中国区域典型植被多尺度光谱数据集

多尺度反射光谱测量主要是观测不同植被覆盖的冠层及叶片光谱数据,包括了中国不同地区的主要森林和作物。农田的光谱是从均匀的大面积覆盖的样方中取样得到的。而对于森林冠层，现场测量平台包括塔吊和UVA。ASD或SVC安装在地面或塔上进行光谱测量，对于UVA，则安装了高光谱成像仪来测量冠层光谱。大多数测量工作在2017年和2018年的生长季节（7月至10月）进行。每个特定目标的光谱为重复观测的平均值，为了收集平滑连续的光谱曲线，应用最小二乘多项式拟合算法去除大气水汽波段的异常波动，并使用分段线性插值函数将光谱曲线插值到1nm光谱分辨率。该数据集有助于植物建模，也可以支持跨尺度生态过程和内在机制的研究，以及生理化参数的反演，也可以作为真实性检验的相对“真值“。

Multi-scale reflective spectral measurement dataset primarily focuses on canopy and leaf spectral data of diverse vegetation covers, including major forests and crops across different regions of China. Spectral data of farmlands are sampled from uniform, large-area sampling plots. For forest canopies, field measurement platforms include tower cranes and UVA. ASD or SVC spectrometers are installed on the ground or towers to perform spectral measurements, while for UVA platforms, hyperspectral imagers are mounted to acquire canopy spectral data. Most measurement campaigns were carried out during the growing seasons (July to October) of 2017 and 2018. The spectral data for each specific target are the average of repeated observations. To generate smooth and continuous spectral curves, a least-squares polynomial fitting algorithm is employed to eliminate abnormal fluctuations in atmospheric water vapor bands, and a piecewise linear interpolation function is used to resample the spectral curves to a spectral resolution of 1 nm. This dataset aids in plant modeling, supports investigations into cross-scale ecological processes and their underlying mechanisms, as well as the inversion of physiological and biochemical parameters, and can also serve as a relative "true value" for validation studies.

搜集汇总

背景与挑战

背景概述

该数据集提供了中国区域典型植被的多尺度光谱数据，覆盖森林和作物，时间范围为2017年至2018年，空间分辨率为1米至10米，数据经过处理并插值到1纳米光谱分辨率，适用于植物建模和生态研究。