Monthly and annual evapotranspiration maps in Berlin (Germany)

  • Stenka Valentinova Vulova (Creator)
  • Alby Duarte Rocha (Creator)
  • Fred Meier (Creator)
  • Hamideh Nouri (Creator)
  • Christian Schulz (Creator)
  • Chris Soulsby (Creator)
  • Doerthe Tetzlaff (Creator)
  • Birgit Kleinschmit (Creator)



    Monthly and annual evapotranspiration (ET) maps of Berlin, Germany at a 10-m resolution are provided. This dataset is related to the manuscript "City-wide, high-resolution mapping of evapotranspiration to guide climate-resilient planning" (under review). The monthly and annual ET sums are provided as rasters (.tif files). The monthly ET sums are given in the files named "ETmonthly_2019_(month).tif" The annual ET sum for 2019 is named "ETannual_2019.tif". The coordinate reference system (CRS) is "+proj=longlat +datum=WGS84 +no_defs." For access to daily ET maps in 2019, please contact Stenka Vulova ( The abstract of the manuscript is given for background information on the dataset: "The impacts of global change, including extreme heat and water scarcity, are threatening an ever-growing urban world population. Evapotranspiration (ET) mitigates the urban heat island, reducing the effect of heat waves. It can also be used as a proxy for vegetation water use, making it a crucial tool to plan resilient green cities. To optimize the trade-off between urban greening and water security, reliable and up-to-date maps of ET for cities are urgently needed. Despite its importance, few studies have mapped urban ET accurately for an entire city in high spatial and temporal resolution. We mapped the ET of Berlin, Germany in high spatial (10-m) and temporal (hourly) resolution for the year of 2019. A novel machine learning (ML) approach combining Sentinel-2 time series, open geodata, and flux footprint modeling was applied. Two eddy flux towers with contrasting surrounding land cover provided the training and testing data. Flux footprint modeling allowed us to incorporate comprehensive land cover types in training the ML models. Open remote sensing and geodata used as model inputs included Normalized Difference Vegetation Index (NDVI) from Sentinel-2, building height, impervious surface fraction, vegetation fraction, and vegetation height. NDVI was used to indicate vegetation phenology and health, as plant transpiration contributes to the majority of terrestrial ET. Hourly reference ET (RET) was calculated and used as input to capture the temporal dynamics of the meteorological conditions. Predictions were carried out using Random Forest (RF) regression. Weighted averages extracted from hourly ET maps using flux footprints were compared to measured ET from the two flux towers. Validation showed that the approach is reliable for mapping urban ET, with a mean R2 of 0.76 and 0.56 and a mean RMSE of 0.0289 mm and 0.0171 mm at the more vegetated site and the city-center site, respectively. Lastly, the variation of ET between Local Climate Zones (LCZs) was analyzed to support urban planning. This study demonstrated the capacity to map urban ET at an unprecedented high spatial and temporal resolution with a novel methodology, which can be used to support the sustainable management of green infrastructure and water resources in an urbanizing world facing climate change."
    Date made available2023

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