School of Information Engineering, China University of Geosciences (Beijing), Beijing 100083, China; [EMAIL_ADDRESS]  
Hebei Key Laboratory of Geospatial Digital Twin and Collaborative Optimization, China University of Geosciences (Beijing), Beijing 100083, China  
The Second Surveying and Mapping Institute of Hunan Province, Changsha 410004, China; [EMAIL_ADDRESS]  
[PERSON] Cao  
tural changes [1], the area planted with non-food crops (e.g., oilseeds, economic forests, medicinal plants) is expanding, directly causing a decrease in food crop yields. Converting cropland to non-food crops shrinks the area available for staple production, heightens food-supply risks, and intensifies ecological pressures, such as pesticide overuse, soil erosion, and biodiversity loss [2]. Precise identification of non-food crops is essential for evaluating land conversion impacts on food security, prioritizing food production, optimizing land resource allocation, and supporting ecological conservation [3]. Traditional monitoring methods, including field surveys and ground statistics, suffer from limited coverage, low efficiency, and inability to provide real-time dynamic monitoring, rendering them insufficient for current demands. In contrast, remote-sensing technology, characterized by extensive spatial coverage, frequent observations, and multi-source data integration, is increasingly an effective tool for monitoring non-food crops. Despite these advantages, medium-resolution sensors face significant limitations. [PERSON] et al. [4], while mapping farmland in complex urban landscapes using Landsat (30 m) and MODIS (ranging from 250 m to 1000 m), found that the spatial resolution of these sensors is insufficient for capturing fine spectral details, particularly where spectral differences between crops are subtle or planting density is high. Cloud cover and rainfall often interrupt optical acquisitions, reducing the temporal resolution required for frequent tracking of crop development. [PERSON] et al. [5] explored an enhanced Landsat-MODIS fusion dataset from 2007, combining object-based image segmentation, decision-tree classification, and targeted feature selection. They found that, despite the availability of multiple spectral bands, the low spectral resolution hindered the separability between spectrally similar crops and those at adjacent phenological stages. The Sentinel-2 (S2) mission, launched in 2017, offers 10 m spatial resolution and a five-day revisit cycle, enabling finer and more frequent crop monitoring. The inclusion of red-edge bands in S2 significantly enhances the ability to distinguish subtle spectral differences between crop species. [PERSON] et al. [6] evaluated Landsat 7/8, Sentinel-2, Sentinel-1 (S1), and MODIS for mapping maize and soybean in the United States, finding that red-edge bands, sensitive to chlorophyll content and plant health, effectively differentiate healthy from stressed vegetation. However, despite the high revisit frequency of Sentinel-2, its optical sensors remain vulnerable to cloud interference, limiting all-weather observations. In contrast, Sentinel-1's synthetic-aperture radar offers all-weather, day-and-night imaging, which is highly sensitive to crop moisture and structural characteristics, making S1 data a critical complement to optical imagery for effective crop-growth monitoring [7]. Due to the data acquisition costs and scale limitations, Sentinel-2 (S2) imagery is widely employed in traditional non-food crop classification methods. However, this approach may not sufficiently capture the spatiotemporal dynamics of crop growth, potentially limiting both the accuracy of classification and the generalizability of the model. To address these limitations, increasing the temporal frequency of data sampling has been recognized as an effective strategy for enhancing the model's ability to capture crop phenology and dynamic growth stages. For example, [PERSON] et al.