Statistical Modeling of Soil Moisture, Integrating Satellite Remote-Sensing (SAR) and Ground-Based Data
Open accessAbstract
We present a flexible, integrated statistical-based modeling approach to improve the robustness of soil moisture data predictions. We apply this approach in exploring the consequence of different choices of leading predictors and covariates. Competing models, predictors, covariates and changing spatial correlation are often ignored in empirical analyses and validation studies. An optimal choice of model and predictors may, however, provide a more consistent and reliable explanation of the high environmental variability and stochasticity of soil moisture observational data. We integrate active polarimetric satellite remote-sensing data (RADARSAT-2, C-band) with ground-based in-situ data across an agricultural monitoring site in Canada. We apply a grouped step-wise algorithm to iteratively select best-performing predictors of soil moisture. Integrated modeling approaches may better account for observed uncertainty and be tuned to different applications that vary in scale and scope, while also providing greater insights into spatial scaling (upscaling and downscaling) of soil moisture variability from the field- to regional scale. We discuss several methodological extensions and data requirements to enable further statistical modeling and validation for improved agricultural decision-support.
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- Authors
- Hosseini, R., Newlands, N., Dean, C., & Takemura, A.
- Affiliations
- IBM (United States) (US)
- Agriculture and Agri-Food Canada (CA)
- Western University (CA)
- Graduate School of Information Science and Technology, The University of Tokyo, Bunkyo, Tokyo 113-8656, Japan
- Corresponding author
- Newlands, Nathaniel
- Venue
- Remote Sensing
- Publisher
- MDPI AG
- Published
- 2015-03-10
- Volume
- 7
- Issue
- 3
- Pages
- 2752-2780
- ISSN
- 2072-4292 (ISSN-L)
- OpenAlex
- W2007231592
- Cited by
- 31
- References
- 52
- Citations by year
- 2026: 3 · 2025: 4 · 2024: 1 · 2023: 6 · 2022: 2 · 2021: 2 · 2020: 6 · 2019: 1 · 2018: 4 · 2016: 2
- FWCI
- 1.44
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Remote Sensing (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Natural Sciences and Engineering Research Council of Canada
- Agriculture and Agri-Food Canada
- Topics
- Physical Sciences › Environmental Science › Environmental Engineering › Soil Moisture and Remote Sensing
- Physical Sciences › Environmental Science › Environmental Engineering › Soil Geostatistics and Mapping
- Physical Sciences › Engineering › Aerospace Engineering › Synthetic Aperture Radar (SAR) Applications and Techniques
- Keywords
- soil moisture prediction, polarimetric SAR, RADARSAT-2, C-band radar, predictor selection, spatial correlation, upscaling and downscaling, soil moisture variability, agricultural monitoring, agricultural decision support
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