TY - JOUR
T1 - Assessing geeSEBAL automated calibration and meteorological reanalysis uncertainties to estimate evapotranspiration in subtropical humid climates
AU - Kayser, Rafael Henrique
AU - Ruhoff, Anderson
AU - Laipelt, Leonardo
AU - Kich, Elisa de Mello
AU - Roberti, Débora Regina
AU - Souza, Vanessa de Arruda
AU - Rubert, Gisele Cristina Dotto
AU - Collischonn, Walter
AU - Neale, Christopher Michael Usher
N1 - Funding Information:
The authors gratefully acknowledge the financial support provided by the Brazilian Agency for the Improvement of Higher Education (CAPES) in partnership with the Brazilian Nacional Water Agency (ANA) in the context of the research project "Estimating land surface evapotranspiration using remote sensing models for water management in Brazil ". This study was also partially funded by the Brazilian National Council for Scientific and Technological Development (CNPq) and by the Research Support Foundation of Rio Grande do Sul (FAPERGS). The authors also acknowledge the in-kind support of the Google Earth Engine and the Daugherty Water for Food Global Institute at the University of Nebraska.
Funding Information:
The authors gratefully acknowledge the financial support provided by the Brazilian Agency for the Improvement of Higher Education ( CAPES ) in partnership with the Brazilian Nacional Water Agency (ANA) in the context of the research project "Estimating land surface evapotranspiration using remote sensing models for water management in Brazil ". This study was also partially funded by the Brazilian National Council for Scientific and Technological Development ( CNPq ) and by the Research Support Foundation of Rio Grande do Sul ( FAPERGS ). The authors also acknowledge the in-kind support of the Google Earth Engine and the Daugherty Water for Food Global Institute at the University of Nebraska.
Publisher Copyright:
© 2021
PY - 2022/3/1
Y1 - 2022/3/1
N2 - The application of energy balance models for estimation of evapotranspiration (ET) still has challenges to be addressed for large scale applications. The algorithm for automated calibration using inverse modeling at extreme conditions (CIMEC) is based on the definition of endmembers that represent the extreme conditions of the ET spectrum, between hot (dry and sparse vegetation) and cold (wet and dense vegetation) surfaces, with pre-defined quantiles for the endmember selection. The main goal was to assess geeSEBAL algorithm uncertainties related to the (i) automated calibration, including the use of additional filters (land cover, homogeneity, and domain area) and (ii) the use of a global climate grid as input data. Based on a sensitivity analysis, we defined new set of quantiles to increase the accuracy of ET estimates in subtropical humid climates, since the default quantiles were adjusted to semiarid climates with dry summers. To validate our ET estimates we used eddy covariance measurements from five flux towers located in the South of Brazil. Processing 132 Landsat cloud free images and using adjusted quantiles, we found a root mean square error (RMSE) of 0.91 mm d − 1 and a coefficient of determination (R²) of 0.82 with geeSEBAL driven by meteorological measurements. Using the pre-defined quantiles, we found an RMSE of 1.16 mm d − 1 (27% higher) and R² of 0.75. The upscaling instantaneous ET to daily ET resulted in an underestimation of the daily ET using the pre-defined quantiles, while the optimized quantiles corrected the daily estimates. Furthermore, our results suggested a low sensitivity of geeSEBAL to meteorological inputs, since RMSE slightly increased to 1.04 mm d − 1 (14.3% higher) and R² decreased to 0.76 (8.5% smaller) when driven by global climate data. For data scarce areas, geeSEBAL is a feasible alternative for cropland ET estimation and water resources management in subtropical humid climates.
AB - The application of energy balance models for estimation of evapotranspiration (ET) still has challenges to be addressed for large scale applications. The algorithm for automated calibration using inverse modeling at extreme conditions (CIMEC) is based on the definition of endmembers that represent the extreme conditions of the ET spectrum, between hot (dry and sparse vegetation) and cold (wet and dense vegetation) surfaces, with pre-defined quantiles for the endmember selection. The main goal was to assess geeSEBAL algorithm uncertainties related to the (i) automated calibration, including the use of additional filters (land cover, homogeneity, and domain area) and (ii) the use of a global climate grid as input data. Based on a sensitivity analysis, we defined new set of quantiles to increase the accuracy of ET estimates in subtropical humid climates, since the default quantiles were adjusted to semiarid climates with dry summers. To validate our ET estimates we used eddy covariance measurements from five flux towers located in the South of Brazil. Processing 132 Landsat cloud free images and using adjusted quantiles, we found a root mean square error (RMSE) of 0.91 mm d − 1 and a coefficient of determination (R²) of 0.82 with geeSEBAL driven by meteorological measurements. Using the pre-defined quantiles, we found an RMSE of 1.16 mm d − 1 (27% higher) and R² of 0.75. The upscaling instantaneous ET to daily ET resulted in an underestimation of the daily ET using the pre-defined quantiles, while the optimized quantiles corrected the daily estimates. Furthermore, our results suggested a low sensitivity of geeSEBAL to meteorological inputs, since RMSE slightly increased to 1.04 mm d − 1 (14.3% higher) and R² decreased to 0.76 (8.5% smaller) when driven by global climate data. For data scarce areas, geeSEBAL is a feasible alternative for cropland ET estimation and water resources management in subtropical humid climates.
KW - Agriculture
KW - Endmember selection
KW - Energy balance
KW - GLDAS
KW - LANDSAT
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U2 - 10.1016/j.agrformet.2021.108775
DO - 10.1016/j.agrformet.2021.108775
M3 - Article
AN - SCOPUS:85122449574
SN - 0168-1923
VL - 314
JO - Agricultural and Forest Meteorology
JF - Agricultural and Forest Meteorology
M1 - 108775
ER -