Spatiotemporal multiresolution modeling to infill missing areal data and enhance the temporal frequency of infrared satellite images

High‐resolution environmental observations from satellite remote sensing are useful for gaining insight into climate variability over large regions of the earth. However, data products derived from infrared remote sensing can have large amounts of missing data due to periodic cloud cover obscuring the earth's surface. In addition, these products are commonly derived from instruments aboard polar‐orbiting satellites, which make relatively infrequent passes over each region of the planet, leaving large blocks of data that are missing in time. The goal of this research is to overcome these issues by spatially and temporally infilling temperature data using satellite observations. To accomplish this, we propose novel statistical modeling techniques to account for dissonance between the areal spatial support of the data and the continuous temporal domain. In addition, we develop a novel multiresolution approach that accommodates the large spatial dimensionality of high‐resolution satellite images. The result of our work is a computationally efficient methodology that provides the means to infill satellite data products both spatially and temporally. The methodology is demonstrated using land surface temperature fields from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument onboard NASA's Terra and Aqua satellites, over Harris County, Texas, USA.