A new method for gridding passive microwave data with mixed measurements and spatial correlation

Abstract

In this article we develop a new method to grid passive microwave data in the presence of spatial correlation patterns. Our proposal combines a Tychonov inverse method with a generalized cross validation procedure to grid the observations over a discrete retrieval grid. To build this grid, the study region is partitioned into objects following an object-based image analysis procedure. Then, this partition is refined into small cells of similar size. Two cells from the same object are considered of the same type. The procedure to estimate the brightness temperature of each cell is based on a least-squares estimation with a cell-type aware Tychonov regularization method. This method assumes that the brightness temperature heterogeneity within each cell can be neglected and that adjacent cells of the same type have similar brightness temperature. In other words, spatial correlations are considered within each object in the scene. The Tychonov regularization parameter is found using a fast generalized cross validation procedure that makes it possible to solve the inverse problem when the observational error variance is not known. We evaluate the proposed method using different synthetic scenarios and compare it with other methods. The evaluation shows an excellent performance of the proposed method when the brightness temperature field varies smoothly over each object in the scene. But it also shows that the method is competitive when the brightness temperature field does not present spatial correlations. We conclude that the proposed algorithm provides a fast and robust method to solve the original inverse problem.