# Application of Soil Moisture Active Passive (SMAP) Satellite Data in Seismic Response Assessment
###### 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 \(\Delta\)_SMAP_ 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. S 202214375 10.3390 10.3390 10.390 10.  
required and immediate estimation of soil moisture before and after a seismic event. Within this context, well-calibrated remote sensing methods can provide effective alternative solutions to understand and evaluate the interaction between soil moisture and earthquake response at global scales. Remote sensing has been used in recent decades to study geohazards such as earthquakes, volcanic eruptions, floods, and landslides [24; 25; 26; 27; 28; 29]. Natural hazards assessments have benefited from remote sensing data for post-event surface damage detection [30; 31; 32; 33], geodetic observations of volcanoes using GPS and InSAR [34], passive microwave measurements of precipitation that are applicable to improve flood forecasting [35; 36], and remotely sensed soil moisture products used for analyzing landslide events [24; 37; 38; 39]. For earthquake engineering, remote sensing techniques have been utilized to appraise different aspects of an event such as liquefaction, landslides, ground displacement, and infrastructure damage assessment [40; 41; 42; 43]. Optical imagery has been widely used to identify ground failures [31; 42] as well as structural damage [44; 45]. However, optical imagery is limited by atmospheric conditions and may not have the temporal resolution and availability for pre- and post-earthquake data. Synthetic aperture radar (SAR) has also provided a tool to measure ground movements [46], detect surface changes [40], and identify structural damage [47] by measuring the reflections from its transmitted signals as well as advanced data-processing techniques [42]. Table 1 presents a summary of the satellite data used to develop remote sensing methods for the assessment of earthquake events, and their applications and challenges. Due to the limited availability of in situ soil saturation measurements at large spatial scales, which are essential for a liquefaction or landslide probability assessment, parameters such as water table depth, precipitation measurements, and topographic conditions have been used as proxies for soil saturation to model and investigate geotechnical engineering events such as landslide and liquefaction [12; 56].