Location-and Time-Specific Hydrological Simulations with Multi-Resolution Remote Sensing Data in Urban Areas

A major challenge in hydrologic modeling remains the mapping of vegetation dynamics in an urban landscape. The impact of vegetation on interception storage varies over time and needs to be quantified in order to enable proper management of water resources in urban areas. However, the heterogeneity and complexity of the urban landscape makes it challenging to monitor urban vegetation. A more detailed spatial and temporal scale is needed. To characterize surface cover at a high spatial resolution, a hyperspectral APEX image (2 m) is used, while a time series of Proba-V images (daily, 100 m) allows a detailed characterization of the seasonal variation of urban greenness. For this study, we use and validate the leaf area index (LAI) maps derived from APEX and Proba-V data for a selected pixel in the Watermaelbeek catchment in Brussels (Belgium). The ground-truthing of the Proba-V pixels includes a detailed mapping of land cover characteristics and more specifically vegetation cover throughout the seasons. LAI values calculated based on the APEX image agree with the LAI values measured from the ground (n = 106, R 2 = 0.68). Further, the aggregated APEX pixels correlate with the Proba-V pixels (R 2 = 0.79), and the Proba-V data can be used to monitor vegetation dynamics. As the seasonal LAI measurements correspond with the Proba-V dynamics, we conclude that Proba-V images allow the characterization of vegetation dynamics at a high spatial resolution in heterogeneous areas. We create a time series of LAI maps at a high resolution (2 m), which allows a location- and time-specific simulation of interception storage and thus contributes to managing water resources in urban areas.