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从单一指数中检索与叶片含水量相关的植被指数:以(拉比尔)和(D. 唐)为例的研究。

Retrieval of Vegetation Indices Related to Leaf Water Content from a Single Index: A Case Study of (Labill.) and (D. Don.).

作者信息

Villacrés Juan, Fuentes Andrés, Reszka Pedro, Cheein Fernando Auat

机构信息

Department of Electronic Engineering, Universidad Técnica Federico Santa María, Av. España 1680, Valparaiso 2390123, Chile.

Department of Industrial Engineering, Universidad Técnica Federico Santa María, Av. España 1680, Valparaiso 2390123, Chile.

出版信息

Plants (Basel). 2021 Apr 5;10(4):697. doi: 10.3390/plants10040697.

Abstract

The vegetation indices derived from spectral reflectance have served as an indicator of vegetation's biophysical and biochemical parameters. Some of these indices are capable of characterizing more than one parameter at a time. This study examines the feasibility of retrieving several spectral vegetation indices from a single index under the assumption that all these indices are correlated with water content. The models used are based on a linear regression adjusted with least squares. The spectral signatures of and , which constitute 97.5% of the forest plantation in Valparaiso region in Chile, have been used to test and validate the proposed approach. The linear models were fitted with an independent data set from which their performance was assessed. The results suggest that from the Leaf Water Index, other spectral indices can be recovered with a root mean square error up to 0.02, a bias of 1.12%, and a coefficient of determination of 0.77. The latter encourages using a sensor with discrete wavelengths instead of a continuum spectrum to estimate the forestry's essential parameters.

摘要

从光谱反射率得出的植被指数已成为植被生物物理和生化参数的一个指标。其中一些指数能够同时表征多个参数。本研究在所有这些指数都与含水量相关的假设下,探讨了从单一指数反演多个光谱植被指数的可行性。所使用的模型基于用最小二乘法调整的线性回归。构成智利瓦尔帕莱索地区97.5%人工林的 和 的光谱特征已被用于测试和验证所提出的方法。线性模型与一个独立数据集拟合,据此评估其性能。结果表明,从叶水指数可以反演其他光谱指数,其均方根误差高达0.02,偏差为1.12%,决定系数为0.77。后者促使使用具有离散波长的传感器而非连续光谱来估计林业的基本参数。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e155/8067298/c16cd895dba7/plants-10-00697-g0A1.jpg

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