Validation of seasonal time series of remote sensing derived LAI for hydrological modelling

In urban environments, due to climate change urban heat waves are predicted to occur more frequently. Urban vegetation and the linked evapotranspiration rate can play a mitigating role. However, a major challenge in urban hydrological modelling remains the mapping of vegetation dynamics and its role in hydrological processes in particular interception storage and evapotranspiration. Conventional mapping of vegetation usually implies intensive labor and time consuming field work. We explore the potential of different remote sensing sensors (Proba-V, Landsat, Sentinel2, Apex) to characterize the urban vegetation dynamics for hydrological modelling. The here proposed remote sensing sensors show differences in the spectral and spatial resolutions as well as in their revisit time. However, in the urban environment we need a high spatial and spectral resolution to distinguish the urban landcover and a frequent revisit time to capture seasonal vegetation dynamics. Therefore, we propose a combination of different remote sensing sensors to derive leaf area index (LAI) timeseries in the urban environment. To improve the consistency in time series generated from different remote sensing sources a harmonization of the multi-sensor time series is proposed and validated with a multi-resolution validation approach using ground-truthing LAI (BELHARMONY project). The LAI timeseries, derived from the different remote sensing sensors, are then introduced into the hydrological modelling framework for a location- and time- specific assessment of the interception storage and evapotranspiration component. The effect of the sensor differences to the LAI timeseries on the hydrological response is analyzed.