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.
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;School of geography and Remote Sensing Sciences, Beijing Normal University, Beijing, China;Beijing Key Lab for Remote Sensing of Environment and Digital Cities, Beijing, China;Northwest Regional Climate Center, Lanzhou, China;School of Atmospheric Sciences, Nanjing University, Nanjing, China;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;School of geography and Remote Sensing Sciences, Beijing Normal University, Beijing, China;Beijing Key Lab for Remote Sensing of Environment and Digital Cities, Beijing, China;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;School of geography and Remote Sensing Sciences, Beijing Normal University, Beijing, China;Beijing Key Lab for Remote Sensing of Environment and Digital Cities, Beijing, China;Institute of Remote Sensing and Geographical Information System, Peking University, Beijing, China;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;School of geography and Remote Sensing Sciences, Beijing Normal University, Beijing, China;Beijing Key Lab for Remote Sensing of Environment and Digital Cities, Beijing, China;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;School of geography and Remote Sensing Sciences, Beijing Normal University, Beijing, China;Beijing Key Lab for Remote Sensing of Environment and Digital Cities, Beijing, China;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;School of geography and Remote Sensing Sciences, Beijing Normal University, Beijing, China;Beijing Key Lab for Remote Sensing of Environment and Digital Cities, Beijing, China
Recommended Citation:
Tianxiang Cui,Yujie Wang,Rui Sun,et al. Estimating Vegetation Primary Production in the Heihe River Basin of China with Multi-Source and Multi-Scale Data[J]. PLOS ONE,2016-01-01,11(4)