https://dx.doi.org/10.3390/rs14174375">
 

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Creative Commons Attribution 3.0 License
This work is licensed under a Creative Commons Attribution 3.0 License.

Abstract

The proven relationship between soil moisture and seismic ground response highlights the need for a tool to track the Earth’s surface soil moisture before and after seismic events. This paper introduces the application of Soil Moisture Active Passive (SMAP) satellite data for global soil moisture measurement during earthquakes and consequent events. An approach is presented to study areas that experienced high level of increase in soil moisture during eleven earthquakes. Two ancillary datasets, Global Precipitation Measurement (GPM) and Global Land Data Assimilation (GLDAS), were used to isolate areas that had an earthquake-induced increase in soil moisture from those that were due to hydrological processes. SMAP-based soil moisture changes were synthesized with seismic records developed by the United States Geological Survey (USGS), mapped ground failures in reconnaissance reports, and surface changes marked by Synthetic Aperture Radar (SAR)-based damage proxy maps. In the majority of the target earthquakes, including Croatia 2020, Greece 2020, Indonesia 2018, Taiwan 2016, Ecuador 2016, and Nepal 2015, a relationship between the SMAP soil moisture estimates and seismic events was evident. For these events, the earthquake-induced soil moisture response occurred in liquefaction-prone seismic zones. The New Zealand 2016 event was the only study region for which there was a clear inconsistency between ΔSMSMAP and the seismic records. The promising relationship between soil moisture changes and ground deformations indicates that SMAP would be a useful data resource for geotechnical earthquake engineering applications and reconnaissance efforts.

Department

Open Access Fund; Civil Engineering

Publication Date

1-1-2022

Journal Title

Remote Sensing

Publisher

Multidisciplinary Digital Publishing Institute

Digital Object Identifier (DOI)

https://dx.doi.org/10.3390/rs14174375

Document Type

Article

Comments

This is an Open Access article published by Multidisciplinary Digital Publishing Institute in Remote Sensing, available online: https://dx.doi.org/10.3390/rs14174375

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