Remote sensing data are an enormous resource for studying the Earth's physical systems, including the climate. Modern spatial statistical methods have been (and are still being) developed to leverage this information in new and creative ways. Increasingly, however, "the data" are less monolithic and more distributed: different space agencies collect, process, and distribute complementary data sets which need to be exploited jointly to realize their full potential. The Working Group on Remote Sensing, formed under the auspices of the Statistical and Applied Mathematical Sciences Institute (SAMSI) Program on Mathematical and Statistical Methods for Climate and the Earth System, has been studying various aspects of this problem during the 2017-2018 academic year. Topics included efficient spatial inference under computational constraints, emulators, optimization, dimension reduction, and the design of data systems specifically for large-scale, spatial statistical analysis. These must be explored in conjunction with computational, transmission, and infrastructure costs that put constraints on the statistical methods that can be used. We will discuss progress made and a path forward.