Abstract: Grain crops are of great significance to the national food security in the world in recent years. Among them, the single remote sensing data source can usually be used to monitor the distribution of grain crops at the pixel scale. However, the monitoring data of grain crop can be constantly contaminated with the serious pepper noise. The current accuracy cannot fully meet the harsh requirements of cropland management in smart agriculture. In this study, a modified strategy was proposed to identify the grain crop information at the field parcel scale using the multi-source satellite data (including high-resolution data (HR), multi-temporal data (MT), and hyperspectral data (HS)), and vector data of land-use type. A study area was selected as the 13 km×12 km complex planting region in the Huangdao District of Qingdao City, Shandong Province, China. The data sources were collected from the ZY1-02D Visible Near-Infrared Camera (VNIC), Advanced Hyperspectral Imager (AHSI), and Landsat-8 Operational Land Imager (OLI). The experiment was performed on the following steps. Firstly, the vector data was used to obtain the cultivated and non-cultivated land boundaries, where the non-cultivated land was masked in the study area. Secondly, the watershed algorithm was utilized to segment the HR for the boundaries of homogeneous crop field parcels. Thirdly, the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) were calculated to determine the thresholds of vegetated field parcels. After that, the spatiotemporal spectrum feature datasets were constructed at the field parcel scale using multi-source satellite remote sensing data, including the spectral features (SFs), Vegetation Indices (VIs), and Texture Features (TFs). Then, an independent sample t-test method was adopted to calculate the separability between the grain crops and non-grain crops within different feature types. The optimal subset of features within each feature type was determined to train the classifier with the diagonal covariance matrix as the discriminant analysis. Seven sets of features were constructed by different combinations of the optimal subset of each feature type. A Quadratic Polynomial Support Vector Machine (QSVM) identification was then carried out to evaluate the accuracy of the system. Subsequently, the contribution of each feature type to the accuracy was analyzed under the different field parcel sizes. Moreover, the optimal feature set was achieved to compare the identification accuracy. Finally, the better accuracy was determined at the field parcel scale and the traditional pixel scale under the optimal feature set. The results showed that: 1) The proposed strategy performed better to acquire the grain crop distribution of cultivated land, with an identification accuracy of 89.7% using the number of field parcels. 2) The maximum identification accuracy of 97.1% was achieved at the field parcel scale using the number of pixels, in terms of the optimal feature set with the SFs, VIs, and TFs. The accuracy was improved by about 24.2 percent points, compared with the traditional pixel scale, and the identification crop field parcels were more complete. 3) The HR_VI and HS_SF can be expected to significantly improve the identification accuracy of small and large field parcels, respectively. In medium-sized field parcels, both HR_VI and HR_TF were contributed to the high identification accuracy of grain crops. The finding can also provide a strong reference to efficiently utilize the cultivated land.