Mapping Soil Moisture with Statistical and spatial analysis Methods


Wednesday, May 22, 2019, 3:30pm to 4:30pm


CGIS Knafel K252, 1737 Cambridge St., Cambridge, MA 02138

by: Yaping Xu, Louisiana State University


Soil moisture is an important factor for accurate prediction of agricultural productivity and rainfall runoff with hydrological models. Remote sensing satellites such as Soil Moisture Active Passive (SMAP) offer synoptic views of soil moisture distribution at a regional-to-global scale. To use the soil moisture product from these satellites, however, requires a downscaling of the data from a usually large instantaneous field of view (i.e. 36 km) to the watershed analysis scales ranging from 30 m to 1 km. The research contains two sections: the first section invented a multi-level soil moisture data assimilation and processing methodology framework based on spatial information theories. We designed a downscaling method that used random forests and regression kriging to model the soil moisture trend at 1 km and the unpredicted variability at local scales to produce downscaled soil moisture. The result shows that the downscaling approach was able to achieve better accuracy than the current statistical downscaling methods. In the second section, we applied SMAP soil moisture to develop a soil moisture drought index called Standardized Soil Moisture Index (SSI) that can provide agriculture drought information for the agricultural and forestry sectors. The present work has its novelty in using the spatial statistic method to reconcile the scale difference from satellite data and ground observations, and therefore propose new theories and solutions for dealing with the modifiable areal unit problem (MAUP) that incurred in soil moisture mapping from satellite and ground stations.

Speaker bio: 

Yaping Xu received his Ph.D. in April 2019 from Louisiana State University. His major is geography, with a concentration on GIScience and Remote Sensing.

His research interests include the application of geostatistics, machine learning, and remote sensing in soil moisture mapping and biological imaging, coupled human-environment system and health, as well as spatio-temporal representation of the geospatial data.

He participated in the Southeast Agriculture project sponsored by NASA and served as the team lead in 2016, and also participated in a project funded by Louisiana Transportation Research Center that measures soil moisture based on infrared and thermal sensors on UAV.

He has won several awards including the “Innovative Application of ESRI GIS Technology Poster Competition at the 2019 AAG Meeting Winner Award”, “The 35th Annual Louisiana Remote Sensing & GIS Workshop Student Poster Competition First Prize Award”, “Louisiana Sea Grant Coastal Connection Competition Thesis Presentation 2nd Place”, “Louisiana State University 2017-2018 Dissertation Year Fellowship”, “NASA Southeast US Agriculture Project Contribution of Project Lead (Nominated)” and so on. In addition, he is a co-founder and president of the GIScience Club, an official student association at Louisiana State University.