Estimating Crop Evapotranspiration in Data-Scare Regions: A Comparative Analysis of Eddy Covariance, Empirical and Remote-Sensing Approaches

Maximizing water productivity amid agricultural water scarcity demands accurate crop evapotranspiration (ETc) estimation. While the Penman-Monteith method is standard, its dependence on extensive meteorological data restricts use in data-scarce regions. Eddy covariance offers precise ETc estimation but is resource-intensive. Satellite remote sensing, like MOD16, offers a promising alternative for ET estimation. Several empirical models are also available, out of which suitable alternatives can also be identified for the regions with limited weather data availability, where eddy covariance and remote sensing techniques become limitations. Consequently, a study was undertaken to investigate the performance of eddy covariance method (Eddy Tower based), empirical models, and a remote sensing technique for computing crop evapotranspiration under rice-wheat cropping system at Naraingarh Seed Farm of Punjab Agricultural University, Ludhiana, for the years 2022-2023. The performance evaluation of all the methods was performed using statistical indicators, including mean absolute error, mean bias error, root mean squared error, coefficient of determination, and index of agreement. The eddy covariance method, selected empirical models, and remote sensing technique demonstrated a good correlation with FAO Penman-Monteith ET, with coefficient of determination values greater than 0.85. The eddy covariance tower gives precise ETc estimates, with MOD-16 satellite data closely trailing. When Eddy Tower data is inaccessible, MODIS products provide a reliable alternative on a broader scale. In the absence of MODIS data, such as during cloud cover, empirical models offer effective ETo and hence ETc estimation. Moreover, for regions lacking weather data, models like Hargreaves and Samani (1985) or Priestley and Taylor (1972) stand out as optimal choices for accurate ETo and thereafter ETc estimation.