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- W4299909526 abstract "A novel algorithm is proposed to downscale microwave brightness temperatures ($mathrm{T_B}$), at scales of 10-40 km such as those from the Soil Moisture Active Passive mission to a resolution meaningful for hydrological and agricultural applications. This algorithm, called Self-Regularized Regressive Models (SRRM), uses auxiliary variables correlated to $mathrm{T_B}$ along-with a limited set of textit{in-situ} SM observations, which are converted to high resolution $mathrm{T_B}$ observations using biophysical models. It includes an information-theoretic clustering step based on all auxiliary variables to identify areas of similarity, followed by a kernel regression step that produces downscaled $mathrm{T_B}$. This was implemented on a multi-scale synthetic data-set over NC-Florida for one year. An RMSE of 5.76~K with standard deviation of 2.8~k was achieved during the vegetated season and an RMSE of 1.2~K with a standard deviation of 0.9~K during periods of no vegetation." @default.
- W4299909526 created "2022-10-03" @default.
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- W4299909526 date "2015-01-30" @default.
- W4299909526 modified "2023-09-23" @default.
- W4299909526 title "Downscaling Microwave Brightness Temperatures Using Self Regularized Regressive Models" @default.
- W4299909526 doi "https://doi.org/10.48550/arxiv.1501.07683" @default.
- W4299909526 hasPublicationYear "2015" @default.
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