Coupling PROSPECT with Prior Estimation of Leaf Structure to Improve the Retrieval of Leaf Nitrogen Content in Ginkgo from Bidirectional Reflectance Factor Spectra.

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Leaf nitrogen content (LNC) is a crucial indicator for assessing the nitrogen status of forest trees. The LNC retrieval can be achieved with the inversion of the PROSPECT-PRO model. However, the LNC retrieval from the commonly used leaf bidirectional reflectance factor (BRF) spectra remains challenging arising from the confounding effects of mesophyll structure, specular reflection, and other chemicals such as water. To address this issue, this study proposed an advanced BRF spectra-based approach, by alleviating the specular reflection effects and enhancing the leaf nitrogen absorption signals from Ginkgo trees and saplings, using 3 modified ratio indices (i.e., mPrior_800, mPrior_1131, and mPrior_1365) for the prior estimation of the Nstruct structure parameter, combined with different inversion methods (STANDARD, sPROCOSINE, PROSDM, and PROCWT). The results demonstrated that the prior Nstruct estimation strategy using modified ratio indices outperformed standard ratio indices or nonperforming prior Nstruct estimation, especially for mPrior_1131 and mPrior_1365 yielding reliable performance for most constituents. With the use of the optimal approaches (i.e., PROCWT_S3 combined with mPrior_1131 or mPrior_1365), our results also revealed that the optimal estimation of LNCarea (normalized root mean square error [NRMSE] = 12.94% to 14.49%) and LNCmass (NRMSE = 10.11% to 10.75%) can be further achieved, with the selected optimal wavebands concentrated in 5 common main domains of 1440 to 1539 nm, 1580 to 1639 nm, 1900 to 1999 nm, 2020 to 2099 nm, and 2120 to 2179 nm. These findings highlight marked potentials of the novel BRF spectra-based approach to improve the estimation of LNC and enhance the understanding of the impact of Nstruct prior estimation on the LNC retrieval in leaves of Ginkgo trees and saplings.