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The rapid growth in population, climate variability, and decreasing water resources necessitate innovative agricultural practices to ensure food security and resource conservation. This study investigates the effectiveness of various multispectral imagery from remote sensing (RS) platforms, Unmanned Aerial Systems (UAS), PlanetDove microsatellites, Sentinel-2, Landsat 8/9, and proximal MSR-5 in assessing crop biophysical characteristics (CBPCs) and actual crop evapotranspiration (ETa) for maize fields in northeastern Colorado. The research aims to evaluate the accuracy of vegetation indices (VIs) derived from these platforms in estimating key CBPCs, including leaf area index (LAI), crop height (Hc), and fractional vegetation cover (Fc), as well as ETa. Field experiments were conducted during 2022 at the USDA-ARS Limited Irrigation Research Farm in Greeley, Colorado, U.S.A., using different irrigation strategies. Surface reflectance data collected using a handled sensor and observed LAI, Hc, and Fc values, served as ground truth for validating RS estimates. The study applied various statistical analyses to compare the performance of different RS platforms and models. Results indicate that higher-resolution platforms, particularly UAS, provided higher accuracy in estimating VIs and CBPCs than satellite platforms. The study also highlights the influence of environmental conditions on the accuracy of RS models, with locally calibrated models outperforming those developed in dissimilar conditions. The findings underscore the potential of advanced RS technologies in enhancing precision agriculture practices and optimizing water resource management.
Al-Majali et al. (Tue,) studied this question.