MODTRAN4: radiative transfer modeling for remote sensing

2000 ◽  
Author(s):  
Gail P. Anderson ◽  
Alexander Berk ◽  
Prabhat K. Acharya ◽  
Michael W. Matthew ◽  
Lawrence S. Bernstein ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Radiative Transfer ◽  
Radiative Transfer Modeling

Hyperspectral Remote Sensing and Radiative Transfer Simulation as a Tool for Monitoring Coral Reef Health

2002 ◽  
Vol 36 (1) ◽  
pp. 4-13 ◽  
Author(s):  
Hiroya Yamano ◽  
Masayuki Tamura ◽  
Yoshimitsu Kunii ◽  
Michio Hidaka
Keyword(s):  
Remote Sensing ◽  
Coral Reef ◽  
Radiative Transfer ◽  
Feasibility Studies ◽  
Hyperspectral Remote Sensing ◽  
Hyperspectral Data ◽  
Dead Coral ◽  
Hyperspectral Sensors ◽  
Reef Health ◽  
Radiative Transfer Modeling

Recent advances in the remote sensing of coral reefs include hyperspectral remote sensing and radiative transfer modeling. Hyperspectral data can be regarded as continuous and the derivative spectroscopy is effective for extracting coral reef components, including sand, macroalgae, and healthy, bleached, recently dead, and old dead coral. Radiative transfer models are effective for feasibility studies of satellite or airborne remote sensing. Using these techniques, we simulate and analyze the apparent reflectance of coral reef benthic features associated with bleaching events, obtained by hyperspectral sensors on various platforms (ROV, boat, airplane, and satellite), and suggest that the coral reef health on reef flats can be discriminated precisely. Remote sensing using hyperspectral sensors should significantly contribute to mapping and monitoring coral reef health.

scholarly journals Correction: Miraglio, T., et al. Monitoring LAI, Chlorophylls, and Carotenoids Content of a Woodland Savanna Using Hyperspectral Imagery and 3D Radiative Transfer Modeling. Remote Sensing 2020, 12, 28

2020 ◽  
Vol 12 (14) ◽  
pp. 2263
Author(s):  
Thomas Miraglio ◽  
Karine Adeline ◽  
Margarita Huesca ◽  
Susan Ustin ◽  
Xavier Briottet
Keyword(s):  
Remote Sensing ◽  
Radiative Transfer ◽  
Hyperspectral Imagery ◽  
Validation Data ◽  
Radiative Transfer Modeling ◽  
Published Paper

The authors are sorry to report that some of the validation data used in their recently published paper [...]

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

10.5772/63887 ◽  
2016 ◽  
Author(s):  
Lucas Landier ◽  
Nicolas Lauret ◽  
Tiangang Yin ◽  
Ahmad Al Bitar ◽  
JeanPhilippe Gastellu-Etchegorry ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Radiative Transfer ◽  
Radiative Transfer Modeling

MODTRAN4: radiative transfer modeling for remote sensing

1999 ◽  
Author(s):  
Gail P. Anderson ◽  
Alexander Berk ◽  
Prabhat K. Acharya ◽  
Michael W. Matthew ◽  
Lawrence S. Bernstein ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Radiative Transfer ◽  
Radiative Transfer Modeling

scholarly journals Simulating Imaging Spectroscopy in Tropical Forest with 3D Radiative Transfer Modeling

2021 ◽  
Vol 13 (11) ◽  
pp. 2120
Author(s):  
Dav M. Ebengo ◽  
Florian de Boissieu ◽  
Grégoire Vincent ◽  
Christiane Weber ◽  
Jean-Baptiste Féret
Keyword(s):  
Remote Sensing ◽  
Radiative Transfer ◽  
Tropical Forest ◽  
Physical Modeling ◽  
Imaging Spectroscopy ◽  
Species Discrimination ◽  
Canopy Reflectance ◽  
Modeling Tools ◽  
Airborne Imaging ◽  
Radiative Transfer Modeling

Optical remote sensing can contribute to biodiversity monitoring and species composition mapping in tropical forests. Inferring ecological information from canopy reflectance is complex and data availability suitable to such a task is limiting, which makes simulation tools particularly important in this context. We explored the capability of the 3D radiative transfer model DART (Discrete Anisotropic Radiative Transfer) to simulate top of canopy reflectance acquired with airborne imaging spectroscopy in a complex tropical forest, and to reproduce spectral dissimilarity within and among species, as well as species discrimination based on spectral information. We focused on two factors contributing to these canopy reflectance properties: the horizontal variability in leaf optical properties (LOP) and the fraction of non-photosynthetic vegetation (NPVf). The variability in LOP was induced by changes in leaf pigment content, and defined for each pixel based on a hybrid approach combining radiative transfer modeling and spectral indices. The influence of LOP variability on simulated reflectance was tested by considering variability at species, individual tree crown and pixel level. We incorporated NPVf into simulations following two approaches, either considering NPVf as a part of wood area density in each voxel or using leaf brown pigments. We validated the different scenarios by comparing simulated scenes with experimental airborne imaging spectroscopy using statistical metrics, spectral dissimilarity (within crowns, within species, and among species dissimilarity) and supervised classification for species discrimination. The simulation of NPVf based on leaf brown pigments resulted in the closest match between measured and simulated canopy reflectance. The definition of LOP at pixel level resulted in conservation of the spectral dissimilarity and expected performances for species discrimination. Therefore, we recommend future research on forest biodiversity using physical modeling of remote-sensing data to account for LOP variability within crowns and species. Our simulation framework could contribute to better understanding of performances of species discrimination and the relationship between spectral variations and taxonomic and functional dimensions of biodiversity. This work contributes to the improved integration of physical modeling tools for applications, focusing on remotely sensed monitoring of biodiversity in complex ecosystems, for current sensors, and for the preparation of future multispectral and hyperspectral satellite missions.

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