Comparison and Uncertainty Analysis in Remote Sensing Based Production Efficiency Models Rui Liu 2010-05-27 公司
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
Terrestrial net primary productivity Terrestrial NPP: Net Photosynthesis Dark Respiration essential to carbon cycle
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
Uncertainty in PEMs Direct observation is unavailable on a regional or global scale; Different data sources and handling methods Which one is better?
Parameters Analysis in PEMs: NPP=ε APAR
Remote Sensing Data Vegetation Distribution Information Vegetation Index Vegetation growth environment information
Meteorology Measurement: Environmental conditions Light Heat Water Radiation Temperature precipitation
Plant Physiological Data: how the plant growth responds to the environmental factors; Light use efficiency (ε): Conversion of APAR to biomass
Uncertainty Analysis: Main differences of parameters in PEMs: Obtaining and applying vegetation distribution; Obtaining fpar and ε Use of meteorology factors
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
Vegetation Distribution Real-time, more accurate vegetation distribution can significantly affect the accuracy of the models. MODIS 12Q1 2009-10 (500m Resolution) Globcover 2006 (300m Resolution) Google Earth Rui LIU: 2009-08 (30m Resolution)
Remote Sensing Based fpar Reflects the status of vegetation canopy s absorption of photosynthetically active radiation NDVI EVI
Experiment: MODIS NDVI and EVI data (1km, 500m and 250m spatial resolution) ground measured spectrum data
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) 0.108 ~ 1.580 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?
Methods for more accurate ε * Is different among biomes; remote sensing retrieval through PEMs and ground measured NPP;
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)
ANUSPLIN in Tibet
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.
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