Year: 2015
Type: conference-paper
Abstract: An novel algorithm is proposed to downscale microwave brightness temperatures (TB), 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 TBalong-with a limited set of in-situ SM observations, which are converted to high resolution TBobservations 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 TB. 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.
Institution: University of Florida
Language: English
Subfield: Environmental Engineering
Field: Environmental Science
Domain: Physical Sciences
SDG: Climate action
Open Access status: green
Awards: HYDROMETEOROLOGICAL MODELS SIMULATE ATMOSPHERIC AND HYDROLOGICAL PROCESSES AT SCALES OF 1-10 KM AND ARE SIGNIFICANTLY INFLUENCED BY THE AVAILABILITY, OPTIMAL USE OF ACTIVE/PASSIVE MICROWAVE OBSERVATIONS AT DIFFERING SPATIAL AND TEMPORAL SCALES TO ESTIMATE ROOT-ZONE SOIL MOISTURE IN DYNAMIC, HETEROG
OpenAlex ID: W1978190814