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In this study, a machine learning algorithm is used for disaggregation ofSMAP brightness temperatures (T$_{\textrm{B}}$) from 36km to 9km. It uses imagesegmentation to cluster the study region based on meteorological and land coversimilarity, followed by a support vector machine based regression that computesthe value of the disaggregated T$_{\textrm{B}}$ at all pixels. High resolutionremote sensing products such as land surface temperature, normalized differencevegetation index, enhanced vegetation index, precipitation, soil texture, andland-cover were used for disaggregation. The algorithm was implemented in Iowa,United States, from April to July 2015, and compared with the SMAP L3_SM_APT$_{\textrm{B}}$ product at 9km. It was found that the disaggregatedT$_{\textrm{B}}$ were very similar to the SMAP-T$_{\textrm{B}}$ product, evenfor vegetated areas with a mean difference $\leq$ 5K. However, the standarddeviation of the disaggregation was lower by 7K than that of the AP product.The probability density functions of the disaggregated T$_{\textrm{B}}$ weresimilar to the SMAP-T$_{\textrm{B}}$. The results indicate that this algorithmmay be used for disaggregating T$_{\textrm{B}}$ using complex non-linearcorrelations on a grid.

Disaggregation of SMAP L3 Brightness Temperatures to 9km using Kernel Machines