Maize Crop Coefficient Estimation Based on Spectral Vegetation Indices and Vegetation Cover Fraction Derived from UAV-Based Multispectral Images

Remote sensing-based crop monitoring has evolved unprecedentedly to supply multispectral imagery with high spatial-temporal resolution for the assessment of evapotranspiration (ETc). Several methodologies have shown a correlation between Vegetation Indices (VIs) and coefficient (Kc). This work analyzes estimation (Kc) as spectral function product two variables: VIs green vegetation cover fraction (fv). Multispectral images from experimental maize plots were classified separate pixels into three classes (vegetation, shade soil) using OBIA (Object Based Image Analysis) approach. Only used estimate fv variables. The Kcfv:VI models compared Kc based on Cumulative Growing Degree Days (CGDD) (Kc-cGDD). maximum average values Normalized Difference Index (NDVI), WDRVI, amd EVI2 indices during growing season 0.77, 0.21, 1.63, respectively. results showed that model strong linear Kc-cGDD (R2 > 0.80). precision increases plant densities, Kcfv:NDVI 80,000 plants/ha had best fitting performance = 0.94 RMSE 0.055). indicate use spatial temporal UAV-images, only compute VI variables, offers powerful simple tool ETc support irrigation scheduling in agricultural areas.