Published September 1, 2022 | Version v1
Dataset Open

Global leaf chlorophyll content product from MERIS imagery (GLOBMAP MERIS LCC)

  • 1. ROR icon Institute of Geographic Sciences and Natural Resources Research
  • 2. ROR icon University of Toronto
  • 3. ROR icon Fujian Normal University

Description

Leaf chlorophyll content (LCC) is an indicator of plant physiological function and is an important parameter in estimating the carbon and water fluxes of terrestrial ecosystems. Here, we produced a new global 7-day LCC product (GLOBMAP MERIS LCC) at 300-m resolution from 2003.01 to 2012.03 using a neural network model based on radiative transfer model simulations from CCI MERIS surface reflectance data. It shows an improvement over the previous MERIS LCC product in capturing LCC seasonal variations in different plant functional types.

The following paper should be cited when using the data:
Xu, M., Liu, R., Chen, J.M., Shang, R., Liu, Y., Qi, L., Croft, H., Ju, W., Zhang, Y., He, Y., Qiu, F., Li, J., Lin, Q., 2022. Retrieving global leaf chlorophyll content from MERIS data using a neural network method. Isprs J. Photogramm. Remote Sens. 192, 66–82. https://doi.org/10.1016/j.isprsjprs.2022.08.003.

Due to the data volume limitation of Zenodo, the data we uploaded here are an example tile (h59v10) at 300-m resolution and a global 0.05° composition of multi-year average LCC with a 7-day interval. The original dataset of GLOBMAP MERIS LCC product can be downloaded through Google Drive sharing link:

https://drive.google.com/drive/folders/1ekfemq5x6UzNwmFadpzV3zCi338-hFZL?usp=sharing

Data format description:
Data type: int16
Projection: GCS_WGS_1984
Scaling factor: 0.1
Unit: ug/cm2

Related dataset: Mingzhu Xu, Ronggao Liu, Jing M. Chen, Yang Liu, & Rong Shang. (2021). Global leaf chlorophyll content (LCC) product from MODIS imagery (2000-2020) (Version V1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5805575

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

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Additional details

Related works

Is continued by
Dataset: 10.5281/zenodo.5805574 (DOI)

References

  • Xu, M., Liu, R., Chen, J.M., Shang, R., Liu, Y., Qi, L., Croft, H., Ju, W., Zhang, Y., He, Y., Qiu, F., Li, J., Lin, Q., 2022. Retrieving global leaf chlorophyll content from MERIS data using a neural network method. Isprs J. Photogramm. Remote Sens. 192, 66–82. https://doi.org/10.1016/j.isprsjprs.2022.08.003