Published December 27, 2021 | Version V1
Dataset Open

Global leaf chlorophyll content (LCC) product from MODIS imagery (2000-2020)

  • 1. Key Laboratory of Humid Subtropical Eco-Geographical Process (Ministry of Education), College of Geographical Sciences, Fujian Normal University
  • 2. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences

Description

The spatial and temporal distribution of leaf chlorophyll content (LCC) is critical for understanding the capacity of vegetation photosynthesis. Here, a global 8-day leaf chlorophyll content (LCC) dataset at 500-m resolution was generated from MODIS data using a matrix system with two pairs of vegetation indices. 

The following paper should be cited when using the data:

Xu, M., Liu, R., Chen, J.M., Liu, Y., Wolanin, A., Croft, H., He, L., Shang, R., Ju, W., Zhang, Y., He, Y., Wang, R., 2022. A 21-year time-series of global leaf chlorophyll content maps from MODIS imagery. IEEE Trans. Geosci. Remote Sens. https://doi.org/10.1109/TGRS.2022.3204185.

Detailed description of data organization can be found in the uploaded document "1Readme.docx".

The whole dataset of MODIS LCC product is from 2000 to 2020. Due to the data volume limitation of Zenodo, currently the data from 2000-2010 can be downloaded through Google Drive sharing link:

https://drive.google.com/drive/folders/11eXetjsAB_ZjFqGs8SXEWzd6byr_8LoW?usp=sharing

Related dataset: Mingzhu, X., Liu, R., Chen, J. M., Shang, R., & Liu, Y. (2022). Global leaf chlorophyll content product from MERIS imagery (GLOBMAP MERIS LCC) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.10467919

For any other questions, please send email to Mingzhu Xu (xumzhu@gmail.com).

Files

2011001-2011089.zip

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Additional details

Related works

Is cited by
Dataset: 10.5281/zenodo.10467919 (DOI)

References

  • Xu, M., Liu, R., Chen, J.M., Liu, Y., Shang, R., Ju, W., Wu, C., Huang, W., 2019. Retrieving leaf chlorophyll content using a matrix-based vegetation index combination approach. Remote Sens. Environ. 224, 60–73. https://doi.org/10.1016/j.rse.2019.01.039