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- W4312796511 abstract "NASA Cyclone Global Navigation Satellite System (CYGNSS) mission has gained attention within the land remote sensing community for estimating soil moisture (SM) by using the Global Navigation System Reflectometry (GNSS-R) technique. CYGNSS constellation generates Delay-Doppler Maps (DDM) that contain valuable earth surface information from GNSS reflection measurements. Existing approaches use predefined features from DDMs to estimate SM. This pa-per presents a deep-learning framework to learn optimal features from DDMs for estimating SM. The proposed approach is applied over the Continental United States (CONUS) by leveraging CYGNSS DDM observations with ancillary re-motely sensed geophysical data. The model is trained and evaluated using the Soil Moisture Active Passive (SMAP) mission's enhanced SM products at a <tex xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>$9text{km}times 9text{km}$</tex> resolution with vegetation water content less than <tex xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>$5kg/m^{2}$</tex> . The mean unbiased root-mean-square difference (ubRMSD) between CYGNSS and SMAP SM retrievals from 2017 to 2020 is 0.0362 <tex xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>$m^{3}/m^{3}$</tex> with a correlation coefficient of 0.9309 over 5-fold cross-validation." @default.
- W4312796511 created "2023-01-05" @default.
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- W4312796511 date "2022-07-17" @default.
- W4312796511 modified "2023-09-26" @default.
- W4312796511 title "A Deep Learning-Based Soil Moisture Estimation in Conus Region Using Cygnss Delay Doppler Maps" @default.
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- W4312796511 doi "https://doi.org/10.1109/igarss46834.2022.9883916" @default.
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