He et al. (2025) APPLE-GO: Modeling high-spatial resolution forest canopy reflectance with effect of Adjacent Pixels using Path Length Extended Geometric Optical theory

Identification

• Journal: Remote Sensing of Environment
• Year: 2025
• Date: 2025-10-01
• Authors: Qunchao He, Siqi Yang, Naijie Peng, Wenjie Fan, Xihan Mu, Biao Cao, Dechao Zhai, Zhicheng Huang, Huazhong Ren, Guangjian Yan
• DOI: 10.1016/j.rse.2025.115043

Research Groups

• Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing, China
• Beijing Key Lab of Spatial Information Integration and Its Application, Peking University, Beijing, China
• State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing, China
• Beijing Engineering Research Center for Global Land Remote Sensing Products, Faculty of Geographical Science, Beijing Normal University, Beijing, China
• State Key Laboratory of Remote Sensing Science, Advanced Interdisciplinary Institute of Satellite Applications, Beijing Normal University, Beijing, China

Short Summary

This study introduces APPLE-GO, a novel high-spatial resolution forest canopy reflectance model that comprehensively accounts for shading and cross-radiation effects from adjacent pixels. The model demonstrates high accuracy in calculating the bi-directional reflectance factor (BRF), validated against a 3D radiative transfer model and satellite observations.

Objective

• To develop a high-spatial resolution forest canopy reflectance model (APPLE-GO) that accurately considers the shading effect and cross-radiation caused by adjacent pixels, thereby overcoming limitations of classic physical models at resolutions below 10 meters.

Study Configuration

• Spatial Scale: High spatial resolution, specifically for pixels below 10 meters.
• Temporal Scale: Instantaneous reflectance modeling.

Methodology and Data

• Models used:
  • APPLE-GO (proposed model)
  • LESS (three-dimensional radiative transfer model, used for evaluation)
• Data sources:
  • Simulations from the 3D radiative transfer model LESS.
  • Satellite observations for larch and mixed forests.

Main Results

• The APPLE-GO model's bi-directional reflectance factor (BRF) showed root mean square errors (RMSEs) of 0.008 and 0.054, with relative root mean square errors (RRMSEs) of 10.2 % and 15.9 %, in the red and near-infrared bands, respectively, when compared to the LESS model.
• Validation with satellite observations yielded RMSEs below 0.01 (RRMSE <27 %) for larch forests and under 0.017 (RRMSE <35 %) for mixed forests in the visible bands.
• The model accurately calculates the BRF in the nadir viewing direction, indicating its potential for extracting vegetation parameters from high-resolution remotely sensed imagery.

Contributions

• Proposes APPLE-GO, a new high-spatial resolution forest canopy reflectance model specifically designed to address the significant radiative influences from adjacent pixels.
• Integrates the comprehensive consideration of shading effects and cross-radiation from adjacent pixels, which are often simplified or ignored in classic physical models.
• Introduces the two-dimensional path length distribution (2-PLD) method and shading factors to quantitatively calculate the area fractions of sunlit components, accounting for adjacent pixel effects.
• Utilizes spectral invariant theory and an eight-neighborhood convolution algorithm for calculating multiple scattering energy.

Funding

Not specified in the provided text.

Citation

@article{He2025APPLEGO,
  author = {He, Qunchao and Yang, Siqi and Peng, Naijie and Fan, Wenjie and Mu, Xihan and Cao, Biao and Zhai, Dechao and Huang, Zhicheng and Ren, Huazhong and Yan, Guangjian},
  title = {APPLE-GO: Modeling high-spatial resolution forest canopy reflectance with effect of Adjacent Pixels using Path Length Extended Geometric Optical theory},
  journal = {Remote Sensing of Environment},
  year = {2025},
  doi = {10.1016/j.rse.2025.115043},
  url = {https://doi.org/10.1016/j.rse.2025.115043}
}