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Proposed methodology for site-specific soil moisture obtainment utilizing coarse satellite-based data

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Abstract

Soil moisture is an important prognostic variable within a soil and climate system. Soil moisture is often used in the analysis of soil and crop health, determining the probability of natural hazard occurrence, and of the overall climatology. However, obtaining soil moisture measurements that are comprehensive with respect to a study area is often a tedious and costly endeavor. Globally available satellite-based soil moisture retrievals yield a unique solution to this problem. Although globally available, these estimates are typically at too coarse a resolution for use in site-specific analyses. For this reason, this study presents a comparative analysis upon the efficacy of methods used to remotely obtain site-specific moisture estimates from these satellite-based moisture data sets. In the geoscience and remote-sensing communities, downscaling or assimilation methods are traditionally used to obtain desired site-specific moisture estimates. This study investigated Random Forest and Soil Evaporative Efficiency (SEE) downscaling methods as well as an Ensemble Kalman Filter (EnKF)-based assimilation method to obtain site-specific moisture data. This study also proposes a less intensive approach which was observed to effectively yield site-specific soil moisture estimates from satellite-based moisture datasets. The proposed approach developed a multivariate regression analysis which characterized relationships between site-specific soil texture data and SMAP L4_SM root zone soil moisture correction factors. This approach was conducted over various in-situ sites across the Commonwealth of Kentucky to yield site-specific L4_SM soil moisture estimates. These sites served as control sites, whereas the developed regression approach was able to be validated. Through qualitative and quantitative analyses, it was found that the EnKF and proposed multivariate regression approaches performed strongly when compared to site-specific in-situ measurements. These analyses accounted for both the accuracy of the site-specific products as compared to in-situ data and the efforts required to complete the approach. The study presented herein shows that the proposed multivariate regression approach is far less intensive, yet still yields site-specific moisture estimates comparable to that of downscaling or assimilated approaches.

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Data availability

The soil texture data, in-situ moisture data, and data for each associated site-specific obtainment approach used in this article can be found on UKnowledge repository, the open access institutional repository hosted by the University of Kentucky, at https://doi.org/10.13023/wzxy-w420.

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Acknowledgements

We acknowledge Dr. Doug Baldwin from SCS Global Services for his invaluable guidance in Ensemble Kalman Filtering (EnKF). This guidance provided pathways through which EnKF assimilation was effectively conducted through this study.

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The authors have not disclosed any funding.

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  1. Department of Civil Engineering, University of Kentucky, 161 Raymond Bldg, Lexington, KY, 40506, USA

    Daniel M. Francis & L. Sebastian Bryson

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  1. Daniel M. Francis
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  2. L. Sebastian Bryson
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DMF prepared the original draft manuscript. LSB reviewed and edited the draft manuscript.

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Correspondence to L. Sebastian Bryson.

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Francis, D.M., Bryson, L.S. Proposed methodology for site-specific soil moisture obtainment utilizing coarse satellite-based data. Environ Earth Sci 82, 377 (2023). https://doi.org/10.1007/s12665-023-11057-0

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