Improving WISE Crop Evapotranspiration Estimates Using Crop Coefficients Derived from Remote-Sensing Algorithms
Publication: Journal of Irrigation and Drainage Engineering
Volume 150, Issue 5
Abstract
Sustainable irrigation water management is achievable only when irrigation scheduling is optimized to conserve water and soil resources in an agricultural setting. This study evaluated the use of remote sensing–based algorithms for determining actual crop evapotranspiration () mapping to update crop coefficients () of an irrigation scheduler software (WISE). The scheduler’s values are based on the FAO-56 approach for crop evapotranspiration () determination. A surface-irrigated (furrow) maize (Zea mays L.) field in Fort Collins, Colorado, was used from July to September 2020 and 2021. An eddy covariance energy balance system () installed on a tower at 3.5 m above the ground surface was used to determine hourly and daily maize data. These EC-based data were used to evaluate the performance of three approaches for maize estimation and the FAO-56–based predictions from WISE. Microsatellite PlanetScope multispectral imagery, at a 3-m-pixel spatial resolution, provided surface reflectance in the red and near-infrared bands for input in the remote sensing of algorithms. On-site micrometeorological data were measured at the exact location of the tower. Optimization of values was done using an ordinary least-squares regression approach. The optimized values were calculated for the maize midseason growth stage. Results indicated that using remote sensing of algorithms has excellent potential to improve irrigation scheduling by integrating optimized crop coefficients. WISE overestimated daily maize predictions by as much as 26%. When remote sensing-based optimized values were introduced, the overestimation of daily maize was reduced significantly, by 18% to 75%, depending on the remote sensing of the algorithm used. The research findings support the combined use of remote sensing data and the FAO-56 approach for irrigation scheduling to improve agricultural water management at the farm level.
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Data Availability Statement
All data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
The authors thank the Irrigation Innovation Consortium (IIC) and Colorado Corn for funding this research. This research effort was supported by Colorado Agricultural Experiment Station (CAES)–USDA NIFA project COL00796, to whom the authors express gratitude for their support. Furthermore, the authors thank Cole Lucero, Rustin Jensen, Mia Morones, Kelsey Walker, and Luke Stark for the field efforts to collect data, install instrumentation, and farm management.
Author contributions: Edson Costa-Filho processed the data, developed the optimization analysis, and wrote the bulk of the manuscript. José L. Chávez, Allan Andales, and Ansley Brown were responsible for the project conceptualization and design, management, financial procurement, fieldwork planning, and manuscript writing contributions and editing.
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Journal of Irrigation and Drainage Engineering
Volume 150 • Issue 5 • October 2024
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© 2024 American Society of Civil Engineers.
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Received: Dec 13, 2022
Accepted: May 2, 2024
Published online: Jul 27, 2024
Published in print: Oct 1, 2024
Discussion open until: Dec 27, 2024
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