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.
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.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
02 Sep 2022
Development and technical paper |
| 02 Sep 2022
| 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
CORRESPONDING AUTHOR
School of Remote Sensing and Information Engineering, Wuhan
University, Wuhan 430079, China
Jingfeng Xiao
Earth Systems Research Center, Institute for the Study of Earth,
Oceans, and Space, University of New Hampshire, Durham, NH 03824, USA
Department of Geography, University of Hong Kong, Hong Kong SAR 999077,
China
Department of Geography, University of Hong Kong, Hong Kong SAR 999077,
China
School of Remote Sensing and Information Engineering, Wuhan
University, Wuhan 430079, China
College of Resources and Environment, University of Chinese Academy
of Sciences, Beijing 100049, China
Xiaobang Liu
School of Remote Sensing and Information Engineering, Wuhan
University, Wuhan 430079, China
Haibo Lu
School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai
519082, China
Viewed
Total article views: 3,299 (including HTML, PDF, and XML)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 2,372 | 856 | 71 | 3,299 | 193 | 55 | 64 |
- HTML: 2,372
- PDF: 856
- XML: 71
- Total: 3,299
- Supplement: 193
- BibTeX: 55
- EndNote: 64
Cumulative views and downloads
(calculated since 18 May 2022)
Total article views: 2,162 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 02 Sep 2022)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 1,525 | 591 | 46 | 2,162 | 107 | 50 | 58 |
- HTML: 1,525
- PDF: 591
- XML: 46
- Total: 2,162
- Supplement: 107
- BibTeX: 50
- EndNote: 58
Total article views: 1,137 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 18 May 2022)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 847 | 265 | 25 | 1,137 | 86 | 5 | 6 |
- HTML: 847
- PDF: 265
- XML: 25
- Total: 1,137
- Supplement: 86
- BibTeX: 5
- EndNote: 6
Viewed (geographical distribution)
Total article views: 3,299 (including HTML, PDF, and XML)
Thereof 3,126 with geography defined
and 173 with unknown origin.
Total article views: 2,162 (including HTML, PDF, and XML)
Thereof 2,069 with geography defined
and 93 with unknown origin.
Total article views: 1,137 (including HTML, PDF, and XML)
Thereof 1,057 with geography defined
and 80 with unknown origin.
| Country | # | Views | % |
|---|---|---|---|
| United States of America | 1 | 1010 | 30 |
| China | 2 | 969 | 29 |
| Germany | 3 | 180 | 5 |
| undefined | 4 | 139 | 4 |
| France | 5 | 117 | 3 |
| Country | # | Views | % |
|---|---|---|---|
| United States of America | 1 | 697 | 32 |
| China | 2 | 564 | 26 |
| Germany | 3 | 115 | 5 |
| undefined | 4 | 105 | 4 |
| India | 5 | 59 | 2 |
| Country | # | Views | % |
|---|---|---|---|
| China | 1 | 405 | 35 |
| United States of America | 2 | 313 | 27 |
| Germany | 3 | 65 | 5 |
| France | 4 | 60 | 5 |
| undefined | 5 | 34 | 2 |
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
- 1010
1
1010
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
- 697
1
697
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
- 405
1
405
Cited
11 citations as recorded by crossref.
- Assimilation of carbonyl sulfide (COS) fluxes within the adjoint-based data assimilation system – Nanjing University Carbon Assimilation System (NUCAS v1.0) H. Zhu et al. 10.5194/gmd-17-6337-2024
- Climate and vegetation change impacts on future conterminous United States water yield H. Duarte et al. 10.1016/j.jhydrol.2024.131472
- Optimizing the terrestrial ecosystem gross primary productivity using carbonyl sulfide (COS) within a two-leaf modeling framework H. Zhu et al. 10.5194/bg-21-3735-2024
- Modeling carbon dynamics from a heterogeneous watershed in the mid-Atlantic USA: A distributed-calibration and independent verification (DCIV) approach S. Tijjani et al. 10.1016/j.scitotenv.2024.177271
- Inner Mongolia grasslands act as a weak regional carbon sink: A new estimation based on upscaling eddy covariance observations C. You et al. 10.1016/j.agrformet.2023.109719
- AgriCarbon-EO v1.0.1: large-scale and high-resolution simulation of carbon fluxes by assimilation of Sentinel-2 and Landsat-8 reflectances using a Bayesian approach T. Wijmer et al. 10.5194/gmd-17-997-2024
- Joint improvement on absorbed photosynthetically active radiation and intrinsic quantum yield efficiency algorithms in the P model betters the estimate of terrestrial gross primary productivity Z. Zhang et al. 10.1016/j.agrformet.2023.109883
- A surrogate modeling method for distributed land surface hydrological models based on deep learning R. Sun et al. 10.1016/j.jhydrol.2023.129944
- Global datasets of hourly carbon and water fluxes simulated using a satellite-based process model with dynamic parameterizations J. Leng et al. 10.5194/essd-16-1283-2024
- A multi-perspective input selection strategy for daily net ecosystem exchange predictions based on machine learning methods Ö. Ekmekcioğlu et al. 10.1007/s00704-022-04265-4
- UFLUX-GPP: A Cost-Effective Framework for Quantifying Daily Terrestrial Ecosystem Carbon Uptake Using Satellite Data S. Zhu et al. 10.1109/TGRS.2024.3439333
11 citations as recorded by crossref.
- Assimilation of carbonyl sulfide (COS) fluxes within the adjoint-based data assimilation system – Nanjing University Carbon Assimilation System (NUCAS v1.0) H. Zhu et al. 10.5194/gmd-17-6337-2024
- Climate and vegetation change impacts on future conterminous United States water yield H. Duarte et al. 10.1016/j.jhydrol.2024.131472
- Optimizing the terrestrial ecosystem gross primary productivity using carbonyl sulfide (COS) within a two-leaf modeling framework H. Zhu et al. 10.5194/bg-21-3735-2024
- Modeling carbon dynamics from a heterogeneous watershed in the mid-Atlantic USA: A distributed-calibration and independent verification (DCIV) approach S. Tijjani et al. 10.1016/j.scitotenv.2024.177271
- Inner Mongolia grasslands act as a weak regional carbon sink: A new estimation based on upscaling eddy covariance observations C. You et al. 10.1016/j.agrformet.2023.109719
- AgriCarbon-EO v1.0.1: large-scale and high-resolution simulation of carbon fluxes by assimilation of Sentinel-2 and Landsat-8 reflectances using a Bayesian approach T. Wijmer et al. 10.5194/gmd-17-997-2024
- Joint improvement on absorbed photosynthetically active radiation and intrinsic quantum yield efficiency algorithms in the P model betters the estimate of terrestrial gross primary productivity Z. Zhang et al. 10.1016/j.agrformet.2023.109883
- A surrogate modeling method for distributed land surface hydrological models based on deep learning R. Sun et al. 10.1016/j.jhydrol.2023.129944
- Global datasets of hourly carbon and water fluxes simulated using a satellite-based process model with dynamic parameterizations J. Leng et al. 10.5194/essd-16-1283-2024
- A multi-perspective input selection strategy for daily net ecosystem exchange predictions based on machine learning methods Ö. Ekmekcioğlu et al. 10.1007/s00704-022-04265-4
- UFLUX-GPP: A Cost-Effective Framework for Quantifying Daily Terrestrial Ecosystem Carbon Uptake Using Satellite Data S. Zhu et al. 10.1109/TGRS.2024.3439333
Latest update: 05 Apr 2025
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.
Parameter optimization can improve the accuracy of modeled carbon fluxes. Few studies conducted...
