HR: 1340h
AN: B13D-1533    [Abstracts]
TI: MODIS NDVI-Based Crop Production Estimates for Zimbabwe in 2006/07
AU: * Freund, J T
EM: freund@geog.ucsb.edu
AF: Climate Hazards Group, Department of Geography University of California, Santa Barbara, Santa Barbara, CA 93106, United States
AU: Funk, C
EM: chris@geog.ucsb.edu
AF: Climate Hazards Group, Department of Geography University of California, Santa Barbara, Santa Barbara, CA 93106, United States
AU: Budde, M E
EM: mbudde@usgs.gov
AF: U. S. Geological Survey Center for Earth Resources Observation and Science (EROS), 47914 252nd Street, Sioux Falls, SD 57198, United States
AU: Michaelsen, J
EM: joel@geog.ucsb.edu
AF: Climate Hazards Group, Department of Geography University of California, Santa Barbara, Santa Barbara, CA 93106, United States
AU: Galu, G
EM: ggalu@fews.net
AF: USGS / Famine Early Warning System Network (FEWS NET), P.O. Box 66613, Nairobi, 00100, Kenya
AB: Traditionally, crop production is estimated as the product of yield and cropped area. Satellite sensors tend to convolve these two sources of 'greeness', rendering independent assessments of yield and cropped area difficult. In this study, we derive an analogous metric of crop productivity based on time-series of Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) imagery. MODIS NDVI data 'cubes' are created and a special temporal filter is used to screen for cloud contamination. Regional NDVI time-series are then composited for cultivated areas, and adjusted in time according to the timing of the onset of rains. This adjustment standardizes the NDVI response vis-a-vis the standard phenological response of maize. A national time-series index is then created by taking the cropped area weighted average of the regional series. This spatio-temporal compositing allows for the identification of NDVI-green up during grain filling in crop growing areas. Cross-validation tests based on Zimbabwe data reveal that this metric is highly correlated with US Department of Agriculture production figures (R2=0.91), possesses desirable linear characteristics, and performs much better than more common indices such as maximum seasonal NDVI (R2=0.52) or seasonally integrated NDVI (R2=0.52). Thus, appropriate agro-phenological filtering of NDVI can improve the utility and accuracy of space-based agricultural monitoring, which in turn can provide an early proxy for national crop production.
DE: 0468 Natural hazards
DE: 0480 Remote sensing
DE: 0520 Data analysis: algorithms and implementation
DE: 1632 Land cover change
DE: 9305 Africa
SC: Biogeosciences [B]
MN: 2007 Fall Meeting