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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 and ground based GPP during the growing season of 2012.(a) croplands. (b)orchard. (c)vegetable field. (d)wetland.
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pone.0153971.g005: Temporal dynamic patterns of modelled and ground based GPP during the growing season of 2012.(a) croplands. (b)orchard. (c)vegetable field. (d)wetland.

Mentions: Among the twenty field measurement sites, we chose seventeen sites covered mainly by vegetation to analyze the temporal dynamic patterns of GPP during the growing season of 2012, which included fourteen croplands, one orchard, one vegetable field and one wetland. Both modelled and observed GPP were obtained. It should be pointed out that we used the mean values in a 3 pixels × 3 pixels window around each sampling site as modelled results to decrease the co-registration errors between image and sampling sites in this study. As shown in Fig 5, both modelled and observed GPP for croplands, orchard and vegetable field increase initially and then decrease after reaching their maximum around July (DOY 181–201). During this period, mountain snow in the southern part of our study region melt as temperature increases, which creates a better water supply. Additionally, the increased temperature, precipitation and solar influx in summer can also lead to higher GPP and NPP values. After August, both temperature and precipitation decrease over time. As environmental conditions getting worse for vegetation growing, GPP and NPP decreases subsequently. Fig 5 also indicates that the modelled GPP have more dramatically increase and decrease patterns than the observed ones, such as the significant differences between DOY 171–180 and DOY 181–190, DOY 201–210 and DOY 211–220, DOY 231–240 and DOY 241–250, which may be attributed to the monthly scaled LAI and FPAR data used in our model. Besides the mismatch between the monthly inputs and daily outputs, the significant differences between the modelled and observed GPP for orchard and vegetable field can also be attributed to their spatial distributions. Compared with croplands and wetland, both these two land cover categories occupy relatively small areas around the EC sites in our study region. Although ground surfaces at the EC sites and around them are relatively homogeneous, land cover categories demonstrate significant variability within large areas like 900 m × 900 m (3 pixels × 3 pixels) especially for orchard and vegetable field with small areas (Fig 6). Fig 6 illustrates the Compact Airborne Spectrographic Imager (CASI) image derived from the HiWATER-MUSOEXE with 5 m spatial resolution acquired on June 29th, 2012 [63], together with the 30 m × 30 m, 300 m × 300 m, and 900 m × 900 m boundaries around the EC sites of orchard and vegetable field. As shown in Fig 6, croplands and urban areas occupy significant proportions within the 900 m × 900 m regions around both the orchard and vegetable field sites. The significant variability of land cover categories will lead to some uncertainties and result in significant differences between the modelled and observed results.


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 and ground based GPP during the growing season of 2012.(a) croplands. (b)orchard. (c)vegetable field. (d)wetland.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0153971.g005: Temporal dynamic patterns of modelled and ground based GPP during the growing season of 2012.(a) croplands. (b)orchard. (c)vegetable field. (d)wetland.
Mentions: Among the twenty field measurement sites, we chose seventeen sites covered mainly by vegetation to analyze the temporal dynamic patterns of GPP during the growing season of 2012, which included fourteen croplands, one orchard, one vegetable field and one wetland. Both modelled and observed GPP were obtained. It should be pointed out that we used the mean values in a 3 pixels × 3 pixels window around each sampling site as modelled results to decrease the co-registration errors between image and sampling sites in this study. As shown in Fig 5, both modelled and observed GPP for croplands, orchard and vegetable field increase initially and then decrease after reaching their maximum around July (DOY 181–201). During this period, mountain snow in the southern part of our study region melt as temperature increases, which creates a better water supply. Additionally, the increased temperature, precipitation and solar influx in summer can also lead to higher GPP and NPP values. After August, both temperature and precipitation decrease over time. As environmental conditions getting worse for vegetation growing, GPP and NPP decreases subsequently. Fig 5 also indicates that the modelled GPP have more dramatically increase and decrease patterns than the observed ones, such as the significant differences between DOY 171–180 and DOY 181–190, DOY 201–210 and DOY 211–220, DOY 231–240 and DOY 241–250, which may be attributed to the monthly scaled LAI and FPAR data used in our model. Besides the mismatch between the monthly inputs and daily outputs, the significant differences between the modelled and observed GPP for orchard and vegetable field can also be attributed to their spatial distributions. Compared with croplands and wetland, both these two land cover categories occupy relatively small areas around the EC sites in our study region. Although ground surfaces at the EC sites and around them are relatively homogeneous, land cover categories demonstrate significant variability within large areas like 900 m × 900 m (3 pixels × 3 pixels) especially for orchard and vegetable field with small areas (Fig 6). Fig 6 illustrates the Compact Airborne Spectrographic Imager (CASI) image derived from the HiWATER-MUSOEXE with 5 m spatial resolution acquired on June 29th, 2012 [63], together with the 30 m × 30 m, 300 m × 300 m, and 900 m × 900 m boundaries around the EC sites of orchard and vegetable field. As shown in Fig 6, croplands and urban areas occupy significant proportions within the 900 m × 900 m regions around both the orchard and vegetable field sites. The significant variability of land cover categories will lead to some uncertainties and result in significant differences between the modelled and observed results.

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