Remote Sensing Using Satellite Derived Products to Assess Sinkhole Occurrence

Karst subsidence or localized cover collapse of subsurface cavities (sinkholes) is a severe hazard in the United States and can be found in all 50 states. Although occurrences of subsidence or sinkholes are challenging to predict, karst areas are more susceptible and conducive to the formation of cover collapse sinkholes. Seasonality and daily conditions in soil moisture, surface temperature, and precipitation collected from satellite-based sensors and remote sensing algorithms can be used to investigate incipient failure factors that initiate a sinkhole: in particular, the NASA Soil Moisture Active Passive (SMAP) Level 4 root zone soil moisture, the Integrated Multi-satellite Retrievals (IMERG) algorithm for the Global Precipitation Measurement (GPM) mission, and the Famine Land Data Assimilation System (FLDAS) Noah Land Surface Model. This study presents a holistic assessment of several known sinkhole occurrences in the United States using coarse low spatial resolution data. It was observed that comparing the spatial and temporal volumetric soil moisture from SMAP_L4 and FLDAS Noah with the precipitation intensity from GPM_IMERG data functioned well in detecting impending conditions at the investigated sites. This research aimed to develop a routine process flow analysis through SMAP L4_SM, FLDAS Noah, and GPM_IMERG data and investigate the normalized difference vegetation index (NDVI) to conduct sinkhole assessment.