2026-10-13T15:36:00Z https://www.scidb.cn/oai

10.57760/sciencedb.j00001.01801 2026-09-03T15:53:15Z

2026-09-03 Multiple remote sensing vegetation index datasets at a resolution of 500 m for the Three-North Shelterbelt Program region from 2000 to 2025 doi:10.57760/sciencedb.j00001.01801 en This dataset was developed based on the MODIS surface reflectance product MCD43A4 and its quality control product MCD43A2. The Near-Infrared Reflectance Vegetation Index (NIRv), Kernel Normalized Difference Vegetation Index (kNDVI), and Plant Phenology Index (PPI) were derived, and time-series harmonic analysis together with the Savitzky–Golay filtering method were applied for missing-value interpolation, outlier removal, and time-series reconstruction. Based on these procedures, a 500 m, 8-day multi-remote-sensing vegetation index dataset for the Three-North Shelterbelt Program (TNSP) region from 2000 to 2025 was produced. The datasets were validated against ground-measured Gross Primary Productivity (GPP) data obtained from 14 flux tower observations during 2000-2025. Results indicate that all three vegetation indices are well correlated with GPP and effectively characterize the temporal variations in vegetation productivity. NIRv exhibited the best fitting performance with GPP (R² = 0.66, RMSE = 2.45, MAE = 1.67), followed by PPI and kNDVI. These datasets provide reliable data support for monitoring vegetation dynamics in the TNSP, investigating ecosystem responses to climate change in arid and semi-arid regions, and assessing the implementation effectiveness of major ecological restoration projects. NIRv; kNDVI; PPI; Three-North Shelterbelt Program Yu Qinyue Zhao Huiling Guo Yan Bao Honghao Bai Yan Wang Hengjie Zou Xiangyu Asgen Liu Feng Liu Yanping PUBLIC https://creativecommons.org/licenses/by-nc/4.0/ dataset Science Data Bank