NASA's Orbiting Carbon Observatory-2 (OCO-2) mission is now actively collecting space-based measurements of atmospheric carbon dioxide (CO2). Data are collected with high spatial and temporal resolution and the data product includes both an estimate of column averaged CO2 dry air mole fraction (XCO2) and an estimate of uncertainty. In this talk we will take a look at how these estimates are obtained. As with any remote sensing method, the measurements are indirect. The OCO-2 instrument measures reflected sunlight in three spectral bands that make a single "sounding”.
With the development of new remote sensing technology, large or even massive spatial datasets covering the globe become available. Statistical analysis of such data is challenging. This manuscript proposes a semiparametric approach to modeling and inference for massive spatial datasets.