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

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 ([Formula: see text]). 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 [Formula: see text] values of 0.89 and 0.79, respectively. Comparisons between observed and simulated ET_a indicated good agreement for daily values ([Formula: see text] of 0.59 mm d^-1, Willmott index of agreement ([Formula: see text]) of 0.86) and for average weekly values ([Formula: see text] of 0.38 mm d^-1, [Formula: see text] 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 [Formula: see text] 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.