## 2 Study Area and Data
### Data
#### 2.2.3 Sample Data  
To ensure the accuracy of crop classification, it is crucial to guarantee the representativeness and stability of the sample data. The sample data used in this study are based on multi-year samples of three non-food crops provided by the Hunan Provincial Institute of Surveying and Mapping, combined with non-food crop planting data from the Ministry of Agriculture and Rural Affairs of China (www.moa.gov.cn). Tile-wise labeling workflow is as follows: Original vector polygons were first rasterized to a 10 m grid that matches the Sentinel-2 ground sampling distance. The entire province was then divided  
Figure 3: Phenology calendar of major non-food crops in Hunan Province. into non-overlapping 512 \(\times\) 512-pixel tiles identical to the model input size. For each tile we calculated the proportion of pixels belonging to each crop polygon. A tile was assigned to class c only if pixels of class c covered at least 80% of its area; otherwise, the tile was excluded from training and validation. This majority-vote criterion minimizes label noise while avoiding the impractical task of per-pixel manual annotation. By overlaying and integrating both datasets, and combining with field verification conducted between October 2021 and October 2022, a total of 9990 valid samples were obtained. The data were divided as follows: 60% for training, 20% for validation, and 20% for testing. To improve the stability and generalization ability of the model, all sample data underwent strict preprocessing. First, noise and low-quality fragmented areas were removed, as remote-sensing images often contain noisy and irrelevant small fragments that can affect classification accuracy. To eliminate these interferences, spatial smoothing and texture analysis techniques were applied to remove small, irrelevant noise areas. Additionally, in terms of spatial distribution of samples, a sample balancing strategy was applied to ensure a balanced distribution of samples for each crop class in the training, validation, and testing datasets. Oversampling or undersampling methods were used to eliminate classification bias caused by class imbalance, enhancing the model's ability to recognize minority crop classes. To further optimize data quality, the data underwent geometric correction, radiometric correction, and standardization of different sensor data to ensure that data from different platforms and time points were compared under the same standard. Furthermore, considering the different spatial and temporal resolutions of remote-sensing data, the time-series data were interpolated and smoothed to reduce the impact of inconsistent time steps or missing data. Finally, by integrating optical and radar data, this study constructed a time-series dataset for non-food crops, providing high-quality input data for subsequent semantic segmentation tasks. A total of 9990 field-verified plots, each representing one 10 m \(\times\) 10 m Sentinel-2 pixel, were collected with sub-3 m RTK-GPS between October 2021 and October 2022 and then divided 60-20-20% into training (5994 plots), validation (1998) and test (1998) sets. To ensure geographic representativeness, plots were allocated in proportion to cropland area across the region's three agro-ecological zones: 3996 plots (\(\approx\)40%) lie in hilly uplands that account for 39% of cropland, 3497 plots (\(\approx\)35%) in alluvial plains covering 37%, and 2497 plots (\(\approx\)25%) in river-basin terrain, whose share is 24%; the deviation from perfect proportionality is therefore no greater than one percentage point in any zone. Because pixel-level statistics in Table 1 show a strong class imbalance--oilseed-rape dominates while cotton is scarce--we applied a mild balancing step after the spatial draw, slightly undersampling rape parcels and oversampling independent cotton parcels, so that the final plot count is 3995 for rapeseed, 3112 for tea and 2883 for cotton.