Analysis of time-series MODIS 250 m vegetation index data for crop classification in the U.S. Central Great Plains

• Published in: Remote sensing of environment Vol. 108; no. 3; pp. 290 - 310
• Main Authors: Wardlow, Brian D., Egbert, Stephen L., Kastens, Jude H.
• Format: Journal Article
• Language: English
• Published: New York, NY Elsevier Inc 15.06.2007; Elsevier Science
• Subjects: Animal, plant and microbial ecology; Applied geophysics; Biological and medical sciences; Crops; Earth sciences; Earth, ocean, space; Exact sciences and technology; Fundamental and applied biological sciences. Psychology; General aspects. Techniques; Internal geophysics; Land used/land cover classification; MODIS; Sorghum; Teledetection and vegetation maps; Triticum aestivum; U.S. Central Great Plains; Vegetation index data; United States; Kansas; USA, Kansas; Land used/land cover classification; U.S. Central Great Plains; Crops; MODIS; Vegetation index data; time series analysis; Cultivated field; Spectroradiometry; data; Vegetation index; Support; global; Information; North America; Economy; land use; spatial resolution; land cover; high resolution; policy; Status; processes; cost; Time series; cartography; classification; varieties; management; Environment; statistical analysis; Community
• ISSN: 0034-4257; 1879-0704
• DOI: 10.1016/j.rse.2006.11.021

The global environmental change research community requires improved and up-to-date land use/land cover (LULC) datasets at regional to global scales to support a variety of science and policy applications. Considerable strides have been made to improve large-area LULC datasets, but little emphasis has been placed on thematically detailed crop mapping, despite the considerable influence of management activities in the cropland sector on various environmental processes and the economy. Time-series MODIS 250 m Vegetation Index (VI) datasets hold considerable promise for large-area crop mapping in an agriculturally intensive region such as the U.S. Central Great Plains, given their global coverage, intermediate spatial resolution, high temporal resolution (16-day composite period), and cost-free status. However, the specific spectral–temporal information contained in these data has yet to be thoroughly explored and their applicability for large-area crop-related LULC classification is relatively unknown. The objective of this research was to investigate the general applicability of the time-series MODIS 250 m Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI) datasets for crop-related LULC classification in this region. A combination of graphical and statistical analyses were performed on a 12-month time-series of MODIS EVI and NDVI data from more than 2000 cropped field sites across the U.S. state of Kansas. Both MODIS VI datasets were found to have sufficient spatial, spectral, and temporal resolutions to detect unique multi-temporal signatures for each of the region's major crop types (alfalfa, corn, sorghum, soybeans, and winter wheat) and management practices (double crop, fallow, and irrigation). Each crop's multi-temporal VI signature was consistent with its general phenological characteristics and most crop classes were spectrally separable at some point during the growing season. Regional intra-class VI signature variations were found for some crops across Kansas that reflected the state's climate and planting time differences. The multi-temporal EVI and NDVI data tracked similar seasonal responses for all crops and were highly correlated across the growing season. However, differences between EVI and NDVI responses were most pronounced during the senescence phase of the growing season.