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Integrating high-resolution remote sensing data for improved agricultural soil water monitoring
Proceedings of the 2020 Mississippi Water Resources Conference
Year: 2020 Authors: Lei F., Moorhead R., Crow W.T., Kurum M.
Improving water usage efficiency is of critical importance for sustainable agriculture water management. Over the past few decades, extensive field investigations and numerical modeling have been conducted to quantify surface water and energy fluxes at different spatiotemporal scales. Meanwhile, with the development of satellite-based sensors, high-resolution land surface hydrological variables can be retrieved remotely to supplement ground-based observations. However, both models and remote sensing retrievals are subject to various sources of errors. An accurate and spatiotemporally continuous soil water monitoring at the subfield-scale is crucial for efficient agriculture water management. Particularly, data assimilation techniques can optimally integrate measurements acquired from various sources (including in-situ and remotely sensed data) with numerical models by considering different uncertainties. In this presentation, we present some recent work on monitoring soil water content over a vineyard in California. Specifically, high-resolution evapotranspiration estimates derived from satellite-based thermal imagery and surface soil moisture retrievals from synthetic aperture radar sensor are optimally incorporated into a Water-Energy-Balance Soil Vegetation Atmosphere Transfer (WEB-SVAT) model via data assimilation methods. Results demonstrate that the simulation of soil water content in the SVAT model can be enhanced through the assimilation of high-resolution remote sensing data with reduced errors compared to independent ground-based measurements. This work can foster improved irrigation strategies with the availability of continuous and accurate soil water monitoring at subfield-scale for agriculture.