An exploratory methodology based on high resolution remote sensing techniques for soil moisture determination with prospective applications in vegetative SuDS

Abstract

Sustainable Drainage Systems (SuDS) monitoring is very often intrusive and need onsite personnel to be carried out. The application of remote sensing in SuDS still is an area for further development, especially in vegetation-based techniques, representing a gap in the field. This research proposes an exploratory method combining Synthetic Aperture Radar (SAR) images data and onsite measurements to develop models of performance. Linear regression models were obtained for the computing of the soil moisture using the following variables: backscatter coefficient (σ°), temperature, normalized difference vegetation index (NDVI) and topographic wetness index (TWI), reaching medium to high values for its predictive capacity, ranging from 0.53 and 0.66 using σ° and temperature. The most influential variable was found to be the temperature. This investigation opens the path for future research in the use of remote sensing tools in vegetation-based SuDS monitoring with homogeneous plant species, highlighting the need for further research.