diagnostic analysis of an irrigation command by satellite remote

INTERNATIONAL JOURNAL OF CIVIL AND STRUCTURAL ENGINEERING

Volume 3, No 1, 2012

© Copyright by the authors - Licensee IPA- Under Creative Commons license 3.0

Research article ISSN 0976 – 4399

Received on May, 2012 Published on August 2012 178

Diagnostic analysis of an irrigation command by satellite Remote Sensing:

Case study of Mehasana district command of Sabarmati right bank main

canal, North Gujarat, India Samta Shah, H.J.Dalwadi

1- Assistant Professor, S.V.I.T-Vasad, Gujarat.

2- Professor, B.V.M Engineering College.

[email protected]

doi:10.6088/ijcser.201203013017

ABSTRACT

Multi temporal, multidate remote sensing data was processed using Erdas imagine software

to study crop growth stages and identify period of full canopy (harvest season).Generation of

vegetation spectral index and crop evapotranspiration for full canopy stage of Rabi season,

February month was carried out over Mehasana district command of Sabarmati Right bank

main canal, North Gujarat, India, The use of Indian Remote Sensing Satellite (IRS)-P6 Linear

Imaging and Self Scanning-III (LISS-III) and Wide Field Sensor (AWiFS) data was done.

Distributary wise, two RS-based performance indices, namely, adequacy (AI), equity (EI)

were computed. AI was computed by comparing the crop water requirement with the water

release data. EI was evaluated by observing the head-to-tail difference in two distributaries. It

was found that water availability was in excess along main canals and branch canals. In

cropped area, it was less and crop condition was poor towards the tail ends of the command

area. The two RS based indices could rank the performance of the distributaries and also

identify those having problems in water allocation. The crop evapotranspiration was

estimated using vegetation index NDVI. Crop coefficients and reference crop

Evapotranspiration were developed for the month of February using LISS-III scene of

February 2008 and local weather data. Crop water requirement for the month of February was

evaluated.

Keywords: Performance-indices, Adequacy; Equity, Crop evapotranspiration, full canopy.

1. Introduction

Performance evaluation is considered to be one of the most essential elements for improving

irrigation management (Abernethy and Pearce, 1987). Successful irrigation scheme is the one

that can apply the right amount of water over the entire region of interest without loss

(Zerihun et al., 1997). Performance evaluation terms quantify how much system is closer to

the ideal irrigation system. Based on a review of the literature concerning, indicators of

irrigation performance, Rao (1993) found that the performance of an irrigation system could

be evaluated in three categories, namely, water delivery system, irrigated agriculture system

and irrigated agricultural economic system. Various workers have used remote sensing (RS)

as a tool for assessment of irrigation performance. Bastiaanssen (1998) has listed the

performance indicators derived from RS algorithms supplemented by ground data. Sabarmati

Right bank main canal is designed to irrigated during Rabi season, which gives it the 50%

irrigating potential. Also North Gujarat being the region with moderate rainfall an attempt is

made to estimate exact Crop water requirement for the month of February, for which crop

evapotranspiration is worked out using RS-technique. Weather data were feeded to

Diagnostic analysis of an irrigation command by satellite Remote Sensing: Case study of Mehasana district

command of Sabarmati right bank main canal, North Gujarat, India

Samta Shah, H.J.Dalwadi

International Journal of Civil and Structural Engineering

Volume 3 Issue 1 2012

179

CROPWAT 8.0 ETo calculator, which is FAO-24 Penman Monteith method based software

developed by FAO-24 to compute reference crop evapotranspiration. Pixel based crop

coefficients are evaluated using vegetation indices based empirical equations developed by

(Gontia et al,) for all Indian conditions.

The results obtained for Kc, ETo and ETc values are validated with FAO values and empirically

estimated field values for the region. The advantage of developing pixel based, RS-derived

crop coefficient over traditional crop coefficient is that it represents real time crop

coefficients that responds to actual crop conditions and also reflects the between field

variability (Ray et al 2000). In present study, multitemporal RS data have been used to

compute two indices for the evaluation of performance of each distributory in the SRBMC

irrigation system, to rank them according to performance, as well as identify problem in

distributaries with respect to irrigation management similar work is done for Mahi Right bank

(Ray et al, 2000). Local field surveys and actual site visits during canal watering of Rabi

season were also made to authentify the results obtained by RS-approach. Distributary level

canal discharge values were collected SRBMC irrigation department located atVisnagar

(Dharoi colony).

1.1 Study area and data used

1.1.1 Study area

The command area of the SRBMC, passing through Mehasana district of North Gujarat state,

in India. Region lies between, Latitudes 230 02’ N to 24

0 09’N, Longitudes, 71

0 26’E to 72

0

51’E,Survey of India toposheet (SOI) numbers 46A05,46A06,46A09 and 46A10.The region

is semi-arid with mean annual rainfall ranging from 600mm to 800mm.The region falls into

water scarce region. The Sabarmati Right bank main canal has two district under its

command, Mehasana district and Sabarkantha district. The Mehasana district command under

present evaluation (Block No.6) has gross command area of 11652 Ha.Region has two major

cropping seasons Rabi from October to February and Kharif from March to June. The

majority of irrigation is done during Rabi season.

1.2 Data used

Multi-temporal remotely sensed data of Wide Field Sensor (AWiFS), on-board the Indian

Remote Sensing Satellite (IRS)-P6 satellite was used to study the Rabi crop growth stages.

AWiFS has a 56m-ground resolution and three spectral bands in the red (620–680 nm), green

and the near-infrared (NIR; 770–860 nm) region. For AWiFS sensor one scene of each month

for two consecutive year’s Rabi seasons were used.

The dates of satellite data collection are 20/12/2007, 18/01/2008, 1/2/2008,for Rabi season

2007-08 and 25/02/2008 and 14/12/08,31/1/09,5/2/09 and20/3/09 for Rabi season of 2008-09 .

IRS-P6 Linear Imaging and Self Scanning-III (LISS-III) digital data (23 m spatial resolution

and four spectral bands in green, red, NIR and middle-infrared region) for 11th

February 2008

and 5th

February 09 was used for cropped area estimation, condition assessment and develop

the crop coefficient maps for full canopy stage of rabi crop. Monthly average temperatures

from different weather stations surrounding the command area were collected, along with

climatic values of monthly averages of daytime wind speed, sunshine hours from

www.imdpune.gov.ac.in.






180

Figure 1: Image Map of Mehsana command

Figure 2: Drainage Map of Mehsana command

2. Methodology

IRS-P6 LISS III digital data was used for crop acreage estimation of selected distributaries.

Crop acreage was estimated using a supervised classification approach with the ground truth

data collected during field trips. Crop condition assessment was done by computing the

normalized difference vegetation index (NDVI) values of crop pixels as:

NDVI =NIR-R 1.

NIR+R






181

Where NIR is the reflectance in Near infrared and IR is the reflectance in IR.

Soil Adjusted Vegetation Index (SAVI), as defined by Huete (1988), was calculated for each

crop pixel. SAVI is defined by:

SAVI = NIR-R (1+L) 2.

NIR+R+L

Where NIR and R are reflectance in red and near-infrared wavelength regions and L an

adjustment factor to minimize soil brightness influences. Although, Huete (1988) found that

the optimal adjustment factor varied with vegetation density, he used a constant L. He

suggested a value of 0.5 for annual field crops; therefore, this value was used for the present

study. The digital number (DN) values from the AWiFS sensor were converted into radiance

using ERDAS Imagine 8.7 software.

2.1 Performance Indices

The irrigation system was evaluated using two performance indices under different categories.

The two indices (adequacy and equity) describe the water delivery system.

2.1.1 Adequacy

Adequacy of an irrigation system describes to what extent is the quantity of water provided

sufficient for the growth needs of the crops (Abernethy, 1989). The relative water supply

(RWS), evaluated by (Ray and Dadhwal, 2000) describes the adequacy of water supply.

RWS = IR+RN 3. IRG

Where IR is the irrigation water supply, RN the rainfall and IRG the gross irrigation

requirement.

The major rainfall season, for this region, is June–September, with almost no rainfall in

October –February (Rabi season), which can be neglected. Therefore, the above equation was

simplified to:

RWS = IR 4. IRG

The gross irrigation requirement is computed as the net irrigation requirement (IRN)

multiplied by irrigation efficiency, to account for losses during conveyance, distribution and

application. Net irrigation requirement (IRN) is computed using following expression:

IRN = ET- ER+ WS +AL 5.

Where ET is the crop evapotranspiration, ER the effective rainfall, WS the water for special

purposes, including land preparation, transplantation, leaching etc. and AL the application

losses in the fields, including percolation, seepage, runoff, etc. To account for the various

components in Eqs. (4) and (5), such as irrigation efficiency, effective rainfall, or water for

special purposes and the application losses, an adjustment factor of 1.3 was used for rabi-

season non-rice crops. This factor was computed from the experimental values of the gross

irrigation requirement (IRG) and the crop evapotranspiration (ET) for the Rabi season for the






182

SRBMC command Four years’ average ratio of the IRG and the ET values was found to be

1.3. This value was multiplied by the crop evapotranspiration to get the gross irrigation

requirement, assuming that the other components in Eq. (5) remained constant.For the present

study, the information about the amount of irrigation water supply was collected from Dharoi

irrigation circle office, Visnagar. The crop evapotranspiration (ETc) value was computed

using the crop coefficients, estimated from NDVI values using equations and the reference

crop evapotranspiration values estimated by FAO software CROPWAT 8.0 ETo calculator.

2.1.2 Equity

Evaluating head to tail difference in the values of NDVI for the selected distributaries

evaluates equity of an irrigation scheme. Dividing each distributory in three equal segments

and identifying the average NDVI values for cluster of pixels for that part of division, note

the difference in vegetation quality from head to tail.

3. Crop Evapotranspiration

The Crop Evapotranspiration was evaluated for Rabi crops of the study area. The

multispectral LISS-III image having four spectral bands was classified for crop type map by

supervised digital classification using remote sensing software; further the crop type map was

processed using ERDAS-imagine software to generate the NDVI maps of the crop type

image. The NDVI map was further processed with to develop crop coefficient, KC maps

using the equation ((Gontia et al, 2009).

Kc FAO = 2.7109 (NDVI) + 0.424 6.

The Kc maps were further developed using same software into the Crop evapotranspiration,

(ETc) maps. The effects of various climatic variables on evapotranspiration are incorporated

into crop ETo (reference crop evapotranspiration), the effects of characteristics that distinguish

cropped surface from reference surface are integrated into crop coefficient Kc. ETc is

estimated as given below (Ray and Dadhwal, 2001)

ETc = Kc x ETO 7.

Figure 3: Landuse map of Mehsana command 1






183

ETO, reference crop evapotranspiration was evaluated using the local climatological data like

wind speed, maximum and minimum temperature, maximum and minimum relative humidity,

saturated vapour pressure, etc from www.imdpune.gov.ac.in.The local weather parameters

were feeded to CROPWAT 8.0 FAO Penman Monteith based software and using its ETO

calculator.

ETO = .408 ∆ (Rn-G) γ {(900)/ (T+273)} µ2 (es-ea)

8.

∆+ γ (1+.34 µ2)

Where,

ETo reference evapotranspiration (mm day−1

)

Rn net radiation at the crop surface (MJ m−2

day−1

)

G soil heat flux density (MJ m−2

day−1

)

T means daily temperature at 2 m height (◦C),

es saturation vapour pressure at Tc (kPa)

ea actual vapour pressure (kPa),

es-ea saturation vapour pressure deficit (kPa),

∆ Slope of the saturation vapour pressure and temperature relationship (kPa ◦C

−1)

γ psychrometric constant (kPa ◦C

−1)

u2 wind speed at 2 m height (m s−1

)

4. Results and discussions

4.1 Crop Evapotranspiration

Net irrigation Water requirement for the Rabi crop was done using equation 5.,for which

reference crop evapotranspiration was estimated using equation no.7 which was found as

5.65mm/day and the average value of crop coefficient was taken as 0.6 , as obtained from

spatially interpolated values of crop coefficients using equation 7. The average crop

evapotranspiration, for whole cropped area was found to be 3.39mm/day.

4.2 Adequacy

Adequacy was found for the distributaries within the command named M3LA and

M5L,dividing them into three parts head, middle portion and tail end and the land patches

growing wheat were classified by supervised classification. The actual location to measure

the exact amount of irrigation water supply was decided based on field visits at the site, and

assistance of the local Rangpur Piyat Mandli actively doing the Participatory irrigation

management over there for the distributaries selected (M3LA and M5L). The IR (irrigation

supply) was found to be 950mm, 910mm and 830mm for M3LA and 1000 mm, 980mm and

870mm for M5LA at head, middle and tail outlets by field measurement ranging for the

period of entire crop period. The crop water requirement was evaluated as 1050 mm.using






184

equation 5. The RWS was evaluated, using equation 4 (table 1). The tail ends suffered from

relatively low adequacy in terms of RWS.

Table 1: Name of dsitributory

Name of Distributory

M3LA M5L

IR

(mm)

IRG

(mm)

RWS REMARK IR

(mm)

IRG

(mm)

RWS REMARK

Head 950 .904 Excellent

adequacy 1000 .956

Excellent

adequacy

Middle 910 .866 Good

adequacy 980 .933 Excellent

adequacy

Tail 830

1050

.792 Moderate

adequacy 870

1050

.826 Moderate

adequacy

4.3 Equity

Differences in cropped area and crop vigor between head and tail zones of two distributaries

were studied. The command areas of the distributaries were divided into three nearly-equal

zones from head to tail. The crop vigor which was estimated as average value of NDVI, (Ray

and Dadhwal, 2000) clearly showed the vigor which was 68 % towards head reach, 59%

towards middle reach and 42% for tail reach for M3LA and for the distributory M5L, the

result was 70% for head reach and 32%for tail reach. The results clearly focused at the

unequitable water distribution by distributaries from head to tail command areas.

Acknowledgement

The project was carried out at Bhaskaracharya Institute of Space application, BISAG,

Gandhinagar, of Gujarat, India. The authors are grateful to Dr.T.P.Singh, director BISAG,

Gandhinagar, for providing the Satellite imageries and other required data related to

Mehasana command of Sabarmati Right bank Main canal. Thanks are due to Dr.S.S.Ray of

SAC-ISRO, Ahmedabad, Dr.Anjali.Bahuguna of SAC-ISRO, Ahmedabad for their valuable

critical suggestions. I am also thankful to Mr. Vijay Singh, Mr. Khalid and Dr.Mahesh

Chodwadiya of BISAG for their valuable help during my study. Authors must also be

thankful to Dharoi Irrigation circle staff of Visnagar Dharoi colony for their valuable help

during data collection stage.

5. References

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systems. In: Proceedings of the Symposium on the Performance Evaluation of

Irrigation Systems. International Irrigation Management Institute (IIMI), Colombo, 23

November 1989.

2. Abernethy, C.L., Pearce, G.R., (1987), Research needs in third world irrigation.

Hydraulics Research Limited, Wallingford, UK.






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3. Bastiaanssen, W.G.M., (1998), Remote sensing in water resources management: the

state of the art. International Water Management Institute (IWMI), Colombo, Sri

Lanka, pp 118-119.

4. Brahmabhatt, V.S., Dalwadi, G.B., Chhabra, S.B., Ray, S.S., Dadhwal, V.K., (2000),

Land use/land cover change mapping in Mahi Canal command area, Gujarat using

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diagnostic analysis of an irrigation command by satellite remote

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