The performance of directional area scattering factor proxies for correcting canopy structural and soil effects on leaf chlorophyll content estimation varies with canopy conditions in agricultural systems
4 affiliations
- 1Jiangsu Key Laboratory of Crop Genetics and Physiology/Jiangsu Key Laboratory of Crop Cultivation and Physiology, Agricultural College, Yangzhou University , ,
- 2Agro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs , ,
- 3Schoolof Geomatics, Zhejiang University of Water Resources and Electric Power , ,
- 4School of Computer and Software, Nanjing University of Industry Technology , ,
Abstract
Accurate leaf chlorophyll content (LCC) estimation from canopy reflectance is essential for agricultural monitoring but remains challenging due to confounding effects of canopy structure and soil background. While spectral invariants theory, implemented through directional area scattering factor (DASF) proxies, offers a framework to decouple these effects from canopy bidirectional reflectance factor (BRF), their comparative performance across diverse canopy conditions remains unexamined. In this study, we comprehensively evaluated four DASF proxies, practical DASF (pDASF), improved DASF (iDASF), near-infrared reflectance of vegetation (NIRv), and NIRv from hyperspectral measurements (NIRVH2), for mitigating structural and soil effects on BRF and improving LCC retrieval. Using PROSAIL-5 simulations with global sensitivity analysis, we quantified the capacity of DASF-corrected BRFs to enhance LCC sensitivity while suppressing confounding factors. We then evaluated their applicability for LCC estimation under diverse canopy conditions using ground hyperspectral and simulated Sentinel-2 MSI data. DASF-corrected BRFs significantly enhanced spectral sensitivity to LCC while minimizing structural and background interference. This correction consistently outperformed uncorrected BRF in LCC estimation, reducing RMSE by 10.90%–15.88% (BRF/pDASF), 0.38%–11.62% (BRF/iDASF), 6.05%–16.47% (BRF/NIRv), and 7.44%–16.62% (BRF/NIRvH2). Notably, NIRv and NIRvH2 demonstrated relatively better estimation performance than pDASF and iDASF in sparse canopies (LAI < 2 m²/m²), whereas pDASF performed reliably in dense canopies (LAI > 3 m²/m²). This study reveals the differential applicability of DASF proxies across canopy conditions and sensor configurations, thereby advancing operational LCC retrieval and offering a robust framework for non-invasive assessment of physiological and biochemical traits in agricultural systems.
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BibTeX
@article{bao2026performance,
title = {{The performance of directional area scattering factor proxies for correcting canopy structural and soil effects on leaf chlorophyll content estimation varies with canopy conditions in agricultural systems}},
author = {Ruisi Bao and Xiaoyu Zhu and Ziwei Wang and Mengdi Wang and Liwen Fang and Lei Ding and Ziwei Zhang and Zhenwang Li and Changwei Tan and Jun Zhu},
journal = {Frontiers in plant science},
year = {2026},
month = sep,
doi = {10.3389/fpls.2026.1884277},
url = {https://europepmc.org/article/PMC/PMC13661556},
note = {CC BY},
}
RIS
TY - JOUR
TI - The performance of directional area scattering factor proxies for correcting canopy structural and soil effects on leaf chlorophyll content estimation varies with canopy conditions in agricultural systems
AU - Ruisi Bao
AU - Xiaoyu Zhu
AU - Ziwei Wang
AU - Mengdi Wang
AU - Liwen Fang
AU - Lei Ding
AU - Ziwei Zhang
AU - Zhenwang Li
AU - Changwei Tan
AU - Jun Zhu
PY - 2026
DA - 2026/09/30
JO - Frontiers in plant science
DO - 10.3389/fpls.2026.1884277
UR - https://europepmc.org/article/PMC/PMC13661556
AB - Accurate leaf chlorophyll content (LCC) estimation from canopy reflectance is essential for agricultural monitoring but remains challenging due to confounding effects of canopy structure and soil background. While spectral invariants theory, implemented through directional area scattering factor (DASF) proxies, offers a framework to decouple these effects from canopy bidirectional reflectance factor (BRF), their comparative performance across diverse canopy conditions remains unexamined. In this study, we comprehensively evaluated four DASF proxies, practical DASF (pDASF), improved DASF (iDASF), near-infrared reflectance of vegetation (NIRv), and NIRv from hyperspectral measurements (NIRVH2), for mitigating structural and soil effects on BRF and improving LCC retrieval. Using PROSAIL-5 simulations with global sensitivity analysis, we quantified the capacity of DASF-corrected BRFs to enhance LCC sensitivity while suppressing confounding factors. We then evaluated their applicability for LCC estimation under diverse canopy conditions using ground hyperspectral and simulated Sentinel-2 MSI data. DASF-corrected BRFs significantly enhanced spectral sensitivity to LCC while minimizing structural and background interference. This correction consistently outperformed uncorrected BRF in LCC estimation, reducing RMSE by 10.90%–15.88% (BRF/pDASF), 0.38%–11.62% (BRF/iDASF), 6.05%–16.47% (BRF/NIRv), and 7.44%–16.62% (BRF/NIRvH2). Notably, NIRv and NIRvH2 demonstrated relatively better estimation performance than pDASF and iDASF in sparse canopies (LAI < 2 m²/m²), whereas pDASF performed reliably in dense canopies (LAI > 3 m²/m²). This study reveals the differential applicability of DASF proxies across canopy conditions and sensor configurations, thereby advancing operational LCC retrieval and offering a robust framework for non-invasive assessment of physiological and biochemical traits in agricultural systems.
SN - 1664-462X
ER -