Global gross primary production from vegetation photosynthesis model for 2000-2016
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| Auteurs principaux: | , , , , , , |
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| Format: | Dataset Open Access |
| Langue: | en |
| Publié: |
PANGAEA
2017
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| _version_ | 1867170471840055296 |
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| author | Zhang, Yao Xiao, Xiangming Wu, Xiaocui Zhou, Sha Zhang, Geli Qin, Yuanwei Dong, Jinwei |
| author_facet | Zhang, Yao Xiao, Xiangming Wu, Xiaocui Zhou, Sha Zhang, Geli Qin, Yuanwei Dong, Jinwei |
| collection | Datos científicos de ciencias marinas y ambientales |
| contents | Accurate estimation of the gross primary production (GPP) of terrestrial vegetation is vital for understanding of the global carbon cycle and predicting the future climate change. Multiple GPP products are currently available based on different methods, but their performances vary substantially when validated against GPP estimates from eddy covariance data. This paper provides a new GPP dataset at moderate spatial (500 m) and temporal (8-day) resolutions over the entire globe for 2000-2016. This GPP dataset is based on an improved light use efficiency theory and is driven by satellite data from MODIS and climate data from NCEP Reanalysis II. It also employs a state-of-the-art vegetation index (VI) gap-filling and smoothing algorithm and a separate treatment for C3/C4 photosynthesis pathways. All these improvements aim to solve several critical problems existing in current GPP products. With a satisfactory performance when validated against in situ GPP estimates, this dataset offers an alternative GPP estimates for regional to global carbon cycle studies. |
| format | Dataset Open Access |
| id | pangaea_https___doi_org_10_1594_PANGAEA_879560 |
| institution | PANGAEA |
| language | en |
| publishDate | 2017 |
| publisher | PANGAEA |
| record_format | pangaea |
| spellingShingle | Global gross primary production from vegetation photosynthesis model for 2000-2016 Zhang, Yao Xiao, Xiangming Wu, Xiaocui Zhou, Sha Zhang, Geli Qin, Yuanwei Dong, Jinwei Accurate estimation of the gross primary production (GPP) of terrestrial vegetation is vital for understanding of the global carbon cycle and predicting the future climate change. Multiple GPP products are currently available based on different methods, but their performances vary substantially when validated against GPP estimates from eddy covariance data. This paper provides a new GPP dataset at moderate spatial (500 m) and temporal (8-day) resolutions over the entire globe for 2000-2016. This GPP dataset is based on an improved light use efficiency theory and is driven by satellite data from MODIS and climate data from NCEP Reanalysis II. It also employs a state-of-the-art vegetation index (VI) gap-filling and smoothing algorithm and a separate treatment for C3/C4 photosynthesis pathways. All these improvements aim to solve several critical problems existing in current GPP products. With a satisfactory performance when validated against in situ GPP estimates, this dataset offers an alternative GPP estimates for regional to global carbon cycle studies. |
| title | Global gross primary production from vegetation photosynthesis model for 2000-2016 |
| topic | |
| url | https://doi.org/10.1594/PANGAEA.879560 |