HR: 1330h
AN: B33D-01 [Abstracts]
TI: Regional Scaling of Cropland Net Primary Production for Nebraska using Satellite Remote Sensing from MODIS and the Ecosystem Process Model BIOME-BGC
AU: * Heinsch, F A
EM: faithann@ntsg.umt.edu
AF: NTSG, College of Forestry and Conservation, The University of Montana, 32 Campus Drive, Missoula, MT
59812 United States
AU: Jolly, W M
EM: mjolly@fs.fed.us
AF: USDA Forest Service, Rocky Mountain Research Station, Fire Sciences Laboratory, 5775 Hwy 10 W,
Missoula, MT 59808 United States
AU: Mu, Q
EM: qiaozhen@ntsg.umt.edu
AF: NTSG, College of Forestry and Conservation, The University of Montana, 32 Campus Drive, Missoula, MT
59812 United States
AU: Kimball, J S
EM: johnk@ntsg.umt.edu
AF: NTSG, College of Forestry and Conservation, The University of Montana, 32 Campus Drive, Missoula, MT
59812 United States
AU: Kimball, J S
EM: johnk@ntsg.umt.edu
AF: The University of Montana Flathead Lake Biological Station, 311 BioStation Lane, Polson, MT 59860 United States
AB:
Crops dominate the Midwestern U.S., and this has major implications for the domestic carbon balance. This research is
designed to test and improve the ability of satellite remote sensing (MODIS) to estimate cropland productivity through the
use of field measurements and ecosystem modeling. The Biome-BGC ecosystem process model (V4.1.2) has been modified for use in agricultural systems, and tested at the field level for both C3 (soybean) and C4 (maize) crops under different
irrigation regimes. Biome-BGC results are verified using local AmeriFlux network site measurements of the net CO2 flux
and cropland biomass. These results are spatially aggregated within a 7x7km window centered over an intensive study area and
compared with satellite-derived GPP and NPP estimates from MODIS. Model results are further extrapolated statewide using
available estimates of crop coverage and irrigation to assess regional patterns and seasonal variability in productivity
captured from both `bottom-up' ecosystem model simulations and `top-down' satellite remote sensing approaches. Finally, we
evaluate regional productivity differences based on model assumptions of both natural (grasslands) and agricultural land
cover types, and assess the potential for improving MODIS GPP and NPP algorithms for agricultural regions. Initial results
indicate that the model works well in estimating productivity of both maize and soybean using different management
techniques. The MODIS algorithm does well when site-specific data are used, but the standard outputs from the MODIS sensor
differ from tower estimates of productivity, most likely a result of the scale mismatch between the two methods. We verify
spatial MODIS products over a heterogeneous landscape by using Biome-BGC to scale tower-specific measurements to MODIS cutout sizes. These results will be used to provide estimates of the regional carbon balance for the larger 20,000 km2 area
within the National Institute for Global Environmental Change (NIGEC) Great Plains and Midwestern study regions.
DE: 0315 Biosphere/atmosphere interactions
DE: 0400 Biogeosciences
DE: 1615 Biogeochemical processes (4805)
SC: Biogeosciences [B]
MN: 2005 Joint Assembly