A comparison of MODIS 250-m EVI and NDVI data for crop mapping: a case study for southwest Kansas

Abstract

Multi-temporal vegetation index (VI) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) are becoming widely used for large-area crop classification. Most crop-mapping studies have applied enhanced vegetation index (EVI) data from MODIS instead of the more traditional normalized difference vegetation index (NDVI) data because of atmospheric and background corrections incorporated into EVI's calculation and the index's sensitivity over high biomass areas. However, the actual differences in the classification results using EVI versus NDVI have not been thoroughly explored. This study evaluated time-series MODIS 250-m EVI and NDVI for crop-related land use/land cover (LULC) classification in the US Central Great Plains. EVI- and NDVI-derived maps classifying general crop types, summer crop types and irrigated/non-irrigated crops were produced for southwest Kansas. Qualitative and quantitative assessments were conducted to determine the thematic accuracy of the maps and summarize their classification differences. For the three crop maps, MODIS EVI and NDVI data produced equivalent classification results. High thematic accuracies were achieved with both indices (generally ranging from 85% to 90%) and classified cropping patterns were consistent with those reported for the study area (> 0.95 correlation between the classified and USDA-reported crop areas). Differences in thematic accuracy (< 3% difference), spatially depicted patterns (> 90% pixel-level thematic agreement) and classified crop areas between the series of EVI- and NDVI-derived maps were negligible. Most thematic disagreements were restricted to single pixels or small clumps of pixels in transitional areas between cover types. Analysis of MODIS composite period usage in the classification models also revealed that both VIs performed equally well when periods from a specific growing season phase (green, peak or senescence) were heavily utilized to generate a specific crop map.

Acknowledgements

This research was supported by NASA Headquarters under the Earth System Science Fellowship Grant NGT5-50421. The work was conducted at the Kansas Applied Remote Sensing (KARS) Program (Edward A. Martinko, Director). We thank the Kansas Farm Service Agency and its county offices in the study area for providing annotated aerial photographs used for field site selection. We also thank Dr Thomas Loveland and Dr Bruce Wylie of the USGS Center for Earth Resources Observation Science (EROS) for their insightful suggestions during this research and Dr Jude Kastens, Dr John Kostelnick and Carleen Roberts for assisting in the preparation of several data sets.