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

Daily NPP for a 10-day period of Heihe River Basin.(a) DOY 131–140. (b) DOY 161–170. (c) DOY 191–200. (d) DOY 221–230. (e) DOY 251–260. (f) DOY 281–290.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0153971.g004: Daily NPP for a 10-day period of Heihe River Basin.(a) DOY 131–140. (b) DOY 161–170. (c) DOY 191–200. (d) DOY 221–230. (e) DOY 251–260. (f) DOY 281–290.

Mentions: The maps of growing season GPP and NPP reveal clear spatial and temporal patterns in their distribution over Heihe River Basin. As shown in Fig 3 and Fig 4, both GPP and NPP are relatively high in the southern part of mountainous areas in the upstream, oasis areas in the midstream, and riparian areas adjacent to the water body in the downstream. Among which, the oasis areas in the midstream covered with crops occupy the highest values of GPP and NPP, followed by the mountainous areas in the upstream covered with grasses. The downstream regions covered with deserts and Gobi demonstrate the lowest production values. Temporal dynamic patterns of GPP and NPP reveal both of these datasets increase over time initially and decreases after reaching their maximum on July and August. The spatial and temporal patterns of GPP and NPP corresponds to the vegetation growth characteristics in Heihe River Basin.


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)

Daily NPP for a 10-day period of Heihe River Basin.(a) DOY 131–140. (b) DOY 161–170. (c) DOY 191–200. (d) DOY 221–230. (e) DOY 251–260. (f) DOY 281–290.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0153971.g004: Daily NPP for a 10-day period of Heihe River Basin.(a) DOY 131–140. (b) DOY 161–170. (c) DOY 191–200. (d) DOY 221–230. (e) DOY 251–260. (f) DOY 281–290.
Mentions: The maps of growing season GPP and NPP reveal clear spatial and temporal patterns in their distribution over Heihe River Basin. As shown in Fig 3 and Fig 4, both GPP and NPP are relatively high in the southern part of mountainous areas in the upstream, oasis areas in the midstream, and riparian areas adjacent to the water body in the downstream. Among which, the oasis areas in the midstream covered with crops occupy the highest values of GPP and NPP, followed by the mountainous areas in the upstream covered with grasses. The downstream regions covered with deserts and Gobi demonstrate the lowest production values. Temporal dynamic patterns of GPP and NPP reveal both of these datasets increase over time initially and decreases after reaching their maximum on July and August. The spatial and temporal patterns of GPP and NPP corresponds to the vegetation growth characteristics in Heihe River Basin.

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