Estimation of Site-specific Crop Coefficients for Major Crops of Lalgudi Block in Tamil Nadu using Remote Sensing based Algorithms

Authors

  • J. Ramachandran Department of Irrigation and Drainage Engineering, Agricultural Engineering College and Research Institute, Tamil Nadu Agricultural University, Kumulur-621712, Trichy, India
  • R. Lalitha Department of Irrigation and Drainage Engineering, Agricultural Engineering College and Research Institute, Tamil Nadu Agricultural University, Kumulur-621712, Trichy, India
  • S. Vallal Kannan Department of Irrigation and Drainage Engineering, Agricultural Engineering College and Research Institute, Tamil Nadu Agricultural University, Kumulur-621712, Trichy, India

Keywords:

Banana, crop coefficient, NDVI, paddy, remote sensing, SEBAL, sugarcane

Abstract

Crop coefficient (Kc) 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-Kc linear regression technique and SEBAL actual evapotranspiration to reference evapotranspiration ratio approach (SEBAL-Kc). The Kc values obtained by remote sensing methods were compared with FAO-56 Adjusted Kc (ClimAdj-Kc) for local climatic conditions and FAO-56 tabulated reference Kc values (FAOTab-Kc). Regression analysis revealed a good agreement between NDVI-Kc and ClimAdj-Kc for paddy (R2=0.95), banana (R2=0.93), and sugarcane (R2=0.79). Compared to FAO56-Kc, the derived Kc values using NDVI-Kc were higher, while the SEBAL-Kc values were lower for all growth stages of paddy. For sugarcane crop, the FAO-56 Kc, NDVI-Kc and ClimAdj-Kc for local climate were almost similar in all stages. In case of banana, NDVI-Kc and SEBAL-Kc were higher as compared to the FAO-56-Kc and ClimAdj-Kc. 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-Kc method. The Kc 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.

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Published

2021-08-24

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