Limits...
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

Spatial distribution of GPP and NPP of Heihe River Basin during the growing season of 2012.(a) GPP. (b) NPP.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4835106&req=5

pone.0153971.g007: Spatial distribution of GPP and NPP of Heihe River Basin during the growing season of 2012.(a) GPP. (b) NPP.

Mentions: We also generated GPP and NPP over the whole growing season. As shows in Fig 7, the growing season GPP and NPP are significantly high in the areas covered by croplands (mainly maize). Due to the fact that maize (C4 crop) occupies a significantly higher potential LUE [33,54], we consider the derived higher GPP and NPP over croplands are partly attributed to the higher potential LUE used in the model. In addition, although precipitation is the major limiting factor for vegetation growth in arid areas, croplands are influenced by irrigation during the growing period. Thus, compared with other land cover categories, croplands occupy a better water supply, water stress of these areas would be much smaller and conditions for vegetation growth would be much better, which will definitely lead to higher GPP and NPP values.


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)

Spatial distribution of GPP and NPP of Heihe River Basin during the growing season of 2012.(a) GPP. (b) NPP.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0153971.g007: Spatial distribution of GPP and NPP of Heihe River Basin during the growing season of 2012.(a) GPP. (b) NPP.
Mentions: We also generated GPP and NPP over the whole growing season. As shows in Fig 7, the growing season GPP and NPP are significantly high in the areas covered by croplands (mainly maize). Due to the fact that maize (C4 crop) occupies a significantly higher potential LUE [33,54], we consider the derived higher GPP and NPP over croplands are partly attributed to the higher potential LUE used in the model. In addition, although precipitation is the major limiting factor for vegetation growth in arid areas, croplands are influenced by irrigation during the growing period. Thus, compared with other land cover categories, croplands occupy a better water supply, water stress of these areas would be much smaller and conditions for vegetation growth would be much better, which will definitely lead to higher GPP and NPP values.

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