Jones, L. A., J. S. Kimball, R. H. Reichle, K. Zhang, K. C. McDonald:
"Satellite Remote Sensing of Net Ecosystem CO2 Exchange using optical-IR and Microwave sensors: Algorithm Development for the SMAP Decadal Survey Mission"
Presentation at the AGU Fall Meeting, San Francisco, CA, USA, 2009.

Abstract:
The global balance between photosynthesis, respiration, and disturbance determines whether ecosystems will continue to offset human CO2 emissions. Changes in temperature and moisture constraints can differentially affect photosynthesis and respiration, whereas disturbance and stand succession can push ecosystems far from steady state, shifting carbon source-sink dynamics. Remote sensing and ecosystem process model simulations allow us to characterize the climatic sensitivity of this balance, but effective model parameters are uncertain at continental scales. We developed a carbon model to derive daily net ecosystem exchange of CO2 (NEE) using MODIS GPP and surface soil moisture and temperature retrievals from AMSR-E as driving data. We apply Bayesian synthesis to parameterize the model with a range of FLUXNET tower CO2 measurements across representative global biomes, while accounting for error in flux observations, driving data, and model structure. We compare model fit diagnostics to determine the relative value of remotely sensed information for accurate prediction of carbon fluxes. Model parameters vary with ecosystem type and indicate that most ecosystems have not reached soil organic carbon pools expected for steady state. Model fit is relatively more impacted by MODIS GPP than by AMSR-E temperature and moisture. AMSR-E moisture explains arid region fluxes, whereas temperature is a stronger predictor for high-latitude locations. The results of this study offer a benchmark for calibrating and assessing the incremental value of Soil Moisture Active Passive (SMAP) mission observations over information available from existing sensors. The Soil Moisture Active Passive (SMAP) mission with scheduled 2013 launch date will provide moderate resolution soil moisture (10 km) and freeze-thaw state (1-3 km) information potentially providing new estimates of land surface processes, including daily NEE. This work was performed at the University of Montana and Jet Propulsion Laboratory under contract to NASA.


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