2026-10-13T22:57:50Z https://www.scidb.cn/oai

10.57760/sciencedb.ecodb.00187 2025-09-10T11:51:55Z

2025-09-10 A 30m fused InENVI NDVI dataset from 2001 to 2020 in China doi:10.57760/sciencedb.ecodb.00187 en This dataset is a fused NDVI (Normalized Difference Vegetation Index) dataset in China from 2001 to 2020. This dataset is based on the Integrating ENvironmental VarIable spatiotemporal fusion model (InENVI), which combines environmental variables (including temperature, atmospheric water vapor pressure difference, and downward shortwave radiation) and simulates the nonlinear relationship between MODIS (Moderate Resolution Imaging Spectroradiometer) NDVI and Landsat NDVI to reconstruct high spatial resolution NDVI data. The data set is in HDF raster data format with a spatial reference of WGS84 (EPSG:4326). The data set has a spatial resolution of 1/3711° (about 30 m at the equator) and a temporal resolution of 8 days. The HDF file contains NDVI, quality control (QC), longitude, latitude, time, and area information for each pixel. A QC value of 0 indicates that the predicted NDVI value is within the minimum and maximum range of the original sequence, 1 indicates that the predicted NDVI value is outside the range, and 2 indicates that the original Landsat is missing. Besides, the annual maximum NDVI for China derived from the fused dataset is also provided in GeoTIFF format with a spatial reference of WGS84 (EPSG:4326).In order to compress the file volume, the data type used in this dataset is UInt8 (Unsigned Integer 8). The scale is 1.2 / 255, and the offset is -0.2. It needs to be converted to NDVI by the following formula:NDVI = 1.2 / 255 * data - 0.2The missing value of the converted NDVI is -0.2.The dataset is organized in 1°×1° grid cells across the entire region. Each year’s data is stored in a separate folder, with 67 compressed packages per annual folder. Each compressed package covers a 5°×5° geographical area and contains HDF files for all 1°×1° grid cells within that area. For detailed information, please refer to the .docx document. NDVI ; data fusion; machine learning Xiangqian Li Qiongyan Peng Ruoque Shen Wenping Yuan Wenfang Xu Zhangcai Qin Shangrong Lin Dongdong Kong PUBLIC https://creativecommons.org/licenses/by/4.0/ dataset http://www.doi.org/10.1038/s41597-025-04497-9 Science Data Bank