**1. Introduction**

In today's world, concerns about sustainable energy production, related environmental issues, and their impact on urban agglomerations and their citizens are of high importance. In this context, the functioning of urban environments, including climate‐alternating interactions between atmosphere and human civilization, is studied globally. Remote sensing is a power‐ ful tool that is increasingly used in such studies due to improvements achieved in sensor

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technology, as well as modeling of remote sensing measurements with respect to three‐ dimensional (3D) radiative and energy budget. One of these studies is the H2020 project URBANFLUXES (http://urbanfluxes.eu), which aims to develop methods estimating the anthropogenic heat flux (QF) of urban environments by employing remote sensing data [1]. In other words, its goal is to estimate the impact of human urban activities on the energy budget of city using satellite images.Important parts ofthe surface energy balance computation are the 3Dradiativebudgetcomponentsspecificallyasurbansurfacealbedoincludingthermalexitance. However, no remote sensing model able to simulate accurately spatial distribution of urban spectral albedo and exitance has been previously available. Three conditions must be fulfilled in order to achieve an acceptable solution of an urban radiative budget simulation:

– The model must consider explicitly the 3D architecture of urban environments and simulate radiance images and radiative budget of urban environment. Hence, apart from physical modeling considerations, the model must be able to work with urban databases, including spatial information on vegetation and digital elevation model.

– The model must work within any atmospheric conditions and possibly with air pollution of an urban environment. This requires to model radiative transfer of both the atmosphere above and the air among urban objects.

– An operational methodology must allow calibrating outputs of the remote sensing model in terms of 2D distribution of albedo and exitance (i.e., to produce image outputs). This calibra‐ tion is important, because one cannot expect to have access to the optical properties of all urban surface elements, which vary in space (e.g., tiles of roofs have different reflectance values depending on their age) and time (e.g., wet and dry roofs will exhibit different anisotropy of their reflectance).

**Figure 1.** DART calibration with a remote sensing image (Landsat‐8) for computation of the urban surface albedo over the city of Basel, Switzerland.

Here, we present a 3D radiative transfer model, DART, that fulfils these requirements and its recent improvements for studying urban and natural Earth landscapes with remote sensing acquisitions. We present also the approach that was recently designed and implemented to assess the spatial distribution of DART input parameters: optical properties of surface elements (e.g., roofs, streets, vegetation). **Figure 1** summarizes this approach. It leads to DART simulated albedo and exitance maps that are calibrated with real‐time satellite acquisition. Ideally, these maps have a spatial resolution that is equal to that of satellite images that are used for the calibration.
