Mapping Maize Evapotranspiration with Two-Source Land Surface Energy Balance Approaches and Multiscale Remote Sensing Imagery Pixel Sizes: Accuracy Determination toward a Sustainable Irrigated Agriculture

Edson Costa-Filho, José L. Chávez () and Huihui Zhang
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Edson Costa-Filho: Civil and Environmental Engineering Department, Colorado State University, Fort Collins, CO 80523, USA
José L. Chávez: Civil and Environmental Engineering Department, Colorado State University, Fort Collins, CO 80523, USA
Huihui Zhang: Water Management and Systems Research Unit, United States Department of Agriculture, Agricultural Research Service, Fort Collins, CO 80526, USA

Sustainability, 2024, vol. 16, issue 11, 1-30

Abstract: This study evaluated the performance of remote sensing (RS) algorithms for the estimation of actual maize evapotranspiration (ET a ) using different spaceborne, airborne, and proximal multispectral data in a semi-arid climate region to identify the optimal platform that provides the best ET a estimates to improve irrigation water management and help make irrigated agriculture sustainable. The RS platforms used in the study included Landsat-8 (30 m pixel spatial resolution), Sentinel-2 (10 m), Planet CubeSat (3 m), multispectral radiometer or MSR (1 m), and a small uncrewed aerial system or sUAS (0.03 m). Two-source surface energy balance (TSEB) models, implementing the series and parallel surface resistance approaches, were used in this study to estimate hourly maize ET a . The data used in this study were obtained from two maize research sites in Greeley and Fort Collins, CO, USA, in 2020 and 2021. Each research site had different irrigation systems. The Greeley site had a subsurface drip system, while the Fort Collins site had surface irrigation (furrow). Maize ET a predictions were compared to observed maize ET a data from an eddy covariance system installed at each research site. Results indicated that the MSR5 proximal platform (1 m) provided optimal RS data for the TSEB algorithms. The MSR5 “point-based” nadir-looking surface reflectance data and surface radiometric temperature combination resulted in the smallest error when predicting hourly (mm/h) maize ET a . The mean bias and root mean square errors (MBE and RMSE, respectively), when predicting maize hourly ET a using the MSR5 sensor data, were equal to −0.02 (−3%) ± 0.07 (11%) mm/h MBE ± RMSE and −0.02 (−3%) ± 0.09 (14%) mm/h for the TSEB parallel and series approaches, respectively. The poorest performance, when predicting hourly TSEB maize ET a , was from Landsat-8 (30 m) multispectral data combined with its original thermal data, since the errors were −0.03 (−5%) ± 0.16 (29%) mm/h and −0.07 (−13%) ± 0.15 (29%) mm/h for the TSEB parallel and series approaches, respectively. These results indicate the need to develop methods to improve the quality of the RS data from sub-optimal platforms/sensors/scales/calibration to further advance sustainable irrigation water management.

Keywords: remote sensing; evapotranspiration; crop coefficient; surface energy balance; irrigation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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