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B0373
Title: Physically constrained spatiotemporal kriging of remotely sensed land surface temperature Authors:  Matthew Heaton - Brigham Young University (United States) [presenting]
Abstract: Satellite remote-sensing is often used to collect important atmospheric and geophysical data that provide insight into spatial and temporal climate variability over large regions of the earth, at high spatial resolutions. Common issues surrounding such data include missing information in space due to cloud cover at the time of a satellite passing and large blocks of time for which measurements are not available due to the infrequent passing of polar-orbiting satellites. While many methods are available to predict missing data in space and time, in the case of land surface temperature (LST) data, these approaches generally ignore the temporal pattern called the diurnal cycle which physically constrains temperatures to peak in the early afternoon and reach a minimum just prior to sunrise. In order to construct a complete, physically justifiable remotely sensed dataset, we parameterize the diurnal cycle into a functional form with unknown spatiotemporal parameters. Using multiresolution basis functions, we estimate these unknown parameters from sparse satellite observations to obtain associated physically constrained predictions of land surface temperature. The methodology is demonstrated using a remote sensing dataset of LST in Houston, Texas USA, collected by the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument, aboard NASAs polar-orbiting Aqua and Terra satellites.