College of Earth Science, Chengdu University of Technology, Chengdu, China.
Geology and Surveying Engineering School, Chongqing Vocational Institute of Engineering, Chongqing, China.
PLoS One. 2021 Mar 30;16(3):e0249351. doi: 10.1371/journal.pone.0249351. eCollection 2021.
The leaf equivalent water thickness (EWT, g cm-2) and fuel moisture content (FMC, %) are key variables in ecological and environmental monitoring. Although a variety of hyperspectral vegetation indices have been developed to estimate the leaf EWT and FMC, most of these indices are defined considered two or three specific bands for a specific plant species, which limits their applicability. In this study, we proposed three new spectral absorption indices (SAI970, SAI1200, and SAI1660) for various plant types by considering the symmetry of the spectral absorption at 970 nm, 1200 nm and 1660 nm and spectral heterogeneity of different leaves. The indices were calculated considering the absorption peak and shoulder bands of each leaf instead of the same specific bands for all leaves. A pooled dataset of three tree species (camphor (VX), capricorn (VJ), and red-leaf plum (VL)) was used to test the performance of the SAIs in terms of the leaf EWT and FMC estimation. The results indicated that, first, SAI1200 was more suitable for estimating the EWT than FMC, whereas SAI970 and SAI1660 were more suitable for estimating the FMC. Second, SAI1200 achieved the most accurate estimation of the EWT with a cross-validation coefficient of determination (Rcv2) of 0.845 and relative cross-validation root mean square error (rRMSEcv) of 8.90%. Third, SAI1660 outperformed the other indices in estimating the FMC at the leaf level, with an Rcv2 of 0.637 and rRMSEcv of 8.56%. Fourth, SAI970 achieved a moderate accuracy in estimating the EWT (Rcv2 of 0.25 and rRMSEcv of 19.68%) and FMC (Rcv2 of 0.275 and rRMSEcv of 12.10%) at the leaf level. These results can enrich the application of the SAIs and demonstrate the potential of using SAI1200 to determine the leaf EWT and SAI1660 to obtain the leaf FMC among various plant types.
叶等效水厚度 (EWT，g cm-2) 和燃料水分含量 (FMC，%) 是生态和环境监测的关键变量。虽然已经开发了多种高光谱植被指数来估计叶 EWT 和 FMC，但这些指数中的大多数是针对特定植物物种的两个或三个特定波段定义的，这限制了它们的适用性。在这项研究中，我们通过考虑 970nm、1200nm 和 1660nm 处的光谱吸收对称性以及不同叶片的光谱异质性，为各种植物类型提出了三个新的光谱吸收指数 (SAI970、SAI1200 和 SAI1660)。该指数是通过考虑每个叶片的吸收峰和肩部波段而不是所有叶片的相同特定波段来计算的。使用三个树种（樟 (VX)、羊齿 (VJ) 和红叶李 (VL)) 的汇总数据集来测试 SAI 在叶 EWT 和 FMC 估计方面的性能。结果表明，首先，SAI1200 更适合估计 EWT，而 SAI970 和 SAI1660 更适合估计 FMC。其次，SAI1200 对 EWT 的估计最为准确，交叉验证决定系数 (Rcv2) 为 0.845，相对交叉验证均方根误差 (rRMSEcv) 为 8.90%。第三，SAI1660 在叶片水平上估计 FMC 的表现优于其他指数，Rcv2 为 0.637，rRMSEcv 为 8.56%。第四，SAI970 在叶片水平上对 EWT (Rcv2 为 0.25，rRMSEcv 为 19.68%) 和 FMC (Rcv2 为 0.275，rRMSEcv 为 12.10%) 的估计具有中等准确性。这些结果可以丰富 SAI 的应用，并证明在各种植物类型中使用 SAI1200 来确定叶 EWT 和 SAI1660 来获取叶 FMC 的潜力。
