Evaluating Land Surface Moisture Conditions Before and After Flash-Flood Storm from Optical and Thermal Data: Models Comparison and Validation

Soil moisture (SM) is an important physical parameter for several hydrological and agricultural applications, weather and climate predictions, as well as early warning of natural hazards such as flood and drought. It varies significantly in space and time, and it is a challenge to map its variations accurately at the regional and local scales. The aim of the present study is a comparison and validation of four models such as NDWI, SIWSI, TOTRAM and OPTRAM for SM mapping before and after flash-flood storm in arid land. The used methodology exploits two pairs of images acquired with Landsat-OLI/TIRS sensors over the study area before and after flood. The first pair was acquired two weeks before the flash flood, and the second one was collected eight days after the flood. These images were radiometrically and atmospherically corrected, as well topographically rectified using SRTM DEM. For validation purposes, before and after storm, two independent SM products were used. Both, they were derived from data acquired simultaneously with those recorded by Landsat-OLI/TIRS. The first is derived from SMOS data, and the second is compiled from rainfall data (SM-RFE) delivered by NOAA climate prediction center RFE (Rainfall Estimator) for Africa. The results revealed that TOTRAM and NDWI models converge towards the same conclusions describing accurately the drastic change of SM before and after flash-flood highlighting the impact of inundation and the mud accumulation over the study area. Their results validation against those of SMOS and SM-RFE products shows a significant agreement (R2 of 0.95). However, in addition to its simplicity and to its easy implementation, the NDWI show a great potential for SM content estimation independently of LST using only optical data such as Landsat-OLI or Sentinel-MSI.