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Estimating Vegetation Primary Production in the Heihe River Basin of China with Multi-Source and Multi-Scale Data.

Cui T, Wang Y, Sun R, Qiao C, Fan W, Jiang G, Hao L, Zhang L - PLoS ONE (2016)

Bottom Line: Estimating gross primary production (GPP) and net primary production (NPP) are significant important in studying carbon cycles.The results indicated that both GPP and NPP exhibit clear spatial and temporal patterns in their distribution over Heihe River Basin during the growing season due to the temperature, water and solar influx conditions.After validated against ground-based measurements, MODIS GPP product (MOD17A2H) and results reported in recent literature, we found the MuSyQ-NPP algorithm could yield an RMSE of 2.973 gC m(-2) d(-1) and an R of 0.842 when compared with ground-based GPP while an RMSE of 8.010 gC m(-2) d(-1) and an R of 0.682 can be achieved for MODIS GPP, the estimated NPP values were also well within the range of previous literature, which proved the reliability of our modelling results.

View Article: PubMed Central - PubMed

Affiliation: State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and the Institute of Remote Sensing and Digital Earth, CAS, Beijing, China.

ABSTRACT
Estimating gross primary production (GPP) and net primary production (NPP) are significant important in studying carbon cycles. Using models driven by multi-source and multi-scale data is a promising approach to estimate GPP and NPP at regional and global scales. With a focus on data that are openly accessible, this paper presents a GPP and NPP model driven by remotely sensed data and meteorological data with spatial resolutions varying from 30 m to 0.25 degree and temporal resolutions ranging from 3 hours to 1 month, by integrating remote sensing techniques and eco-physiological process theories. Our model is also designed as part of the Multi-source data Synergized Quantitative (MuSyQ) Remote Sensing Production System. In the presented MuSyQ-NPP algorithm, daily GPP for a 10-day period was calculated as a product of incident photosynthetically active radiation (PAR) and its fraction absorbed by vegetation (FPAR) using a light use efficiency (LUE) model. The autotrophic respiration (Ra) was determined using eco-physiological process theories and the daily NPP was obtained as the balance between GPP and Ra. To test its feasibility at regional scales, our model was performed in an arid and semi-arid region of Heihe River Basin, China to generate daily GPP and NPP during the growing season of 2012. The results indicated that both GPP and NPP exhibit clear spatial and temporal patterns in their distribution over Heihe River Basin during the growing season due to the temperature, water and solar influx conditions. After validated against ground-based measurements, MODIS GPP product (MOD17A2H) and results reported in recent literature, we found the MuSyQ-NPP algorithm could yield an RMSE of 2.973 gC m(-2) d(-1) and an R of 0.842 when compared with ground-based GPP while an RMSE of 8.010 gC m(-2) d(-1) and an R of 0.682 can be achieved for MODIS GPP, the estimated NPP values were also well within the range of previous literature, which proved the reliability of our modelling results. This research suggested that the utilization of multi-source data with various scales would help to the establishment of an appropriate model for calculating GPP and NPP at regional scales with relatively high spatial and temporal resolution.

No MeSH data available.


Related in: MedlinePlus

Temporal dynamic patterns of modelled GPP generated with simulated daily LAI and FPAR together with ground based ones during the growing season of 2012.(a) croplands. (b) orchard. (c) vegetable field. (d) wetland. For modelled and ground-based values, error bars represent mean and maximum/minimum for GPP in a 10-day period.
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pone.0153971.g009: Temporal dynamic patterns of modelled GPP generated with simulated daily LAI and FPAR together with ground based ones during the growing season of 2012.(a) croplands. (b) orchard. (c) vegetable field. (d) wetland. For modelled and ground-based values, error bars represent mean and maximum/minimum for GPP in a 10-day period.

Mentions: Another advantage of the MuSyQ-NPP algorithm is the exclusive use of multi-source and multi-scale data that are openly accessible. MODIS GPP/NPP algorithm adopts the Collection 6 MODIS products for LAI/FPAR and land cover type with spatial resolution of 0.5 km, it also deploys hourly meteorological data provided by the GMAO which are distributed at a resolution of 0.5° by 0.67°. Compared with the MODIS GPP/NPP algorithm, we occupied a series of remotely sensed and meteorological data with higher spatial resolutions in the MuSyQ-NPP algorithm. Among these data, remotely sensed data, such as LAI, FPAR, and land cover categories, are all designed at a 30 m spatial resolution, GLDAS based meteorological data are distributed at a spatial resolution of 0.25 degree. However, it should be noted that lower spatial resolution data including GLDAS-based meteorological data, DEM and forest biomass were all converted to higher spatial resolution using simple spatial interpolation algorithms. Both the quality of these input data and the interpolating methods will affect our estimated results, especially for the forest biomass data obtained from 1998 to 2003, which is quite different from our study period. The temporal mismatch between the forest biomass data used and our study period will introduce significant uncertainties. Additional studies should focus on the collection and adoption of data at more proper spatial and temporal resolution, such as the openly accessed NASA Shuttle Radar Topographic Mission (SRTM) 90m DEM data, which would significantly improve modelling results. The LAI and FPAR data used in this study have a temporal resolution of 1 month, which may be insufficient in characterizing their variations over monthly scale when used in generating daily GPP and NPP. To evaluate the impacts of monthly scaled inputs, we tested the MuSyQ-NPP algorithm based on simulated daily LAI and FPAR. Since daily LAI and FPAR were not available for our study area during the study period, in this study, daily LAI and FPAR were generated by performing linear interpolation algorithm to the monthly scaled data. We then calculated daily GPP based on the daily inputs (including daily LAI, FPAR and meteorological data). To compare with the previously generated 10-day averaged GPP, the simulated data were also plotted in a 10-day scale. As shown in Fig 9, the modelled GPP varied more continuous when used daily inputs. Additionally, the temporal dynamic patterns of modelled GPP were found to be better corresponded with the ground-based ones. We also noticed the modelled and observed GPP for orchard and vegetable field still demonstrated significant differences, which can be attributed to the spatial variability around these two EC sites. Fig 9 indicated that using LAI and FPAR at a more proper scale would help to address some model uncertainties caused by monthly inputs.


Estimating Vegetation Primary Production in the Heihe River Basin of China with Multi-Source and Multi-Scale Data.

Cui T, Wang Y, Sun R, Qiao C, Fan W, Jiang G, Hao L, Zhang L - PLoS ONE (2016)

Temporal dynamic patterns of modelled GPP generated with simulated daily LAI and FPAR together with ground based ones during the growing season of 2012.(a) croplands. (b) orchard. (c) vegetable field. (d) wetland. For modelled and ground-based values, error bars represent mean and maximum/minimum for GPP in a 10-day period.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4835106&req=5

pone.0153971.g009: Temporal dynamic patterns of modelled GPP generated with simulated daily LAI and FPAR together with ground based ones during the growing season of 2012.(a) croplands. (b) orchard. (c) vegetable field. (d) wetland. For modelled and ground-based values, error bars represent mean and maximum/minimum for GPP in a 10-day period.
Mentions: Another advantage of the MuSyQ-NPP algorithm is the exclusive use of multi-source and multi-scale data that are openly accessible. MODIS GPP/NPP algorithm adopts the Collection 6 MODIS products for LAI/FPAR and land cover type with spatial resolution of 0.5 km, it also deploys hourly meteorological data provided by the GMAO which are distributed at a resolution of 0.5° by 0.67°. Compared with the MODIS GPP/NPP algorithm, we occupied a series of remotely sensed and meteorological data with higher spatial resolutions in the MuSyQ-NPP algorithm. Among these data, remotely sensed data, such as LAI, FPAR, and land cover categories, are all designed at a 30 m spatial resolution, GLDAS based meteorological data are distributed at a spatial resolution of 0.25 degree. However, it should be noted that lower spatial resolution data including GLDAS-based meteorological data, DEM and forest biomass were all converted to higher spatial resolution using simple spatial interpolation algorithms. Both the quality of these input data and the interpolating methods will affect our estimated results, especially for the forest biomass data obtained from 1998 to 2003, which is quite different from our study period. The temporal mismatch between the forest biomass data used and our study period will introduce significant uncertainties. Additional studies should focus on the collection and adoption of data at more proper spatial and temporal resolution, such as the openly accessed NASA Shuttle Radar Topographic Mission (SRTM) 90m DEM data, which would significantly improve modelling results. The LAI and FPAR data used in this study have a temporal resolution of 1 month, which may be insufficient in characterizing their variations over monthly scale when used in generating daily GPP and NPP. To evaluate the impacts of monthly scaled inputs, we tested the MuSyQ-NPP algorithm based on simulated daily LAI and FPAR. Since daily LAI and FPAR were not available for our study area during the study period, in this study, daily LAI and FPAR were generated by performing linear interpolation algorithm to the monthly scaled data. We then calculated daily GPP based on the daily inputs (including daily LAI, FPAR and meteorological data). To compare with the previously generated 10-day averaged GPP, the simulated data were also plotted in a 10-day scale. As shown in Fig 9, the modelled GPP varied more continuous when used daily inputs. Additionally, the temporal dynamic patterns of modelled GPP were found to be better corresponded with the ground-based ones. We also noticed the modelled and observed GPP for orchard and vegetable field still demonstrated significant differences, which can be attributed to the spatial variability around these two EC sites. Fig 9 indicated that using LAI and FPAR at a more proper scale would help to address some model uncertainties caused by monthly inputs.

Bottom Line: Estimating gross primary production (GPP) and net primary production (NPP) are significant important in studying carbon cycles.The results indicated that both GPP and NPP exhibit clear spatial and temporal patterns in their distribution over Heihe River Basin during the growing season due to the temperature, water and solar influx conditions.After validated against ground-based measurements, MODIS GPP product (MOD17A2H) and results reported in recent literature, we found the MuSyQ-NPP algorithm could yield an RMSE of 2.973 gC m(-2) d(-1) and an R of 0.842 when compared with ground-based GPP while an RMSE of 8.010 gC m(-2) d(-1) and an R of 0.682 can be achieved for MODIS GPP, the estimated NPP values were also well within the range of previous literature, which proved the reliability of our modelling results.

View Article: PubMed Central - PubMed

Affiliation: State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and the Institute of Remote Sensing and Digital Earth, CAS, Beijing, China.

ABSTRACT
Estimating gross primary production (GPP) and net primary production (NPP) are significant important in studying carbon cycles. Using models driven by multi-source and multi-scale data is a promising approach to estimate GPP and NPP at regional and global scales. With a focus on data that are openly accessible, this paper presents a GPP and NPP model driven by remotely sensed data and meteorological data with spatial resolutions varying from 30 m to 0.25 degree and temporal resolutions ranging from 3 hours to 1 month, by integrating remote sensing techniques and eco-physiological process theories. Our model is also designed as part of the Multi-source data Synergized Quantitative (MuSyQ) Remote Sensing Production System. In the presented MuSyQ-NPP algorithm, daily GPP for a 10-day period was calculated as a product of incident photosynthetically active radiation (PAR) and its fraction absorbed by vegetation (FPAR) using a light use efficiency (LUE) model. The autotrophic respiration (Ra) was determined using eco-physiological process theories and the daily NPP was obtained as the balance between GPP and Ra. To test its feasibility at regional scales, our model was performed in an arid and semi-arid region of Heihe River Basin, China to generate daily GPP and NPP during the growing season of 2012. The results indicated that both GPP and NPP exhibit clear spatial and temporal patterns in their distribution over Heihe River Basin during the growing season due to the temperature, water and solar influx conditions. After validated against ground-based measurements, MODIS GPP product (MOD17A2H) and results reported in recent literature, we found the MuSyQ-NPP algorithm could yield an RMSE of 2.973 gC m(-2) d(-1) and an R of 0.842 when compared with ground-based GPP while an RMSE of 8.010 gC m(-2) d(-1) and an R of 0.682 can be achieved for MODIS GPP, the estimated NPP values were also well within the range of previous literature, which proved the reliability of our modelling results. This research suggested that the utilization of multi-source data with various scales would help to the establishment of an appropriate model for calculating GPP and NPP at regional scales with relatively high spatial and temporal resolution.

No MeSH data available.


Related in: MedlinePlus