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A 2001-2015 Archive of Fractional Cover of Photosynthetic and Non-Photosynthetic Vegetation for Beijing and Tianjin Sandstorm Source Region  ( EI收录)   被引量:3

文献类型:期刊文献

英文题名:A 2001-2015 Archive of Fractional Cover of Photosynthetic and Non-Photosynthetic Vegetation for Beijing and Tianjin Sandstorm Source Region

作者:Li, Xiaosong[1] Li, Zengyuan[2] Ji, Cuicui[1] Wang, Hongyan[1] Sun, Bin[2] Wu, Bo[3] Gao, Zhihai[2]

第一作者:Li, Xiaosong

通信作者:Gao, ZH[1]

机构:[1]Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China;[2]Chinese Acad Forestry, Inst Forest Resources Informat Tech, Beijing 100091, Peoples R China;[3]Chinese Acad Forestry, Inst Desertificat Res, Beijing 100091, Peoples R China

年份:2017

卷号:2

期号:3

外文期刊名:DATA

收录:EI(收录号:20224413045835);Scopus(收录号:2-s2.0-85063595133);WOS:【ESCI(收录号:WOS:000424475100007)】;

基金:This work was funded by National Key Research and Development Program (No. 2016YFC0500806), National Natural Science Foundation of China (No. 41571421).

语种:英文

外文关键词:non-photosynthetic vegetation; land degradation surveillance; linear spectral mixture model; endmember; MODIS NBAR

摘要:Fractional covers of photosynthetic and non-photosynthetic vegetation are key indicators for land degradation surveillance in the dryland of China. However, there are no available, well validated, and multispectral-based products. Aiming for this, we selected the Beijing and Tianjin Sandstorm Source Region as the study area, and utilized the linear spectral mixture model for generating the fractional cover of PV, NPV, and bare soil, with endmember spectra retrieved from the field measured endmember spectral library, based on the MODIS NBAR data from 2001 to 2015. The unmixing results were validated through comparison with the field samples. The results show the method adopted could acquire rational and accurate estimation of fractional cover of photosynthetic vegetation (R-2 = 0.6297, RMSE = 0.2443) and non-photosynthetic vegetation (R-2 = 0.3747, RMSE = 0.2568). The dataset could provide key data support for the users in land degradation surveillance fields.

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