Articles | Volume 16, issue 3
https://doi.org/10.5194/essd-16-1601-2024
https://doi.org/10.5194/essd-16-1601-2024
Data description paper
 | 
26 Mar 2024
Data description paper |  | 26 Mar 2024

HiQ-LAI: a high-quality reprocessed MODIS leaf area index dataset with better spatiotemporal consistency from 2000 to 2022

Kai Yan, Jingrui Wang, Rui Peng, Kai Yang, Xiuzhi Chen, Gaofei Yin, Jinwei Dong, Marie Weiss, Jiabin Pu, and Ranga B. Myneni

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Cited articles

Bai, G., Dash, J., Brown, L., Meier, C., Lerebourg, C., Ronco, E., Lamquin, N., Bruniquel, V., Clerici, M., and Gobron, N.: GBOV (Ground-Based Observation for Validation): A Copernicus Service for Validation of Vegetation Land Products, GARSS 2019 – 2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, 4592–4594, https://doi.org/10.1109/IGARSS.2019.8898634, 2019. 
Baret, F., Morissette, J. T., Fernandes, R. A., Champeaux, J. L., Myneni, R. B., Chen, J., Plummer, S., Weiss, M., Bacour, C., Garrigues, S., and Nickeson, J. E.: Evaluation of the representativeness of networks of sites for the global validation and intercomparison of land biophysical products: Proposition of the CEOS-BELMANIP, IEEE T. Geosci. Remote, 44, 1794–1802, https://doi.org/10.1109/TGRS.2006.876030, 2006. 
Baret, F., Weiss, M., Lacaze, R., Camacho, F., Makhmara, H., Pacholcyzk, P., and Smets, B.: GEOV1: LAI and FAPAR essential climate variables and FCOVER global time series capitalizing over existing products. Part1: Principles of development and production, Remote Sens. Environ., 137, 299–309, https://doi.org/10.1016/j.rse.2012.12.027, 2013. 
Boussetta, S., Balsamo, G., Beljaars, A., Kral, T., and Jarlan, L.: Impact of a satellite-derived leaf area index monthly climatology in a global numerical weather prediction model, Int. J. Remote Sens., 34, 3520–3542, https://doi.org/10.1080/01431161.2012.716543, 2013. 
Brown, L. A., Meier, C., Morris, H., Pastor-Guzman, J., Bai, G., Lerebourg, C., Gobron, N., Lanconelli, C., Clerici, M., and Dash, J.: Evaluation of global leaf area index and fraction of absorbed photosynthetically active radiation products over North America using Copernicus Ground Based Observations for Validation data, Remote Sens. Environ., 247, 111935, https://doi.org/10.1016/j.rse.2020.111935, 2020. 
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Short summary
Variations in observational conditions have led to poor spatiotemporal consistency in leaf area index (LAI) time series. Using prior knowledge, we leveraged high-quality observations and spatiotemporal correlation to reprocess MODIS LAI, thereby generating HiQ-LAI, a product that exhibits fewer abnormal fluctuations in time series. Reprocessing was done on Google Earth Engine, providing users with convenient access to this value-added data and facilitating large-scale research and applications.
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