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ARS Home » Plains Area » Fort Collins, Colorado » Center for Agricultural Resources Research » Water Management and Systems Research » Research » Publications at this Location » Publication #411712
Research Project: Improving Crop Performance and Precision Irrigation Management in Semi-Arid Regions through Data-Driven Research, AI, and Integrated Models

Location: Water Management and Systems Research

Title: 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

Author
item COSTA-FILHO, EDSON - Colorado State University
item CHAVEZ, JOSE - Colorado State University
item Zhang, Huihui

Submitted to: Sustainability
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/27/2024
Publication Date: 6/6/2024
Citation: Costa-Filho, E., Chavez, J.L., Zhang, H. 2024. Assessing maize evapotranspiration estimation from two-source surface energy balance approaches using several remote sensing sensors. Sustainability. 16(11). Article e4850. https://doi.org/10.3390/su16114850.
DOI: https://doi.org/10.3390/su16114850

Interpretive Summary: This research investigated the efficacy of various remote sensing algorithms in estimating corn water use (evapotranspiration, ETa) across different platforms—ranging from spaceborne to proximal sensors—in a semi-arid climate. The aim was to pinpoint the most optimal platform for obtaining accurate ETa estimates. The study considered diverse remote sensing platforms, including Landsat-8 (30 m), Sentinel-2 (10 m), Planet CubeSat (3 m), multispectral radiometer or MSR (1 m), and unmanned aerial vehicle or UAS (0.03 m). The results showed that no single type of technology was the best for all situations. However, MSR5 worked the best with the models and made the smallest errors in figuring out how much water the corn needed. The satellite, Landsat-8, didn't work as well with the largest errors in estimating crop water needs. These findings tell us the importance of developing methods to enhance remote sensing data from sub-optimal platforms, so farmers can use these methods more effectively to save water when growing crops.

Technical Abstract: This study evaluated the performance of remote sensing (RS) algorithms for the estimation of actual maize evapotranspiration (ETa) using different spaceborne, airborne, and proximal multispectral data in a semi-arid climate region to identify the optimal platform that gives the best ETa 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 ETa. The data used in this study were obtained from two maize research sites in Greeley and Fort Collins, Colorado, 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 ETa predictions were compared to observed maize ETa data from an Eddy Covariance system installed at each research site. Results indicated that no unique RS platform provides more accurate ETa results across both TSEB RS of ETa algorithms evaluated. 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 maize ETa. The errors when predicting maize hourly ETa when using the MSR5 sensor were MBE ± RMSE equals to -0.02 (-3%) ± 0.07 (11%) mm/h 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 ETa, 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/calibration to further advance sustainable irrigation water management.