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

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

doi:https://doi.org/10.5194/gmd-15-6637-2022

Articles | Volume 15, issue 17

https://doi.org/10.5194/gmd-15-6637-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/gmd-15-6637-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 15, issue 17

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

Supplement

https://doi.org/10.5194/gmd-15-6637-2022-supplement

Model code and software

Modified adaptive surrogate modeling (MASM) Rui Ma, Jingfeng Xiao, Shunlin Liang, Han Ma, Tao He, Da Guo, Xiaobang Liu, and Haibo Lu https://doi.org/10.5281/zenodo.6953354

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.