A hybrid model coupling PROSAIL and continuous wavelet transform based on multi-angle hyperspectral data improves maize chlorophyll retrieval

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Chlorophyll is both a cornerstone of plant photosynthesis and an important indicator for assessing crop growth and health. Although many previous studies have explored the use of remote sensing to retrieve chlorophyll content, there is room for improvement in the proposed retrieval models, especially the hybrid model, and its performance in combination with multi-angle remote sensing remains unknown. To this end, we developed a hybrid chlorophyll retrieval model by coupling PROSAIL, Gaussian process regression, and continuous wavelet transform (CWT) based on multi-angle (−60° to 60°) hyperspectral observations of maize. The CWT converts PROSAIL-modeled and measured spectral reflectance into wavelet features (WF) that finely capture signals due to chlorophyll changes, making WF-based hybrid models (HMWF) promising for enhanced chlorophyll retrieval. Our results show that for leaf chlorophyll content (LCC) and canopy chlorophyll content (CCC) retrieval, combining low and medium scale WFs (scales3-5) with hybrid models is more advantageous than using other scale WFs. The accuracy of the HMWF based on the best-scale WF was significantly higher than that of the hybrid model based on original spectrum or vegetation indices. Additionally, our evaluation of the effect of viewing zenith angles (VZAs) on HMWF showed that the accuracies of HMWF acquired at non-nadir angles were generally higher than those acquired at nadir angle. Among all models, the HMWF based on the scale3 WF had the highest accuracy at −10°, with R2 = 0.85 and RMSE=3.55 for LCC retrievals, and R2 = 0.78 and RMSE=0.22 for CCC retrievals. Furthermore, the HMWF showed the least sensitivity to changes in VZAs, especially in the range of −10° to −40°. Overall, these findings highlight the effectiveness of HMWF with multi-angle hyperspectral data in improving chlorophyll retrieval accuracy. This study serves as a reference for crop parameter retrieval, crucial for advancing agricultural monitoring and management.

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