Surface land cover is a special landscape feature modeled by natural and anthropogenic activities, significantly influencing global or local climate, hydrothermal circulation, material transport, and diversity of terrestrial ecosystem communities. Although numerous global or regional land cover datasets have been published, challenges exist in the retrieval of detailed surface cover information, especially on the automatic identification of crop types. Furthermore, the classification accuracy of land-use and land cover maps needs improvement, specifically on crops in the agricultural management community. Using temporal Landsat 8 OLI multispectral images, we analyzed the spectral features of several dominant land cover types from temporal remote sensing images, with a specific emphasis on the unique phenological rhythm of individual plants and investigated the automatic retrieval of land cover types. We organized temporal Landsat 8 OLI surface reflectance data covering Fukang City of Xinjiang, China in the 2016 vegetation growth season. According to the Fukang statistical yearbook, prior knowledge of crop phenology, and the visible texture transition of vegetation in the growing season, we systematically selected the training samples of typical surface covers. We investigated the spectrum, the normalized difference vegetation index, and the enhanced vegetation index temporal curves of 10 typical ground objects, such as agriculture fields of wheat, corn, and cotton, to extract the feature vectors modulated by the growth phase of each crop type. The Euclidean distance was calculated to measure the similarity between unknown surface targets and apriori templates built-up using the above feature vectors and assigned the land cover type according to the K-nearest neighbor rule. As a case study, we applied this approach to the land cover identification of Fukang using the time series of Landsat spectral data in 2018. We validated the retrieval accuracy using 2500 ground samples randomly collected from the land cover map of the investigated area, followed by the manual interpretation of the land cover types using the high-resolution images of 2018 and our field surveys. The results correlate well with the overall accuracy of 97.2% and the Kappa coefficient of 0.9655. Specifically, combined with the field investigation results, the method provides multi-dimensional data for ground object identification and potentially outlines the growth and development progress of different crops, enriches classification information, effectively reduces the phenomenon of foreign matters with the same spectrum in a single time phase, and has potential advantages in improving the identification accuracy of re-plant fields. The time series spectral information captured by this research well reflects the seasonal rhythm and phenological change law of crops in the Fukang region of Xinjiang and provides a novel technical method for the needs of agricultural monitoring.