Detecting Major Phenological Stages of Rice Using MODIS-EVI Data and Symlet11 Wavelet in Northeast China

Plant phenology refers to the emergence of an annual cycle of natural phenomena in plants affected by climate and other environmental factors. Remote sensing technique is an important method to detect vegetation phenology with high spatial-temporal scales. In this study, we developed a method for detecting phenological stages of rice in Northeast China based on the EOS-MODIS multi-temporal remote sensing data, including MOD09A1 and MCD12Q1 in 2008. The rice crop phenological stages were detected by using EOS-MODIS Enhanced Vegetation Index (EVI) data, and compared with the observed rice phenological stages in 24 selected agro-meteorological sites in Northeast China. The twenty six types of wavelet: Daubechies(7-20), Coiflet(3-5) and Symlet(7-15) were used when filtering EVI time profile. The results showed that, the case using Symlet11 shows a remarkably good result in determining phenological stages, which is compared with the observed data. Most of the RMSE in planting date were less than 16 days. Most of the RMSE in heading date and ripening date were less than 8 days. It was shown that the Symlet11 filtering is the best method, which can be used to detect rice phenology.