Remote sensing-based evapotranspiration and soil water balance estimation for a tropical pasture in Brazil using the SETMI model

Developing strategies to improve pasture yield and mitigate environmental impacts is crucial in Southeastern Brazil, where livestock farming faces competition for areas with profitable crops. Accurate evapotranspiration (ET) estimation is an essential component of managing soil water balance and evaluating pasture response to drought and water productivity. The objectives of this study were to: (1) develop a relationship between the basal crop coefficient (K cb ) derived from observed ET, and the soil-adjusted vegetation index (SAVI) from PlanetScope images; (2) integrate the K cb -SAVI relationship into remote sensing-based soil water balance (RSWB) to generate daily K cb and then simulate the actual evapotranspiration (ET a ) of an intensively grazed tropical pasture in the state of São Paulo, Brazil; and (3) use the spatialized estimate of ET a to assess the crop water productivity ( WP_ETa mod ). The K cb -SAVI relationship developed in this study showed a strong positive correlation between SAVI and K cb , with Pearson correlation coefficient ( ρ ) and R² values of 0.89 and 0.79, respectively. Comparisons between observed and simulated ET a indicated good agreement for daily values ( RMSE of 0.59 mm d −1 , Willmott index of agreement ( D ) of 0.86) and for average weekly values ( RMSE of 0.38 mm d −1 , D of 0.93). Biases of -5% and 3% were obtained for modelled cumulative ET a in the years 2021–2022 and 2022–2023, respectively. The average WP_ETa mod values were similar for both years, 2.2 kg m −3 in 2021–2022 and 1.8 kg m −3 in 2022–2023. These findings demonstrate the potential of RSWB, ET a , and WP assessments to enhance decision-making, monitoring, and management of water resources in pasture-based livestock farming.