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Global Scale
Data Details

Yearly/250-m Raster Dataset of Ecosystem Quality Index (EQI) of China (2007-2024)


QING Ao1,2
1 Institute of Geographic Sciences and Natural Resources Research，Chinese Academy of Sciences，Beijing 100101，China2 University of Chinese Academy of Sciences，Beijing 100049，China

DOI:10.3974/geodb.2026.03.02.V1

Published:Mar. 2026

Visitors：60       Data Files Downloaded：9      
Data Downloaded：6710.28 MB      Citations：

Key Words：

Ecosystem Quality Index (EQI),Fractional Vegetation Cover (FVC),Leaf Area Index (LAI),Gross Primary Productivity (GPP)

Abstract：

The Ecosystem Quality Index (EQI) is an important indicator for characterizing the structural integrity and functional stability of ecosystems. The yearly/250-m raster dataset of Ecosystem Quality Index (EQI) of China (2007-2024) was developed based on the Technical specifications for national ecological status survey and assessment (HJ 1172—2021), integrated with multi-source remote sensing data, including Fractional Vegetation Cover (FVC), Leaf Area Index (LAI), and Gross Primary Productivity (GPP). The dataset has a spatial resolution of 250 m. The dataset is archived in .tif format, and consists of 54 data files with data size of 31.3 GB (compressed into 9 files with 6.55 GB). The dataset provides high-resolution and long-term data support for ecosystem monitoring, regional differentiation analysis, and territorial spatial planning.

Foundation Item：

Data Citation：

QING Ao. Yearly/250-m Raster Dataset of Ecosystem Quality Index (EQI) of China (2007-2024)[J/DB/OL]. Digital Journal of Global Change Data Repository, 2026. https://doi.org/10.3974/geodb.2026.03.02.V1.

References：


     [1] Ministry of Ecology and Environment of P. R. China. Technical specification for national ecological status assessment: ecosystem quality assessment (HJ 1172—2021) [S]. Beijing: China Standards Press, 2021.
     [2] Jia, K., Yang, L. Q., Liang, S. L., et al. Long-term global land surface satellite (GLASS) fractional vegetation cover product derived from MODIS and AVHRR data [J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019, 12(2): 508-518.
     [3] Liang, S. L., Cheng, J., Jia, K., et al. The global land surface satellite (GLASS) product suite [J]. Bulletin of the American Meteorological Society, 2021, 102(2): E323-E337.
     [4] Liu, H. M., Lu, J. Y., Li, X. C., et al. Evaluating human-nature relationships at a grid scale in China, 2000-2020 [J]. Habitat International, 2025, 156: 103282.
     [5] Ma, H., Liang, S. L. Development of the GLASS 250-m leaf area index product (version 6) from MODIS data using the bidirectional LSTM deep learning model [J]. Remote Sensing of Environment, 2022, 273: 112985.
     [6] Xiao, Z. Q., Liang, S. L, Jiang, B. Evaluation of four long time-series global leaf area index products [J]. Agricultural and Forest Meteorology, 2017, 246: 218-230.
     [7] Xiao, Z. Q., Liang, S. L., Wang, J. D., et al. Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance [J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(9): 5301-5318.
     [8] Zhang, X. T., Liang, S. L., Zhou, G. Q., et al. Generating global land surface satellite incident shortwave radiation and photosynthetically active radiation products from multiple satellite data [J]. Remote Sensing of Environment, 2014, 152: 318-332.

Data Product:

ID Data Name Data Size Operation
1 eqi_2007_2008.rar 757195.91KB
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2 eqi_2009_2010.rar 756642.51KB
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3 eqi_2011_2012.rar 760226.28KB
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4 eqi_2013_2014.rar 758132.66KB
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5 eqi_2015_2016.rar 759315.81KB
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6 eqi_2017_2018.rar 761036.22KB
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7 eqi_2019_2020.rar 759247.60KB
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8 eqi_2021_2022.rar 793291.66KB
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9 eqi_2023_2024.rar 766235.22KB
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