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Leveraging Unmanned Aerial System Remote Sensing to Inform Energy and Water Balance Models for Spatial Soil Water Content Monitoring and Irrigation Management
Abstract
Irrigation has provided a means to produce more food and fiber throughout the world, converting low producing land into high yielding cropping systems in certain scenarios. The managing of irrigation has taken on various approaches as different locations have been constrained by different factors. Certain areas have significant ground and surface water available for irrigation while other areas struggle to meet irrigation demands due to limited water resources. These factors, along with the desire to increase crop water use efficiency, has provided the motivation to better understand crop water demands spatially within a field. A sub-field scale irrigation management study was conducted between 2018 and 2020 at a production research field in eastern Nebraska comparing uniform and spatial irrigation management approaches. Spatial irrigation was managed using a remote-sensing-based hybrid modeling approach that incorporated the two-source energy balance model and soil water balance model updated with reflectance-based crop coefficients. The models were informed with satellite and unmanned aerial system remotely-sensed imagery. Management approaches were assessed based on the response of irrigation applied and dry grain yield. The remote-sensing-based spatial irrigation management approaches consistently applied less water than the uniform irrigation approach while producing non-statistically significant differences in yield. The accuracy of modeled energy balance fluxes and daily evapotranspiration from the two-source energy balance, water balance, and hybrid models were assessed based on comparison of modeled and measured fluxes and evapotranspiration using data from eddy covariance flux towers. The two-source energy balance model using the Priestley-Taylor formulation produced the highest agreement in modeled and measured fluxes. The hybrid model produced the highest agreement in estimated daily evapotranspiration in comparison to the two-source energy balance and water balance models, individually. The increased accuracy of estimated daily evapotranspiration from the hybrid model indicated that the water balance model benefited from the assimilation of the two-source energy balance estimated evapotranspiration, which may enhance spatial irrigation management.
Subject Area
Agricultural engineering|Agriculture|Energy|Remote sensing
Recommended Citation
Maguire, Mitchell S, "Leveraging Unmanned Aerial System Remote Sensing to Inform Energy and Water Balance Models for Spatial Soil Water Content Monitoring and Irrigation Management" (2021). ETD collection for University of Nebraska-Lincoln. AAI28713282.
https://digitalcommons.unl.edu/dissertations/AAI28713282