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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

Model test against ground-based GPP.(a) Relationship between estimated GPP and ground-based GPP. (b) Relationship between MODIS GPP and ground-based GPP (b).
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pone.0153971.g008: Model test against ground-based GPP.(a) Relationship between estimated GPP and ground-based GPP. (b) Relationship between MODIS GPP and ground-based GPP (b).

Mentions: To decrease the co-registration errors between image and sampling sites, mean values in a 3 pixels × 3 pixels window around each sampling site were extracted as modelled results. Scatter plot between the modelled and ground-based GPP together with scatter plot between the MODIS GPP product and ground-based GPP are presented in Fig 8. As Fig 8 illustrates, an RMSE of 2.973 gC m-2 d-1 and an R of 0.842 can be yield between our modelled GPP and the ground-based measurements. Although a strong relationship exists between these two datasets, some individual pixels show relatively high scattering in the plot. The reason for the scattering can be attributed to several reasons. The most primary one is that we assumed LAI and FPAR hold constant within each month in our study, which may result in a mismatch between the monthly inputs and daily outputs. In addition, we directly compared the modelled GPP with ground measurements in this study, although ground surface at the EC sites and around them are relatively homogeneous for our study region, land cover categories will demonstrate significant variability within a 900 m × 900 m area. This kind of variation will definitely lead to some differences between modelled results and ground-based measurements. As for the MODIS GPP product, an RMSE of 8.010 gC m-2 d-1 and an R of 0.682 can be achieved. Scatter plot between the MODIS GPP product and ground-based GPP prove that the relationship between these two data sets deviate from the 1:1 line significantly: the former is generally lower than the latter, especially for croplands. The significant underestimation of MODIS GPP for croplands compared with our model can be attributed to the lower potential LUE used for C4 crops (mainly maize for our study area) in the MODIS GPP algorithm. As potential LUE for crops is assigned as 1.044 gC MJ-1 and shows no difference between C3 and C4 species in the MODIS GPP algorithm while C4 crops actually demonstrate a much higher potential LUE than C3 crops [54]. Additionally, the MODIS GPP algorithm utilizes the Collection 6 MODIS product for land cover type (MCD12Q1) in generating GPP. Since Collection 6 MCD12Q1 is designed at a 0.5 km grid scale, it can be difficult to obtain accurate land cover in areas with complex land surface conditions. For our study area, one cropland is misclassified as urban/built-up, which result in the incorrectly calculated GPP for the cropland.


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)

Model test against ground-based GPP.(a) Relationship between estimated GPP and ground-based GPP. (b) Relationship between MODIS GPP and ground-based GPP (b).
© Copyright Policy
Related In: Results  -  Collection

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

pone.0153971.g008: Model test against ground-based GPP.(a) Relationship between estimated GPP and ground-based GPP. (b) Relationship between MODIS GPP and ground-based GPP (b).
Mentions: To decrease the co-registration errors between image and sampling sites, mean values in a 3 pixels × 3 pixels window around each sampling site were extracted as modelled results. Scatter plot between the modelled and ground-based GPP together with scatter plot between the MODIS GPP product and ground-based GPP are presented in Fig 8. As Fig 8 illustrates, an RMSE of 2.973 gC m-2 d-1 and an R of 0.842 can be yield between our modelled GPP and the ground-based measurements. Although a strong relationship exists between these two datasets, some individual pixels show relatively high scattering in the plot. The reason for the scattering can be attributed to several reasons. The most primary one is that we assumed LAI and FPAR hold constant within each month in our study, which may result in a mismatch between the monthly inputs and daily outputs. In addition, we directly compared the modelled GPP with ground measurements in this study, although ground surface at the EC sites and around them are relatively homogeneous for our study region, land cover categories will demonstrate significant variability within a 900 m × 900 m area. This kind of variation will definitely lead to some differences between modelled results and ground-based measurements. As for the MODIS GPP product, an RMSE of 8.010 gC m-2 d-1 and an R of 0.682 can be achieved. Scatter plot between the MODIS GPP product and ground-based GPP prove that the relationship between these two data sets deviate from the 1:1 line significantly: the former is generally lower than the latter, especially for croplands. The significant underestimation of MODIS GPP for croplands compared with our model can be attributed to the lower potential LUE used for C4 crops (mainly maize for our study area) in the MODIS GPP algorithm. As potential LUE for crops is assigned as 1.044 gC MJ-1 and shows no difference between C3 and C4 species in the MODIS GPP algorithm while C4 crops actually demonstrate a much higher potential LUE than C3 crops [54]. Additionally, the MODIS GPP algorithm utilizes the Collection 6 MODIS product for land cover type (MCD12Q1) in generating GPP. Since Collection 6 MCD12Q1 is designed at a 0.5 km grid scale, it can be difficult to obtain accurate land cover in areas with complex land surface conditions. For our study area, one cropland is misclassified as urban/built-up, which result in the incorrectly calculated GPP for the cropland.

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