Modelling forest canopy reflectance over sloping terrain with an extended GSV and PROSAIL Model

Canopy reflectance (CR) models describe the transfer and interaction of radiation from the soil background to the canopy layer and play a vital role in the retrieval of biophysical variables. However, few efforts have focused on estimating soil background scattering operators, resulting in uncertainties in CR modelling, especially over sloping terrain. This study developed a canopy reflectance model for simulating CR over sloping terrain, which combines the general spectral vector (GSV) model, the PROSPECT model, and 4SAIL model coupled with topography (GSV-PROSAILT). The canopy reflectance simulated by GSV-PROSAILT was validated against two datasets: discrete anisotropic radiative transfer (DART) simulations and remote sensing observations. A comparison with DART simulations under various conditions revealed that the GSV-PROSAILT model captures terrain-induced CR distortion with high accuracy (red band: coefficient of determination [Formula: see text], root-mean-square error (RMSE) = 0.007; near infrared (NIR) band: [Formula: see text], RMSE = 0.0098). The results of remote sensing observation verification revealed that the GSV-PROSAILT model can be successfully used in CR modelling. These validations confirmed the performance of GSV-PROSAILT in soil and canopy reflectance modelling over sloping terrain, indicating that it can provide a potential tool for biophysical variable retrieval over mountainous areas.

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Publication Details

Journal
International Journal of Digital Earth
Published
2025-06-18
DOI
https://doi.org/10.1080/17538947.2025.2520026
Primary Topic
Remote Sensing in Agriculture
Type
article
Field-Weighted Citation Impact
0.00

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article

Modelling forest canopy reflectance over sloping terrain with an extended GSV and PROSAIL Model

Junxiang Tan, Chenghao Liu, Ronghao Yang, Hu Guyue et al.
International Journal of Digital Earth
Remote Sensing in Agriculture
article

Modelling forest canopy reflectance over sloping terrain with an extended GSV and PROSAIL Model

Junxiang Tan, Chenghao Liu, Ronghao Yang, Hu Guyue, Shaoda Li, Siqi Li, Wang Xiao
article en

Abstract

Canopy reflectance (CR) models describe the transfer and interaction of radiation from the soil background to the canopy layer and play a vital role in the retrieval of biophysical variables. However, few efforts have focused on estimating soil background scattering operators, resulting in uncertainties in CR modelling, especially over sloping terrain. This study developed a canopy reflectance model for simulating CR over sloping terrain, which combines the general spectral vector (GSV) model, the PROSPECT model, and 4SAIL model coupled with topography (GSV-PROSAILT). The canopy reflectance simulated by GSV-PROSAILT was validated against two datasets: discrete anisotropic radiative transfer (DART) simulations and remote sensing observations. A comparison with DART simulations under various conditions revealed that the GSV-PROSAILT model captures terrain-induced CR distortion with high accuracy (red band: coefficient of determination [Formula: see text], root-mean-square error (RMSE) = 0.007; near infrared (NIR) band: [Formula: see text], RMSE = 0.0098). The results of remote sensing observation verification revealed that the GSV-PROSAILT model can be successfully used in CR modelling. These validations confirmed the performance of GSV-PROSAILT in soil and canopy reflectance modelling over sloping terrain, indicating that it can provide a potential tool for biophysical variable retrieval over mountainous areas.

International Journal of Digital EarthVol. 18(1)
Planetary Science Institute (US), Chengdu University of Technology (CN), Chengdu University (CN)
National Natural Science Foundation of China, National Key Research and Development Program of China
Life in Land
Openalex Percentile: Top 86%
Remote Sensing in Agriculture
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Modelling forest canopy reflectance over sloping terrain with an extended GSV and PROSAIL Model — Junxiang Tan, Chenghao Liu, et al. · International Journal of Digital Earth (2025) | TGRS Research Map | TGRS