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Browsing by Author "O'Connel, Jessica, committee member"

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    Comparative analysis of remote sensing platforms for assessing maize crop biophysical characteristics and evapotranspiration estimation
    (Colorado State University. Libraries, 2024) Al-Majali, Zaid, author; Chávez, José L., advisor; Davenport, Frances, committee member; O'Connel, Jessica, committee member
    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 (such as Unmanned Aircraft Systems (UAS), PlanetDove microsatellites, Sentinel-2, Landsat 8/9, and proximal MSR-5) in the appropriate estimation of crop biophysical characteristics (CBPCs) and actual crop evapotranspiration (ETa) for maize fields in northeastern Colorado. The research aimed at evaluating the accuracy of vegetation indices (VIs) derived from different sources of RS data in estimating key CBPCs, including leaf area index (LAI), crop height (Hc), and fractional cover (Fc), as well as the ETa. Field experiments were conducted at the USDA-ARS Limited Irrigation Research Farm in Greeley, Colorado, in 2022. Different irrigation strategies were used to assess the maize's water use response. Surface reflectance data was collected using the MSR sensor, and observed LAI, Hc, and Fc values served as ground truth for validating remote sensing estimates. The study applied various statistical analyses to compare the performance of different remote sensing platforms and models. Results indicate that higher-resolution platforms, particularly UAS, provided higher accuracy in estimating VIs and CBPCs than other satellite platforms. The study also highlights the influence of environmental conditions on the accuracy of remote sensing models, with locally calibrated models outperforming those developed in dissimilar conditions. The findings underscore the potential of advanced remote sensing technologies in enhancing precision agriculture practices and optimizing water resource management.