Using a Vegetation Index-Based Mixture Model to Estimate Fractional Vegetation Cover Products by Jointly Using Multiple Satellite Data: Method and Feasibility Analysis
Using a Vegetation Index-Based Mixture Model to Estimate Fractional Vegetation Cover Products by Jointly Using Multiple Satellite Data: Method and Feasibility Analysis
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使用基于植被指数的混合模型联合使用多卫星数据来估计植被覆盖率产品：方法和可行性分析
DOI：
10.3390/f13050691
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发表时间：
2022-04
期刊：
影响因子：
2.9
通讯作者：
Zheng Niu
中科院分区：
文献类型：
--
作者：
Wanjuan Song;Tian Zhao;Xihan Mu;Bo Zhong;Jing Zhao;Guangjian Yan;Li Wang;Zheng Niu
遥感植被覆盖率(FVC)在气候和生态系统研究中既需要更高的分辨率，也需要更高的频率。更高分辨率(≤30米)遥感数据的日益普及使这一点成为可能。然而，不同卫星的数据在空间分辨率、光谱响应函数等方面存在较大差异，难以联合使用。在此，我们证明了基于植被指数(VI)的混合模型利用多角度VI方法(MultiVI)从MODIS BRDF产品中获取的纯植被(Vv)和裸地(Vs)的VI值是合适的，用于利用多个卫星数据估算FVC是可行的。对MODIS与Landsat 8、中国GF1和ZY3等卫星的空间分辨率和光谱响应函数差异的分析表明：(1)由于卫星间的定位差异，Vv和Vv对FVC估计不确定度的影响是不同的；(2)谱归一化后，FVC估计的不确定度(RMSD)比未进行谱归一化的结果降低了约2.6%。多卫星数据的FVC估计将有助于提高FVC产品的时空分辨率，这对于众多生物物理应用来说是一个重要的发展。在这里，我们证明了基于虚拟仪器的混合模型是一个很好的候选模型。
Remote sensing fractional vegetation cover (FVC) requires both finer-resolution and high-frequency in climate and ecosystem research. The increasing availability of finer-resolution (≤ 30 m) remote sensing data makes this possible. However, data from different satellites have large differences in spatial resolution, spectral response function, and so on, making joint use difficult. Herein, we showed that the vegetation index (VI)-based mixture model with the appropriate VI values of pure vegetation (Vv) and bare soil (Vs) from the MODIS BRDF product via the multi-angle VI method (MultiVI) was feasible to estimate FVC with multiple satellite data. Analyses of the spatial resolution and spectral response function differences for MODIS and other satellites including Landsat 8, Chinese GF 1, and ZY 3 predicted that (1) the effect of Vv and Vs downscaling on FVC estimation uncertainty varied from satellite to satellite due to the positioning differences, and (2) after spectral normalization, the uncertainty (RMSDs) for FVC estimation decreased by ~2.6% compared with the results without spectral normalization. FVC estimation across multiple satellite data will help to improve the spatiotemporal resolution of FVC products, which is an important development for numerous biophysical applications. Herein, we proved that the VI-based mixture model with Vv and Vs from MultiVI is a strong candidate.
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影响因子：
3.7
作者：
Gan M;Deng J;Zheng X;Hong Y;Wang K
通讯作者：
Wang K
DOI：
10.3390/rs9060555
发表时间：
2017-06
期刊：
Remote. Sens.
影响因子：
--
作者：
B. Zhong;Shanlong Wu;A. Yang;Qinhuo Liu
通讯作者：
B. Zhong;Shanlong Wu;A. Yang;Qinhuo Liu
DOI：
10.1016/j.isprsjprs.2012.11.008
发表时间：
2013-03
影响因子：
12.7
作者：
Guijun Yang;R. Pu;Jixian Zhang;Chunjiang Zhao;Haikuan Feng;Jihua Wang
通讯作者：
Guijun Yang;R. Pu;Jixian Zhang;Chunjiang Zhao;Haikuan Feng;Jihua Wang
中国高分一号大视场数据植被覆盖度分数估计算法
影响因子：
13.5
作者：
Jia, Kun;Liang, Shunlin;Li, Yuwei
通讯作者：
Li, Yuwei
影响因子：
13.5
作者：
Feret, J. -B.;Gitelson, A. A.;Jacquemoud, S.
通讯作者：
Jacquemoud, S.