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

Abstract

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 (CIRE). The empirical model between CCC and CIRE 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-CIRE relationships and then evaluated CCC estimation using the CIRE-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 CIRE 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 R 2 of 0.82 and an RMSE of 0.36 g m−2, which were substantially better than the model without considering the prior information (R 2 = 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.