Multi-source automatic crop pattern mapping based on historical vegetation index profiles

Multi-temporal vegetation index (VI) reflects phonological calendar feature, which is one of the most distinguishing features between different crops. It has been widely used in detecting the dynamics of vegetation characteristics over time and land cover classification. However, medium to high spatial resolution time series VI data (spatial resolution > 30m) has not been utilized because single sensor cannot provide the images of all time phrases. In addition, the comprehensive utilization of multi-source images is limited as the difference between sensors. The objective of this research was to evaluate the applicability of crop mapping using multi-source medium resolution time series VI data (TM + HJ-1) based on reference historical time series VI profiles in Bole County. Since all of MODIS, TM and HJ-1 have red band and NIR band, three VIs: NDVI, EVI2 and WRDVI calculated from these two bands were employed in this paper. Then, MODIS and field-plot data were utilized to build annual time series VI profiles of different crops between 2006 and 2010, and the historical references profiles by iteratively calculating the weighted average of annual VI profiles were got. Therefore, to reduce VI difference among TM, HJ-1 and MODIS, TM/HJ-1 VI to MODIS VI using linear regression method, and got medium spatial resolution time series VI data composed of TM and HJ-1 in 2011. To extract crop patterns were translated, historical reference profiles were interpolated by time phrases of TM/HJ-1, Euclidean distance between time series VI data and reference profiles was computed and threshold of crop mapping using field samples between 2006 and 2010 was calculated. Nevertheless, as reference time series VI profiles of cotton and corn were similar (correlation coefficient > 0.99), the sixth time phrase (DOY=253) where cotton and corn reference profiles had the largest difference was utilized to distinguish cotton and corn. Finally, the crop mapping accuracy was compared with supervised classification, the result showed that crop mapping accuracy based on historical profiles using NDVI, EVI2 and WRDVI were 90.53%, 91.35% and 90.83%, and Kappa coefficient were 0.78, 0.81 and 0.80, which were better than that of supervised classification. It was concluded that crop mapping based on historical reference time series VI profiles omitted choosing training sites procedure, and interfaced the automatic crop mapping. What is more, this paper also offered a new method to comprehensive utilization of multi-source remote sensing data.