As a powerful tool for simulating bi-directional reflectance and radiative transfer (RT) in complex canopies, the radiosity graphics model (RGM) suffers from a reduced runtime speed or even crashes when facing considerable computation load of the view factor for fine-grained simulation of heterogeneous canopy. In this work, the RGM model at pixel scale (RGMPS model) is proposed with the open accelerator (OpenACC) acceleration techniques and two improved algorithms, which solve the overloaded view factor calculation and enhance scene availability without sacrificing accuracy. Two heterogeneous canopy scenario experiments were used for validation, including a realistic single-tree experiment and a large-scale synthetic heterogeneous canopy experiment. The RGMPS model has increased by nearly 70 times the speed of the original RGM model, demonstrating its capability to model large-scale scenes spanning ten thousand square meters. The${R} ^{2}$between RGM and RGMPS is over 0.94 and the root-mean-square error (RMSE) is below 0.0038. The cross-model validation between the RGMPS model and the discrete anisotropic RT (DART) model achieved a high agreement with${R} ^{2}$as high as 0.98 in the near-infrared (NIR) band. An assessment conducted using airborne multiangle measurements also demonstrated that the accuracy of the proposed solution was deemed satisfactory for bi-directional reflectance factor (BRF) simulation, with RMSEs of 0.0031 and 0.0340 for the red and NIR bands, respectively. Our research contributes to the development of more efficient and accurate BRF simulations for large heterogeneous canopy scenes using the RGM model.

Land surface temperature plays a crucial role in ecosystem energy balance and material exchanges. Remote sensing is vital for investigating brightness temperature variations. The intricate canopy structure poses challenges, especially with strong directional anisotropy in brightness temperature, leading to assessment inaccuracies. The radiative transfer model provides valuable insights into how canopy structure influences directional brightness temperature (DBT). Traditional models, assuming a turbid medium or randomly distributed ideal geometry, exhibit notable errors. However, the PATH_RT model, incorporating path length distribution, shows commendable performance in the optical domain. To enhance applicability, we modify the PATH_RT model, successfully implementing path length distributions for simulating DBT in the thermal domain. Validation using abstract scenes, cross-validated with SAIL and FRT, and referencing DART, highlights significant improvement attributed to the efficacy of path length distribution.

We integrate the time series simulation capability of the maize model within an extended L-system (ELSYS) using the growth equations from a 4-D maize and a leaf breakpoint model. These models simulate maize growth from emergence to male anthesis, accounting for 3-D architecture during the vegetative season. We employ two methods to achieve time series simulation in ELSYS: directly use the growth equations in the 4-D maize and leaf breakpoint models, and name ELSYS coupling 4-D maize (ELSYS$_{\mathrm {4Dmaize}}$). Alternatively, replace the stem radius-leaf order function with a stem radius-height function and employ linear interpolation to transform the leaf width-length ratio from a constant value to a function that varies with leaf order, thereby simulating a 4-D maize structure, and name the dynamic L-system-based architectural maize (DLAmaize) model. The DLAmaize model is applied to maize canopy reflectance simulations using the discrete anisotropic radiative transfer (DART) model and radiosity-graphics combined method (RGM), along with a comparison with the 1-D scattering by arbitrarily inclined leaves (SAIL) model. The simulated reflectance of maize canopy from ELSYS4Dmaize and DLAmaize differs significantly in the hotspot direction (the absolute value of the relative difference can be up to 67.4%). In addition, comparisons among RT models show that the DART model and RGM simulate close reflectances. The SAIL model yields significant differences (e.g., the absolute value of the relative difference can be up to 41.16% in the nadir direction) owing to its assumption of the homogeneous canopy. DLAmaize enhances remote sensing of dynamic 4-D vegetation canopies modeling and holds promise for remote sensing applications.

In this study, we demonstrated feasibility of a full three-dimensional (3D) reconstruction of a spatially and structurally heterogeneous forest stand of tall Australian eucalypt trees from terrestrial laser scanning (TLS) of individual trees. We provide a direct validation of the Discrete Anisotropic Radiative Transfer (DART) model bidirectional Monte Carlo path tracing (DART-Lux) by comparing a drone-based hyperspectral acquisition of the studied forest with a corresponding DART forward simulation. Finally, we successfully retrieved from a drone hyperspectral imagery contents of leaf chlorophylls a+b (Cab), total carotenoids (Ccar), and anthocyanins (Cant) for sunlit parts of the eucalypt canopy with random forest regressions trained on 2268 spectral simulations of the study site carried out in coupled Fluspect-Cx and DART models.

Optical canopy models that connect land surface properties and satellite-observed radiance must be validated before being used. These models include the bidirectional reflectance distribution function (BRDF) models in the visible and near-infrared domains, and directional brightness temperature (DBT) models in the thermal infrared domain. Near-surface experiments have been extensively conducted to evaluate the modeling accuracy, including ground-, tower-, and aircraft-based measurements. Indeed, it should be noted that in situ measured BRDF/DBT results are sensitive to the experiment protocol, such as sensor moving orientation, flight height, and sampling frequency. A practical tool for optimizing the in situ measurement protocols is needed in the community of remote sensing modeling. For that, we devised a virtual experiment framework based on the discrete anisotropic radiative transfer (DART) 3-D radiative transfer model that is capable of simultaneously simulating both the BRDF/DBT pattern and the images acquired by in situ cameras. Here, as an optimization case, we use it to determine the optimal sensor flight orientation over heterogeneous vegetated canopies (a row-planted scene with three solar angles and a discrete scene with three solar angles) for measuring their DBT distribution. Results showed considerable errors (i.e., image-extracted DBT minus DART-simulated DBT) exist for sensor flight orientation along the canopy rows ($R^{2}$= 0.24 and root mean square error (RMSE) = 4.32 K), and they become much smaller ($R^{2}$= 0.94 ~ 0.98 and RMSE = 0.82 ~ 1.03 K) in other typical orientations (e.g., cross row plane, solar principal plane, and cross solar principal plane). The critical azimuth offset relative to the row direction that can ensure an acceptable RMSE < 1 K is quantified as atan(3*Unitwidth/Scenesize) based on a series of intensive simulations by this new tool. However, the RMSE of the discrete scene is not sensitive to the flight orientation. Such accuracy differences in various protocols were experimentally verified over row-planted maize using a 4-D tower in Huailai, Hebei, China. The result highlights the great potential of this newly designed DART-based virtual experiment to optimize near-surface experiment protocols.

The anisotropy of land surfaces reflectance of greatly impacts the acquisition and analysis of satellite images. Normalization methods using semi empirical kernel-driven models commonly remove these directional effects. Here, we present a physically-based approach to assess the accuracy of 2 normalization methods of Sentinel-2 (S2) and 3 (S3) images. A ReBeLS-based process fits the parameters of a kernel-driven model with S3 and S2 images simulated by the DART RT model in agricultural and urban areas. Here, normalization methods (c-factor, NDVI-disaggregation) hardly validate their assumptions: surface type and orientation have a great influence. We show the potential of RT models to assess and improve normalization methods.

Three-dimensional (3D) radiative transfer (RT) models are frequently recognized as a prerequisite when using high spatial resolution remote sensing data in heterogeneous surfaces. However, most studies of 3D RT models have been restricted to limited applications due to the low computational efficiency. Therefore, this study proposed a graphic processing unit (GPU)-based solution for the ray tracing 3D RT model. A state-of-the-art graphics and compute application programming interface, Vulkan, was introduced to implement the RT process. A bounding box method was adopted for the computation acceleration. By comparison with a central processing unit (CPU)-based solution, the performance efficiency of the proposed solution is significantly better: the simulation time of a GPU model is significantly reduced by more than 99% when facing a large-scale simulation mission. The simulation accuracy of the two solutions is similar, with root mean squared errors (RMSEs) lower than 0.005, 0.032 and 0.31 K for the red, near-infrared (NIR) and brightness temperature images, respectively. An evaluation based on airborne multiangle measurements also indicated that the accuracy of the proposed solution was satisfactory for simulating the red and NIR bidirectional reflectance factor and brightness temperature directional anisotropies, with RMSEs lower than 0.003, 0.020 and 0.20 K, respectively, when treating the whole scene as a pixel. Considering the simulation accuracy and efficiency, a GPU-based model will be an important supplement to the CPU model.

Surface albedo plays a key role in global climate modeling as a factor controlling the energy budget. Satellite observations were utilized to estimate surface albedo at global and regional scales with good precision over flat areas. However, because topography greatly complicates radiative transfer (RT) processes, estimating the albedo of rugged terrain with satellite data remains a challenge. In addition, albedo definitions over sloping terrain differ from that for flat areas. They include horizontal/horizontal sloped surface albedo (HHSA) and inclined/inclined sloped surface albedo (IISA). Methods for retrieving HHSA and IISA in mountains have not been well-explored. Here, we retrieved HHSA and IISA on sloping terrain from Landsat 8 using a direct estimation algorithm. We simulated a dataset of Landsat top-of-atmosphere (TOA) reflectance and surface albedo with discrete anisotropic radiative transfer (DART) model, for variable atmospheric, vegetation, soil, and topography properties. Then, we used artificial neural networks (ANNs) to derive an empirical relationship between TOA reflectance and surface albedo. The accuracy of our method was verified within situmeasurements: root mean squared error (RMSE) and bias equal to 0.029 and −0.010 for HHSA, and 0.023 and −0.001 for IISA, respectively. Several albedo results (HHSA, IISA, values without topographic consideration) were evaluated and compared. HHSA was found similar to albedo without topographic consideration, but IISA, considered as the “true albedo” for sloping terrain, showed large difference from them. This study demonstrated the feasibility of surface albedo estimation from Landsat TOA reflectance directly in rugged terrains and advanced our understanding of energy budget in mountains.

Sky radiance field at the bottom of the atmosphere and building façades are invisible in the remote sensing images, but they are the two main light sources of ground surfaces in the shadows cast by buildings in urban areas. This work is interested in evaluating the impact of the anisotropic sky and the reflection of the building on the irradiance of shaded surfaces. The assessment is based on 3D radiative transfer simulations of urban scenes with different sky radiance distributions and different building façade reflectance. The results show that without taking into account anisotropic sky, the average error of sky irradiance estimation in cast shadows can reach 183.75% in a visible band centered at 550 nm. According to the geometry and reflectivity of the building façade, the contribution of the building reflection to the irradiance of the shaded surfaces varies from 0.95% to 84.23%.

Physical models simulating the radiative budget (RB) and remote sensing (RS) observation of three-dimensional (3D) landscapes are critical to better understand human and natural components of the Earth system and further develop RS technology. DART is one of the most comprehensive 3D models of Earth-atmosphere optical radiative transfer (RT), from ultraviolet (UV) to thermal infrared (TIR). It simulates the optical signal of proximal, aerial and satellite imaging spectrometers and laser scanners, the 3D RB and solar induced chlorophyll fluorescence (SIF) signal, for any urban or natural landscape and any experimental or instrument configuration. It is freely available for research and teaching activities (https://dart.omp.eu). Here, five recent advances are presented. 1) Atmosphere RT. 2) RT in non repetitive topography. 3) Monte Carlo modelling for fast RS image simulation of large landscapes. 4) SIF modelling for vegetation simulated as facets and turbid cells. 5) RS image inversion for mapping the optical properties of urban material and the urban radiative budget.

This paper presents new approaches to forecast the plant growth based on statistical methods: autoregressive and Markov chain models, using a time series of the normalized different vegetation index. Here, a monthly normalized different vegetation index time series was derived from Sentinel-2 over Limaya olive tree fields from January 2016 to November 2019. To ensure consistent prediction, processing is done over homogeneous clusters of vegetation. Finally, the performance of our approach is evaluated by means of the root mean square error between the predicted and true values.

The potential of solar-induced chlorophyll fluorescence (SIF) to monitor photosynthesis and plant stress has attracted considerable interest in SIF remote sensing (RS). However, canopy SIF and RS observations are impacted by topography, vegetation three dimension (3D) structure, leaf orientation, non foliar elements (e.g., tree woody skeleton), ... Physically based downscaling of canopy SIF RS data to leaf-level (i.e., to leaf photosynthesis) requires 3D radiative transfer (RT) models simulating canopy SIF and its observation. These models are necessary to better exploit the potential of SIF, by linking leaf SIF and SIF in RS observations as a function of canopy 3D architecture and experimental configurations (sun and viewing directions, etc.). The Discrete Anisotropic Radiative Transfer (DART) model is a comprehensive 3D radiative transfer (RT) model for urban and natural landscapes. This paper presents its SIF modeling for vegetation simulated with facets, its validation with the SCOPE/mSCOPE 1D models, and its recent extension to SIF modelling for landscapes simulated with 3D turbid medium.

Soil-Adjusted Vegetation Index (SAVI) is found to be undesirable to estimate Leaf Area Index (LAI) with heterogeneous canopy structure in low vegetation cover. In this article, three new vegetation indices (VIs), such as Normalized Hotspot-Signature Vegetation Index 2 (NHVI2), Hotspot-Signature Soil-Adjusted Vegetation Index (HSVI), and Hotspot-Signature 2-Band Enhanced Vegetation Index (HEVI2), are proposed for a better quantitative estimation of LAI and soil-noise resistance than with SAVI. To obtain these new indices, the angular index called Normalized Difference between Hotspot and Darkspot (NDHD) is introduced which represents the distribution of foliage in vegetation canopy. The validity of new VIs is statistically verified using simulated data and field measurements. The Discrete Anisotropic Radiative Transfer (DART) model is used to simulate both the homogeneous and heterogeneous canopy for analyzing vegetation isolines behaviors, soil-noise resistance, and LAI estimation. In situ measurements of LAI and bidirectional reflectance factor from the Boreal Ecosystem-Atmosphere Study (BOREAS) are also used to test the robustness of the new VIs for the estimation of LAI. By considering the distribution of the foliage, the accuracy of LAI estimation of SAVI for heterogeneous canopy improved almost 16% using exponential regression analysis. With the improvement of multiangular remote-sensing and Bidirectional Reflectance Distribution Function (BRDF) models in the future, hotspot-signature VIs have the potential to provide a more accurate LAI estimation for heterogeneous canopy in strong soil-noise interference area.

This work proposes a new approach to estimate two canopy structure properties: leaf area index (LAI) and leaf angle distribution (LAD) using terrestrial LiDAR system (TLS) data. Our methodology consists of two steps. First, a forward model was developed to simulate TLS observations of a vegetation scene having known structure variables (i.e. LAI and LAD) which permit obtaining 3D point cloud representing the studied scene. Second, a backward model was designed to retrieve LAI and LAD based on the relationship between light transmittance and foliage density. Our approach was validated with results obtained with different homogenous vegetation covers.

3D radiative transfer modeling in forest canopies is of great importance to upscale leaf level observations to canopy level, which, however, is particularly difficult in heterogeneous areas due to the complexity of forests. A common solution is to use physically based radiative transfer models. In this paper, we parameterized the LESS (LargE-Scale remote sensing data and image Simulation framework) model through a voxel-based reconstruction of airborne LiDAR data. For that, an airborne spectral image was simulated and compared with actual ASIA hyperspectral image. The results show a good agreement with R-squared being 0.5 and 0.56 for near infrared and red band, respectively. This demonstrates that the proposed voxel-based parameterization approach can successfully capture the fine-scale structures of forest canopies, and it can provide reliable data source for 3D radiative transfer models.

Many physical models have been proposed to simulate the directional anisotropy in the thermal infrared (TIR) region over vegetation canopies to produce angular corrected directional brightness temperature or land surface temperature. However, too many input parameters obstruct their operational use. Semiempirical kernel-driven models are designed to be a tradeoff between physical accuracy and operationality. Recently, four kernel-driven models have been proposed: the first two are direct extensions of kernel models in the visible- and near-infrared region and the last two were directly designed for the TIR region. In this paper, 153 continuous and 153 discrete canopies with varying structures and temperature distributions were considered in order to evaluate their accuracies against two physical models (4SAIL and DART). Their error distribution, scatterplots, and directional anisotropy patterns are compared. LSF-Li model, followed by Ross-Li, Vinnikov, and RL model, gave the best fitting results for all the scenes. The R2of all four kernel models can reach up to 0.82 for discrete scenes; however, the kernel-driven models underestimate the hotspot effect from continuous scenes; therefore, further improvements are necessary for operational use with future TIR satellite missions.

Imaging spectroscopy is frequently used to assess traits and functioning of vegetated ecosystems. Applied reflectance- and radiance-based approaches critically rely on accurate estimates of surface irradiance. Accurate retrievals of surface irradiance are, however, nontrivial and often error-prone, thus causing inaccurate estimates of vegetation information. We analyze the irradiance field surrounding an isolated tree using the 3-D radiative transfer model DART in high spatial (25 cm) and spectral (1 nm, 350-2500 nm) resolution. We validate modeled irradiance with in situ measurements and quantify the impact of erroneous surface irradiance estimates on the retrieval of vegetation indices. We observe the irradiance gradients in the cast shadows of <;560% in the blue spectral range, while this gradient decreases with increasing wavelength and becomes negligible in the near infrared (NIR). Furthermore, we quantify a vegetation-induced decrease in the irradiance of <;6% in the visible spectral region and an increase of <;7% in the NIR outside the cast shadow. Commonly employed vegetation indices are also affected by such brightening or darkening effects. Outside the cast shadow, indices sensitive to the relative content of chlorophyll (CHL) and carotenoids (CAR) show an overestimation of <;14%. The photochemical reflectance index shows an underestimation of <;5%. This paper provides first quantitative insight in high spatial and spectral resolution, on the impact of vegetation on its surrounding irradiance field. Findings highlight important implications for vegetation assessments and provide the fundamental base to advance retrievals of vegetation traits and functioning from imaging spectroscopy data.

In this paper, we study the estimation of olive tree biophysical properties driven by Sentinel-2 (S2) image inversion. The latter is based on the forward/backward radiative transfer (RT) model. The forward step is done simulating DART on a realistic tree mock-up, whereas the backward is done based on a coupling between the Look UP Table (LUT) and the Markov Chain Monte Carlo (MCMC). The parameters Leaf area index (LAI), chlorophyll (Cab) water (Cw) contents and mesophyll structure (N) are derived. The results are promising, in particular LAI and Cab values are close to those found in literature.

Models that simulate the radiative budget (RB) and remote sensing (RS) observation of landscapes with physical approaches and consideration of the three-dimensional (3-D) architecture of Earth surfaces are increasingly needed to better understand the life-essential cycles and processes of our planet and to further develop RS technology. DART (Discrete Anisotropic Radiative Transfer) is one of the most comprehensive physically based 3-D models of Earth-atmosphere optical radiative transfer (RT), from ultraviolet to thermal infrared. It simulates the optical 3-D RB and signal of proximal, aerial and satellite imaging spectrometers and laser scanners, for any urban and/ or natural landscapes and for any experimental and instrumental configurations. It is freely available for research and teaching activities. Here, an application is presented after a summary of its theory and recent advances: inversion of Sentinel 2 images for simulating time series of urban radiative budget `Q*sw' maps through the determination of maps of urban surface material. Results are very encouraging: satellite and in-situ Q*sware very close (RMSE ≈ 15W/m2; i.e., 2.7% mean relative difference).

Potential of solar-induced chlorophyll fluorescence (SIF) to track time variable environmental stress of vegetation explains high interest in SIF remote sensing. There is an increasing need for physical models that consider the 3D structure of Earth surfaces, in order to better understand the relationships between SIF, vegetation three-dimensional (3D) architecture, irradiance and remote sensing configuration at canopy level. The Discrete Anisotropic Radiative Transfer (DART) model is one of the most comprehensive physically based 3D models of Earth-atmosphere radiative transfer (RT), covering the spectral domain from ultraviolet to thermal infrared wavelengths. This paper presents the determination of the sun and shade adapted leaf elements of a 3D vegetation canopy in DART, which is required for accurate RT simulations of SIF in geometrically explicit 3D canopy representations.

Three dimensional (3D) radiative transfer simulation is becoming an important and essential tool to understand the interaction between solar radiation and forest canopies. However, conducting a 3D simulation usually needs a lot of input parameters, especially the 3D information of forest scene, which is difficult to obtain and reconstruct. This paper presents a voxel approach that derives forest mockups from LiDAR data. These 3D mock-ups are adapted to DART model, using its recently introduced data access objects (DAO) tool. Here, they are used to simulate multispectral images with DART.

Light Detection And Ranging (LiDAR) techniques have been extensively applied in spaceborne, airborne and ground-based platforms. Understanding LiDAR data requires modeling approaches that can precisely account for the physical interactions between the emitted laser pulse and reflecting targets. Diverse LiDAR data types arise from different systems, platforms, and applications. However, most existing physical models consider only single pulse configurations to simulate large footprint LiDAR waveforms, which do not correspond to standard data formats. Hence, in many cases, model outputs are not well adapted to research conducted with actual LiDAR systems, especially for Aerial and Terrestrial Laser Scanning (ALS and TLS) systems. The Discrete Anisotropic Radiation Transfer (DART) model provides accurate and efficient simulations of multiple LiDAR pulses from all platform types. This paper presents the latest development of the DART LiDAR module: Gaussian decomposition of the simulated ALS and TLS waveforms followed by the provision of LiDAR point cloud and waveforms in text and standard ASPRS LAS formats.

In this paper, we investigate the estimation of crop biophysical properties from small footprint LiDAR waveforms inversion. Due to crop heterogeneity within the same agricultural field, a classification on similar waveform clusters is performed before inversion. A Look up table (LUT) approach was adapted then, to derive the height and LAI of maize and wheat crops. The LUT was generated using the Discrete Anisotropic Radiative Transfer (DART) by simulating the LiDAR observations which are used to search for the suitable set of biophysical properties describing the different crops clusters. The results are promising. Crops height is accurately estimated with a root mean square error (RMSE) of 0.06m and 0.03m for maize and wheat, respectively. LAI was well estimated with RMSE of 0.07 and 0.43 for maize and wheat, respectively.

The fast development of the light detection and ranging (LiDAR) technique, especially with scanning and multi-beam systems that launch pulses along different directions, requires efficient and accurate simulation tools to analyze existing data and to design future systems. This work presents the recent advantages of the discrete anisotropic radiative transfer (DART) model in LiDAR data simulation. A more comprehensive comparison between DART-simulated waveforms and Laser Vegetation Imaging Sensor (LVIS) over Howland forest, Maine is presented. Results show that in addition to waveform shape simulation, radiometric calibration coefficients could be inferred from the comparison. This new discovery could provide an approach for possible radiometric modeling of LiDAR data, that convert the digital number of the waveform into actual energy.

We introduced an approach to simulate and separate atmospheric contribution in ground-based thermal-infrared (TIR) camera measurements. Different from the traditional approach which uses the look-up table built from 1-D radiative transfer model (RTM), this approach directly simulates 3-D ray propagations and interactions in the heterogeneous urban environment by using the Discrete Anisotropic Radiative Transfer (DART) model. The atmospheric turbid cells that occupy every part of the urban scene are created using the vertical constituent distribution and the optical property profiles in the existing databases or from the actual meteorological measurements. The two components of atmospheric effects on the TIR at-sensor radiance are attenuated transmission and path thermal emission. Taking both into account, the at-surface radiance corresponding to the signal emitted only from the urban surface can be derived.

The recently launched H2020 project URBANFLUXES (URBan ANthrpogenic heat FLUX from Earth observation Satellites) investigates the potential of EO to retrieve anthropogenic heat flux, as a key component in the Urban Energy Budget (UEB). URBANFLUXES advances existing Earth Observation (EO) based methods for estimating spatial patterns of turbulent sensible and latent heat fluxes, as well as urban heat storage flux at city scale and local scale. Independent methods and models are engaged to evaluate the derived products and statistical analyses provide uncertainty measures. Optical, thermal and SAR data are exploited to improve the accuracy of the UEB components spatial distribution calculation. Synergistic use of different types and of various resolution EO data allows estimates in local and city scale. Ultimate goal of the URBANFLUXES is to develop a highly automated method for estimating UEB components to use with Copernicus Sentinel data, enabling its integration into applications and operational services.

The need of better accuracy for analyzing remote sensing (RS) data of complex Earth surfaces explains the increasing need of models that simulate RS data with physical approaches. Similarly, the study of Earth surfaces functioning requires physical models that simulate the 3D radiative budget (RB) of these surfaces. DART (Discrete Anisotropic Radiative Transfer is one of the most comprehensive physically based 3D models that model the Earth-atmosphere radiation interaction from visible to thermal infrared wavelengths. It simulates optical signals at the entrance of terrain/airborne/satellite imaging radiometers and laser scanners, as well as the 3D RB, of urban/natural landscapes for any experimental and instrumental configurations. Its licenses are free for research and teaching activities. Here, we present its major recent advances.

In this paper we present a study on the use of remote sensing data combined to the 3D modeling of radiative transfer (RT) and energy balance in urban canopies in the aim to improve our knowledge on anthropogenic heat fluxes in several European cities (London, Basel, Heraklion, and Toulouse). The approach is based on the forcing by the use of LandSAT8 data of a coupled radiative transfer model DART (Direct Anisotropic Radiative Transfer) (www.cesbio.upstlse.fr/dart) with an energy balance module. LandSAT8 visible remote sensing data is used to better parametrize the albedo of the urban canopy and thermal remote sensing data is used to enhance the anthropogenic component in the coupled model. This work is conducted in the frame of the H2020 project URBANFLUXES, which aim is to improve the efficiency of remote-sensing data usage for the determination of the anthropogenic heat fluxes in urban canopies [5].

Multi-sensor systems are increasingly demanding in recent remote sensing (RS) applications. Combination of LiDAR and imaging spectrometers is an emerging technique used by several recent airborne systems. The combined data provide both functional and structural information, which makes this technique a unique tool for understanding and management of the Earth's ecosystems. The rapid development of this technique demands the simulation and validation of the combined data. In this paper, we introduce a new method to simulate data fusion of multi-sensor system which combined LiDAR and imaging spectrometer, with any experimental, instrumental, and geometrical configurations of systems. This method is implemented in the latest release of discrete anisotropic radiative transfer (DART) model.

This paper gives an overview of French studies realized in the frame of the CNES (French Space Agency) working group on spaceborne lidar missions. These studies include (1) the development of forest scenery and radiative transfer models for the simulation of lidar waveforms under forest cover, (2) preliminary instrumental studies to ensure the feasibility of the scientific requirements and (3) evaluation and improvement of inversion methods to retrieve forest parameters from large footprint lidar data.

DART model was extended for simulating satellite lidar signal of Earth-Atmosphere systems. The adopted approach combines Monte Carlo and flux tracking methods. It improves a lot signal to noise ratios of simulated waveforms. For accurate simulation of atmosphere photon tracing, the atmosphere modelling was modified for obtaining continuous vertical distribution of extinction coefficients. It leads to a much better accuracy than the use of atmosphere layers with constant extinction coefficients. This improvement is valid with any type of atmosphere, exponential or not.

DART model was extended for simulating satellite Lidar data of 3D Earth scenes with Monte Carlo based methods. 2 major modeling methods were developed. (1) Monte Carlo method for efficiently handling complex phase functions: once scattering directions with close occurrence probabilities are grouped within classes, a 1strandom pulling gives the class of scattering directions, and a 2ndrandom pulling gives the scattering direction within the class. (2) A so-called RayCarlo method combines the classical Monte Carlo forward photon tracing method and the flux tracking method, which allows one to decrease computer time of classical Monte Carlo method by factors that can reach 108. Simulation results are very encouraging. Validation tests are being conducted.

Thermal infrared satellite observations of the Earth's surface are key components in estimating the surface skin temperature over global land areas. However, depending on sun illumination and viewing directional configurations, satellites measure different surface radiometric temperatures, particularly over sparsely vegetated regions where the radiometric contributions from soil and vegetation vary with the sun and viewing geometry. Over an oak tree woodland located near the town of Evora, Portugal, we compare different satellite-based land surface temperature (LST) products from the Moderate Resolution Imaging Spectroradiometer on board the Terra and Aqua polar-orbiting satellites and from the Spinning Enhanced Visible and Infrared Imager on board the geostationary Meteosat satellite with ground-based LST. The observed differences between LSTs derived from polar and geostationary satellites are up to 12 K due to directional effects. In this letter, we develop a methodology based on a radiative transfer model and dedicated field radiometric measurements to interpret and validate directional remote sensing measurements. The methodology is used to estimate the quantitative uncertainty in LST products derived from polar-orbiting satellites over a sparse vegetation canopy.

The aim of the topographic normalization of remotely sensed imagery (TNRSI) is to reduce reflectance variability caused by steep terrain and, subsequently, to improve land-cover classification. Recently, multiple-forward-mode (FM) (MFM) reflectance models for topographic normalizations of medium-resolution (20-30 m) satellite imagery have improved the classification of forested covers with respect to more conventional topographic corrections. We propose an FM 3-D reflectance (FM3DR) model, based on the Discrete Anisotropic Radiative Transfer simulator, for the topographic normalization of high-resolution (1-5 m) imagery. The feasibility of this approach was first verified on real IKONOS imagery for three forest types within major biomes (oak, pine, and high tropical forest) in Mexico. Next, we formalized the topographic normalization performance index and variability as relevant criteria to test TNRSI across incident angles in terms of maximum likelihood classification effectiveness. The FM3DR model outperformed five previously published topographic corrections (cosine, Minnaert, sun-canopy-sensor (SCS), Civco two-stage, and slope matching corrections), and image-based statistical strategies (Civco two-stage and slope matching corrections) tended to perform better than more analytical strategies (cosine, Minnaert, and SCS corrections). An asset of this approach versus former models is the realistic account of terrain-related variation of understory and crown cover within a cover type. On top of that, once validated across forest types, the model is sufficient for the application of a full MFM 3-D reflectance-based topographic normalization without additional field measurement.

A DART based data processing chain was developed for assessing the potential of a high spatial resolution geostationary satellite. It simulates time series of TOA and BOA radiance values and their spatial variability at any satellite spatial resolution, for any experimental, instrumental and view configuration (e.g., geosynchronous orbit). Account of sources of noise (e.g., atmosphere, sensor, anisotropy of surface optical properties,...) gives the radiance SNR (i.e., domain of validity). First results with the desert and tree savannah sites are very encouraging.

Many radiative transfer (RT) models combine exact kernel and discrete ordinate techniques for solving the transport equation. They discretize the 4π space into a finite number of angular sectors, with directions along which radiation propagates. They can be more or fewer and equally spaced or not. RT model improvement is usually focused on 3D landscapes simulation and RT mathematical modeling. The angular variable Ω discretization is a much less addressed problem, although it can strongly influence the simulation of satellite signals, especially with small numbers of discrete directions. Here, we present a new Ω discretization that improves the accuracy of simulated result.

This study was conducted to answer two research questions: (1) what is the spatial variability of the leaf optical properties between 400-1600 nm (hemispherical-directional reflectance, transmittance, absorption) within young Norway spruce crowns, and (2) how to design a suitable physically-based approach retrieving the total chlorophyll content of a complex coniferous canopy from very high spatial resolution (0.4 m) hyperspectral data? It was proved that sun-exposed needles of current age-class statistically differ (alpha-level = 0.01) from rest of the needles in reflectance between 510-760 nm. Last four age-classes of sun-exposed needles were also found to be significantly different from almost all age-classes of sun-shaded needles in transmittance from 760-1350 nm. An operational estimation of chlorophyll a+b content (Cab) from an airborne AISA Eagle hyperspectral image was proposed by means of a PROSPECT-DART inversion employing an artificial neural network (ANN). A spatial pattern of estimated Cabwas successfully validated against the Cabmap produced by a vegetation index ANCB650-720. Coefficients of determination (R2) between ground measured and retrieved Cabwere 0.81 and 0.83, respectively, with root mean square errors (RMSE) of 2.72 mug cm-2for ANN and 3.27 mug cm-2for ANCB650-720.

International audience

DART (Discrete Anisotropic Radiative Transfer) was developed in 1996 for simulating radiative transfer in 3D scenes. Since then, it was greatly improved to make it more accurate, comprehensive and operational (e.g., simulation of thermal infrared and atmospheric radiative transfer). Presently, a single DART simulation gives 2 major products. (1) 3-D radiation budget of the Earth-Atmosphere system. (2) Optical remote sensing images at any altitude from bottom up to top of the atmosphere, for many view directions, simultaneously in several spectral bands, from the visible up to thermal infrared. DART works with natural landscapes (i.e., forests, field mosaics, etc.) made of trees, grass, rivers, etc. and urban landscapes made of buildings, roads, etc. Topography is simulated with digital elevation models. Atmosphere (vertical profiles, etc.) and Earth surface (spectral reflectance, etc.) databases can be used, sensor characteristics can be accounted for, etc. Moreover, a Graphic User Interface (GUI) is used to input scene parameters and to display scene and DART simulations. Recent improvements of DART (patent (PCT/FR 02/01181)) are presented here.

DART (Discrete Anisotropic Radiative Transfer) model was designed in 1996 for simulating optical directional images and 3-D radiation budget of heterogeneous 3-D scenes with various landscape elements (i.e., trees, water, grass, soil, etc.). It was already used for many scientific works; e.g., impact of canopy structure on satellite images texture and 3-D canopy photosynthesis rate and primary production rate. It was successfully tested against reflectance measurements and also against radiative transfer (RT) models in the frame of the RAMI exercise. Recently, DART was greatly improved to make it more comprehensive and operational. The new DART 2003 model simulates directional images in the sensor plane, for any altitude, simultaneously in several spectral bands in the whole optical domain, for natural, agricultural and urban landscapes with topography, with/without the simulation of the atmospheric radiative transfer, with/without the use of spectral databases (0.3 /spl mu/m-15 /spl mu/m), etc. In order to validate these improvements, DART 2003 simulations were tested against red and NIR reflectance values that were simulated by some RT models used in the frame of RAMI exercise: (1) two 1-D RT models (ProSAIL, 1/2 Discrete) and (2) five 3-D RT models (Flight, DART, Sprint, RAYTRAN, RGM). Results stress that DART accuracy is compatible with that of other models. Work is being conducted for generalizing this first result. Unfortunately, a few major features of DART (i.e., simulation of directional images, atmospheric RT, thermal inferred) could not be tested because other RT models do not simulate them.

A simulator of high spatial resolution satellite images is introduced. It is based on the coupling of two radiative transfer models: discrete anisotropic radiative transfer (DART) for terrestrial landscapes and second simulation of the satellite signal in the solar spectrum (6S) for the atmosphere. It works in the visible, near infrared, and midinfrared domains. The simulation procedure involves four steps: 1) assessment of the optical properties of the atmosphere, 2) simulation of high-resolution reflectance images of the Earth surface, 3) transformation of the bottom of the atmosphere (BOA) images to top of the atmosphere (TOA) images, and 4) convolution of the TOA spectral images with the sensor spectral response. Two applications of the simulator are presented: the detection of targets and the analysis of the spatial information. This simulator is being used as a radiative transfer reference model at the Centre National d'Etudes Spatiales (CNES), Toulouse, France.