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Remote Sens. 2015, 7(3), 2752-2780; doi:10.3390/rs70302752

Statistical Modeling of Soil Moisture, Integrating Satellite Remote-Sensing (SAR) and Ground-Based Data

1
IBM Research Collaboratory, 9 Changi Business Park Central 1, Singapore 486048, Singapore
2
Science and Technology, Agriculture and Agri-Food Canada, Lethbridge Research Centre, 5403 1st Avenue South, P.O. Box 3000, Lethbridge, AB T1J 4B1, Canada
3
Department of Statistics and Actuarial Sciences, University of Western Ontario, 262 Western Science Centre, 1151 Richmond Street, London, ON N6A 5B7, Canada
4
Graduate School of Information Science and Technology, The University of Tokyo, Bunkyo, Tokyo 113-8656, Japan
*
Author to whom correspondence should be addressed.
Academic Editors: Nicolas Baghdadi and Prasad S. Thenkabail
Received: 9 September 2014 / Accepted: 27 February 2015 / Published: 10 March 2015
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Abstract

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. View Full-Text
Keywords: agriculture; cross-validation, multi-scale; prediction, RADARSAT; soil moisture; uncertainty agriculture; cross-validation, multi-scale; prediction, RADARSAT; soil moisture; uncertainty
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Hosseini, R.; Newlands, N.K.; Dean, C.B.; Takemura, A. Statistical Modeling of Soil Moisture, Integrating Satellite Remote-Sensing (SAR) and Ground-Based Data. Remote Sens. 2015, 7, 2752-2780.

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