TY - JOUR
T1 - City-wide, high-resolution mapping of evapotranspiration to guide climate-resilient planning
AU - Vulova, Stenka
AU - Rocha, Alby Duarte
AU - Meier, Fred
AU - Nouri, Hamideh
AU - Schulz, Christian
AU - Soulsby, Chris
AU - Tetzlaff, Doerthe
AU - Kleinschmit, Birgit
N1 - Funding Information:
This work was supported by the German Research Foundation (DFG) within the Research Training Group ‘Urban Water Interfaces’ ( GRK 2032-2 ). Fred Meier acknowledges funding for instrumentation of the Urban Climate Observatory (UCO) Berlin from DFG grants SCHE 750/8 and SCHE 750/9 within Research Unit 1736 “Urban Climate and Heat Stress in Mid Latitude Cities in View of Climate Change (UCaHS)” and the research program “Urban Climate Under Change ([UC]2)”, funded by the German Ministry of Research and Education ( FKZ 01LP1602A ). The authors are grateful to Ann-Kathrin Holtgrave for generously allowing models to run on her computer. The authors also thank Harro Jongen for implementing the Kljun footprint model for comparison. The authors thank the DWD, the Chair of Climatology at the Technische Universität Berlin, the European Commission, Google, and the Berlin Senate Department for Urban Development and Housing for providing data used in this paper. They also express their gratitude to the four anonymous reviewers for their constructive comments that enhanced the quality of the manuscript.
Funding Information:
This work was supported by the German Research Foundation (DFG) within the Research Training Group ‘Urban Water Interfaces’ (GRK 2032-2). Fred Meier acknowledges funding for instrumentation of the Urban Climate Observatory (UCO) Berlin from DFG grants SCHE 750/8 and SCHE 750/9 within Research Unit 1736 “Urban Climate and Heat Stress in Mid Latitude Cities in View of Climate Change (UCaHS)” and the research program “Urban Climate Under Change ([UC]2)”, funded by the German Ministry of Research and Education (FKZ 01LP1602A). The authors are grateful to Ann-Kathrin Holtgrave for generously allowing models to run on her computer. The authors also thank Harro Jongen for implementing the Kljun footprint model for comparison. The authors thank the DWD, the Chair of Climatology at the Technische Universität Berlin, the European Commission, Google, and the Berlin Senate Department for Urban Development and Housing for providing data used in this paper. They also express their gratitude to the four anonymous reviewers for their constructive comments that enhanced the quality of the manuscript.
Publisher Copyright:
© 2023 The Authors
PY - 2023/3/15
Y1 - 2023/3/15
N2 - 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.
AB - 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.
KW - Cooling cities
KW - High resolution
KW - Latent heat flux
KW - Local Climate Zones (LCZs)
KW - Nature-based solutions
KW - NDVI
KW - Phenology
KW - Satellite remote sensing
KW - Transpiration
KW - Urban heat island (UHI)
KW - Urban planning
KW - Water scarcity
UR - http://www.scopus.com/inward/record.url?scp=85149697119&partnerID=8YFLogxK
U2 - 10.1016/j.rse.2023.113487
DO - 10.1016/j.rse.2023.113487
M3 - Article
AN - SCOPUS:85149697119
VL - 287
JO - Remote Sensing of Environment
JF - Remote Sensing of Environment
SN - 0034-4257
M1 - 113487
ER -
