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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 #395523
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: Evaluating reflectance-based maize evapotranspiration modeling under different irrigation systems

Author
item COSTA-FILHO, EDSON - Colorado State University
item CHÁVEZ, JOSÉ - Colorado State University
item Zhang, Huihui
item ANDALES, ALLAN - Colorado State University
item BROWN, ANSLEY - Colorado State University

Submitted to: United States Committee on Irrigation and Drainage Conference
Publication Type: Abstract Only
Publication Acceptance Date: 7/14/2022
Publication Date: N/A
Citation: N/A

Interpretive Summary:

Technical Abstract: Understanding the accuracy of daily evapotranspiration (ETa) estimates from remotely sensed crop coefficient models under different field irrigation systems is critical to interpreting results and improving irrigation water management at the farm scale. This study aimed to identify the strengths and limitations of estimating daily maize ETa using a reflectance-based crop coefficient (RBCC) model and spaceborne surface reflectance data from two different field irrigation systems in Colorado, US. The RBCC approach for daily maize ETa uses real-time fractional vegetation cover (fc) and daily alfalfa-based reference evapotranspiration (ETrd) as inputs. This study's data were obtained from August to September of 2020. A total of eight cloudless Sentinel-2 multispectral images provided red and near-infrared surface reflectance with a spatial resolution of 10 m. Two maize research farms in Fort Collins and Greeley, CO, provided the grounds for in-situ measured ETa data using soil water content sensors at multiple depths covering the optimal root zone layer. The irrigation systems used at the Fort Collins and Greeley farms were surface (furrow) and subsurface drip, respectively. None of the fields had water stress conditions during the fieldwork campaign. An agricultural weather station nearby Greeley, CO, provided micrometeorological data for calculating ETrd. Preliminary results have indicated that the RBCC model for daily maize ETa applied to the subsurface drip outperformed the surface irrigation site by 21%. The normalized root mean square error for the subsurface drip and surface irrigation fields were 20% and 26%, respectively. Daily maize ETa underestimation happened in both fields, with the furrow irrigated site having the highest normalized mean bias error (8%) compared to the subsurface drip field (1%). Research findings suggest low daily maize ETa underestimation due to subsurface drip is associated with low top-soil evaporation conditions. Also, improvements in daily maize ETa estimation for surface irrigated fields will rely on incorporating soil evaporation estimates to reduce the RBCC model underestimation of ETa.