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Integration of Remote Sensing and Proximal Sensing for Improvement of Field Scale Water Management
Abstract
Water is one of the most precious natural resources, and sustainable water resources development is a significant challenge facing water managers over the coming decades. Accurate estimation of the different components of the hydrologic cycle is key for water managers and planners in order to achieve sustainable water resources development. The primary goal of this dissertation was to investigate techniques to combine datasets acquired by remote and proximal sensing and in-situ sensors for the improvement of monitoring near surface water fluxes. This dissertation is separated into three site-specific case studies. First study, investigated the feasibility of using inverse vadose zone modeling for field actual evapotranspiration (ETa) estimation. Results show reasonable estimates of ETa, both daily and annually, from soil water content (SWC) sensors and Cosmic-Ray Neutron Probes (CRNPs). Second study, combined remote and proximal sensing methods to explore the spatial correlation between hydrological state variables and ET flux. Comparison of the datasets reveal that SWC and ETa were linearly correlated but the correlation between depth to the water table and ETa was weak. A simple multivariate linear regression model was used to estimate ET a. The estimated ETa values were then compared to the time ETa integration spline method. The comparison indicates similar seasonal ETa between the two methods in 2015 (wet) but a 20% reduction in 2016 (dry). The study highlights the challenge of connecting hydrologic state variables with hydrologic flux estimates. Third study, evaluated the functionality of automatically calibrated Earth Engine Evapotranspiration Flux (EEFlux) to the existing mapping evapotranspiration at high resolution with internalized calibration (METRIC) images in different locations. The comparison results showed that EEFlux is able to calculate Reference evapotranspiration Fraction (ETrF) and ETa in agricultural areas comparable (RMSE=0.13) to the ones from trained expert METRIC users. However, the EEFlux algorithm needs to be improved to calculate ETrF and ET a in non-agricultural areas (RMSE=0.21). Given the paucity of in-situ data across much of the globe the field of remote sensing offers an alternative but requires users to be cautious and realistic about associated errors and uncertainty on using such information to help construct a hydrologic budget.
Subject Area
Hydraulic engineering|Civil engineering|Water Resource Management
Recommended Citation
Foolad, Foad, "Integration of Remote Sensing and Proximal Sensing for Improvement of Field Scale Water Management" (2018). ETD collection for University of Nebraska-Lincoln. AAI10841913.
https://digitalcommons.unl.edu/dissertations/AAI10841913