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.

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.