Name
Abstract | ||
|---|---|---|
The remote sensing based Production Efficiency Models (PEMs), springs from the concept of "Light Use Efficiency" and has been applied more and more in estimating terrestrial Net Primary Productivity (NPP) regionally and globally. However, global NPP estimates vary greatly among different models in different data sources and handling methods. Because direct observation or measurement of NPP is unavailable at global scale, the precision and reliability of the models cannot be guaranteed. Though, there are ways to improve the accuracy of the models from input parameters. In this study, five remote sensing based PEMs have been compared: CASA, GLO-PEM, TURC, SDBM and VPM. We divided input parameters into three categories, and analyzed the uncertainty of (1) vegetation distribution, (2) fraction of photosynthetically active radiation absorbed by the canopy (fPAR) and (3) light use efficiency (epsilon). Ground measurements of Hulunbeier typical grassland and meteorology measurements were introduced for accuracy evaluation. Results show that a real-time, more accurate vegetation distribution could significantly affect the accuracy of the models, since it's applied directly or indirectly in all models and affects other parameters simultaneously. Higher spatial and spectral resolution remote sensing data may reduce uncertainty of fPAR up to 51.3%, which is essential to improve model accuracy. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/IGARSS.2010.5649553 | 2010 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM |
Keywords | Field | DocType |
NPP, Model, PEM, Remote Sensing, Accuracy, Uncertainty, Comparison | Primary production,Vegetation,Production efficiency,Computer science,Remote sensing,Vegetation remote sensing,Photosynthetically active radiation,Group method of data handling | Conference |
ISSN | Citations | PageRank |
2153-6996 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rui Liu | 1 | 0 | 0.34 |
| Jiulin Sun | 2 | 14 | 4.00 |
| Juanle Wang | 3 | 16 | 11.47 |
| Xiaolei Li | 4 | 2 | 0.75 |
| Fei Yang | 5 | 0 | 1.69 |
| Pengfei Chen | 6 | 62 | 13.05 |