Improving characteristic band selection in leaf biochemical property estimation considering interrelations among biochemical parameters based on the PROSPECT-D model.

At present, many studies have mainly focused on analyzing the sensitivity and correlation to select characteristic bands. However, the interrelations between biochemical parameters were ignored, which may significantly influence the accuracy of biochemical concentration retrieval. The study aims to propose a new band selection method and to focus on the improving magnitude of characteristic band combination in leaf trait estimation when taking interrelations among different traits into consideration. Thus, in this study, firstly a ranking- and searching-based method considering the sensitivity and correlation between different wavelengths, which can enhance the reliability of spectral band selection, was proposed to select a subset of characteristic bands for leaf structure index and five leaf biochemical parameters (including chlorophyll (Chl), carotenoid (Car), leaf dry matter per area (LMA), equivalent water thickness (EWT), and anthocyanin (Anth)) based on the PROSPECT-D model. These characteristic bands were then validated based on a physical model for retrieving five biochemical properties using one synthetic dataset and six experimental datasets on leaf-level spectra. Secondly, and more innovatively, to explore interrelations among different biochemical parameters, trait-trait band combinations were adopted to retrieve and analyze how the five biochemical participants above affected each other. The results demonstrated that the combination of LMA (809 and 2278 nm), EWT (1386, 1414, and 1894 nm) is more beneficial in LMA and EWT estimation than respective retrieval: LMA-EWT band combination retrieval improves R by 0.5782 and 0.1824 in two datasets, respectively, compared with solely LMA characteristic bands retrieval. What's more, the accuracy of Chl, EWT, Car, and Anth estimation can be also improved when considering interrelations between biochemical parameters. The experimental results show that the ranking- and searching-based method is an effective and efficient way to select a set of spectral bands related to the foliar information about plant traits, and trait-trait combinations, which focus on exploring latent interrelations between leaf traits, are useful in furthering improve retrieval accuracy. This research will provide notably advanced insight into identifying the spectral responses of biochemical traits in foliage and canopies.