Name
Title | ||
|---|---|---|
Assimilation of Remotely-Sensed Leaf Area Index into a Dynamic Vegetation Model for Gross Primary Productivity Estimation. |
Abstract | ||
|---|---|---|
Quantitative estimation of the magnitude and variability of gross primary productivity (GPP) is required to study the carbon cycle of the terrestrial ecosystem. Using ecosystem models and remotely-sensed data is a practical method for accurately estimating GPP. This study presents a method for assimilating high-quality leaf area index (LAI) products retrieved from satellite data into a process-oriented Lund-Potsdam-Jena dynamic global vegetation model (LPJ-DGVM) to acquire accurate GPP. The assimilation methods, including the Ensemble Kalman Filter (EnKF) and a proper orthogonal decomposition (POD)-based ensemble four-dimensional (4D) variational assimilation method (PODEn4DVar), incorporate information provided by observations into the model to achieve a better agreement between the model-estimated and observed GPP. The LPJ-POD scheme performs better with a correlation coefficient of r = 0.923 and RMSD of 32.676 gC/m(2)/month compared with the LPJ-EnKF scheme (r = 0.887, RMSD = 38.531 gC/m(2)/month) and with no data assimilation (r = 0.840, RMSD = 45.410 gC/m(2)/month). Applying the PODEn4DVar method into LPJ-DGVM for simulating GPP in China shows that the annual amount of GPP in China varied between 5.92 PgC and 6.67 PgC during 2003-2012 with an annual mean of 6.35 PgC/yr. This study demonstrates that integrating remotely-sensed data with dynamic global vegetation models through data assimilation methods has potential in optimizing the simulation and that the LPJ-POD scheme shows better performance in improving GPP estimates, which can provide a favorable way for accurately estimating dynamics of ecosystems. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.3390/rs9030188 | REMOTE SENSING |
Keywords | Field | DocType |
gross primary production,leaf area index,Lund-Potsdam-Jena dynamic global vegetation model,EnKF,PODEn4DVar,China | Primary production,Meteorology,Correlation coefficient,Leaf area index,Vegetation,Dynamic global vegetation model,Remote sensing,Terrestrial ecosystem,Atmospheric sciences,Data assimilation,Ensemble Kalman filter,Geology | Journal |
Volume | Issue | ISSN |
9 | 3 | 2072-4292 |
Citations | PageRank | References |
0 | 0.34 | 5 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rui Ma | 1 | 0 | 0.34 |
| Li Zhang | 2 | 17 | 4.92 |
| Xiangjun Tian | 3 | 0 | 0.34 |
| Jiancai Zhang | 4 | 0 | 0.34 |
| Wenping Yuan | 5 | 18 | 9.69 |
| Yi Zheng | 6 | 83 | 16.52 |
| Xiang Zhao | 7 | 41 | 9.19 |
| Tomomichi Kato | 8 | 0 | 0.68 |