Large-area crop mapping using time-series MODIS 250 m NDVI data: An assessment for the U.S. Central Great Plains

摘要: Abstract Improved and up-to-date land use/land cover (LULC) data sets that classify specific crop types associated use practices are needed over intensively cropped regions such as the U.S. Central Great Plains, to support science policy applications focused on understanding role response of agricultural sector environmental change issues. The Moderate Resolution Imaging Spectroradiometer (MODIS) holds considerable promise for detailed, large-area crop-related LULC mapping in this region given its global coverage, unique combination spatial, spectral, temporal resolutions, cost-free status data. objective research was evaluate applicability time-series MODIS 250 m normalized difference vegetation index (NDVI) Plains. A hierarchical protocol, which applied a decision tree classifier multi-temporal NDVI collected growing season, tested state Kansas. classification approach produced series four maps progressively classified: 1) crop/non-crop, 2) general (alfalfa, summer crops, winter wheat, fallow), 3) (corn, sorghum, soybeans), 4) irrigated/non-irrigated crops. quantitative qualitative assessments were made at sub-state levels overall map quality highlight areas misclassification each map. NDVI-derived generally had accuracies greater than 80%. Overall ranged from 94% 84% state-level patterns classified consistent with cropping across usually within 1–5% USDA reported area most classes. Sub-state comparisons found areal discrepancies classes be relatively minor throughout state. In eastern Kansas, some small cropland could not resolved MODIS' resolution led an underclassification crop/non-crop map, propagated subsequent classifications. Notable regional differences also few selected locations related climate factors (i.e., omission marginal, dryland irrigated crops western Kansas), localized precipitation (overclassification northeast (double southeast Kansas).