Anthropogenic Heat Flux Estimation from Space: Results of the first phase of the URBANFLUXES Project

H2020-Space project URBANFLUXES (URBan ANthrpogenic heat FLUX from Earth observation Satellites) investigates the potential of Copernicus Sentinels to retrieve anthropogenic heat flux, as a key component of the Urban Energy Budget (UEB). URBANFLUXES advances the current knowledge of the impacts of UEB fluxes on urban heat island and consequently on energy consumption in cities. This will lead to the development of tools and strategies to mitigate these effects, improving thermal comfort and energy efficiency. In URBANFLUXES, the anthropogenic heat flux is estimated as a residual of UEB. Therefore, the rest UEB components, namely, the net all-wave radiation, the net change in heat storage and the turbulent sensible and latent heat fluxes are independently estimated from Earth Observation (EO), whereas the advection term is included in the error of the anthropogenic heat flux estimation from the UEB closure. The project exploits Sentinels observations, which provide improved data quality, coverage and revisit times and increase the value of EO data for scientific work and future emerging applications. These observations can reveal novel scientific insights for the detection and monitoring of the spatial distribution of the urban energy budget fluxes in cities, thereby generating new EO opportunities. URBANFLUXES thus exploits the European capacity for space-borne observations to enable the development of operational services in the field of urban environmental monitoring and energy efficiency in cities. H2020-Space project URBANFLUXES (URBan ANthrpogenic heat FLUX from Earth observation Satellites)investigates the potential of Copernicus Sentinels to retrieve anthropogenic heat flux, as a key component of the UrbanEnergy Budget (UEB). URBANFLUXES advances the current knowledge of the impacts of UEB fluxes on urban heatisland and consequently on energy consumption in cities. This will lead to the development of tools and strategies tomitigate these effects, improving thermal comfort and energy efficiency. In URBANFLUXES, the anthropogenic heatflux is estimated as a residual of UEB. Therefore, the rest UEB components, namely, the net all-wave radiation, the netchange in heat storage and the turbulent sensible and latent heat fluxes are independently estimated from EarthObservation (EO), whereas the advection term is included in the error of the anthropogenic heat flux estimation from theUEB closure. The project exploits Sentinels observations, which provide improved data quality, coverage and revisittimes and increase the value of EO data for scientific work and future emerging applications. These observations canreveal novel scientific insights for the detection and monitoring of the spatial distribution of the urban energy budgetfluxes in cities, thereby generating new EO opportunities. URBANFLUXES thus exploits the European capacity forspace-borne observations to enable the development of operational services in the field of urban environmentalmonitoring and energy efficiency in cities

The short term debt vs. long term debt puzzle: a model for the optimal mix

This paper argues that the existing finance literature is inadequate with respect to its coverage of capital structure of small and medium sized enterprises (SMEs). In particular it is argued that the cost of equity (being both conceptually ill defined and empirically non quantifiable) is not applicable to the capital structure decisions for a large proportion of SMEs and the optimal capital structure depends only on the mix of short and long term debt. The paper then presents a model, developed by practitioners for optimising the debt mix and demonstrates its practical application using an Italian firm's debt structure as a case study

Calibration of DART Radiative Transfer Model with Satellite Images for Simulating Albedo and Thermal Irradiance Images and 3D Radiative Budget of Urban Environment

Remote sensing is increasingly used for managing urban environment. In this context, the H2020 project URBANFLUXES aims to improve our knowledge on urban anthropogenic heat fluxes, with the specific study of three cities: London, Basel and Heraklion. Usually, one expects to derive directly 2 major urban parameters from remote sensing: the albedo and thermal irradiance. However, the determination of these two parameters is seriously hampered by complexity of urban architecture. For example, urban reflectance and brightness temperature are far from isotropic and are spatially heterogeneous. Hence, radiative transfer models that consider the complexity of urban architecture when simulating remote sensing signals are essential tools. Even for these sophisticated models, there is a major constraint for an operational use of remote sensing: the complex 3D distribution of optical properties and temperatures in urban environments. Here, the work is conducted with the DART (Discrete Anisotropic Radiative Transfer) model. It is a comprehensive physically based 3D radiative transfer model that simulates optical signals at the entrance of imaging spectro-radiometers and LiDAR scanners on board of satellites and airplanes, as well as the 3D radiative budget, of urban and natural landscapes for any experimental (atmosphere, topography,…) and instrumental (sensor altitude, spatial resolution, UV to thermal infrared,…) configuration. Paul Sabatier University distributes free licenses for research activities. This paper presents the calibration of DART model with high spatial resolution satellite images (Landsat 8, Sentinel 2, etc.) that are acquired in the visible (VIS) / near infrared (NIR) domain and in the thermal infrared (TIR) domain. Here, the work is conducted with an atmospherically corrected Landsat 8 image and Bale city, with its urban database. The calibration approach in the VIS/IR domain encompasses 5 steps for computing the 2D distribution (image) of urban albedo at satellite spatial resolution. (1) DART simulation of satellite image at very high spatial resolution (e.g., 50cm) per satellite spectral band. Atmosphere conditions are specific to the satellite image acquisition. (2) Spatial resampling of DART image at the coarser spatial resolution of the available satellite image, per spectral band. (3) Iterative derivation of the urban surfaces (roofs, walls, streets, vegetation,…) optical properties as derived from pixel-wise comparison of DART and satellite images, independently per spectral band. (4) Computation of the band albedo image of the city, per spectral band. (5) Computation of the image of the city albedo and VIS/NIR exitance, as an integral over all satellite spectral bands. In order to get a time series of albedo and VIS/NIR exitance, even in the absence of satellite images, ECMWF information about local irradiance and atmosphere conditions are used. A similar approach is used for calculating the city thermal exitance using satellite images acquired in the thermal infrared domain. Finally, DART simulations that are conducted with the optical properties derived from remote sensing images give also the 3D radiative budget of the city at any date including the date of the satellite image acquisition

3D modeling of the radiative budget of urban landscapes via the inversion of satellites images into urban materials reflectance and temperature maps

Du fait de son impact sur le climat urbain, le suivi temporel du bilan radiatif urbain Q*, avec prise en compte de sa variabilité spatiale, est un axe de recherche en développement. Q* est la différence entre l'éclairement (i.e., rayonnement incident) et l'exitance (rayonnement sortant) sur le domaine spectral qui englobe l'essentiel du rayonnement solaire (i.e., courtes longueurs d'ondes ) et de l'émission thermique terrestre (i.e., grandes longueurs d'ondes). Les images satellites optiques fournissent une information unique et indispensable mais très partielle, car uniquement pour la configuration d'observation (direction de visée et bandes spectrales du capteur satellite), alors que Q* est une quantité intégrée sur toutes les directions de l'espace et sur l'ensemble des courtes (Qsw*) et grandes (Qlw*) longueurs d'onde. Ces intégrations appliquées aux images satellites sont très compliquées du fait de la complexité de l'architecture tridimensionnelle (3D) urbaine, et de l'hétérogénéité spatiale des propriétés optiques et températures des matériaux urbains. Durant cette thèse, une approche originale a été conçue pour effectuer ces intégrations et ainsi obtenir des séries temporelles de cartes de Q* à la résolution spatiale des images satellites utilisées (i.e., Sentinel-2, Landsat-8, etc.). Elle s'appuie uniquement sur un modèle de transfert radiatif 3D, des images satellites et une base de données géométriques urbaine incluant le relief, le bâti (i.e., immeubles, maisons, routes, etc.) et la végétation (i.e., arbres, pelouses, etc.). De manière schématique, le modèle de transfert radiatif DART (www.cesbio.ups-tlse.fr/dart), développé au CESBIO, est utilisé en mode inverse pour transformer des images satellites en cartes de propriétés optiques et de température de matériaux urbains, puis en mode direct pour calculer des cartes de bilan radiatif par bande spectrale satellite Q*Δλ. L'intégration spectrale des cartes Q*Δλ donne alors les cartes Q* recherchées. Toute série temporelle de carte Qsw* est alors générée efficacement à partir de cartes d'albédo direct (i.e., black sky albedo) et diffus (i.e., white sky albedo) pré- calculées par DART avec la base de données géométrique urbaine et des cartes de propriétés des matériaux dérivées de l'image satellite la plus proche. Ces cartes sont complétées par des données externes thermiques pour la construction des séries temporelles. Cette approche a été conçue et mise au point avec 3 villes de géométries et propriétés optiques très diverses : Londres (Royaume-Uni), Bâle (Suisse), et Héraklion (Grèce). Le projet H2020 URBANFLUXES de la Communauté Européenne a utilisé les cartes de Q* simulées pour estimer les flux urbains de chaleur anthropogénique via le calcul du bilan énergétique urbain à partir d'images satellites. La précision de l'approche développée a été évaluée via l'écart relatif EL des luminances des images DART et satellites (EL < 2% pour toute bande spectrale) et via l'écart relatif EQ* des bilans Q* simulés et mesurés par les tours de flux. En 2016, |EQ*|< 4.5% pour la série temporelle de 321 cartes de Q* de Bâle, et |EQ*|< 4.4% pour les 278 cartes de Q* de Londres. Cette possibilité de dériver d'images satellites des cartes précises de Q* est très prometteuse au vu de la disponibilité croissante des bases de données urbaines et des séries temporelles d'images satellites à haute résolution spatiale, et de l'amélioration des modèles de transfert radiatif 3D.Optical remote-sensing imagery provide a unique and very needed information, but still a partial one, because only in the observation configuration of the satellite sensor (i.e. viewing direction and spectral bands), whereas Q* is an integrated quantity over all the directions and over the whole shortwave (Qsw*) and longwave (Qlw*) spectral domain. These integrations applied to satellite images are very complicated because of the complexity of the urban tri-dimensional (3D) architecture, and because of the urban materials temperature and optical properties spatial heterogeneity. Over the course of this PhD, an innovative approach has been conceived in order to achieve those integrations and thus obtain temporal series of Q* maps at the spatial resolution of the used satellite sensors (i.e. Sentinel-2, Landsat-8, etc.). This approach is using solely a 3D radiative transfer model, satellite images, and a geometrical urban database including the topology, the urban constructions (i.e. buildings, roads, etc.) and the vegetation (i.e. trees, gardens, etc.). Schematically speaking, the radiative transfer model DART (www.cesbio.ups-tlse/dart), developed at CESBIO, is used in inverse mode in order to transform satellite images into urban materials optical properties and temperature maps, and then in direct mode in order to compute radiative budget Q*Δλ maps for each spectral band of the used satellite sensor. Then, the spectral integral of those Q*Δλ maps leads to the desired Q* maps. Each temporal series of Qsw* maps is then generated efficiently from direct albedo maps (i.e. black sky albedo) and diffuse (i.e. white sky albedo) pre-computed using DART from the geometrical urban database of the considered city and optical properties derived from the closest satellite image. These maps are complemented by external thermal data for the computation of the temporal series. This method has been conceived and refined using 3 cities with very varying geometries and optical properties: London (United- Kingdom), Basel (Switzerland), and Heraklion (Greece). The H2020 project URBANFLUXES of the European Community used the simulated Q* maps in order to estimate the urban anthropogenic heat fluxes using the derivation of urban energy budget computed from satellite imagery. The precision of the developed method has been estimated using the relative error ER between the radiance images simulated by DART and measured by satellite sensors (ER<2% for any spectral band) and the relative error EQ* between Q* simulated and measured by flux towers. For the year 2016, |EQ*|< 4.5% for 321 Q* maps over Basel, and |EQ*|< 4.4% for 278 London Q* maps. This capacity of deriving from satellite imagery precis Q* maps is really promising in light of the always increasing availability of urban geometrical databases, of high resolution temporal series of satellite images, and of the improvement of 3D radiative transfer modeling

Modélisation 3D du Bilan Radiatif des Milieux Urbains par Inversion d'Images Satellites en Cartes de Réflectance et de Température des Matériaux Urbains

Optical remote-sensing imagery provide a unique and very needed information, but still a partial one, because only in the observation configuration of the satellite sensor (i.e. viewing direction and spectral bands), whereas Q^* is an integrated quantity over all the directions and over the whole shortwave (Q_sw^*) and longwave (Q_lw^*) spectral domain. These integrations applied to satellite images are very complicated because of the complexity of the urban tri-dimensional (3D) architecture, and because of the urban materials temperature and optical properties spatial heterogeneity. Over the course of this PhD, an innovative approach has been conceived in order to achieve those integrations and thus obtain temporal series of Q^* maps at the spatial resolution of the used satellite sensors (i.e. Sentinel-2, Landsat-8, etc.). This approach is using solely a 3D radiative transfer model, satellite images, and a geometrical urban database including the topology, the urban constructions (i.e. buildings, roads, etc.) and the vegetation (i.e. trees, gardens, etc.).Schematically speaking, the radiative transfer model DART (www.cesbio.ups-tlse/dart), developed at CESBIO, is used in inverse mode in order to transform satellite images into urban materials optical properties and temperature maps, and then in direct mode in order to compute radiative budget Q_Δλ^* maps for each spectral band of the used satellite sensor. Then, the spectral integral of those Q_Δλ^* maps leads to the desired Q^* maps.Each temporal series of Q_sw^* maps is then generated efficiently from direct albedo maps (i.e. black sky albedo) and diffuse (i.e. white sky albedo) pre-computed using DART from the geometrical urban database of the considered city and optical properties derived from the closest satellite image. These maps are complemented by external thermal data for the computation of the temporal series. This method has been conceived and refined using 3 cities with very varying geometries and optical properties: London (United-Kingdom), Basel (Switzerland), and Heraklion (Greece). The H2020 project URBANFLUXES of the European Community used the simulated Q^* maps in order to estimate the urban anthropogenic heat fluxes using the derivation of urban energy budget computed from satellite imagery.The precision of the developed method has been estimated using the relative error ϵ_R between the radiance images simulated by DART and measured by satellite sensors (ϵ_R<2% for any spectral band) and the relative error ϵ_(Q^* ) between Q^* simulated and measured by flux towers. For the year 2016, |ϵ_(Q^* )|< 4.5% for 321 Q^* maps over Basel, and |ϵ_(Q^* )|< 4.4% for 278 London Q^* maps. This capacity of deriving from satellite imagery precis Q^* maps is really promising in light of the always increasing availability of urban geometrical databases, of high resolution temporal series of satellite images, and of the improvement of 3D radiative transfer modeling.Du fait de son impact sur le climat urbain, le suivi temporel du bilan radiatif urbain Q^*, avec prise en compte de sa variabilité spatiale, est un axe de recherche en développement. Q^* est la différence entre l'éclairement (i.e., rayonnement incident) et l'exitance (rayonnement sortant) sur le domaine spectral qui englobe l'essentiel du rayonnement solaire (i.e., courtes longueurs d'ondes ) et de l'émission thermique terrestre (i.e., grandes longueurs d'ondes). Les images satellites optiques fournissent une information unique et indispensable mais très partielle, car uniquement pour la configuration d'observation (direction de visée et bandes spectrales du capteur satellite), alors que Q^* est une quantité intégrée sur toutes les directions de l'espace et sur l'ensemble des courtes (Q_sw^*) et grandes (Q_lw^*) longueurs d'onde. Ces intégrations appliquées aux images satellites sont très compliquées du fait de la complexité de l'architecture tridimensionnelle (3D) urbaine, et de l'hétérogénéité spatiale des propriétés optiques et températures des matériaux urbains. Durant cette thèse, une approche originale a été conçue pour effectuer ces intégrations et ainsi obtenir des séries temporelles de cartes de Q^* à la résolution spatiale des images satellites utilisées (i.e., Sentinel-2, Landsat-8, etc.). Elle s'appuie uniquement sur un modèle de transfert radiatif 3D, des images satellites et une base de données géométriques urbaine incluant le relief, le bâti (i.e., immeubles, maisons, routes, etc.) et la végétation (i.e., arbres, pelouses, etc.). De manière schématique, le modèle de transfert radiatif DART (www.cesbio.ups-tlse.fr/dart), développé au CESBIO, est utilisé en mode inverse pour transformer des images satellites en cartes de propriétés optiques et de température de matériaux urbains, puis en mode direct pour calculer des cartes de bilan radiatif par bande spectrale satellite Q_Äλ^*. L'intégration spectrale des cartes Q_Δλ^* donne alors les cartes Q^* recherchées. Toute série temporelle de carte Q_sw^* est alors générée efficacement à partir de cartes d'albédo direct (i.e., black sky albedo) et diffus (i.e., white sky albedo) pré-calculées par DART avec la base de données géométrique urbaine et des cartes de propriétés des matériaux dérivées de l'image satellite la plus proche. Ces cartes sont complétées par des données externes thermiques pour la construction des séries temporelles. Cette approche a été conçue et mise au point avec 3 villes de géométries et propriétés optiques très diverses : Londres (Royaume-Uni), Bâle (Suisse), et Héraklion (Grèce). Le projet H2020 URBANFLUXES de la Communauté Européenne a utilisé les cartes de Q^* simulées pour estimer les flux urbains de chaleur anthropogénique via le calcul du bilan énergétique urbain à partir d'images satellites. La précision de l'approche développée a été évaluée via l'écart relatif ϵ_L des luminances des images DART et satellites (ϵ_L < 2% pour toute bande spectrale) et via l'écart relatif ϵ_(Q^* ) des bilans Q^* simulés et mesurés par les tours de flux. En 2016, |ϵ_(Q^* )|< 4.5% pour la série temporelle de 321 cartes de Q^* de Bâle, et |ϵ_(Q^* )|< 4.4% pour les 278 cartes de Q^* de Londres. Cette possibilité de dériver d'images satellites des cartes précises de Q^* est très prometteuse au vu de la disponibilité croissante des bases de données urbaines et des séries temporelles d'images satellites à haute résolution spatiale, et de l'amélioration des modèles de transfert radiatif 3D

Assessment of the Performance of the Atmospheric Correction Algorithm MAJA for Sentinel-2 Surface Reflectance Estimates

International audienceThe correction of atmospheric effects on optical remote sensing products is an essential component of Analysis Ready Data (ARD) production lines. The MAJA processor aims at providing accurate time series of surface reflectances over land for satellite missions, such as Sentinel-2, Venμs, and Landsat 8. The Centre d’Études Spatiales de la Biosphère (CESBIO) and the Centre National d’Études Spatiales (CNES) share a common effort to maintain, validate, and improve the MAJA processor, using state-of-the-art ground measurement sites, and participating in processor inter-comparisons, such as the Atmospheric Correction Intercomparison Exercise (ACIX). While contributing to the second ACIX-II Land validation exercise, it was found that the candidate MAJA dataset could not adequately be compared to the main reference dataset. MAJA reflectances were corrected for adjacency and topography effects while the reference dataset was not, excluding MAJA from a part of the performance metrics of the exercise. The first part of the following study aims at providing complementary performance assessment to ACIX-II by reprocessing MAJA surface reflectances without adjacency nor topographic correction, allowing for an un-biased full resolution comparison with the reference Sentinel-2 dataset. The second part of the study consists of validating MAJA against surface reflectance measurements time series of up to five years acquired at three automated stations. Both approaches provide extensive insights on the quality of MAJA Sentinel-2 Level 2 products

Modeling parameters and remote sensing acquisition of urban canopies

In the frame of the H2020 project, URBANFLUXES (http://urbanfluxes.eu/), new modeling is developed to improve the efficiency of the study of urban canopies with remote sensing, with the modeling of satellite and in- situ acquisitions (reflectance, albedo, brightness temperature), and also 3D radiative and energy budgets. The 3D radiative transfer model DART (Direct Anisotropic Radiative Transfer) (www.cesbio.ups-tlse.fr/dart) is adapted and used with its coupled model DARTEB to simulate the 3D energy budget of urban scenes. Here we present DART improvements to simulate imaging spectroscopy of urban landscapes with atmosphere, including the perspective projection of airborne acquisitions, and also the operating process of DARTEB. Applications conducted in the frame of the H2020 project URBANFLUXES are presented

Chapter Remote Sensing Studies of Urban Canopies: 3D Radiative Transfer Modeling

Need for better understanding and more accurate estimation of radiative fluxes in urban environments, specifically urban surface albedo and exitance, motivates development of new remote sensing and three‐dimensional (3D) radiative transfer (RT) modeling methods. The discrete anisotropic radiative transfer (DART) model, one of the most comprehensive physically based 3D models simulating Earth/atmosphere radiation interactions, was used in combination with satellite data (e.g., Landsat‐8 observations) to better parameterize the radiative budget components of cities, such as Basel in Switzerland. After presenting DART and its recent RT modeling functions, we present a methodological concept for estimating urban fluxes using any satellite image data