Abstract:In this paper, the author proposes to identify Land Surface Phenology from remote sensing data by using segmented Morlet wavelet transform. Land Surface Phenology is a necessary parameter for human understanding the Earth's ecological system, and the essential basis for protecting animals and plants, farming and other activities. The author finds that there are some defects in the existing methods, such as inaccurate in identifying phenology, poor at removing noise, while Morlet wavelet performs very good in cycle identification and noise removal. Therefore, the Morlet wavelet transform is used to deal with NDVI of Qinghai Lake Basin from 2003 to 2014. Then it is found that there is a case where the transformed curve is not fit with the original NDVI or the phenological period is shifted. So the author proposes an improvement method:segmenting Morlet wavelet transform, which means to divide each NDVI cycle into two sections according to the NDVI maximum, and then use Morlet wavelet transform on two segments respectively, and finally select appropriate parameter automatically. With this method, the phenology identification will be more reasonable and accurate. The authors extract the LSP parameters of Qinghai Lake Basin by segmenting Morlet wavelet transform and maximum slope, analysis of LSP parameters on time, space, and special year scales, and reveal the characteristics of Land Surface Phenology in the Qinghai Lake Basin. At the same time, it is proved that the Land Surface Phenology remote sensing recognition method based on segmented Morlet wavelet transform has improved both its accuracy and efficiency.