@article{Chrysoulakis2018,

title = {Urban energy exchanges monitoring from space},

author = {Nektarios Chrysoulakis and Sue Grimmond and Christian Feigenwinter and Fredrik Lindberg and Jean-Philippe Gastellu-Etchegorry and Mattia Marconcini and Zina Mitraka and Stavros Stagakis and Ben Crawford and Frans Olofson and Lucas Landier and William Morrison and Eberhard Parlow },

editor = {nature.com},

url = {https://urbanfluxes.eu/wp-content/uploads/2018/07/UF_Overview_final.pdf},

doi = {10.1038/s41598-018-29873-x},

issn = {2045-2322},

year  = {2018},

date = {2018-07-31},

journal = { nature.com },

volume = {Scientific Reports volume},

number = {8},

pages = {11498},

abstract = {One important challenge facing the urbanization and global environmental change community is to understand the relation between urban form, energy use and carbon emissions. Missing from the current literature are scientific assessments that evaluate the impacts of different urban spatial units on energy fluxes; yet, this type of analysis is needed by urban planners, who recognize that local scale zoning affects energy consumption and local climate. Satellite-based estimation of urban energy fluxes at neighbourhood scale is still a challenge. Here we show the potential of the current satellite missions to retrieve urban energy budget fluxes, supported by meteorological observations and evaluated by direct flux measurements. We found an agreement within 5% between satellite and in-situ derived net all-wave radiation; and identified that wall facet fraction and urban materials type are the most important parameters for estimating heat storage of the urban canopy. The satellite approaches were found to underestimate measured turbulent heat fluxes, with sensible heat flux being most sensitive to surface temperature variation (−64.1, +69.3 W m−2 for ±2 K perturbation).  They also underestimate anthropogenic heat fluxes. However, reasonable spatial patterns are obtained for the latter allowing hot-spots to be identified, therefore supporting both urban planning and urban climate modelling.},

keywords = {anthropogenic heat emission, Anthropogenic Heat Flux, Concentration profiles, Copernicus Sentinels, Earth Observation, Eddy-covariance, Flux measurements, Ground-based thermography, Image classification, reducing, Sensor view modelling, Street canyon, Thermographic camera modelling, Understanding, Upwelling longwave radiation, urban, Urban Climate, Urban Energy Budget, urban heat fluxes, Urban meteorology},

pubstate = {published},

tppubtype = {article}

}

@article{Feigenwinter2018,

title = {Spatial Distribution of Sensible and Latent Heat Flux in the city of Basel (Switzerland)},

author = {Christian Feigenwinter and Roland Vogt and Eberhard Parlow and Fredrik Lindberg and Mattia Marconcini and Fabio Del Frate and Nektarios Chrysoulakis},

doi = {10.1109/JSTARS.2018.2807815},

issn = {2151-1535},

year  = {2018},

date = {2018-03-18},

journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},

abstract = {Urban surfaces are a complex mixture of different land covers and surface materials; the relative magnitudes of the surface energy balance components therefore vary widely across a city. Eddy covariance (EC) measurements provide the best estimates of turbulent heat fluxes but are restricted to the source area. Land surface modeling with earth observation (EO) data is beneficial for extrapolation of a larger area since citywide information is possible. Turbulent sensible and latent heat fluxes are calculated by a combination of micrometeorological approaches (the aerodynamic resistance method, ARM), EO data, and GIS techniques. Input data such as land cover fractions and surface

temperatures are derived from Landsat 8 OLI and TIRS, urban morphology was calculated from high-resolution digital building models and GIS data layers, and meteorological data were provided by flux tower measurements. Twenty-two Landsat scenes covering all seasons and different meteorological conditions were analyzed. Sensible heat fluxes were highest for industrial areas, railway stations, and areas with high building density, mainly corresponding to the pixels with highest surface-to-air  temperature differences.

The spatial distribution of latent heat flux is strongly related to the

saturation deficit of vapor and the (minimum) stomatal resistance

of vegetation types. Seasonal variations are highly dependent on

meteorological conditions, i.e., air temperature, water vapor saturation

deficit, and wind speed. Comparison of measured fluxes

with modeled fluxes in the weighted source area of the flux towers

is moderately accurate due to known drawbacks in the modeling

approach and uncertainties inherent to EC measurements, particularly

in urban areas.},

keywords = {Basel, Flux measurements, Urban Energy Budget},

pubstate = {published},

tppubtype = {article}

}

@article{Chrysoulakis2018,

title = {Urban energy exchanges monitoring from space},

author = {Nektarios Chrysoulakis and Sue Grimmond and Christian Feigenwinter and Fredrik Lindberg and Jean-Philippe Gastellu-Etchegorry and Mattia Marconcini and Zina Mitraka and Stavros Stagakis and Ben Crawford and Frans Olofson and Lucas Landier and William Morrison and Eberhard Parlow },

editor = {nature.com},

url = {https://urbanfluxes.eu/wp-content/uploads/2018/07/UF_Overview_final.pdf},

doi = {10.1038/s41598-018-29873-x},

issn = {2045-2322},

year  = {2018},

date = {2018-07-31},

journal = { nature.com },

volume = {Scientific Reports volume},

number = {8},

pages = {11498},

abstract = {One important challenge facing the urbanization and global environmental change community is to understand the relation between urban form, energy use and carbon emissions. Missing from the current literature are scientific assessments that evaluate the impacts of different urban spatial units on energy fluxes; yet, this type of analysis is needed by urban planners, who recognize that local scale zoning affects energy consumption and local climate. Satellite-based estimation of urban energy fluxes at neighbourhood scale is still a challenge. Here we show the potential of the current satellite missions to retrieve urban energy budget fluxes, supported by meteorological observations and evaluated by direct flux measurements. We found an agreement within 5% between satellite and in-situ derived net all-wave radiation; and identified that wall facet fraction and urban materials type are the most important parameters for estimating heat storage of the urban canopy. The satellite approaches were found to underestimate measured turbulent heat fluxes, with sensible heat flux being most sensitive to surface temperature variation (−64.1, +69.3 W m−2 for ±2 K perturbation).  They also underestimate anthropogenic heat fluxes. However, reasonable spatial patterns are obtained for the latter allowing hot-spots to be identified, therefore supporting both urban planning and urban climate modelling.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Feigenwinter2018,

title = {Spatial Distribution of Sensible and Latent Heat Flux in the city of Basel (Switzerland)},

author = {Christian Feigenwinter and Roland Vogt and Eberhard Parlow and Fredrik Lindberg and Mattia Marconcini and Fabio Del Frate and Nektarios Chrysoulakis},

doi = {10.1109/JSTARS.2018.2807815},

issn = {2151-1535},

year  = {2018},

date = {2018-03-18},

journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},

abstract = {Urban surfaces are a complex mixture of different land covers and surface materials; the relative magnitudes of the surface energy balance components therefore vary widely across a city. Eddy covariance (EC) measurements provide the best estimates of turbulent heat fluxes but are restricted to the source area. Land surface modeling with earth observation (EO) data is beneficial for extrapolation of a larger area since citywide information is possible. Turbulent sensible and latent heat fluxes are calculated by a combination of micrometeorological approaches (the aerodynamic resistance method, ARM), EO data, and GIS techniques. Input data such as land cover fractions and surface

temperatures are derived from Landsat 8 OLI and TIRS, urban morphology was calculated from high-resolution digital building models and GIS data layers, and meteorological data were provided by flux tower measurements. Twenty-two Landsat scenes covering all seasons and different meteorological conditions were analyzed. Sensible heat fluxes were highest for industrial areas, railway stations, and areas with high building density, mainly corresponding to the pixels with highest surface-to-air  temperature differences.

The spatial distribution of latent heat flux is strongly related to the

saturation deficit of vapor and the (minimum) stomatal resistance

of vegetation types. Seasonal variations are highly dependent on

meteorological conditions, i.e., air temperature, water vapor saturation

deficit, and wind speed. Comparison of measured fluxes

with modeled fluxes in the weighted source area of the flux towers

is moderately accurate due to known drawbacks in the modeling

approach and uncertainties inherent to EC measurements, particularly

in urban areas.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}