Comparison and Uncertainty Analysis in Remote Sensing Based Production Efficiency Models. Rui Liu

Transcription

1 Comparison and Uncertainty Analysis in Remote Sensing Based Production Efficiency Models Rui Liu 公司

2 Outline: Why I m doing this work? Parameters Analysis in Production Efficiency Model (PEM) Uncertainty Analysis Vegetation Distribution fpar Light Use Efficiency Interpolation of Meteorology Conclusion

3 Terrestrial net primary productivity Terrestrial NPP: Net Photosynthesis Dark Respiration essential to carbon cycle

4 Estimate Terrestrial NPP NPP Estimation Field Measurement Modeling Productivity Obtain the key parameters by remote sensing Statistical Model Process Model Production Efficiency Model(PEM) Easy access to regional data CASA GLO-PEM SDBM VPM TURC

5 Uncertainty in PEMs Direct observation is unavailable on a regional or global scale; Different data sources and handling methods Which one is better?

6 Parameters Analysis in PEMs: NPP=ε APAR

7 Remote Sensing Data Vegetation Distribution Information Vegetation Index Vegetation growth environment information

8 Meteorology Measurement: Environmental conditions Light Heat Water Radiation Temperature precipitation

9 Plant Physiological Data: how the plant growth responds to the environmental factors; Light use efficiency (ε): Conversion of APAR to biomass

10 Uncertainty Analysis: Main differences of parameters in PEMs: Obtaining and applying vegetation distribution; Obtaining fpar and ε Use of meteorology factors

11 Vegetation Distribution The most important: Applying vegetation distribution; affect up to 40% of NPP estimate in temperate mixed forests and deciduous forest (Ruimy et al. 1999); 65% in the south portion of NSTEC (Gao et al. 2003); Determine other parameters: Applied directly or indirectly in other parameters such as ε *, R A, P L and EET Vegetation type

12 Vegetation Distribution Real-time, more accurate vegetation distribution can significantly affect the accuracy of the models. MODIS 12Q (500m Resolution) Globcover 2006 (300m Resolution) Google Earth Rui LIU: (30m Resolution)

13 Remote Sensing Based fpar Reflects the status of vegetation canopy s absorption of photosynthetically active radiation NDVI EVI

14 Experiment: MODIS NDVI and EVI data (1km, 500m and 250m spatial resolution) ground measured spectrum data

15 Maximum Value of Light Use Efficiency (ε * ) Capability of the plants capture and transform environmental resources to dry matter production CASA: 0.389gC MJ 1 (Potter et al. 1993) ~ gc MJ 1 (Ruimy et al. 1994) GLO-PEM: 0.2 ~ 1.2 gc MJ 1 (Prince 1991) 0.69 ~ 1.05 gc MJ 1 (Peng et al. 2000) Which one is more accurate?

16 Methods for more accurate ε * Is different among biomes; remote sensing retrieval through PEMs and ground measured NPP;

17 Spatial Interpolation of Meteorology Measurements Inhibition of ε * Station measurement regional and global meteorology distribution Interpolation Methods: Multiple regression equation (Collins 1995) Gradient Plus Inverse-distance-squared (GIDS) method (Lin et al. 2002) ANUSPLIN (Price et al. 2000, Feng 2004)

18 ANUSPLIN in Tibet

19 CONCLUSION Vegetation distribution is the fundamental element among all parameters; High precision vegetation index is needed. Better spectral and spatial resolution can provide more accurate fpar; Remote sensing retrieval with accurate vegetation map can bring us ε * precisely; ANUSPLIN method can improve accuracy of spatialized meteorology.

20 公司