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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 #404116

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: Assessing CO2 exchange, water use and yield of maize crops under full and deficit irrigation using UAV and satellite imagery

item Zhang, Huihui
item SCHUH, ANDREW - Colorado State University
item CHÁVEZ, JOSÉ - Colorado State University
item Yemoto, Kevin
item Wenz, Joshua
item Comas, Louise

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 4/19/2023
Publication Date: N/A
Citation: N/A

Interpretive Summary: N/A

Technical Abstract: With increasing interest in understanding crop water use and carbon dioxide (CO2) exchange in agricultural ecosystems, various methods have been developed to monitor these processes at different spatial and temporal scales. In this study, an integrated approach was employed to investigate crop water use and CO2 exchange in maize under full and deficit irrigation at USDA-ARS Limited Irrigation Research Farm (LIRF) in Greeley, Colorado. The net ecosystem exchange (NEE) of CO2 between the crop and atmosphere and evapotranspiration (ET) were measured using an eddy covariance (EC) system and crop sap flow rate was measured by sap flow sensors. Additionally, Unmanned Aerial Vehicle (UAV) multispectral and thermal imagery, and Planet/Sentinel satellite multispectral imagery were acquired to assess crop growth and water stress. Our preliminary results show a significant relationship with decreased NEE associated with increase sap flow rate from late July to end growing season, although the significance and slope of the correlations were different at different growth stages. Significant positive correlations were found between UAV Normalized Difference Red Edge Index (NDRE) and daily average and mid-day NEE. Both UAV and Satellite derived vegetation indices were positively correlated to crop biomass, leaf area index and yield. Next, combining UAV and satellite images for CO2 exchange, water use and yield prediction will be evaluated. Overall, the study aims to provide a valuable tool for monitoring and understanding crop water use and carbon dioxide exchange in agricultural ecosystems. The results can be used to inform and improve management practices, leading to more sustainable and efficient use of water resources in agriculture.