Skip to main content
ARS Home » Plains Area » Fort Collins, Colorado » Center for Agricultural Resources Research » Water Management and Systems Research » Research » Publications at this Location » Publication #396809
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: Improved estimation of crop water use by linking multi-scale thermal and solar-induced fluorescence measurements

Author
item Zhang, Huihui
item MAGNEY, TROY - University Of California, Davis
item ULEP, FRANCIS - University Of California, Davis
item Comas, Louise
item SCHUH, ANDREW - Colorado State University

Submitted to: American Geophysical Union
Publication Type: Abstract Only
Publication Acceptance Date: 10/5/2022
Publication Date: N/A
Citation: N/A

Interpretive Summary: N/A

Technical Abstract: Agricultural water supplies in the US are experiencing a significant decline due to climate change, increasing population, and competition for water use. As a result, there is an increasing need for accurate estimation of crop water use at high spatial and temporal resolutions for precision irrigation management. Field campaigns were held at USDA-ARS Limited Irrigation Research Farm (LIRF) in Greeley, and Central Great Plains Research Station (CPRS) in Akron, Colorado, in 2022. A set of surface temperature measurements were collected from three platforms: ECOSTRESS satellite, UAV thermal camera, and proximal IRT sensors for dryland winter wheat at CPRS (Apr-Jun 2022) and maize plants with differences in soil water at LIRF (Jun-Oct 2022). In the meantime, a scanning tower spectrometer gathered high spectral and temporal vegetation reflectance (400-1000nm) and solar-induced fluorescence (SIF) data at LIRF complemented by leaf fluorescence and sap flow measurements. The goals of the study are 1) to test the possibility of integrating thermal data at multi-scales, both temporal and spatial, to improve the estimation of crop water use and stress using single-pass satellite or UAV data; 2) to link UAV, leaf fluorescence, sap flow, and tower spectrometer data to evaluate the scale-up of SIF from leaf to canopy level, and single plant transpiration to field evapotranspiration; and 3) to investigate if diurnal SIF, in addition to IRT and vegetation indices, will improve the evaluation of water stress on crop yield in a farm-scale at semi-arid and dryland cropping regions.