Spatio-Temporal Data Fusion for Very Large Remote Sensing Datasets

摘要

Developing global maps of carbon dioxide (CO_2) mole fraction (in units of parts per million) near the Earth's surface can help identify locations where major amounts of CO_2 are entering and exiting the atmosphere, thus providing valuable insights into the carbon cycle and mitigating the greenhouse effect of atmospheric CO_2. Existing satellite remote sensing data do not provide measurements of the CO_2 mole fraction near the surface. Japan's Greenhouse gases Observing SATellite (GOSAT) is sensitive to average CO_2 over the entire column, and NASA's Atmospheric InfraRed Sounder (AIRS) is sensitive to CO_2 in the middle troposphere. One might expect that lower-atmospheric CO_2 could be inferred by differencing GOSAT column-average and AIRS mid-tropospheric data. However, the two instruments have different footprints, measurement-error characteristics, and data coverages. In addition, the spatio-temporal domains are large, and the AIRS dataset is massive. In this article, we describe a spatio-temporal data-fusion (STDF) methodology based on reduced-dimensional Kalman smoothing. Our STDF is able to combine the complementary GOSAT and AIRS datasets to optimally estimate lower-atmospheric CO_2 mole fraction over the whole globe. Further, it is designed for massive remote sensing datasets and accounts for differences in instrument footprint, measurement-error characteristics, and data coverages. This article has supplementary material online.