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High Spatial Resolution and High Temporal Frequency(30-m/15-day) Fractional Vegetation Cover Estimation over China Using multiple remote sensing datasets:Method Development and Validation

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Journal of Meteorological Research 2021年第1期35卷 128-147页

作者： Xihan MU Tian ZHAO Gaiyan RUAN Jinling SONG Jindi WANG Guangjian YAN Tim RMCVICAR Kai YAN Zhan GAO Yaokai LIU Yuanyuan WANG State Key Laboratory of remote sensing Science Faculty of Geographical ScienceBeijing Normal UniversityBeijing 100875China Beijing Engineering Research Center for Global Land remote sensing Products Faculty of Geographical ScienceBeijing Normal UniversityBeijing 100875China CSIRO Land and Water ACT2601Australia Department of Earth and Environment Boston UniversityBostonMA 02215USA Jiangsu Institute of Urban Planning and Design Nanjing 210036China Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China National Satellite Meteorological Center China Meteorological AdministrationBeijing 100081China

High spatial resolution and high temporal frequency fractional vegetation cover(FVC) products have been increasingly in demand to monitor and research land surface processes. This paper develops an algorithm to estimate FVC at a 30-m/15-day resolution over China by taking advantage of the spatial and temporal information from different types of sensors: the 30-m resolution sensor on the Chinese environment satellite(HJ-1) and the 1-km Moderate Resolution Imaging Spectroradiometer(MODIS). The algorithm was implemented for each main vegetation class and each land cover type over China. First, the high spatial resolution and high temporal frequency normalized difference vegetation index(NDVI) was acquired by using the continuous correction(CC) data assimilation method. Then, FVC was generated with a nonlinear pixel unmixing model. Model coefficients were obtained by statistical analysis of the MODIS NDVI. The proposed method was evaluated based on in situ FVC measurements and a global FVC product(GEOV1 FVC). Direct validation using in situ measurements at 97 sampling plots per half month in 2010 showed that the annual mean errors(MEs) of forest, cropland, and grassland were-0.025, 0.133, and 0.160, respectively, indicating that the FVCs derived from the proposed algorithm were consistent with ground measurements [R2 = 0.809,root-mean-square deviation(RMSD) = 0.065]. An intercomparison between the proposed FVC and GEOV1 FVC demonstrated that the two products had good spatial–temporal consistency and similar magnitude(RMSD approximates 0.1). Overall, the approach provides a new operational way to estimate high spatial resolution and high temporal frequency FVC from multiple remote sensing datasets.

关键词： fractional vegetation cover(FVC) high spatial resolution and high temporal frequency data fusion normalized difference vegetation index(NDVI) pixel unmixing model multiple remote sensing datasets

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