Monitoring of winter wheat distribution and phenological phases based on MODIS time-series：A case study in the Yellow River Delta, China

摘要

Accurate winter wheat identiifcation and phenology extraction are essential for ifeld management and agricultural policy making. Here, we present mechanisms of winter wheat discrimination and phenological detection in the Yelow River Delta (YRD) region using moderate resolution imaging spectroradiometer (MODIS) time-series data. The normalized difference vegetation index (NDVI) was obtained by calculating the surface relfectance in red and infrared. We used the Savitzky-Golay iflter to smooth time series NDVI curves. We adopted a two-step classiifcation to identify winter wheat. The ifrst step was designed to mask out non-vegetation classes, and the second step aimed to identify winter wheat from other vegetation based on its phenological features. We used the double Gaussian model and the maximum curvature method to extract phenology. Due to the characteristics of the time-series proifles for winter wheat, a double Gaussian function method was selected to ift the temporal proifle. A maximum curvature method was performed to extract phenological phases. Pheno-logical phases such as the green-up, heading and harvesting phases were detected when the NDVI curvature exhibited local maximum values. The extracted phenological dates then were validated with records of the ground observations. The spatial patterns of phenological phases were investigated. This study concluded that, for winter wheat, the accuracy of classiifcation is 87.07%, and the accuracy of planting acreage is 90.09%. The phenological result was comparable to the ground observation at the municipal level. The average green-up date for the whole region occurred on March 5, the average heading date occurred on May 9, and the average harvesting date occurred on June 5. The spatial distribution of the phenology for winter wheat showed a signiifcant gradual delay from the southwest to the northeast. This study demon-strates the effectiveness of our proposed method for winter wheat classiifcation and phenology detection.