Estimation of site-specific crop coefficients for major crops of lalgudi block in tamil nadu using remote sensing based algorithms

Crop coefficient (K_c) is an important parameter in estimating the crop water requirements during different crop growth stages. The Kc values for a particular crop are highly site-and region-specific, and needs to be precisely determined for each agro-climatic region for better irrigation scheduling and improved water and crop productivity. The site-specific crop coefficients for paddy, sugarcane, and banana cultivated in Lalgudi block, Tiruchirapalli district, Tamil Nadu, India, were estimated using two remote sensing-based methods viz. NDVI-K_c linear regression technique and SEBAL actual evapotranspiration to reference evapotranspiration ratio approach (SEBAL-K_c). The K_c values obtained by remote sensing methods were compared with FAO-56 Adjusted K_c (ClimAdj-K_c) for local climatic conditions and FAO-56 tabulated reference K_c values (FAOTab-K_c). Regression analysis revealed a good agreement between NDVI-K_c and ClimAdj-K_c for paddy (R²=0.95), banana (R²=0.93), and sugarcane (R²=0.79). Coinpared to FAO56-K, the derived K_c values using NDVI-K_c were higher, while the SEBAL-K_c values were lower for all growth stages of paddy. For sugarcane crop, the FAO-56 K_c, NDVI-K_c and ClimAdj-K_c for local climate were almost similar in all stages. In case of banana, NDVI-K_c and S^cEBAL-K_c were higher as compared to the FAO-56-K_c and ClimAdj-K_c. SEBAL approach performs well as it accounted for local climatic conditions and crop canopy changes, whereas NDVI considered only crop canopy. However, SEBAL method is computationally intensive as compared to NDVI-K_c method. The K_c values estimated in this study can be important in quantifying the crop evapotranspiration at regional and field scale, leading to better decision making in irrigation scheduling.