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The MUltiscale Satellite remotE Sensing (MUSES) global vegetation productivity dataset includes gross primary productivity (GPP) and net primary productivity (NPP) data from 1981 to 2018. GPP and NPP were estimated with a light use efficiency (LUE) model and MUSES leaf area index (LAI) and fraction of absorbed photosynthetically active radiation (FAPAR) products. The MUSES product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (https://muses.bnu.edu.cn/). The detail information of the MUSES 5-km global GPP and NPP products are as below: Name: MUSES 5-km global GPP and NPP products Period: 1981-2018 Spatial resolution: 0.05° Temporal resolution: 8 days Projection: geographic latitude/longitude Data format: Tiff Data type: integer (16bit) Upper left coordinates: -180°E, 90°N Scale factor: 100 Unit: gCm-2d-1 Citation (Please cite these papers when these data are used) 1. Wang, J.M., Sun, R., Zhang, H.L., Xiao, Z.Q., Zhu A.R., Wang, M.J., Yu, T., Xiang, K.L., New global MuSyQ GPP/NPP remote sensing products from 1981 to 2018. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14, 5596-5612. 2. Wang, M.J.; Sun, R.; Zhu, A.R.; Xiao, Z. Q. Evaluation and Comparison of Light Use Efficiency and Gross Primary Productivity Using Three Different Approaches. Remote Sensing. 2020, 12, 1003. 3. Yu, T.; Sun, R.; Xiao, Z.Q. ;Zhang , Q.; Liu, G.; Cui, T.X.; Wang, J.M. Estimation of Global Vegetation Productivity from Global LAnd Surface Satellite Data. Remote sensing. 2018, 10, 327.
This dataset was supported by the National Key R&D Program of China (2017YFA0603002, 2016YFB0501502).
remote sensing, carbon cycle, light use efficiency, vegetation productivity, global
remote sensing, carbon cycle, light use efficiency, vegetation productivity, global
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