Dependence of Soil Moisture and Strength on Topography and Vegetation Varies Within a SMAP Grid Cell

Off-road vehicle mobility assessments rely on fine-resolution (~10 m) estimates of soil moisture and strength across the region of interest. Such estimates are often produced by downscaling soil moisture from a microwave satellite like SMAP, then using the soil moisture in a soil strength model. Soil moisture downscaling methods typically assume consistent relationships between the moisture and topographic, vegetation, and soil composition characteristics within the microwave satellite grid cells. The objective of this study is to examine whether soil moisture and strength exhibit heterogenous dependencies on topography, vegetation, and soil composition characteristics within a SMAP grid cell. Soil moisture and strength data were collected at four geographically separated regions within a 9 km SMAP grid cell in the Front Range foothills of northern Colorado. Laboratory methods and pedotransfer functions were used to characterize soil attributes, and remote sensing data were used to determine topographic and vegetation attributes. Pearson correlation analyses were used to quantify the direction, strength, and significance of the relationships of both soil moisture and strength with topography, vegetation, and soil composition. Contrary to the common assumption, spatial variations in the slope and correlation of the relationships are observed for both soil moisture and strength. The findings indicate that improved predictions of soil moisture and soil strength may be achievable by soil moisture downscaling procedures that use spatially variable parameters across the downscaling extent.

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Publication Details

Journal
Hydrology
Published
2025-02-15
DOI
https://doi.org/10.3390/hydrology12020034
Citations
1
Primary Topic
Soil Moisture and Remote Sensing
Type
article
Field-Weighted Citation Impact
0.80

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Dependence of Soil Moisture and Strength on Topography and Vegetation Varies Within a SMAP Grid Cell

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Dependence of Soil Moisture and Strength on Topography and Vegetation Varies Within a SMAP Grid Cell

Peter J. Grazaitis, Joseph R. Bindner, Holly Proulx, Timothy R. Green, Joseph Scalia, Jeffrey D. Niemann, Kevin Wickham
article en
1 citations

Abstract

Off-road vehicle mobility assessments rely on fine-resolution (~10 m) estimates of soil moisture and strength across the region of interest. Such estimates are often produced by downscaling soil moisture from a microwave satellite like SMAP, then using the soil moisture in a soil strength model. Soil moisture downscaling methods typically assume consistent relationships between the moisture and topographic, vegetation, and soil composition characteristics within the microwave satellite grid cells. The objective of this study is to examine whether soil moisture and strength exhibit heterogenous dependencies on topography, vegetation, and soil composition characteristics within a SMAP grid cell. Soil moisture and strength data were collected at four geographically separated regions within a 9 km SMAP grid cell in the Front Range foothills of northern Colorado. Laboratory methods and pedotransfer functions were used to characterize soil attributes, and remote sensing data were used to determine topographic and vegetation attributes. Pearson correlation analyses were used to quantify the direction, strength, and significance of the relationships of both soil moisture and strength with topography, vegetation, and soil composition. Contrary to the common assumption, spatial variations in the slope and correlation of the relationships are observed for both soil moisture and strength. The findings indicate that improved predictions of soil moisture and soil strength may be achievable by soil moisture downscaling procedures that use spatially variable parameters across the downscaling extent.

HydrologyVol. 12(2)
United States Army Combat Capabilities Development Command (US), United States Army (US), Agricultural Research Service - Plains Area (US), Colorado State University (US)
Army Research Laboratory
Openalex Percentile: Top 32%
Soil Moisture and Remote Sensing
0.80
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