A 500-m annual raster dataset of vegetation-oriented ecosystem quality index (EQI) in China

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Authors: Xiang Jia, Sanjun Yin, Danni He, Xiaoli Zhang, Lingting Lei, Guoqi Chai, Xin Tian, Shuangquan Li, Shike Qiu, Ran Wang, Jun Du, Jingxu Wang
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Abstract

This dataset provides annual vegetation-oriented Ecosystem Quality Index (EQI) products for China from 2001 to 2025 at 500 m spatial resolution. The EQI was constructed from three MODIS-derived ecosystem indicators: Fractional Vegetation Cover (FVC), Leaf Area Index (LAI), and Gross Primary Productivity (GPP), with the aim of providing a temporally consistent measure of vegetation structure and ecosystem functioning across China. To improve long-term comparability, the dataset uses a fixed vegetation mask and a fixed ecosystem classification derived from the 2001–2024 MCD12Q1 land-cover series. Stable vegetated pixels were grouped into four functional ecosystem classes: forest, shrubland, grassland/savanna, and cropland. For each indicator and ecosystem class, normalization limits were determined once from the complete 2001–2025 population of valid pixel-year observations using fixed 5th and 95th percentiles. The normalized FVC, LAI, and GPP components were then combined with equal weights to derive annual EQI values on a 0–100 scale. The dataset includes annual 500 m EQI raster products for 2001–2025 and is designed for long-term ecosystem-quality assessment, spatiotemporal trend analysis, regional comparison, and climate–ecosystem studies. The fixed-reference framework reduces artificial interannual variation caused by annually changing normalization ranges or land-cover classifications and improves temporal consistency across the full study period. This version replaces the previous dataset release and reflects the revised methodological framework used in the associated manuscript. Users are encouraged to cite both this dataset and the associated publication when using these data.

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
22 September 2026
Language
EN
Document type
Dataset