Improving characteristic band selection in leaf biochemical property estimation considering interrelations among biochemical parameters based on the PROSPECT-D model.
Improving characteristic band selection in leaf biochemical property estimation considering interrelations among biochemical parameters based on the PROSPECT-D model.
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DOI：
10.1364/oe.414050
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发表时间：
2020-12
期刊：
影响因子：
3.8
通讯作者：
Jian Yang;Songxi Yang;Yangyang Zhang;S. Shi;L. Du
中科院分区：
文献类型：
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作者：
Jian Yang;Songxi Yang;Yangyang Zhang;S. Shi;L. Du
目前许多研究主要集中在分析敏感性和相关性来选择特征波段。然而，生化参数之间的相互关系被忽略，这可能会显着影响生化浓度反演的准确性。本研究旨在提出一种新的波段选择方法，并着眼于在考虑不同性状之间的相互关系时，提高叶片性状估计中特征波段组合的幅度。因此，本研究首先提出了一种考虑不同波长之间的敏感性和相关性的基于排序和搜索的方法，该方法可以增强光谱带选择的可靠性，为叶片结构指数和五个叶片生化参数（包括叶绿素（Chl）、类胡萝卜素（Car）、叶片单位面积干物质（LMA）、等效水厚度（EWT）和叶面积）选择特征波段子集。 花青素 (Anth)) 基于 PROSPECT-D 模型。然后根据物理模型对这些特征带进行验证，该物理模型使用一个合成数据集和六个叶级光谱实验数据集检索五种生化特性。其次，更具创新性的是，为了探索不同生化参数之间的相互关系，采用性状-性状带组合来检索和分析上述五个生化参与者如何相互影响。结果表明，LMA（809 和 2278 nm）、EWT（1386、1414 和 1894 nm）的组合在 LMA 和 EWT 估计中比各自检索更有利：与单独的 LMA 特征波段检索相比，LMA-EWT 波段组合检索在两个数据集中分别将 R2 提高了 0.5782 和 0.1824。此外，在考虑生化参数之间的相互关系时，Chl、EWT、Car 和 Anth 估计的准确性也可以得到提高。实验结果表明，基于排序和搜索的方法是选择一组与植物性状叶片信息相关的光谱带的有效且高效的方法，而性状-性状组合侧重于探索叶片性状之间潜在的相互关系，有助于进一步提高检索精度。这项研究将为识别树叶和冠层生化特征的光谱响应提供非常先进的见解。
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 R2 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.