Downscaling Microwave Brightness Temperatures Using Self Regularized Regressive Models
This addresses the need for higher-resolution data in hydrology and agriculture, but it is incremental as it builds on existing downscaling methods with a novel algorithm.
The paper tackles the problem of downscaling microwave brightness temperatures from 10-40 km to higher resolutions for hydrological and agricultural applications, achieving an RMSE of 5.76 K during vegetated seasons and 1.2 K during non-vegetated periods.
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.