The apple cultivation industry is a pivotal sector for elevating agriculture and enriching farmers in Shaanxi Province. The apple-producing regions on the Loess Plateau face annual demands for blossom period frost protection and yield assessment. Blossom period monitoring is particularly crucial， and conventional methods suffer from high monitoring costs and low accuracy. This study introduces a method based on reconstructing crop reference curves using remote sensing. It compares the accuracy of this method with others applied to common crops. Subsequently， a set of NDVI time series is extracted from MODIS pure pixels identified by geographic national survey land cover classification data. These time series are then used to fit observations similar to Sentinel-2. The method is applied to apple crops in the Loess Plateau region. Based on the spatial phenological differences between the Normalized Difference Vegetation Index （NDVI） sequences of apple sample points （crop reference curve） and the reconstructed apple NDVI sequences， historical high-resolution onset blossom period monitoring results at 10 meters are generated， achieving the transformation of blossom period monitoring from point to area. The research results indicate that the detection results for the onset blossom period of apples in the validation region for the years 2019~2021 have an average absolute error within 1~3 days compared to ground data. The average absolute error is 0.926 days， and the root mean square error is 1.503 days. This method can be used for high-precision monitoring of the apple blossom period， and the predicted results can serve as a reference for frost protection and yield assessment in actual apple production. Additionally， this method can be applied to study the phenological characteristics of other crops such as wheat and maize， promising more accurate decision support for agricultural production.