Estimating Regional Changes in Soil Carbon with High Spatial Resolution: Integrating Field Measurements, Inventory Data, and Remote Sensing Products
Tristram
O.
West, Oak Ridge National Laboratory, westto@ornl.gov
(Presenting)
Craig
C
Brandt, Oak Ridge National Laboratory, brandtcc@ornl.gov
Bradly
Wilson, University of Tennessee, driver8@utk.edu
Chad
Hellwinckel, University of Tennessee, chellwin@utk.edu
Marcella
Mueller, Oak Ridge National Laboratory, muellerma@ornl.gov
Donald
D.
Tyler, University of Tennessee, dtyler@utk.edu
Daniel
De La Torre Ugarte, University of Tennessee, danieltu@utk.edu
To improve estimates of regional carbon dynamics, it is important to better represent landscape heterogeneity and local land management. We are currently developing a carbon accounting framework that can estimate carbon dynamics and net greenhouse gas emissions associated with changes in land management at a high spatial resolution. One component of this framework integrates field measurements, inventory data, and remote sensing products to monitor changes in soil carbon at a sub-county level (900m^2 resolution) caused by inter-annual changes in tillage and crop management. We applied this framework component to a mid-western region of the US that consists of 679 counties approximately centered around Iowa. We estimate the 1990 baseline soil carbon for this region to be 4,099,199,793 Mg to a 3m maximum depth. Soil carbon accumulation of 57,274,560 Mg is estimated to have occurred in this region between 1991-2000. Without accounting for soil carbon loss associated with changes to more intense tillage practices, our estimate increases to 66,338,751 Mg. This indicates that on-site permanence of soil carbon is approximately 86% with no additional economic incentives provided for soil carbon sequestration practices. This carbon accounting framework offers a method to integrate new inventory and remote sensing data on an annual basis, account for alternating annual trends in land management without the need for model equilibration, and provide a transparent means to monitor changes soil carbon. Our method of integration is capable of estimating regional or national changes in soil carbon while still representing heterogeneity at the sub-county level. Future research will include predictive changes in soil carbon based on socio-economic drivers, and a sensitivity analysis using high-resolution remote sensing products.