Crop And Irrigation Water Requirement Estimation By Remote .

Gaurav Pakhale et al. /International Journal of Engineering and Technology Vol.2(4), 2010, 207-211Crop And Irrigation Water RequirementEstimation By Remote Sensing And GIS: A CaseStudy Of Karnal District, Haryana, IndiaGaurav Pakhale #1, Prasun Gupta #2, Jyoti Nale *3#Indian Institute of Remote Sensing4 Kalidas Road, Dehradun, India*College of Agricultural Engineering, MPKV,Rahuri, Ahemadnagar, IndiaAbstract- The paper focuses on analyzing the irrigation waterrequirement of wheat crop for rabi season from 1999 to 2003 inKarnal district of Haryana state, India. Area under wheatcultivation has been determined using Landsat ETM image byapplying Artificial Neural Network (ANN) classificationtechnique. Potential evapotranspiration has been estimated usingHargreaves model. Potential Evapotraspiraiton and cropcoefficient for wheat was used for estimating crop waterrequirement. Effective rainfall was determined using IndiaMeteorological Department gridded rainfall data. Effectiverainfall and crop water requirement was used for determiningirrigation water requirement. By assuming 35% losses, netirrigation water requirement was estimated. Multiplying thewheat cropped area and net irrigation water requirement thevolume of water required for wheat during the rabi season wasestimated.I. INTRODUCTIONExperts’ estimates that demand for food crops will doubleduring the next 50 years with limited land and waterresources, farmers need to increase their output from existingcultivated areas to satisfy the food demand of increasingpopulation. Irrigation systems will be essential to enhancecrop productivity in order to meet future food needs andensure food security. However, the irrigation sector must berevitalized to unlock its potential, by introducing innovativemanagement practices and changing the way it is governed.Haryana in India, became much lower than in predominantlyrain fed states such as Orissa and Madhya Pradesh.II. STUDY AREA:Karnal district lies on western bank of river Yamuna.Karnal is located at 29.43o N latitude and 76.58o E longitudesand is about 250 meters above mean sea level. [2]The topography of Karnal district is almost plain and wellirrigated through canals and tube-wells. Irrigated area is about205627 ha. While the gross irrigated area is 388917 ha.Theimportant crops grown in this district include wheat, rice,sugarcane, sorghum, maize and berseem.The climate of the district is dry and hot in summer andcold in winter. Its maximum and minimum temperatures varyfrom 43oC to 21.5oC in June and from 22oC to 4oC in January.Developments in irrigation are often instrumental inachieving high rates of agricultural goals but proper watermanagement must be given due weightage in order toeffectively manage water resources. Better management ofexisting irrigated areas is required for growing the extra foodto fulfill the demand of increasing population. [1]Irrigation contributed in number of ways. It enables farmersto increase yields and cropping intensities, stabilizeproduction by providing a buffer against the vagaries ofweather, and create employments in rural areas. Rural povertyin intensively irrigated areas, such as states of Punjab andISSN : 0975-4024Fig. 1. Study areaThe land of Karnal district is plain and productive. Thesoil texture varies from sandy loam to clay loam. The soils arealluvial and are ideal for crops like wheat, rice, sugarcane,vegetables etc. [2]207

Gaurav Pakhale et al. /International Journal of Engineering and Technology Vol.2(4), 2010, 207-211III. MATERIALS AND METHODSIn order to accomplish the task, the data used for the studyincludes Landsat ETM imagery. The image contains 8 bandsincluding a panchromatic band covering a swath of 185 km.The meteorological data was obtained from IMD (Indiameteorological Department, Pune). The data consists of 0.5 x0.5 gridded daily data of rainfall, maximum and minimumtemperature. The data was subsequently processed in a GISenvironment and converted into TIFF format to facilitate GISanalysis.Landsat ETM image was processed in order to preparecrop mask for the area. The processing includes conversion ofDN values into radiance; the following formula was used forconversion. [3] LMAX LMIN X DN QCALMIN LMINL QCALMAX QCALMIN where LMAX, LMIN, QCALMAX and QCALMIN weretaken from header file of the image for each band. Theequations prepared for calculating radiance values for eachband are as follows,R1 [1.18 * (DN-1)] – 6.2R2 [1.204 * (DN-1)] - 6.4R3 [0.945* (DN-1)] - 5R4 [0.639* (DN-1)] – 5.1R5 [0.126* (DN-1)] 1R61 [0.067* (DN-1)]R62 [0.037* (DN-1)] 3.2R7 [0.044* (DN-1)] – 0.350Fig. 2. Radiance Image (Landsat ETM )The radiance image was subjected to supervisedclassification using Artificial Neural Network (ANN). Themulti layer perception (MLP) network with the backpropagation (BP) algorithm is used in ENVI software. [4]Training parameters used during the classification are asfollows: Number of Training Iterations: 1000, Training RMSExit Criteria: 0.1, Initial learning rate: 0.01, TrainingThreshold Contribution: 0.9, Training Momentum: 0.9 andMinimum Output Activation Threshold: 1.00e-8.Ground truth data has been collected during rabi season andthat was provided as training sites for various land use types.Information about crops and cropping pattern were obtainedfrom the farmers of the area. Based on the field visits, andclassified image obtained from ANN, an area of interest (AOI)for wheat crop has been identified. Crop mask of the area hasbeen prepared based on extracted wheat area and as indicatedbelow in Fig. 3.R8 [0.975* (DN-1)] – 4.7where, Rband is the radiance image of a particular bandand DN is the digital number of the input pixel. TheKarnal district boundary map was overlaid on the radianceimage to extract the study area.Fig. 3. Wheat crop maskISSN : 0975-4024208

Gaurav Pakhale et al. /International Journal of Engineering and Technology Vol.2(4), 2010, 207-211With the help of meteorological data, daily potentialevapotranspiration was estimated using the Hargreavesequation as shown below.PETHG 0.0023 * Ra * Tmean 17.8 * Tmax Tmin where, PETHG: potential evapotranspiration rate, Ra:extraterrestrial radiation (calculated from latitude and time ofyear), Tmean: mean temperature, Tmax: maximum temperature,Tmin: minimum temperature. [5]The Hargreaves equation was adopted due to nonavailability of other meteorological variables in the study area.In order to compute the crop water requirement (CWR), cropcoefficient (Kc) values for the wheat crop were obtained andmultiplied with the potential evapotranspirationIn the present study, the net irrigation water requirementshave been computed on monthly basis. IWR values areconverted into discharge units (million cubic meters / month)by multiplying with crop acreage values.IV. RESULTS AND DISCUSSIONThe accuracy of classification is based on ground truthsamples. From ground truth samples the confusion matrix iscreated. Confusion Matrix shows the accuracy of aclassification result by comparing a classification result withground truth information. The accuracy of classification wasfound to be 96.80% and the kappa value was 0.9418 for thecropping season. From the classified image area under thewheat crop was found out to be 127340 ha.Daily potential evapotranspiration was calculated usingHargreaves method. The daily values were aggregated tomonthly values for the five years (1999-2003).CWR Kc * PETHGThe crop coefficient values of wheat crop for Karnal stationare as follows, [6]TABLE ICROP COEFFICIENT VALUES OF WHEAT CROPGrowth stageCrop coefficientInitialCrop BLE IIIVALUES OF MONTHLY POTENTIAL 9183.04Effective rainfall is estimated based on FAO approach(Food and Agricultural Organization). The empirical equationgiven by FAO is as follows, [7]y 0.0011x 2 0.4422 xFig. 4. Variation of PET across different years during rabi seasonwhere, y is effective rainfall (ER) in mm and x is totalrainfall in mm. The effective rainfall (ER) and the crop waterrequirement decide the amount of irrigation water that has tobe applied. The effective rainfall is subtracted from the cropwater requirement to calculate the irrigation waterrequirements (IWR).IWR CWR - ERNet irrigation water requirements (NIWR) is the totalwater to be supplied to the crops during their life cycle,considering the losses due to infiltration into the subsoiland conveyance losses. Based on soil types, field lossesand conveyance losses are assumed as 35% of theirrigation water requirements.NIWR IWR LOSSESISSN : 0975-4024From the above graph it is observed that the potential cropevapotranspiration is at peak at the beginning stage i.e in themonth of November, slightly reduced at growing stage, than atmid stage and at late-season stage it shows increasing trend asthe temperature was on higher side.Crop water requirement was estimated on a monthly basisby multiplying the PET with crop coefficient values.TABLE IIIIICROP WATER REQUIREMENT FOR 63200383.67196.11175.12188.3678.38209

Gaurav Pakhale et al. /International Journal of Engineering and Technology Vol.2(4), 2010, 207-211Fig. 5. Variation of CWR for wheat across different years during rabi seasonThe crop water requirement graph shows that wheat waterrequirement is increasing with the passage of time and requiremaximum amount of water at the crop development and midseason stage. The crop water requirement varied from 78.63mm/month to 201.14 mm/month. The maximum CWR wasobserved in the month of December while the minimum wasobserved in the month of March. CWR decreases in the monthof March as wheat was in maturity stage. It was also foundthat crop water requirement was less in the maturity stage ascompared to the initial stage.From the CWR and effective rainfall the irrigation waterrequirement (IWR) is calculated. The simulated values ofirrigation water requirement (IWR) for the wheat crop inKarnal district are given below.Fig. 7. Scatterplot between CWR and IWRAssuming 35% of conveyance and field losses, the netirrigation water requirement (NWIR) for wheat crop wascalculated.TABLE VNWIR FOR 105.842003110.72247.55235.07244.4395.43The net irrigation water requirement was multiplied withcropped area of wheat to estimate the volume of waterrequired for the entire growing season.TABLE IVVIRRIGATION WATER REQUIREMENT FOR 778.7982.43184.30175.01181.9871.04TABLE VIVOLUME OF WATER REQUIRED FOR .95136.682003142.99319.70303.59315.68123.24Fig. 6. Variation of IWR for wheat across different years during rabi seasonAs wheat is grown in rabi season the amount of rainfall isvery less. The irrigation water requirement is directlydependent on crop water requirement.A scatter plot was generated by plotting the monthly meanvalues of crop water requirement with irrigation waterrequirement. A strong relationship was observed between thetwo variables with a coefficient of determination (R2) of 0.994.ISSN : 0975-4024Fig. 8. Volume of water required for wheat crop across different years duringrabi seasonV. CONCLUSIONThe present study shows that Remote Sensing and GISintegrated approach can be used for estimation of crop waterrequirement and irrigation water requirement. In the studyarea the wheat water requirement was higher in the vegetativeand mid-season stage and shows decreasing trend towards thematurity stage. In the study area it was found that irrigationwater requirement highly correlated with crop water210