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Title: Downscaling Satellite Soil Moisture Using a Modular Spatial Inference Framework
Soil moisture is an important parameter that regulates multiple ecosystem processes and provides important information for environmental management and policy decision-making. Spaceborne sensors provide soil moisture information over large areas, but information is commonly available at coarse resolution with spatial and temporal gaps. Here, we present a modular spatial inference framework to downscale satellite-derived soil moisture using terrain parameters and test the performance of two modeling methods (Kernel-Weighted K-Nearest Neighbor and Random Forest ). We generate monthly and weekly gap-free spatial predictions on soil moisture at 1 km using data from the European Space Agency Climate Change Initiative (ESA-CCI; version 6.1) over two regions in the conterminous United States. RF was the method that performed better in cross-validation when comparing with the reference ESA-CCI data, but KKNN showed a slightly higher agreement with ground-truth information as part of independent validation. We postulate that more heterogeneous landscapes (i.e., high topographic variation) may be more challenging for downscaling and predicting soil moisture; therefore, moisture networks should increase monitoring efforts across these complex landscapes. Future opportunities for development of modular cyberinfrastructure tools for downscaling satellite-derived soil moisture are discussed.  more » « less
Award ID(s):
2103836 2103845
PAR ID:
10382232
Author(s) / Creator(s):
; ; ; ;
Date Published:
Journal Name:
Remote Sensing
Volume:
14
Issue:
13
ISSN:
2072-4292
Page Range / eLocation ID:
3137
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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