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Data Details

MODIS Global Leaf Area Index Product Reprocessing Dataset (2001-2021)

LIU Li1ZHANG Yinghui*2HU Zhongwen*2GAO Xing3WANG Jingzhe4WU Guofeng2
1 Guangdong Polytechnic of Industry & Commerce,Guangzhou 510510,China2 MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area,Shenzhen University,Shenzhen 518060,China3 LREIS Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences,Beijing 100101,China4 School of Artificial Intelligence Shenzhen Polytechnic,Shenzhen 518055,China


Published:Oct. 2023

Visitors:3435       Data Files Downloaded:191      
Data Downloaded:150246.03 MB      Citations:

Key Words:

MODIS,Leaf area index,Global Change,spatial-temporal


Based on the MODIS leaf area index (LAI) products, the authors designed an algorithm with maximum fraction of absorbed photosynthetically active radiation in a day to recompile a new reprocessing dataset, in which the vegetation types were input to the model for the LAI analysis. The resolution of the dataset were changed from 500 m to 0.05°. By validation with 280 public measured LAI test sites, the square of the linear fitting correlation coefficient of the reprocessing dataset reaches 0.748. The dataset includes 8-day, monthly, and yearly global LAI with resolution of 0.05°. The scale factor is 0.1. The dataset is archived in .tif format, and consists of 1239 data files with data size of 29.9 GB (Compressed into three files with 2.48 GB).Browse

Foundation Item:

National Natural Science Foundation of China (42201347); China Postdoctoral Science Foundation (2022M712163); Guangdong Province (2021A1515110910, 2023A1515011273); Chinese Academy of Sciences (XDA23090503); Shenzhen (JCYJ20220818101617037, 20220811173316001)

Data Citation:

LIU Li, ZHANG Yinghui*, HU Zhongwen*, GAO Xing, WANG Jingzhe, WU Guofeng. MODIS Global Leaf Area Index Product Reprocessing Dataset (2001-2021)[J/DB/OL]. Digital Journal of Global Change Data Repository, 2023.

LIU Li, ZHANG Yinghui, HU Zhongwen, et al. MODIS global leaf area index product reprocessing dataset (2001-2021) [J]. Journal of Global Change Data & Discovery, 2023, 7(3): 242-251.


     [1] Fang, H., Baret, F., Plummer, S., et al. An overview of global leaf area index (LAI): methods, products, validation, and applications [J]. Reviews of Geophysics, 2019, 57(3): 739-799.
     [2] Liu, Y. , Liu, R. G., Chen, J. M., et al. Current status and perspectives of leaf area index retrieval from optical remote sensing data [J]. Geo-information Science, 2013, 15(5): 734.
     [3] Chen, J. M., Black, T. A. Defining leaf area index for non‐flat leaves [J]. Plant, Cell & Environment, 1992, 15(4): 421-429.
     [4] GCOS. The Global Observing System For Climate Implementation Needs [R], 2016.
     [5] Xiao, Z., Liang, S., Wang, J., et al. Use of general regression neural networks for generating the glass leaf area index product from time-series MODIS surface reflectance [J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(1): 209-223.
     [6] Baret, F., Hagolle, O., Geiger, B., et al. LAI, fAPAR and fCover CYCLOPES global products derived from VEGETATION [J]. Remote Sensing of Environment, 2007, 110(3): 275-286.
     [7] Knyazikhin, Y., Martonchik, J. V., Myneni, R. B., et al. Synergistic algorithm for estimating vegetation canopy leaf area index and fraction of absorbed photosynthetically active radiation from MODIS and MISR data [J]. Journal of Geophysical Research: Atmospheres, 1998, 103(D24): 32257-32275.
     [8] Chen, C., Park, T., Wang, X., et al. China and India lead in greening of the world through land-use management [J]. Nat Sustain, 2019, 2(2): 122-129.
     [9] Zhang, Y., Hu, Z., Wang, J., et al. Temporal upscaling of MODIS instantaneous FAPAR improves forest gross primary productivity (GPP) simulation [J]. International Journal of Applied Earth Observation and Geoinformation, 2023, 121.
     [10] Fang, H., Zhang, Y., Wei, S., et al. Validation of global moderate resolution leaf area index (LAI) products over croplands in northeastern China [J]. Remote Sensing of Environment, 2019, 233: 111377.
     [11] Ma, P. P., Li, J., Liu, Q. H., et al. 2019. Multisensor synergistic quantitative leaf area index product of China. Journal of Remote Sensing, 23(6): 1232–1252.
     [12] Yuan, H., Dai, Y., Xiao, Z., et al. Reprocessing the MODIS leaf area index products for land surface and climate modelling [J]. Remote Sensing of Environment, 2011, 115(5): 1171-1187.
     [13] Wang, J., Yan, K., Gao, S., et al. Improving the quality of MODIS LAI products by exploiting spatiotemporal correlation information [J]. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61: 1-19.
     [14] Zhang, Y., Fang, H., Wang, Y., et al. Variation of intra-daily instantaneous FAPAR estimated from the geostationary Himawari-8 AHI data [J]. Agricultural and Forest Meteorology, 2021, 307: 108535.
     [15] Pu, J., Yan, K., Gao, S., et al. Improving the MODIS LAI compositing using prior time-series information [J]. Remote Sensing of Environment, 2023, 287.
     [16] Zeng, Y., Li, J., Liu Q. Global LAI ground validation dataset and product validation framework [J]. Advances in Earth Science, 2012, 27(2) : 165-174.
     [17] Weiss, M., Baret, F., Block, T., et al. On line validation exercise (OLIVE): a web based service for the validation of medium resolution land products. application to FAPAR products [J]. Remote Sensing, 2014, 6(5): 4190-4216.

Data Product:

ID Data Name Data Size Operation
1 8-Day_LAI_2001_2010.rar 961743.10KB
2 8-Day_LAI_2011_2021.rar 1078138.67KB
3 Monthly_Yearly_LAI_2001_2021.rar 565512.28KB