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Biogeosciences, 10, 2451-2466, 2013
www.biogeosciences.net/10/2451/2013/
doi:10.5194/bg-10-2451-2013
© Author(s) 2013. This work is distributed
under the Creative Commons Attribution 3.0 License.
A data assimilation framework for constraining upscaled cropland carbon flux seasonality and biometry with MODIS
O. Sus1,*, M. W. Heuer2, T. P. Meyers2, and M. Williams1
1The University of Edinburgh, School of GeoSciences and SAGES, West Mains Road, Edinburgh, EH9 3JN, UK
2Atmospheric Turbulence and Diffusion Division, NOAA/ARL, Oak Ridge, TN, USA
*now at: CREAF, Universitat Autònoma de Barcelona, Barcelona, Spain

Abstract. Agroecosystem models are strongly dependent on information on land management patterns for regional applications. Land management practices play a major role in determining global yield variability, and add an anthropogenic signal to the observed seasonality of atmospheric CO2 concentrations. However, there is still little knowledge on spatial and temporal variability of important farmland activities such as crop sowing dates, and thus these remain rather crudely approximated within carbon cycle studies. In this study, we present a framework allowing for spatio-temporally resolved simulation of cropland carbon fluxes under observational constraints on land management and canopy greenness. We apply data assimilation methodology in order to explicitly account for information on sowing dates and model leaf area index. MODIS 250 m vegetation index data were assimilated both in batch-calibration for sowing date estimation and sequentially for improved model state estimation, using the ensemble Kalman filter (EnKF), into a crop carbon mass balance model (SPAc). In doing so, we are able to quantify the multiannual (2000–2006) regional carbon flux and biometry seasonality of maize–soybean crop rotations surrounding the Bondville Ameriflux eddy covariance site, averaged over 104 pixel locations within the wider area. (1) Validation at the Bondville site shows that growing season C cycling is simulated accurately with MODIS-derived sowing dates, and we expect that this framework allows for accurate simulations of C cycling at locations for which ground-truth data are not available. Thus, this framework enables modellers to simulate current (i.e. last 10 yr) carbon cycling of major agricultural regions. Averaged over the 104 field patches analysed, relative spatial variability for biometry and net ecosystem exchange ranges from ∼7% to ∼18%. The annual sign of net biome productivity is not significantly different from carbon neutrality. (2) Moreover, observing carbon cycling at one single field with its individual sowing pattern is not sufficient to constrain large-scale agroecosystem carbon flux seasonality. Study area average growing season length is 20 days longer than observed at Bondville, primarily because of an earlier estimated start of season. (3) For carbon budgeting, additional information on cropland soil management and belowground carbon cycling has to be considered, as such constraints are not provided by MODIS.

Citation: Sus, O., Heuer, M. W., Meyers, T. P., and Williams, M.: A data assimilation framework for constraining upscaled cropland carbon flux seasonality and biometry with MODIS, Biogeosciences, 10, 2451-2466, doi:10.5194/bg-10-2451-2013, 2013.
 
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