SOMOSPIE: A Modular SOil MOisture SPatial Inference Engine Based on Data-Driven Decisions

Abstract

The current availability of soil moisture data over large areas comes from satellite remote sensing technologies (i.e., radar-based systems), but these data have coarse resolution and often exhibit large spatial information gaps. Where data are too coarse or sparse for a given need (e.g., precision agriculture), one can leverage machine-learning techniques coupled with other sources of environmental information (e.g., topography) to generate gap-free information and at a finer spatial resolution (i.e., increased granularity). To this end, we develop a spatial inference engine consisting of modular stages for processing spatial environmental data, generating predictions with machine-learning techniques, and analyzing these predictions. We demonstrate the functionality of this approach and the effects of data processing choices via multiple prediction maps over a United States ecological region with a highly diverse soil moisture profile (i.e., the Middle Atlantic Coastal Plains). The relevance of our work derives from a pressing need to improve the spatial representation of soil moisture for applications in environmental sciences (e.g., ecological niche modeling, carbon monitoring systems, and other Earth system models) and precision agriculture (e.g., optimizing irrigation practices and other land management decisions).

Downscaling Satellite Soil Moisture Using a Modular Spatial Inference Framework

“…This variant is based on the definition of kernel functions (i.e., Triangular, Epanechnikov, Gaussian, Optimal) that serve to find the number of neighbors (k) to be used in the prediction. The number of neighbors and the optimal kernel function are automatically selected through 10-fold cross validation [44,45].…”

“…The KKNN code used in the SOMOSPIE framework has been described previously [45] and has been successfully used to downscale satellite-derived soil moisture at different spatial scales [44]. The code is based on the 'kknn' package [60] developed for the Rstatistical platform [56].…”

“…Random Forest (RF) in the SOMOSPIE framework has been described previously [45] and is based on the 'quantregForest' package [61] developed for the R-statistical platform [56]. It is based on an ensemble of decision trees through a "bootstrap aggregation" process (bagging), which is a method to generate multiple versions of a predictor and then uses these versions to generate an aggregated predictor that depends on the values of a random vector independently sampled and weighed [62,63].…”

“…Techniques such as RF do not K-nearest neighbors (KKNN) in its traditional form is a regression technique that builds many simple models from local data [58], and is based upon decision rules that classify an unsampled point, based on the values of the nearest set of previously classified points or reference values in the sampling space [59]. This method assumes a different level of influence in the prediction space, where the nearest k-points to the target location are the ones with the most relevant influence, while the influence in the construction of the prediction model decreases with distance [45]. To assign distance-related relevance to predict soil moisture, a weighted mean of the k-nearest soil moisture ratios is calculated.…”

“…Topography has been explored previously as a meaningful environmental variable for downscaling soil moisture at the catchment scale [41][42][43] and across the United States [44]. We used a modular spatial inference framework, which is the foundation of a cyberinfrastructure tool named SOil Moisture SPatial Inference Engine (SOMOSPIE) [45][46][47]. We tested the performance of two modeling methods coupled with geoinformation from terrain parameters to downscale satellitederived soil moisture.…”