MUSES Normalized Difference Vegetation Index (NDVI) Monthly Global 0.05º Geographic Grid Since 1982
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The MUltiscale Satellite remotE Sensing (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 NDVI product at 0.05º spatial resolution and monthly temporal resolution. The MUSES NDVI product is provided on Geographic grid and spans from 1982 to 2015 (continuously updated). It was generated from the Land Long-Term Data Record (LTDR) Advanced very high resolution radiometer (AVHRR) daily surface reflectance product (Version 4) using a temporally continuous vegetation indices-based land-surface reflectance reconstruction (VIRR) method (Xiao <em>et al</em>., 2015; Xiao <em>et al</em>., 2017). The MUSES NDVI product is spatially complete and temporally continuous. <strong>Dataset Characteristics:</strong> Spatial Coverage: 180º W – 180º E, 90º S – 90º N Temporal Coverage: 1982 – 2015 Spatial Resolution: 0.05º (approximately 5 km) Temporal Resolution: 1 month Projection: Geographic Data Format: HDF Scale: 0.0001 Valid Range: 0 – 10000 <strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong> Xiao Zhiqiang, <em>et al</em>. (2015). Reconstruction of Satellite-Retrieved Land-Surface Reflectance Based on Temporally-Continuous Vegetation Indices. <em>Remote Sensing</em>, 7, 9844-9864 Xiao Zhiqiang, <em>et al</em>. (2017). Reconstruction of Long-Term Temporally Continuous NDVI and Surface Reflectance From AVHRR Data. <em>IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing</em>, 10, 5551-5568 If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).
