Abstract

This paper proposes a novel methodology for high spatial resolution near-surface daily Soil Water Content (SWC) estimation using remote sensing data and deep computer vision techniques. Current SWC estimation methods have accuracy, spatial resolution, and globalization limitations. To overcome these limitations, the proposed approach integrates diverse data sources, including satellite images (land elevation, SAR data, and a vegetation index), time-series data (precipitation, temperature, global radiation, and wind), and soil and land cover properties. To do this, we use a hybrid deep model consisting of a U-Net for image processing, a Temporal Convolutional Network (TCN) for time-series features, and a Feed-Forward Network (FNN) for constant properties. This solution makes it possible to capture spatial variability and temporal dynamics simultaneously. We used the HOBE dataset as SWC ground truth to conduct initial experiments that showed promising results in accurately estimating SWC across different soil depths. Further refinements are needed for a potential solution for various hydrology, environmental, and agricultural applications.

Original languageEnglish
Title of host publicationNSG 2024 30th European Meeting of Environmental and Engineering Geophysics
Number of pages5
PublisherEuropean Association of Geoscientists and Engineers
Publication date2024
Pages1-5
ISBN (Electronic)9789462825055
DOIs
Publication statusPublished - 2024
SeriesEAGE Conference Proceedings
Volume2024
ISSN2214-4609

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