Soil moisture downscaling using space-time geospatial statsitics and localized methods

Authors: Lei Wang*, Louisiana State University, Yaping Xu, Louisiana State University
Topics: Spatial Analysis & Modeling, Water Resources and Hydrology, Remote Sensing
Keywords: Soil moisture, satellite, geostatistics
Session Type: Paper
Day: 4/5/2019
Start / End Time: 3:05 PM / 4:45 PM
Room: Jackson, Marriott, Mezzanine Level
Presentation File: No File Uploaded

Downscaling soil moisture data is important yet challenging work given the large discrepancy between the scales of the field observation and the satellite. The spatial continuity of soil moisture is broken by the complex landscape conditions. We propose the use of multiple spatial analysis methods including kriging and localized sampling to solve the multi-scale problem exists in the data and models. We argue that the temporal continuity of soil moisture shall play more important roles than spatial autocorrelation because it is lower in the chance to break if there were no major precipitation or irrigation events. Therefore, an integration of geostatistics and the autoregressive temporal Kalman Filter model was used to make space-time predictions. The results were compared with traditional statistical downscaling methods and showed the superiority of our approach in soil moisture downscaling.

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