Extraction of snow cover information in sparse vegetation area based on spectral measurement and SRF by using MODIS data
LIU Yan1, LI Yang1, ZHANG Pu2
1. Institute of Desert Meteorology, China Meteorological Administration, Urumqi 830002, China;
2. Urumqi Meteorological Satellite Ground Station, Urumqi 830011, China
In this paper, the linear spectral mixture model (LSMM) was used for the spectral unmixing analysis of the Moderate Resolution Imaging Spectrometer (MODIS) data of the study area in Gurbantunggut desert. Using the spectral response function (SRF) of MODIS1-7 bands,the authors transformed the end-member spectrum quasi-synchronously collected by the full-band spectrometer (ASD) to the pixel spectra, thus generating the discrete spectrum of MODIS1-7 bands. Compared with the MODIS end-member spectra obtained by minimum noise fraction (MNF) transform and pixel purity index(PPI),the end-member spectral values of the first band of MODIS were much larger than the transformed spectrum values,but the spectral values of the MODIS2-7 bands were close to the transformed values. Therefore,selecting the image end-member spectral values of the MODIS2-7 bands,the authors used LSMM to estimate the abundance of snow in the sparse vegetation area appropriately. Fitting the estimated snow component value to the normalized difference snow index(NDSI),the authors found that a significant correlation exists between them after excluding MODIS1 band. The correlation coefficients show that the snow component can be a typical index of the snow cover.
刘艳, 李杨, 张璞. 基于实测光谱和SRF的稀疏植被区MODIS积雪信息提取[J]. 国土资源遥感, 2013, 25(1): 26-32.
LIU Yan, LI Yang, ZHANG Pu. Extraction of snow cover information in sparse vegetation area based on spectral measurement and SRF by using MODIS data. REMOTE SENSING FOR LAND & RESOURCES, 2013, 25(1): 26-32.
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2013, Vol. 25 
