Using a Support Vector Machine and a Land Surface Model to Estimate Large-Scale Passive Microwave Brightness Temperatures Over Snow-Covered Land in North America

Abstract

A support vector machine (SVM), a machine learning technique developed from statistical learning theory, is employed for the purpose of estimating passive microwave (PMW) brightness temperatures over snow-covered land in North America as observed by the advanced microwave scanning radiometer (AMSR-E) satellite sensor. The capability of the trained SVM is compared relative to the artificial neural network (ANN) estimates originally presented in [16] . The results suggest that the SVM outperforms the ANN at 10.65, 18.7, and 36.5 GHz for both vertically and horizontally polarized PMW radiation. When compared against daily AMSR-E measurements not used during the training procedure and subsequently averaged across the North American domain over the 9-year study period, the root-mean-squared error (RMSE) in the SVM output is 8 K or less, while the anomaly correlation coefficient is 0.7 or greater. When compared relative to the results from the ANN at any of the six frequency and polarization combinations tested, the RMSE was reduced by more than 18%, while the anomaly correlation coefficient was increased by more than 52%. Furthermore, the temporal and spatial variability in the modeled brightness temperatures via the SVM more closely agrees with that found in the original AMSR-E measurements. These findings suggest that the SVM is a superior alternative to the ANN for eventual use as a measurement operator within a data assimilation framework.