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ORIGINAL RESEARCH article

Front. Remote Sens.
Sec. Atmospheric Remote Sensing
Volume 5 - 2024 | doi: 10.3389/frsen.2024.1474088

A Novel Validation of Satellite Soil Moisture Using SM2RAIN-Derived Rainfall Estimates

Provisionally accepted
Son K Do Son K Do 1*Thanh-Nhan-Duc Tran Thanh-Nhan-Duc Tran 1Manh-Hung Le Manh-Hung Le 2John Bolten John Bolten 2Venkataraman Lakshmi Venkataraman Lakshmi 1
  • 1 University of Virginia, Charlottesville, United States
  • 2 Laboratory of Hydrological Sciences, Goddard Space Flight Center, Greenbelt, Maryland, United States

The final, formatted version of the article will be published soon.

    Despite the importance of soil moisture (SM) in various applications and the need to validate satellite SM products, the current in situ SM network is still inadequate, even for developed country such as the United States. Recently, SM2RAIN (Soil Moisture to Rain) algorithm has prominently emerged as a bottom-up approach to derive rainfall data from SM. In this study, we evaluated whether SM2RAIN algorithm and rain gauges, which are more abundant and readily available than in situ SM, can be used to validate satellite-based SMAP SM estimates. Since errors in SMAP SM propagate to SMAP-derived rainfall, the skills of SM2RAIN might be able to provide insights on the accuracy of SMAP SM observations. While the correlation between SM2RAIN skills and SMAP SM skills was found to be statistically significant, the strength of the correlation varied among different climate zones and annual rainfall classes. Specifically, weaker correlations were observed in arid and lower rainfall regions (median R value of 0.12), while stronger correlations were found in temperate and higher rainfall regions (median R value of 0.54). In term of over/under-estimation tendencies, 56% of the stations had the same tendencies (SM2RAIN skills and satellite SM skills both have positive or negative PBIAS value).

    Keywords: soil moisture, precipitation, SMAP, SM2RAIN algorithm, United States

    Received: 01 Aug 2024; Accepted: 08 Nov 2024.

    Copyright: © 2024 Do, Tran, Le, Bolten and Lakshmi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Son K Do, University of Virginia, Charlottesville, United States

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.