Improved maize cultivated area estimation over a large scale combining MODIS-EVI time series data and crop phenological information

The accurate and timely information of crop area is vital for crop production and food security. In this study, the Enhanced Vegetation Index (EVI) data from MODerate resolution Imaging Spectroradiometer (MODIS) integrated crop phenological information was used to estimate the maize cultivated area over a large scale in Northeast China. The fine spatial resolution China's Environment Satellite (HJ-1 satellite) images and the support vector machine (SVM) algorithm were employed to discriminate distribution of maize in the reference area. The mean MODIS-EVI time series curve of maize was extracted in the reference area by using multiple periods MODIS-EVI data. By analysing the temporal shift of crop calendars from northern to southern parts in Northeast China, the lag value was derived from phenological data of twenty-one agro-meteorological stations; here integrating with the mean MODIS-EVI time series image of maize, a standard MODIS-EVI time series image of maize was obtained in the whole study area. By calculating mean absolute distances (MAD) map between standard MODIS-EVI image and mean MODIS-EVI time series images, and setting appropriate thresholds in three provinces, the maize cultivated area was extracted in Northeast China. The results showed that the overall classification accuracy of maize cultivated area was approximately 79%. At the county level, the MODIS-derived maize cultivated area and statistical data were well correlated (R-2 = 0.82, RMSE = 283.98) over whole Northeast China. It demonstrated that MODIS-EVI time series data integrated with crop phenological information can be used to improve the extraction accuracy of crop cultivated area over a large scale. (C) 2014 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.