Assessing multi-sensor hourly maize evapotranspiration estimation using a one-source surface energy balance approach

Authors: COSTA-FILHO, EDSON - COLORADO STATE UNIVERSITY; CHAVEZ, JOSE - COLORADO STATE UNIVERSITY; Zhang, Huihui

Submitted to: Irrigation and Drainage
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/18/2023
Publication Date: 1/23/2024
Citation: Costa-Filho, E., Chavez, J.L., Zhang, H. 2024. Assessing multi-sensor hourly maize evapotranspiration estimation using a one-source surface energy balance approach. Journal of Irrigation and Drainage. https://doi.org/10.1002/ird.2923.
DOI: https://doi.org/10.1002/ird.2923

Interpretive Summary: In today's world, where we have access to a wide range of remote sensing technology, finding the right tool to predict water-related environmental changes has become crucial for better water management in agriculture. This study aimed to evaluate the performance of a remote sensing technique called the one-source surface energy balance (OSEB) in predicting the water needs of crops, specifically maize. The OSEB method combines data from various remote sensing devices, such as Landsat-8, Sentinel-2, Planet CubeSat, handheld multispectral radiometers, and unmanned aerial vehicles, which offer different levels of detail, from 30 meters down to 0.03 meters. It turns out that the Planet CubeSat multispectral sensor, with its 3-meter resolution and the addition of on-site temperature data, provided the most accurate predictions for maize water needs. This study highlights the urgent need to improve the quality of remote sensing data to support more sustainable irrigation water management practices. These improvements will be essential in helping farmers make better decisions about when and how much to water their crops, ultimately contributing to more efficient and sustainable agriculture.

Technical Abstract: With the current wide range availability of different remote sensing (RS) spectral and spatial resolution sensors, finding the best RS sensor product (multispectral imagery) for predicting environmental fluxes related to water vapor has become critical to improving irrigation water management. In this study, the performance of a one-source surface energy balance (OSEB) RS of crop actual evapotranspiration (ETa), incorporating data from different spaceborne, airborne, and proximal multispectral data, was evaluated. The RS platforms in this study included Landsat-8 (30 m pixel size), Sentinel-2 (10 m), Planet CubeSat (3 m), a handheld (proximal) multispectral radiometer or MSR (1 m), and an unmanned aerial vehicle or UAS (0.03 m). The OSEB RS of ETa algorithm uses surface aerodynamic resistance and temperature models for the estimation of sensible heat flux (H). A two-year dataset (2020 and 2021) from two maize research sites in Greeley and Fort Collins, Colorado, USA, provided the ground-based data for estimating and evaluating hourly ETa from the OSEB algorithm. Further, on-site measurements of radiometric surface temperature were considered as input data to the OSEB RS of ETa algorithm. The accuracy of OSEB hourly maize ETa estimates were evaluated using calculated hourly maize ETa using high frequency data collected with an Eddy Covariance Energy Balance system installed at each research site. Results indicated the Planet CubeSat multispectral sensor (3 m), combined with on-site radiometric surface temperature data, yielded the least errors when predicting hourly maize ETa. The hourly ETa estimation errors, for the Planet CubeSat platform/imagery, were MBE ± RMSE of -0.02 (-3%) ± 0.07 (13%) mm/h. The poorest performance when predicting hourly OSEB maize ETa resulted from Landsat-8 (30 m) multispectral data combined with its original thermal data since the errors were -0.05 (-10%) ± 0.17 (31%) mm/h. These results suggest the urgent need for a specific approach to improve RS multispectral and thermal radiometric data (quality) to better support sustainable irrigation water management practices.