Downscaling Satellite Soil Moisture Using a Modular Spatial Inference Framework

Abstract

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 <KKNN> and Random Forest <RF>). 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.

Spatial Downscaling of Satellite-Based Soil Moisture Products Using Machine Learning Techniques: A Review

“…The advantage of using the geomorphometry combined with ML in downscaling is the ability to compare the spatiotemporal relationships of the downscaled SM with vegetation and land cover data without using them as ancillary data. Llamas et al [121] also postulated a terrain-parameters-based downscaling model using kernel-weighted KNN and RF. The target downscaling resolution for the ESA-CCI data was 1 km.…”

“…The target downscaling resolution for the ESA-CCI data was 1 km. Llamas et al [121] suggested terrain parameters as the most suitable predictors in the regional-scale SM subjected to the influence of seasonality. For instance, geomorphometry can highly influence the SM in autumn and spring.…”

Microwave Remote Sensing of Soil Moisture

“…They found that SEE was an optimal auxiliary variable for the scaling and mapping of soil moisture, and the combination of multiple auxiliary variables (LST, NDVI, and SEE) was recommended for improving the scaling and mapping accuracy of soil moisture. Llamas et al [19] proposed a modular spatial inference framework, which was the foundation of a cyberinfrastructure tool named SOil MOisture SPatial Inference Engine (SOMOSPIE), to downscale ESA CCI soil moisture products to 1 km using terrain parameters and examined the skill of two modeling methods, i.e., Kernel-Weighted K-Nearest Neighbor (KKNN) and Random Forest (RF). The results indicated that the SO-MOSPIE framework provided a feasible approach to downscaling satellite soil moisture data, and RF performed better in the cross-validation compared to the reference ESA CCI data, but as part of independent validation, KKNN had a slightly higher consistency with ground soil moisture observations.…”