Winter wheat mapping using temporal signatures of MODIS vegetation index data

Abstract

Because most land-cover types have distinct seasonal changes and corresponding reflectance characteristics in remotely sensed images, the signatures in time-series data are useful for discriminating different land covers. Although temporal signatures have been used to classify different land-cover types, they have not been fully exploited to classify specific crops, and the influence of low resolution should be evaluated. The aims of this study were to seek an effective method to classify specific crops using the temporal signatures in coarse time-series data and to examine the applicability of the data for crop classification as well. A winter wheat-producing region in China was selected for this case study. Moderate-Resolution Imaging Spectroradiometer (MODIS) 8-day composite land surface reflectance product (MOD09Q1) data with a 250 m spatial resolution were used to calculate the vegetation index data, which was applied to detect the properties of live green plants. The noise in the time series was filtered to minimize the classification uncertainties. The curve shape in the time-series vegetation index profile was used as the major metric to classify winter wheat, and other phenological metrics extracted from the data were used conjunctly as auxiliary functions to improve the separability. The metrics for winter wheat classification were quantified in the large fields with relatively pure pixels. Winter wheat was successfully extracted from the MODIS vegetation index data, and the MODIS-derived result was validated with a fine-resolution (19.5 m) thematic map derived from images collected by the charge-coupled device sensor on board the China–Brazil Earth Resources Satellite (CBERS). It showed that the MODIS-derived result had inevitable low-resolution bias, and the errors of commission and omission were 32.3 and 33.8%, respectively. The overall classification effect of the MODIS-derived result relied upon the distribution of pixel purity in the study area.

Acknowledgements

This research was supported by the Natural Science Foundation of the Jiangsu Higher Education Institutions of China (Grant No. 10KJB420003) and the Research Fund of Xuzhou Normal University (Grant No. 09XLR15). The MODIS data were distributed by the Land Processes Distributed Active Archive Center (LP DAAC), located at the US Geological Survey (USGS) Center for Earth Resources Observation and Science (EROS) (http://www.lpdaac.usgs.gov). The CBERS-2 CCD images were provided by the China Centre for Resources Satellite Data and Application (http://www.cresda.com). The statistics of crop calendars was provided by the National Meteorological Bureau of China (http://cdc.cma.gov.cn). We are very grateful for their generous support. In addition, we thank Yu Li and two anonymous reviewers for their constructive comments on an earlier version of the manuscript.