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

• Published in: Remote sensing of environment Vol. 112; no. 3; pp. 1096 - 1116
• Main Authors: Wardlow, Brian D., Egbert, Stephen L.
• Format: Journal Article
• Language: English
• Published: New York, NY Elsevier Inc 18.03.2008; Elsevier Science
• Subjects: Agronomy. Soil science and plant productions; Applied geophysics; Biological and medical sciences; Crop; Earth sciences; Earth, ocean, space; Exact sciences and technology; Fundamental and applied biological sciences. Psychology; Generalities. Biometrics, experimentation. Remote sensing; Internal geophysics; LULC mapping; MODIS; NDVI; Remote sensing; Sorghum; Triticum aestivum; U.S. Central Great Plains; United States; Great Plains; Kansas; Crop; LULC mapping; U.S. Central Great Plains; NDVI; MODIS; time series analysis; Protocols; Spectroradiometry; Vegetation index; Growing season; vegetation; Classifier; North America; Decision tree; evaluation; land use; Plant cover; land cover; policy; cost; maps; Hierarchical classification; Space remote sensing; cartography; classification; Type; Environment; Multidate observation
• ISSN: 0034-4257; 1879-0704
• DOI: 10.1016/j.rse.2007.07.019

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