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

Ebengo, Dav M.; Boissieu, Florian de; Vincent, Grégoire; Weber, Christiane; Féret, Jean‐Baptiste

doi:10.20944/preprints202104.0644.v1

Cited by 3 publications

(1 citation statement)

References 99 publications

(137 reference statements)

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“…Even in 3D RTMs, most studies use simple 3D geometries (e.g., ellipsoid or cone archetypes) to represent the 3D structure of trees (Cescatti, 1997; Kobayashi & Iwabuchi, 2008; Ni et al., 1999). The combination of these archetypes with the other inputs of the RTMs permits the generation of 3D scenes of different levels of complexity (Ebengo et al., 2021). Within‐canopy clumping is typically not considered by crown archetypes.…”

Section: Introductionmentioning

confidence: 99%

Implications of 3D Forest Stand Reconstruction Methods for Radiative Transfer Modeling: A Case Study in the Temperate Deciduous Forest

et al. 2022

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This study investigated the implications of different assumptions of 3D forest stand reconstructions for the accuracy and efficiency of radiative transfer (RT) modeling based on two highly detailed 3D stand representations: 3D‐explicit and voxel‐based. The discrete anisotropic radiative transfer (DART) model was used for RT simulations. The 3D‐explicit and voxel‐based 3D forest scenes were used as structural inputs for the DART model, respectively. Using the 3D‐explicit RT simulation as the benchmark, the accuracy and efficiency of the voxel‐based RT simulation were evaluated under multiple simulation conditions. The results showed that for voxel‐based RT simulations: with voxel sizes 0.1, 1, and 10 m and in blue, green, red, and near‐infrared wavebands, the normalized deviations of simulated directional reflectance exceeded the 5% tolerance limit in 89% viewing directions; with voxel sizes 0.2, 1, and 10 m, the normalized deviations of simulated spectral albedo exceeded the 5% tolerance limit in 90.5% wavelengths; for simulated spectral albedo in blue, green, red, and near‐infrared wavebands and fraction of absorbed photosynthetically active radiation, the normalized deviations exceeded the 5% tolerance limit in 65.3% voxel sizes and spatial resolutions. The two major causes for differences in the 3D‐explicit versus voxel‐based RT simulations were: (a) the difference between light interaction in spatially explicit objects and in turbid medium, and (b) the structural difference of 3D contours between voxel‐based and 3D‐explicit models. However, voxel‐based RT simulations were substantially more computationally efficient than 3D‐explicit RT simulations in large voxel sizes (≥1 m) and coarse spatial resolutions (≥1 m).

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Section: Introductionmentioning

confidence: 99%

Implications of 3D Forest Stand Reconstruction Methods for Radiative Transfer Modeling: A Case Study in the Temperate Deciduous Forest

et al. 2022

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Gaussian Process Regression Hybrid Models for the Top-of-Atmosphere Retrieval of Vegetation Traits Applied to PRISMA and EnMAP Imagery

Pascual-Venteo,

Garcia,

Berger

et al. 2024

Remote Sensing

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The continuous monitoring of the terrestrial Earth system by a growing number of optical satellite missions provides valuable insights into vegetation and cropland characteristics. Satellite missions typically provide different levels of data, such as level 1 top-of-atmosphere (TOA) radiance and level 2 bottom-of-atmosphere (BOA) reflectance products. Exploiting TOA radiance data directly offers the advantage of bypassing the complex atmospheric correction step, where errors can propagate and compromise the subsequent retrieval process. Therefore, the objective of our study was to develop models capable of retrieving vegetation traits directly from TOA radiance data from imaging spectroscopy satellite missions. To achieve this, we constructed hybrid models based on radiative transfer model (RTM) simulated data, thereby employing the vegetation SCOPE RTM coupled with the atmosphere LibRadtran RTM in conjunction with Gaussian process regression (GPR). The retrieval evaluation focused on vegetation canopy traits, including the leaf area index (LAI), canopy chlorophyll content (CCC), canopy water content (CWC), the fraction of absorbed photosynthetically active radiation (FAPAR), and the fraction of vegetation cover (FVC). Employing band settings from the upcoming Copernicus Hyperspectral Imaging Mission (CHIME), two types of hybrid GPR models were assessed: (1) one trained at level 1 (L1) using TOA radiance data and (2) one trained at level 2 (L2) using BOA reflectance data. Both the TOA- and BOA-based GPR models were validated against in situ data with corresponding hyperspectral data obtained from field campaigns. The TOA-based hybrid GPR models revealed a range of performance from moderate to optimal results, thus reaching R2 = 0.92 (LAI), R2 = 0.72 (CCC) and 0.68 (CWC), R2 = 0.94 (FAPAR), and R2 = 0.95 (FVC). To demonstrate the models’ applicability, the TOA- and BOA-based GPR models were subsequently applied to imagery from the scientific precursor missions PRISMA and EnMAP. The resulting trait maps showed sufficient consistency between the TOA- and BOA-based models, with relative errors between 4% and 16% (R2 between 0.68 and 0.97). Altogether, these findings illuminate the path for the development and enhancement of machine learning hybrid models for the estimation of vegetation traits directly tailored at the TOA level.

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Bitemporal Radiative Transfer Modeling Using Bitemporal 3D-Explicit Forest Reconstruction from Terrestrial Laser Scanning

Liu,

Calders,

Origo

et al. 2024

Remote Sensing

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Radiative transfer models (RTMs) are often used to retrieve biophysical parameters from earth observation data. RTMs with multi-temporal and realistic forest representations enable radiative transfer (RT) modeling for real-world dynamic processes. To achieve more realistic RT modeling for dynamic forest processes, this study presents the 3D-explicit reconstruction of a typical temperate deciduous forest in 2015 and 2022. We demonstrate for the first time the potential use of bitemporal 3D-explicit RT modeling from terrestrial laser scanning on the forward modeling and quantitative interpretation of: (1) remote sensing (RS) observations of leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR), and canopy light extinction, and (2) the impact of canopy gap dynamics on light availability of explicit locations. Results showed that, compared to the 2015 scene, the hemispherical-directional reflectance factor (HDRF) of the 2022 forest scene relatively decreased by 3.8% and the leaf FAPAR relatively increased by 5.4%. At explicit locations where canopy gaps significantly changed between the 2015 scene and the 2022 scene, only under diffuse light did the branch damage and closing gap significantly impact ground light availability. This study provides the first bitemporal RT comparison based on the 3D RT modeling, which uses one of the most realistic bitemporal forest scenes as the structural input. This bitemporal 3D-explicit forest RT modeling allows spatially explicit modeling over time under fully controlled experimental conditions in one of the most realistic virtual environments, thus delivering a powerful tool for studying canopy light regimes as impacted by dynamics in forest structure and developing RS inversion schemes on forest structural changes.

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

Cited by 3 publications

References 99 publications

Implications of 3D Forest Stand Reconstruction Methods for Radiative Transfer Modeling: A Case Study in the Temperate Deciduous Forest

Implications of 3D Forest Stand Reconstruction Methods for Radiative Transfer Modeling: A Case Study in the Temperate Deciduous Forest

Gaussian Process Regression Hybrid Models for the Top-of-Atmosphere Retrieval of Vegetation Traits Applied to PRISMA and EnMAP Imagery

Bitemporal Radiative Transfer Modeling Using Bitemporal 3D-Explicit Forest Reconstruction from Terrestrial Laser Scanning

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