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

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 (& sigma;& DEG;), 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 & sigma;& DEG; 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.

Automated summary

Monitoring Sustainable Drainage Systems (SuDS) often requires intrusive methods and onsite personnel. Remote sensing in SuDS, especially vegetation-based techniques, still requires further development. This study proposes an exploratory method combining Synthetic Aperture Radar (SAR) images and onsite measurements to develop performance models. Linear regression models were used to compute soil moisture, with variables such as backscatter coefficient (& sigma;& DEG;), temperature, normalized difference vegetation index (NDVI), and topographic wetness index (TWI). The models showed medium to high predictive capacity, ranging from 0.53 to 0.66, with temperature being the most influential variable. This research opens the path for future use of remote sensing tools in vegetation-based SuDS monitoring, highlighting the need for further research.