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Abstract The Consistent Artificial Intelligence (AI)-based Soil Moisture (CASM) dataset is a global, consistent, and long-term, remote sensing soil moisture (SM) dataset created using machine learning. It is based on the NASA Soil Moisture Active Passive (SMAP) satellite mission SM data and is aimed at extrapolating SMAP-like quality SM back in time using previous satellite microwave platforms. CASM represents SM in the top soil layer, and it is defined on a global 25 km EASE-2 grid and for 2002–2020 with a 3-day temporal resolution. The seasonal cycle is removed for the neural network training to ensure its skill is targeted at predicting SM extremes. CASM comparison to 367 globalin-situSM monitoring sites shows a SMAP-like median correlation of 0.66. Additionally, the SM product uncertainty was assessed, and both aleatoric and epistemic uncertainties were estimated and included in the dataset. CASM dataset can be used to study a wide range of hydrological, carbon cycle, and energy processes since only a consistent long-term dataset allows assessing changes in water availability and water stress.
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This content will become publicly available on February 1, 2026
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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- Award ID(s):
- 2312319
- PAR ID:
- 10614770
- Publisher / Repository:
- Hydrology
- Date Published:
- Journal Name:
- Hydrology
- Volume:
- 12
- Issue:
- 2
- ISSN:
- 2306-5338
- Page Range / eLocation ID:
- 34
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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