Automated Surface Runoff Estimation with the Spectral Unmixing of Remotely Sensed Multispectral Imagery

This work presents a methodology for the hydrological characterization of natural and urban landscapes, focusing on accurate estimations of infiltration capacity and runoff characteristics. By combining existing methods from the literature, we created a systemic process that integrates satellite-based vegetation maps, topography, and soil permeability data. This process generates a detailed vegetation classification and slope-corrected composite curve number (CN_cα) map using information at the subpixel level, which is crucial for estimating excess runoff during intense precipitation events. The algorithm designed with this methodology is automated and utilizes freely accessible multispectral imagery. Leveraging the vegetation–impervious–soil (V-I-S) model, it is assumed that land cover comprises V-I-S components at each pixel. Automated Music and spectral Separability-based Endmember Selection is employed on a generic spectral library to obtain the most relevant V-I-S endmember spectra for a particular image, which is then employed in multiple endmember spectral mixture analysis to obtain V-I-S fraction maps. The derived fractions are utilized in combination with the Normalized Difference Vegetation Index and the Modified Normalized Difference Water Index to adapt the CN_cα map to different seasons and climatic conditions. The methodology was applied to Esch-sur-Alzette, Luxembourg, over a four-year period to validate the methodology and quantify the increase in the impervious surface area in the commune and the relationship with the runoff dynamics. This approach provides valuable insights into infiltration and runoff dynamics across diverse temporal and geographic ranges.