Data analytics for modeling soil moisture patterns across united states ecoclimatic domains

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).

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“…Fig. 2: Illustration of how satellites collect raster data across the surface of the Earth [10], [18].…”

Section: Our Datasetsmentioning

confidence: 99%

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

Rorabaugh¹,

Guevara

Llamas

et al. 2019

2019 15th International Conference on eScience (eScience)

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“…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.…”

Section: Introductionmentioning

confidence: 99%

Downscaling Satellite Soil Moisture Using a Modular Spatial Inference Framework

et al. 2022

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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.

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Data analytics for modeling soil moisture patterns across united states ecoclimatic domains

Cited by 2 publications

References 3 publications

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

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

Downscaling Satellite Soil Moisture Using a Modular Spatial Inference Framework

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