Unifying radiative transfer models in computer graphics and remote sensing, Part I: A survey
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Author/Creator ORCID
Date
2023-12-02
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Citation of Original Publication
Salesin, Katherine, Kirk D. Knobelspiesse, Jacek Chowdhary, Peng-Wang Zhai, and Wojciech Jarosz. ?Unifying Radiative Transfer Models in Computer Graphics and Remote Sensing, Part I: A Survey.? Journal of Quantitative Spectroscopy and Radiative Transfer 314 (February 1, 2024): 108847. https://doi.org/10.1016/j.jqsrt.2023.108847.
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This work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.
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Public Domain
Abstract
The constellation of Earth-observing satellites continuously collects measurements of scattered radiance, which must be transformed into geophysical parameters in order to answer fundamental scientific questions about the Earth. Retrieval of these parameters requires highly flexible, accurate, and fast forward and inverse radiative transfer models. Existing forward models used by the remote sensing community are typically accurate and fast, but sacrifice flexibility by assuming the atmosphere or ocean is composed of plane-parallel layers. Monte Carlo forward models can handle more complex scenarios such as 3D spatial heterogeneity, but are relatively slower. We propose looking to the computer graphics community for inspiration to improve the statistical efficiency of Monte Carlo forward models and explore new approaches to inverse models for remote sensing. In Part 1 of this work, we examine the evolution of radiative transfer models in computer graphics and highlight recent advancements that have the potential to push forward models in remote sensing beyond their current periphery of realism.