Cropland phenology provides key information in managing agricultural practices and modelling crop yield. However, most of the existing phenological products have coarse spatial resolutions of 250-8,000 m, which are not sufficient to capture the spatial variations of cropland phenology at landscape scales. Landsat imagery provides an unprecedented data source to generate a 30-meter spatial resolution phenological product. This paper explored the potential of utilizing multi-year Landsat enhanced vegetation index to derive annual phenological metrics of a double-season agricultural land from 1993 to 2009 in a sub-urban area of Shanghai, China. We used all available Landsat TM and ETM+ observations (538 scenes) and developed a Landsat Double-Cropping Phenology (LDCP) algorithm. LDCP captures the temporal trajectory of multi-year EVI time series very well with degree of fitness ranges from 0.78 to 0.88 over the study regions. We found good agreements between derived annual phenological metrics and in-situ observation with adjusted R2 of 0.95 (p-value=1.15*10-13), indicating the proposed LDCP is reliable to detect key double-season cropland phenology transition dates. LDCP could also reveal the spatial heterogeneity of cropland phenology at parcel scales. Phenology metrics were able to be retrieved approximately one-third and two-thirds of the17 years for the first and second cropping cycles, respectively, depending on the number of good quality Landsat data. In addition, we found advanced peak of season for both cropping cycles and a delayed start of season for the second cropping cycle in 50%-60% and 50%-70% of our study area, respectively. The potential drivers of those trends might be climate warming and changes of agricultural practices. The derived cropland phenology can be used to help estimating historical crop yields at Landsat spatial resolution, providing insights on evaluating the effects of climate change on temporal variations of crop growth, and contributing to food security policy making.