A Modified Approach for Estimation of Crop Coefficients using Satellite Remote Sensing Data

Abstract

Crop coefficient (Kc) based estimation of crop evapotranspiration (ETc) is one of the most commonly used methods for irrigation water management. However the standard FAO Penman-Monteith approach for estimating ETc from reference evapotranspiration and tabulated generalized Kc values has some limitations. In this paper, we present a modified approach towards estimating Kc values from remotely sensed data. Surface Energy Balance Algorithm for Land (SEBAL) model was used for estimating spatial distribution of ETc during 2005 growing season in south-central Nebraska. The alfalfa based reference evapotranspiration (ETr) was calculated using multi-automatic weather station data with geostatistical analysis. Based upon the mean absolute error (MAE) and coefficient of determination (r2), the ordinary Kriging method resulted as the best interpolation technique for determining the reference evapotranspiration. The crop coefficient was estimated based on crop evapotranspiration and reference evapotranspiration. Land use map was used for sampling and profiling the crop coefficients on dates of satellite overpass for various major crops grown in south-central Nebraska. Finally a regression based model was developed to establish the relationship between the Normalized Difference Vegetation Index (NDVI) and the ETr based crop coefficient (Kcr) for corn, soybean, sorghum, and alfalfa under irrigated and dryland conditions. Validation of the model for the corn using Bowen ratio energy balance system based Kcr and estimated Kcr has shown good correlation (r2=0.73). This approach can be very useful for estimation of evapotranspiration using NDVI based crop coefficient and reference evapotranspiration.