Discrete Anisotropic Radiative Transfer (DART 5) for Modeling Airborne and Satellite Spectroradiometer and LIDAR Acquisitions of Natural and Urban Landscapes

By J. Gastellu‐Etchegorry, Tiangang Yin, N. Lauret, T. Cajgfinger, T. Grégoire, Eloi Grau, Jean-Baptiste F'eret, Maïlys Lopes, J. Guilleux, Gérard Dedieu and 8 more. Remote Sensing, 2015

321 citations recorded by the source corpus.

Satellite and airborne optical sensors are increasingly used by scientists, and policy makers, and managers for studying and managing forests, agriculture crops, and urban areas. Their data acquired with given instrumental specifications (spectral resolution, viewing direction, sensor field-of-view, etc.) and for a specific experimental configuration (surface and atmosphere conditions, sun direction, etc.) are commonly translated into qualitative and quantitative Earth surface parameters. However, atmosphere properties and Earth surface 3D architecture often confound their interpretation. Radiative transfer models capable of simulating the Earth and atmosphere complexity are, therefore, ideal tools for linking remotely sensed data to the surface parameters. Still, many existing models are oversimplifying the Earth-atmosphere system interactions and their parameterization of sensor specifications is often neglected or poorly considered. The Discrete Anisotropic Radiative Transfer (DART) model is one of the most comprehensive physically based 3D models simulating the Earth-atmosphere radiation interaction from visible to thermal infrared wavelengths. It has been developed since 1992. It models optical signals at the entrance of imaging radiometers and laser scanners on board of satellites and airplanes, as well as the 3D radiative budget, of urban and natural landscapes for any experimental configuration and instrumental specification. It is freely distributed for research and teaching activities. This paper presents DART physical bases and its latest functionality for simulating imaging spectroscopy of natural and urban landscapes with atmosphere, including the perspective projection of airborne acquisitions and LIght Detection And Ranging (LIDAR) waveform and photon counting signals.

Subfields: Ecology, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/aa892fb9cc70066f7d359f052432fc955d30ef52

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Citations in the corpus

PROSPECT-PRO for estimating content of nitrogen-containing leaf proteins and other carbon-based constituents (2020, methodology): “…of PROSPECT-PRO for simulating sufficiently accurate imaging spectroscopy data of canopies when being coupled with canopy RT models such as SAIL (Berger et al., 2018; Jacquemoud et al., 2009; Verhoef et al., 2007), SCOPE (van der Tol et al., 2009) or DART (Gastellu-Etchegorry et al., 2017, 2015).”
A general framework of kernel-driven modeling in the thermal infrared domain (2021, background): “DART is one of the most comprehensive three-dimensional models to simulate the VNIR-TIR radiative transfer in natural and urban landscapes (Gastellu-Etchegorry et al., 2015, 2017).”
Remote sensing of solar-induced chlorophyll fluorescence (SIF) in vegetation: 50 years of progress. (2019, methodology): “The DART model (Gastellu-Etchegorry et al., 2017, 2015, 1996) was designed to simulate both the 3D radiative budget and remote sensing observations of any urban and natural landscape with a 3D topography.”
Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods (2018, background): “The latest DART optical development includes also the specular reflectance and the light polarization (Gastellu-Etchegorry et al. 2015).”