HR: 1340h
AN: B43B-0262    [Abstracts]
TI: Monitoring of Crop Production Using a new Satellite-Based Climate-Variability Impact Index
AU: * Zhang, P
EM: zhping@crsa.bu.edu
AF: Department of Geography, Boston University 675 Commonwealth Avenue , Boston, MA 02215 United States
AU: Anderson, B
EM: brucea@bu.edu
AF: Department of Geography, Boston University 675 Commonwealth Avenue , Boston, MA 02215 United States
AU: Tan, B
EM: tanbin@crsa.bu.edu
AF: Department of Geography, Boston University 675 Commonwealth Avenue , Boston, MA 02215 United States
AU: Huang, D
EM: dh@crsa.bu.edu
AF: Department of Geography, Boston University 675 Commonwealth Avenue , Boston, MA 02215 United States
AU: Myneni, R
EM: rmyneni@bu.edu
AF: Department of Geography, Boston University 675 Commonwealth Avenue , Boston, MA 02215 United States
AB: The capabilities of the MODerate resolution Imaging Spectroradiometer (MODIS) present some exciting possibilities for improved and timely monitoring of crop production. A quantitative index is introduced in this paper to study the relationship between remotely-sensed leaf area index (LAI) and crop production. The Climate-Variability Impact Index (CVII), defined as the monthly contribution to anomalies in annual growth, quantifies the percentage of the climatological production either gained or lost due to climatic variability during a given month. By examining the integrated CVII over the growing season, this LAI-based index can provide both fine-scale and aggregated information on vegetation productivity for various crop types. Once the relationship between the CVII and crop production is developed based on the historical record, a trained statistical model can be applied to produce homogeneous production forecasts (in which the model is trained and tested for a particular region), as well as heterogeneous forecasts (in which the model is trained in a particular region and applied to a different region). Both the homogeneous and the heterogeneous model predictions are consistent with USDA/FAO estimates at regional scales. Finally, by determining the estimated production as a function of the growing-season months the CVII can provide significant in-season predictions for end-of-year production. Overall, the high temporal and spatial resolution of the satellite LAI products makes the CVII a useful tool in near-real time crop monitoring and production estimation. Case-studies from recent droughts in Niger and the U.S. Midwest Corn Belt will be presented.
DE: 0402 Agricultural systems
DE: 0429 Climate dynamics (1620)
DE: 0480 Remote sensing
DE: 1812 Drought
SC: Biogeosciences [B]
MN: Fall Meeting 2005