Mapping Cropland and Major Crop Types across the Great Lakes Basin using MODIS-NDVI Data

This research evaluated the potential for using the MODIS Normalized Difference Vegetation Index (NDVI) 16-day composite (MOD13Q) 250 m time-series data to develop an annual crop type mapping capability throughout the 480,000 km2 Great Lakes Basin (GLB). An ecoregion-stratified approach was developed using a two-step processing approach that included an initial differentiation of cropland versus non-cropland and subsequent identification of individual crop types. Major crop types were mapped for the calendar years of 2002 and 2007. National Agricultural Statistics Service (NASS) census data were used to assess county level accuracies on a unit area basis (2002), and the NASS Crop Data Layer (CDL) was used to generate 231,616 reference data points to support a pixel-wise assessment of the MODIS crop type classification (2007) accuracy across the US portion of the GLB. County level comparisons for 2002 indicated 2.2, 6.8, 6.0, and 5.8 percent of area bias errors for corn, soybeans, wheat, and hay, respectively. Detailed pixel-wise accuracy assessments resulted in an overall crop type classification accuracy of 84 percent (Kappa 0.73) for 2007. Kappa coefficients ranged from 0.74 to 0.69 for individual ecoregions. The user's accuracies for corn, soybean, wheat, and hay were 87, 82, 81, and 70 percent, respectively. There were spatial variations of classification performances across ecoregions, especially for soybean and hay. Field sizes had a direct impact on the variable classification performances across the GLB.