Articles | Volume 14, issue 12
https://doi.org/10.5194/essd-14-5333-2022
https://doi.org/10.5194/essd-14-5333-2022
Data description paper
 | 
07 Dec 2022
Data description paper |  | 07 Dec 2022

Global land surface 250 m 8 d fraction of absorbed photosynthetically active radiation (FAPAR) product from 2000 to 2021

Han Ma, Shunlin Liang, Changhao Xiong, Qian Wang, Aolin Jia, and Bing Li

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Cited articles

Bacour, C., Baret, F., Beal, D., Weiss, M., and Pavageau, K.: Neural network estimation of LAI, fAPAR, fCover and LAI×C ab, from top of canopy MERIS reflectance data: Principles and validation, Remote Sens. Environ., 105, 313–325, 2006. 
Baret, F., Weiss, M., Allard, D., Garrigues, S., Leroy, M., Jeanjean, H., Fernandes, R., Myneni, R., Privette, J., and Morisette, J.: VALERI: a network of sites and a methodology for the validation of medium spatial resolution land satellite productss, ffhal-03221068, https://hal.archives-ouvertes.fr/hal-03221068/ (last access 1 November 2022), 2021. 
Baret, F., Hagolle, O., Geiger, B., Bicheron, P., Miras, B., Huc, M., Berthelot, B., Niño, F., Weiss, M., and Samain, O.: LAI, fAPAR and fCover CYCLOPES global products derived from VEGETATION: Part 1: Principles of the algorithm, Remote Sens. Environ., 110, 275–286, 2007. 
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. Part 1: Principles of development and production, Remote Sens. Environ., 137, 299–309, https://doi.org/10.1016/j.rse.2012.12.027, 2013. 
Baret, F., Weiss, M., Verger, A., and Smets, B.: ATBD For LAI, FAPAR And FCOVER From PROBA-V Products At 300 m Resolution (GEOV3), Imagines_rp2.1_atbd-lai, 300, https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/ImagineS_RP2.1_ATBD-LAI300m_I1.73.pdf, (last access 1 November 2022), 2016. 
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Short summary
The fraction of absorbed photosynthetically active radiation (FAPAR) is one of the essential climate variables. This study generated a global land surface FAPAR product with a 250 m resolution based on a deep learning model that takes advantage of the existing FAPAR products and MODIS time series of observation information. Direct validation and intercomparison revealed that our product better meets user requirements and has a greater spatiotemporal continuity than other existing products.
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