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 (Kcb) derived from observed ET, and the soil-adjusted vegetation index (SAVI) from PlanetScope images; (2) integrate the Kcb-SAVI relationship into remote sensing-based soil water balance (RSWB) to generate daily Kcb and then simulate the actual evapotranspiration (ETa) of an intensively grazed tropical pasture in the state of São Paulo, Brazil; and (3) use the spatialized estimate of ETa to assess the crop water productivity (<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\({\text{W}\text{P}}_{{\text{E}\text{T}}_{\text{a} \text{m}\text{o}\text{d}}}\)</EquationSource> </InlineEquation>). The Kcb-SAVI relationship developed in this study showed a strong positive correlation between SAVI and Kcb, with Pearson correlation coefficient (<InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(\rho\)</EquationSource> </InlineEquation>) and <InlineEquation ID="IEq3"> <EquationSource Format="TEX">\({\text{R}}^{2}\)</EquationSource> </InlineEquation> values of 0.89 and 0.79, respectively. Comparisons between observed and simulated ETa indicated good agreement for daily values (<InlineEquation ID="IEq4"> <EquationSource Format="TEX">\(\text{R}\text{M}\text{S}\text{E}\)</EquationSource> </InlineEquation> of 0.59 mm d−1, Willmott index of agreement (<InlineEquation ID="IEq5"> <EquationSource Format="TEX">\(\text{D}\)</EquationSource> </InlineEquation>) of 0.86) and for average weekly values (<InlineEquation ID="IEq6"> <EquationSource Format="TEX">\(\text{R}\text{M}\text{S}\text{E}\)</EquationSource> </InlineEquation> of 0.38 mm d−1, <InlineEquation ID="IEq7"> <EquationSource Format="TEX">\(\text{D}\)</EquationSource> </InlineEquation> of 0.93). Biases of -5% and 3% were obtained for modelled cumulative ETa in the years 2021–2022 and 2022–2023, respectively. The average <InlineEquation ID="IEq8"> <EquationSource Format="TEX">\({\text{W}\text{P}}_{{\text{E}\text{T}}_{\text{a} \text{m}\text{o}\text{d}}}\)</EquationSource> </InlineEquation> 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, ETa, and WP assessments to enhance decision-making, monitoring, and management of water resources in pasture-based livestock farming.

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

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2026年6月30日

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Vitor de J. M. Bianchini,
Ivo Z. Gonçalves,
Christopher M. U. Neale,
Alex da S. Sechi,
Thieres G. F. da Silva,
Fábio R. Marin

摘要

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 ( \({\text{W}\text{P}}_{{\text{E}\text{T}}_{\text{a} \text{m}\text{o}\text{d}}}\) ). The K_cb-SAVI relationship developed in this study showed a strong positive correlation between SAVI and K_cb, with Pearson correlation coefficient ( \(\rho\) ) and \({\text{R}}^{2}\) values of 0.89 and 0.79, respectively. Comparisons between observed and simulated ET_a indicated good agreement for daily values ( \(\text{R}\text{M}\text{S}\text{E}\) of 0.59 mm d⁻¹, Willmott index of agreement ( \(\text{D}\) ) of 0.86) and for average weekly values ( \(\text{R}\text{M}\text{S}\text{E}\) of 0.38 mm d⁻¹, \(\text{D}\) 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 \({\text{W}\text{P}}_{{\text{E}\text{T}}_{\text{a} \text{m}\text{o}\text{d}}}\) 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.