Semi-empirical models for estimating canopy chlorophyll content: the importance of prior information

ABSTRACT

The canopy chlorophyll content (CCC) provides valuable information about the crop growth status. CCC can be estimated using remote sensing techniques, such as through the red-edge-based chlorophyll index (CI_RE). The empirical model between CCC and CI_RE calibrated using the measured dataset lacks generality. Therefore, the semi-empirical model is a better choice, which is calibrated on the physical model simulations. However, the effect of parameter settings of physical models on semi-empirical models is not clear. This study first investigated the effects of dry matter content (LMA) and mesophyll structural coefficient (N_s) on the CCC-CI_RE relationships and then evaluated CCC estimation using the CI_RE-based semi-empirical model calibrated on simulated datasets with different ranges of LMA and N_s. The results showed that the relationships between CCC and CI_RE were sensitive to N_s and LMA. Therefore, after considering the prior information of N_s (1.0–1.5) and LMA (20–80 g m⁻²) for the crop, the best estimation of CCC was obtained with an R² of 0.82 and an RMSE of 0.36 g m⁻², which were substantially better than the model without considering the prior information (R² = 0.40 and RMSE = 0.67 g m⁻²). These findings improved our understanding of CCC estimation using the semi-empirical model and would facilitate the accurate mapping of CCC for agricultural management.

KEYWORDS:

• canopy chlorophyll content
• vegetation index
• semi-empirical model
• prior information

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data used in this study are available upon request.

Additional information

Funding

This work was supported by grants from the National Natural Science Foundation of China (42101360, 32021004), the Jiangsu Funding Program for Excellent Postdoctoral Talent (2022ZB333), the Fellowship of China Postdoctoral Science Foundation (2022M710070), and Collaborative Innovation Center for Modern Crop Production co-sponsored by Province and Ministry. We are grateful to the reviewers for their suggestions and comments, which significantly improved the quality of this paper.