segment based Edge detection method

Answer: Visual is the best method of canal network extraction with manual digitisation. Object/ segment based and edge detection methods were attempted, which could not yield satisfactory results.


63

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

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

Answer: It was found that upto distributary level canal network can be extracted. The distributary level minimum width that has been extracted from the image was 2.29 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. There is no minor and sub-minor canal network. The water courses are existing directly from the distributary.

From the study it is found that the crop phonological stage extraction from the temporal satellite image is very positive. It needs to adopt some other parameters to derive crop phonological stages from one satellite images in combination with known crop period.

In this ever changing world we observe that one system designed for a time needs renovation to suit for the next time. In the present study an analysis was 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 was found that the agricultural area has been increased by 8.96 % form 3955 ha to 4310 ha 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 and the demand is 1085 ha-m against supply of 581 ha-m for Bugbuga distributary. To meet this demand water conveyance system needs to be renovated. Also an attempt was made to extract canal inventory from the Resourcesat1 (P6) LISS IV image, which has 5.8 m spatial resolution. The error of canal extraction from LISS IV image is an order of (-) 2.36 % to 9.10 %.

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 images of same sensor or in combination with other satellite images having nearly similar sensor characteristics like Landsat ETM, ASTER. The study can be extended to other annual crops.


64

Appendix:

Figure A.1: Model for normalization

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


65

Allen, e.a., 1998. Crop evapotranspiration. Guidelines for computing crop water requirements. FAO

Irrigation and Drainage Paper 56. Rome,Italy, 300 pp. Allen, R.G., Jensen, M.E., Burman, R.D.,, 1990. Evapotranspiration and irrigation water requirement.

ASCE Manual and Report on Engineering Practice, no. 70. American Society of Civil Engineers, New York, USA, pp. 123..

Bastiaanssen, W.G.M., 1998. Remte sensing in water resources management: the state of art. International Water Management Institute, Colombo, Srilanka, 118 pp.

Bastiaanssen, W.G.M., Molden, D.J.,Makin, I.W., 2000. Remote sensing for irrigated agriculture : examples for research and possible applications. Agricultural Water Management, 46(2): 137-155.

Bastiaanssen, W.G.M.a.B., M.G., 1999. Irrigation performance indicators based on remotely sensed data: a review of literature. Irrigation and Drainage systems, 13: 291-311.

Bouman, B.A.M., M.J. Kropff, T.P. Tuong, M.C.S. Wopereis, H.F.M. ten Berge, and H.H. Van and Laar, 2001. ORYZA2000: Modeling Lowland Rice. International Rice Research Institute, Los Baños, Philippines and Wageningen University and Research Centre, Wageningen, The Netherlands.

Clarke, D., 1998. Cropwat for windows user guide. FAO,Rome. Counce, P.A., Keisling,T.C.,Mitchell,A.J., 2000. A uniform, objective, and adaptive system for

expressing rice development. Crop Science, 40: 436-443. Doll, P., Siebert,S, 2002. Global modeling of irrigation water requirements. Water Resources

Research, 38(4). Doorenbos, J., W.O. Pruitt, 1984. Guidelines for predicting Crop water requirement, FAO Irrigation

and Drainage paper 24. FAO, Rome, 144 pp. Farah, H.O., Feddes, R.A.,Bastiaanssen, W.G.M., 2001. Estimation of regional evaporation under

different weather condition from satellite and meteorological data : a case study in the Naivasha basin, Kenya, Wageningen University.

Farmwest.com, 2004. Determining Crop Water Use - Crop Coefficients. Fortes, P.S., Platonov, A.E. and Pereira, L.S., 2005. GISAREG--A GIS based irrigation scheduling

simulation model to support improved water use. Agricultural Water Management, 77(1-3): 159-179.

Goswami, B., Mahi,G.S.,Hunai,S.S.,Saikia,U.S., 2003. Growing degree days for rice and wheat in Ludhiana region. Journal of Agrometeoroly, 5(1): 117-119.

Guerra, L.C., Bhuiyan, S.I., Tuong, T.P., Barker, R.,, 1998. Producing more rice with less water from irrigated systems, Colombo, Sri Lanka.

Huete, A.R., 1988. A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25: 89-105.

Indiaagronet, 2005. Crop Planning considering Water requirements and availability of water,http://www.indiaagronet.com/indiaagronet/water_management/CONTENTS/Crop%20Planning.htm, Access date 21 November 2005.

IRRI, 2005. Water Stress Effects, Water Management Systems, and Irrigation Requirements for Rice in Sri Lanka.

Jehangir, W.A., Turral,H. and Masih,I., 2004. Water productivity of rice crop in irrigated areas. Kar, G.a.V., H.N., 2005a. Climatic water balance, probable rainfall,rice crop water requirements and

cold periods in AER 12.0 in India. Agricultural Water Management, 72(1): 15-32. Kar, G.a.V., H.N., 2005b. Phonology based irrigation scheduling and determination of crop coefficient

of winter maize in rice fallow of eastern India. Agricultural Water Management, 75(3): 169-183.

Kiyoshi, H., 2003. Introduction to remote sensing and its application for natural resource management,http://www.star.ait.ac.th/~honda/textbooks/remotesensing/handsout/BasicRS_6.pdf, Access date: 12-November-2005.


66

Kuo, S.-F., Ho,Shin-Shen, and Liu,Chen-Wuing ,, 2005. Estimation irrigation water requirements with derived crop coefficients for upland and paddy crops in ChiaNan Irrigation Association, Taiwan. Agricultural Water Management.

Lee, T.S., Najim,M. M. M., and Aminul,M. H., 2004. Estimating evapotranspiration of irrigated rice at the West Coast of the Peninsular of Malaysia. Journal of Applied Irrigation Science, 39(1): 103-117. Mandal, C., D.K.Mandal, C.V.Srinivas, J.Sengal,M.Velayutham, 1999. Soil Climatic database for crop

planning in India. National Bureau of Soil Survey and Land Use Planning.NBSS Publ.53, Nagpur, 1014 pp.

Mandal, U.K., Singh,G.,Victor,U.S.,Sharma,K.L., 2003. Green manuring: its effect on soil properties and crop growth under rice–wheat cropping system. European Journal of Agronomy, 19(2): 225-237.

NRSA, W.R.D., 2004. Performance Evaluation of Hirakud Project Command Area using Satellite Remote Sensing Technique, NRSA, Hyderabad.

Oguro, Y., Suga,Y., Takeuchi, S., Ogawa,H. and Tsuchiya,k., 2003. Monitoring of a rice field using Landsat-5-TM and Landsat-7-ETM+ data. Adv. Space Res., 32(11): 2223-2228.

Ray, S.S., Dadhwal,V.K. ,, 2001. Estimation of crop evapotranspiration of irrigation command area using remote sensing and GIS. Agricultural Water Management, 49(3): 239-249.

Ray, S.S., Dadhwal,V.K. ,Navalgund,R.R., 2002. Performance evaluation of an irrigation command area using remote sensing: a case study of Mahi command, Gujrat, India. Agricultural Water Management, 56(2): 81-91.

Sakamoto, T. et al., 2005. A crop phenology detection method using time-series MODIS data. Remote Sensing of Environment, 96(3-4): 366-374.

Sakthivadivel, R., Thiruvengadachari,S.,Amerasinghe,U,Bastaanssen,W.G.M., Molden,D., 1999. Performance evaluation of the Bhakra irrigation system, India, using remote sensing and GIS techniques, Research Report 28.

Schott, J.R., C.Salvaggio, W.J. Volchok, 1988. Radiometric Scene Normalization Using Pseudoinvariant Features. Remote Sensing of Environment, 26: 1-16.

Shah, M.H., Bhatti,M.A, and Jensen,J.R., 1986. Crop coefficient over a rice field in the central plain of Thailand. Field Crops Research, 13: 251-256.

Thiruvengadachari, S., 1996. Assessing Irrigation Performance of Rice-Based Bhadra Project in India, http://www.gisdevelopment.net/aars/acrs/1996/ts1/ts1008.shtml, Access date: 14-November-2005.

Tomar, V.S.a.O.T., J.C., 1980. Water use in lowland rice cultivation in Asia: A review of evapotranspiration. Agricultural Water Management, 3(2): 83-106.

Tripathy, R.P., 2004. Evapotranspiration and crop coefficients for rice, wheat and pulses under shallow water table conditions of Tarai region of Uttaranchal. Journal of Agrometeoroly, 6(1): 17-29.

Tucker, C.J., 1979. Red and photographic infrared linear combination for monitoring vegetation. Remote Sensing of Environment, 8: 127-150.

Tyagi, N.K., Sharma,D. K. and Luthra,S. K., 2000. Determination of evapotranspiration and crop coefficients of rice and sunflower with lysimeter. Agricultural Water Management, 45(1): 41-54.

Waterwatch, 1998. Remote Sensing Services for quantifing water management. Xiao, X., Boles,S.,Liu,J.,Zhuang,D.,Frolking,S., LI,C.,Salas,W.,Moore III,B., 2005. Mapping paddy

rice agriculture in southern China using multi-temporal MODIS images. Remote Sensing of Environment, 95(4): 480-492.

Documents

AN ANALYSIS USING LISS III DATA FOR ESTIMATING WATER DEMAND … · · 2012-03-31old command area an attempt has been made for estimating water demand for rice cropping using