MUSES Net Primary Productivity (NPP) 8-Day Global 500m SIN Grid in 2017

The MUltiscale Satellite remotE Sensing (MUSES) 8-Day Global 500m net primary productivity (NPP) data 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/).

This dataset is the MUSES global NPP product at 500m spatial resolution and 8-day temporal resolution. The MUSES NPP product is provided on a Sinusoidal grid and spans from 2001 to 2019. .

This dataset is the MUSES NPP product in 2017.

The global data were divided into different tiles as same as MODIS data, and the size of raw data is 2400 columns and 2400 lines for each tile. The file were compressed for the same horizontal number of tiles. The detail information of this dataset is as below:

• Spatial Coverage: Global
• Temporal Coverage: 2017
• Spatial Resolution: 500m
• Temporal Resolution: 8 days
• Projection: Sinusoidal projection
• Data Format: HDF-5
• Data type: integer (16bit)
• Scale factor: 1000

·        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.