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Flux observation: Integrating fluxes derived from ground station and satellite remote sensing 王鹤松 Hesong Wang Institute of atmospheric physics, Chinese.

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Presentation on theme: "Flux observation: Integrating fluxes derived from ground station and satellite remote sensing 王鹤松 Hesong Wang Institute of atmospheric physics, Chinese."— Presentation transcript:

1 Flux observation: Integrating fluxes derived from ground station and satellite remote sensing 王鹤松 Hesong Wang Institute of atmospheric physics, Chinese Academy of Sciences wanghs@tea.ac.cn

2 李正权, 2006 Why integrating ? (precision and scale) The fluxes of water, energy and carbon are important variables in climate modeling, are also key processes in land surface–atmosphere interactions. Regional extrapolation of field based flux measurements is still a challenging task due to the high spatial and temporal variability of terrestrial ecosystems across complex landscapes and regions. The application of satellite remote sensing has greatly enhanced global scale observations of flux over heterogeneous landscapes.

3 Focus on the flux in terrestrial ecosystem (especially in northern China) Observation in station scale (such as EC, TDP, Licor series etc.) and regional scale (such as Landsat, MODIS, MERIS etc.); Integration of remote sensing and flux measurements to the generate regional products with high accuracy.

4 Observation of fluxes in ground stations Photosynthesis Respiration Sap flow Eddy covariance (EC) measurement in Tongyu, a CEOP reference station (grassland and cropland)

5 Regional Integration: Coordinated flux measurements in northern China Intensive calibration was done weeks before the coordinated enhanced observation period to make those site scale data comparable.

6 LUEmax derived from our method LUEmax derived from MOD17 Deriving maximal light use efficiency regional gross primary production modeling

7 EC measured GPP (g C/m 2 ) in 8-day Predict ed GPP (g C/m 2 ) in 8- day Grass sitesForest sites Crop sitesAll sites y = 0.9576x + 0.6142 R 2 = 0.82, p<0.01 y = 1.0732x + 2.4747 R 2 = 0.57, p<0.05 y = 0.7235x + 13.483 R 2 = 0.69, p<0.01 y = 0.8564x + 5.9414 R 2 = 0.79, p<0.01 Simulated GPP VS. Measured GPP

8 Spatial pattern of GPP in northern China

9 Main landcover types and their dynamics of GPP in northern China For more details, see paper published in RSE (Wang et al., 2010)

10 DOY 185 DOY 201 DOY 209 DOY 225 NDVI LST difference in day and night (K) Triangle of Ts-VI method to estimate EF and to calculate ET in northern China

11 Simulated EF VS. Measured EF

12 mm/Day Seasonality of ET in northern China

13 Main landcover types and their dynamics of ET in northern China

14 Heterogeneity in simulating flux

15 谢谢! Thank you !


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