Bi-directional Monte-Carlo modelling of solar-induced chlorophyll fluorescence images for 3D vegetation canopies in the DART model
•Monte Carlo solar-induced fluorescence (SIF) modeling was introduced in DART-Lux.•SIF emission equations were adapted to the bi-directional path tracing algorithm.•The new modeling results were compared to SCOPE and DART-FT for 3 types of canopies.•The potential of DART-Lux to simulate large scale...
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| Published in | International journal of applied earth observation and geoinformation Vol. 118; p. 103254 |
|---|---|
| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
01.04.2023
Elsevier |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1569-8432 1872-826X 1872-826X |
| DOI | 10.1016/j.jag.2023.103254 |
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| Abstract | •Monte Carlo solar-induced fluorescence (SIF) modeling was introduced in DART-Lux.•SIF emission equations were adapted to the bi-directional path tracing algorithm.•The new modeling results were compared to SCOPE and DART-FT for 3 types of canopies.•The potential of DART-Lux to simulate large scale SIF images was shown.
Remote sensing (RS) of solar-induced chlorophyll fluorescence (SIF) has a great potential for monitoring plant photosynthetic activity. Radiative transfer models (RTM) are essential to better interpret and extract information from SIF signals. DART is one of the most comprehensive and accurate 3D RTMs. Its standard mode DART-FT simulates SIF using a discrete ordinates method but is not adapted to large landscapes due to computational constraints. DART-Lux, the new mode based on a bi-directional path tracing algorithm, greatly improves DART computational efficiency for simulating images. This paper presents the theory of a novel SIF modelling algorithm in DART-Lux. We verified its accuracy with DART-FT and the SCOPE model for three types of canopies: turbid medium, maize field and forest. DART-Lux closely matches DART-FT (relative difference < 2%) with much better computational efficiency depending on the scene complexity, number of spectral bands and needed accuracy. For example, simulation time is reduced by a factor of ≈48, and memory usage by ≈50 for a maize field at 1 cm resolution. It allowed to simulate SIF images of large scenes as the 3×3km2 Ripperdan agricultural site that DART-FT could not simulate. The new SIF modelling algorithm opens new horizons for RS studies of large and complex landscapes. It is available as part of released DART versions (v1152 onwards) (https://dart.omp.eu/). |
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| AbstractList | Remote sensing (RS) of solar-induced chlorophyll fluorescence (SIF) has a great potential for monitoring plant photosynthetic activity. Radiative transfer models (RTM) are essential to better interpret and extract information from SIF signals. DART is one of the most comprehensive and accurate 3D RTMs. Its standard mode DART-FT simulates SIF using a discrete ordinates method but is not adapted to large landscapes due to computational constraints. DART-Lux, the new mode based on a bi-directional path tracing algorithm, greatly improves DART computational efficiency for simulating images. This paper presents the theory of a novel SIF modelling algorithm in DART-Lux. We verified its accuracy with DART-FT and the SCOPE model for three types of canopies: turbid medium, maize field and forest. DART-Lux closely matches DART-FT (relative difference < 2%) with much better computational efficiency depending on the scene complexity, number of spectral bands and needed accuracy. For example, simulation time is reduced by a factor of ≈48, and memory usage by ≈50 for a maize field at 1 cm resolution. It allowed to simulate SIF images of large scenes as the 3 x 3 km$^2$ Ripperdan agricultural site that DART-FT could not simulate. The new SIF modelling algorithm opens new horizons for RS studies of large and complex landscapes. It is available as part of released DART versions (v1152 onwards) (https://dart.omp.eu/). Remote sensing (RS) of solar-induced chlorophyll fluorescence (SIF) has a great potential for monitoring plant photosynthetic activity. Radiative transfer models (RTM) are essential to better interpret and extract information from SIF signals. DART is one of the most comprehensive and accurate 3D RTMs. Its standard mode DART-FT simulates SIF using a discrete ordinates method but is not adapted to large landscapes due to computational constraints. DART-Lux, the new mode based on a bi-directional path tracing algorithm, greatly improves DART computational efficiency for simulating images. This paper presents the theory of a novel SIF modelling algorithm in DART-Lux. We verified its accuracy with DART-FT and the SCOPE model for three types of canopies: turbid medium, maize field and forest. DART-Lux closely matches DART-FT (relative difference < 2%) with much better computational efficiency depending on the scene complexity, number of spectral bands and needed accuracy. For example, simulation time is reduced by a factor of ≈48, and memory usage by ≈50 for a maize field at 1 cm resolution. It allowed to simulate SIF images of large scenes as the 3×3km2 Ripperdan agricultural site that DART-FT could not simulate. The new SIF modelling algorithm opens new horizons for RS studies of large and complex landscapes. It is available as part of released DART versions (v1152 onwards) (https://dart.omp.eu/). Remote sensing (RS) of solar-induced chlorophyll fluorescence (SIF) has a great potential for monitoring plant photosynthetic activity. Radiative transfer models (RTM) are essential to better interpret and extract information from SIF signals. DART is one of the most comprehensive and accurate 3D RTMs. Its standard mode DART-FT simulates SIF using a discrete ordinates method but is not adapted to large landscapes due to computational constraints. DART-Lux, the new mode based on a bi-directional path tracing algorithm, greatly improves DART computational efficiency for simulating images. This paper presents the theory of a novel SIF modelling algorithm in DART-Lux. We verified its accuracy with DART-FT and the SCOPE model for three types of canopies: turbid medium, maize field and forest. DART-Lux closely matches DART-FT (relative difference < 2%) with much better computational efficiency depending on the scene complexity, number of spectral bands and needed accuracy. For example, simulation time is reduced by a factor of ≈48, and memory usage by ≈50 for a maize field at 1 cm resolution. It allowed to simulate SIF images of large scenes as the 3×3km2 Ripperdan agricultural site that DART-FT could not simulate. The new SIF modelling algorithm opens new horizons for RS studies of large and complex landscapes. It is available as part of released DART versions (v1152 onwards) (https://dart.omp.eu/). •Monte Carlo solar-induced fluorescence (SIF) modeling was introduced in DART-Lux.•SIF emission equations were adapted to the bi-directional path tracing algorithm.•The new modeling results were compared to SCOPE and DART-FT for 3 types of canopies.•The potential of DART-Lux to simulate large scale SIF images was shown. Remote sensing (RS) of solar-induced chlorophyll fluorescence (SIF) has a great potential for monitoring plant photosynthetic activity. Radiative transfer models (RTM) are essential to better interpret and extract information from SIF signals. DART is one of the most comprehensive and accurate 3D RTMs. Its standard mode DART-FT simulates SIF using a discrete ordinates method but is not adapted to large landscapes due to computational constraints. DART-Lux, the new mode based on a bi-directional path tracing algorithm, greatly improves DART computational efficiency for simulating images. This paper presents the theory of a novel SIF modelling algorithm in DART-Lux. We verified its accuracy with DART-FT and the SCOPE model for three types of canopies: turbid medium, maize field and forest. DART-Lux closely matches DART-FT (relative difference < 2%) with much better computational efficiency depending on the scene complexity, number of spectral bands and needed accuracy. For example, simulation time is reduced by a factor of ≈48, and memory usage by ≈50 for a maize field at 1 cm resolution. It allowed to simulate SIF images of large scenes as the 3×3km2 Ripperdan agricultural site that DART-FT could not simulate. The new SIF modelling algorithm opens new horizons for RS studies of large and complex landscapes. It is available as part of released DART versions (v1152 onwards) (https://dart.omp.eu/). |
| ArticleNumber | 103254 |
| Author | Wang, Yingjie Guilleux, Jordan Chavanon, Eric Regaieg, Omar Lauret, Nicolas Gastellu-Etchegorry, Jean-Philippe |
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| Cites_doi | 10.1016/0034-4257(84)90057-9 10.1016/j.rse.2018.04.012 10.1016/j.rse.2019.04.030 10.6028/NBS.TN.910-2 10.1016/j.rse.2018.05.035 10.5194/bg-6-3109-2009 10.1016/0034-4257(95)00253-7 10.1016/j.rse.2019.111274 10.1080/02757250009532389 10.1016/j.rse.2020.111902 10.1016/j.rse.2016.10.036 10.1016/j.rse.2021.112497 10.1016/j.rse.2017.02.012 10.1016/j.rse.2017.08.029 10.1111/gcb.14302 10.1145/218380.218498 10.5194/gmd-13-4041-2020 10.1016/0034-4257(91)90023-Y 10.1007/s10712-018-9478-y 10.1016/j.rse.2020.112195 10.1016/j.rse.2021.112564 10.1016/j.rse.2021.112673 10.1016/j.rse.2015.06.004 10.1073/pnas.1320008111 10.1093/jxb/eru191 10.1016/j.rse.2022.112973 10.1016/j.jqsrt.2020.107183 10.1016/j.rse.2020.111722 |
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| Keywords | DART SIF 3D vegetation structure Bi-directional path tracing Remote sensing |
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| Snippet | •Monte Carlo solar-induced fluorescence (SIF) modeling was introduced in DART-Lux.•SIF emission equations were adapted to the bi-directional path tracing... Remote sensing (RS) of solar-induced chlorophyll fluorescence (SIF) has a great potential for monitoring plant photosynthetic activity. Radiative transfer... |
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| SubjectTerms | 3D vegetation structure algorithms Bi-directional path tracing chlorophyll corn DART Environmental Engineering Environmental Sciences forests memory photosynthesis radiative transfer Remote sensing SIF spatial data |
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| Title | Bi-directional Monte-Carlo modelling of solar-induced chlorophyll fluorescence images for 3D vegetation canopies in the DART model |
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