Omar Regaieg

Centre d'Études Spatiales de la Biosphère

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Nicolas Lauret

Centre National de la Recherche Scientifique

Jean‐Philippe Gastellu‐Etchegorry

Centre d'Études Spatiales de la Biosphère

Yingjie Wang

Hohai University

Tiangang Yin

Hong Kong Polytechnic University

Jordan Guilleux

Centre d'Études Spatiales de la Biosphère

Eric Chavanon

Centre d'Études Spatiales de la Biosphère

Douglas C. Morton

Goddard Space Flight Center

Zhijun Zhen

Jilin University

A. Kallel

Digital Research Centre of Sfax

Zbyněk Malenovský

University of Bonn

Cooperative Institutions

Centre d'Études Spatiales de la Biosphère

Université Toulouse III - Paul Sabatier

Centre National de la Recherche Scientifique

Université de Toulouse

Institut de Recherche pour le Développement

Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement

Goddard Space Flight Center

Chinese Academy of Sciences

Centre National d'Études Spatiales

University of Maryland, College Park

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Simulation of Thermal Infrared Images from Simulated Fire Scenes

Journal of Physics Conference Series (2024)

Ronan Paugam Hadi Bordbar Alba Àgueda Omar Regaieg Eric Chavanon

Abstract We summarize here the current development of a simulation strategy aiming at creating a 3D virtual fire lab that can model radiative transfer in simulated fire scenes, and render images in the infrared spectrum. While the end objective is to simulate open landscape scale vegetation fires to help improving fire monitoring Earth Observation products, this work presents a validation exercise performed using a small scale fire scene focusing on flame emission. The fire scene is simulated using the Fire Dynamics Simulator (FDS) model to generate 3D distribution of temperature, soot volume fraction, and CO 2 , CO and H 2 O gases molar fractions. It is then passed to the 3D Discrete Anisotropic Radiative Transfer (DART) model to simulate radiative transfer in multiple infrared bands at a spectral resolution of 0.25 μ m, and render images or intensity spectrum in the infra red spectra that can be compared against direct measurements.

Thermal Radiation

Fire Detection

10.1088/1742-6596/2885/1/012074

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Forest Woody Structures Decrease the Radiative Transfer Modelled Far-red Solar-induced Fluorescence Radiance More Than Its Canopy Escape Ratio

AGU Fall Meeting Abstracts (2020)

Zbyněk Malenovský Růžena Janoutová Lucie Homolová Jan Novotný Tiangang Yin

Far-red

Source

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Simulation of Solar-Induced Chlorophyll Fluorescence from 3D Canopies with the Dart Model

IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium (2020)

Omar Regaieg Yingjie Wang Zbyněk Malenovský Tiangang Yin A. Kallel

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.

Dart

10.1109/igarss39084.2020.9323616

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Calibration of DART 3D model with UAV and Sentinel-2 for studying the radiative budget of conventional and agro-ecological maize fields

Remote Sensing Applications Society and Environment (2023)

Paul Boitard B. Coudert Nicolas Lauret S. Queguiner Claire Marais-Sicre

Quantifying the radiative budget (RB) of maize, the world's predominant cereal, is crucial to managing its growth in a context of global warming. Its microclimate including temperature, water, and energy flux depend on crop management practices. This paper compares the influence of agroecological practices (AE) and conventional agriculture (CA) on the RB of maize using three-dimensional (3D) radiative transfer modelling and remote sensing (RS) observations. We studied two neighbour maize fields in south-west France, respectively with AE and CA practices. Time series of photosynthetically active radiation absorbed (APAR) by plants (APARplant) and soil (APARsoil) were simulated with the DART radiative transfer model. First, realistic 3D models of the CA and AE fields were created and validated with a new method based on DART and Sentinel 2 and UAV reflectance and vegetation indices acquired in July 2019. At that date, around midday, APARplant was larger by 21.5 W/m2 in the AE field and APARsoil was larger by 20.1 W/m2 in the CA field. We explained these differences by differences in geometric and optical parameters (OP) between the two fields. OPsoil causes ≈45% of the differences in APARplant and APARsoil. The shape of plant causes ≈14% of the difference in APARplant and ≈17% in APARsoil. OPplant causes ≈14% of the difference in APARplant and ≈1% in APARsoil. Field geometry (row orientation, inter-row and plant distance) causes ≈7% of the difference in APARplant and 2% in APARsoil. It stresses the great role of OPsoil associated to AE practices. Because it integrates the non-linear effects of all above parameters, the LAI (Leaf Area Index) accounts for ≈40% difference in APARplant and APARsoil. The sensitivity of APAR to LAI is twice as low in the AE field than in the CA field. We showed that APAR differences between the AE and CA fields can be even greater with other maize row orientations. We also highlighted the role of the field model on simulated APAR by comparing the use of APAR simulated by a 3D field model and its corresponding 1D turbid field: 1D turbid models resulted in a 16% increase in APARplant and in similar APARsoil, compared to the 3D CA and AE models.

Microclimate

Photosynthetically active radiation

Dart

Energy budget

10.1016/j.rsase.2023.101079

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TRUST-DART: Land Surfaces Temperature and Emissivity Nonlinear Mapping from Nonisothermal Mixed Pixels of Satellite Images with the DART 3D Radiative Transfer Model

ISPRS annals of the photogrammetry, remote sensing and spatial information sciences (2024)

Zhijun Zhen J. León-Tavares A. Kallel Yingjie Wang Omar Regaieg

Abstract. Coarse spatial resolution of Thermal Infrared (TIR) satellites hampers measuring the temperature and emissivity of scene elements from space that improves our understanding of land surfaces' thermal behavior. The stringent conditions of current TIR unmixing methods hinder the production of extensive component temperature and emissivity products. To address this, we designed a gradient-based multi-pixel physical model, TRUST-DART, to derive the temperature and emissivity of urban features from non-isothermal mixed pixels of satellite images using the DART 3D radiative transfer model. Unlike traditional TIR unmixing methods, TRUSTDART is not constrained by issues related to spatial, spectral, temporal resolution, angular, scene, field measurement requirements, or manual operations. Its inputs include an at-surface radiance image, downwelling sky irradiance, a 3D urban mock-up with feature information, and DART input parameters such as spatial resolution. It generates maps of emissivity and temperature per urban feature. Its accuracy is validated for two vegetation and urban scenes and two types of images (DART simulated pseudo satellite and ASTER observed images). The accuracy of the TRUST-DART depends heavily on the fraction of components. TRUST-DART proves robust for high-fraction components. However, its accuracy decreases with decreasing fractions. TRUST-DART is distributed with DART and is available for education and research via Toulouse III University (https://dart.omp.eu).

Dart

10.5194/isprs-annals-x-3-2024-453-2024

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Why To Model Remote Sensing Measurements In 3d? Recent Advances In Dart: Atmosphere, Topography, Large Landscape, Chlorophyll Fluorescence And Satellite Image Inversion

Jean‐Philippe Gastellu‐Etchegorry Yingjie Wang Omar Regaieg Tiangang Yin Zbyněk Malenovský

Remote sensing (RS) dedicated to the study of land surfaces benefits from more and more advanced sensors. However, the interpretation of RS data is often is often inaccurate due to the complexity of the observed land surfaces. Therefore, RS models, in particular physical models, that simulate RS observations of the three-dimensional (3D) landscapes are critical to correctly interpret RS data. DART is one of the most comprehensive 3D models of Earthatmosphere 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 (dart.omp.eu). After illustrating three significant sources of inaccuracy in RS interpretation, five recent DART advances are presented: RT in the atmosphere and topography, fast RS image simulation of large landscapes, SIF modelling, and satellite image inversion.

Dart

10.1109/atsip49331.2020.9231884

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Recent Improvements in the Dart Model for Atmosphere, Topography, Large Landscape, Chlorophyll Fluorescence, Satellite Image Inversion

IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium (2020)

Jean‐Philippe Gastellu‐Etchegorry Yingjie Wang Omar Regaieg Tiangang Yin Zbyněk Malenovský

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.

Dart

Atmospheric models

Imaging spectrometer

10.1109/igarss39084.2020.9323458

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DART-Lux: An unbiased and rapid Monte Carlo radiative transfer method for simulating remote sensing images

Remote Sensing of Environment (2022)

Yingjie Wang A. Kallel Xuebo Yang Omar Regaieg Nicolas Lauret

Dart

10.1016/j.rse.2022.112973

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Citations (49)

Comprehensive LiDAR simulation with efficient physically-based DART-Lux model (I): Theory, novelty, and consistency validation

Remote Sensing of Environment (2022)

Xuebo Yang Yingjie Wang Tiangang Yin Cheng Wang Nicolas Lauret

Dart

Ranging

10.1016/j.rse.2022.112952

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Citations (25)

DART: a 3D radiative transfer model for urban studies

Zhijun Zhen Najmeddine Benromdhane A. Kallel Yingjie Wang Omar Regaieg

The three-dimensional (3D) geometry of cities greatly influences their radiation regime and, consequently, their temperature. However, current remote sensing algorithms often treat urban pixels as Lambertian planes. 3D radiative transfer models such as DART that simulate the radiative budget as well as sensor images of cities (short and long waves) avoid this "Lambertian plane" simplification, which should allow a more accurate assessment of the urban radiation regime. DART can simulate any experimental (e.g., urban geometry with vegetation and topography, sun direction, atmosphere conditions) and sensor (e.g., spatial and spectral resolution) configurations. DART is freely available for education and research (dart.omp.eu). Here we present the application of DART to the inversion of satellite images as maps per type of urban element (i. e. , roofs, streets, …): optical properties in the short wave, and emissivity and temperature in the long wave.

Dart

10.1109/jurse57346.2023.10144212

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Citations (5)