Articles | Volume 15, issue 17
https://doi.org/10.5194/gmd-15-6637-2022
https://doi.org/10.5194/gmd-15-6637-2022
Development and technical paper
 | 
02 Sep 2022
Development and technical paper |  | 02 Sep 2022

Pixel-level parameter optimization of a terrestrial biosphere model for improving estimation of carbon fluxes with an efficient model–data fusion method and satellite-derived LAI and GPP data

Rui Ma, Jingfeng Xiao, Shunlin Liang, Han Ma, Tao He, Da Guo, Xiaobang Liu, and Haibo Lu

Viewed

Total article views: 2,777 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
2,062 657 58 2,777 161 41 51
  • HTML: 2,062
  • PDF: 657
  • XML: 58
  • Total: 2,777
  • Supplement: 161
  • BibTeX: 41
  • EndNote: 51
Views and downloads (calculated since 18 May 2022)
Cumulative views and downloads (calculated since 18 May 2022)

Viewed (geographical distribution)

Total article views: 2,777 (including HTML, PDF, and XML) Thereof 2,633 with geography defined and 144 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 26 Jul 2024
Download
Short summary
Parameter optimization can improve the accuracy of modeled carbon fluxes. Few studies conducted pixel-level parameterization because it requires a high computational cost. Our paper used high-quality spatial products to optimize parameters at the pixel level, and also used the machine learning method to improve the speed of optimization. The results showed that there was significant spatial variability of parameters and we also improved the spatial pattern of carbon fluxes.