Accurate and rapid assessment of canopy chlorophyll content (CCC) is essential for monitoring rice growth and development. Canopy structure strongly influences canopy spectral variations, affecting CCC estimation accuracy. This study utilized the canopy scattering coefficient (CSC) to normalize canopy structural effects, thereby improving the accuracy of CCC estimation. Field experiments conducted in 2023 and 2024 in Hubei Province, China, measured canopy reflectance, leaf area index (LAI), aboveground biomass (AGB), leaf chlorophyll content, and leaf spectra of paddy rice at different growth stages. The CSC was calculated based on spectral invariant theory and used to replace red-edge and near-infrared bands in traditional vegetation indices (VIs). Results showed significant differences between CCC derived from LAI (CCC LAI ) and from AGB (CCC AGB ). For the same VI, CCC LAI generally exhibited higher correlation than CCC AGB . CSC-optimized VIs further enhanced these correlations, confirming that CCC LAI was more suitable for CCC estimation. Incorporating CSC improved the relationship between VIs and CCC, significantly enhancing estimation accuracy. Among CSC-optimized VIs, the CSC MERIS Terrestrial Chlorophyll Index, CSC Canopy Chlorophyll Content Index, CSC Chlorophyll Indexed Edge, CSC Normalized Difference Red-edge, and the CSC Multiplicative Vegetation Index (CSC LCI × CSC NDRE) achieved high correlations with CCC LAI , all with R² values around 0.8. Overall, this study highlights the potential of the CSC for improving CCC estimation and provides a valuable reference for remote sensing-based phenotyping in rice.
Ma et al. (Sun,) studied this question.