## 5 Discussion
### Innovations in Multi-Sensor Mapping of Crop Distribution in the FPEC  
Extensive evidence supports that multi-source data fusion significantly improves crop classification accuracy, particularly in fragmented agricultural regions [12; 65]. In China, smallholder farming systems with highly fragmented fields often lead to mixed classifications when using moderate-resolution imagery alone. Our study addresses this challenge by integrating SAR and optical data and optimizing phenological separability to achieve accurate crop-type mapping in a topographically complex zone. Our methodological framework innovatively combines three key components: (1) temporal optimization of optical features through fused Sentinel-2 and Landsat 8/9 time series, (2) strategic selection of phenological separation windows based on crop calendar analysis, and (3) incorporation of Sentinel-1 dual-polarization (VV/VH) time sequences at quarterly temporal resolution. The resultsindicate that Sentinel-1 backscatter from July to September served as a stable and discriminative input for improving crop classification performance (Figure 10). This multi-sensor integration approach achieves 10 m resolution crop mapping from 2019 to 2023, offering a transferable and effective solution tailored for smallholder agricultural landscapes.  
Compared with existing studies, our approach exhibits several notable advances. While previous studies achieved 10 m resolution maize maps [66] but largely focused on a single crop, in contrast, our study captures multi-crop and rotation-specific dynamics and provides finer-scale, higher-accuracy classification tailored to the FPEC, which presents considerable challenges, including field fragmentation, phenological overlap, and intra-class variability. Similar studies in Southern China (e.g., rice intensification monitoring [31]) or the North China Plain (winter wheat-summer maize rotation [29; 30]) achieved promising results, yet often neglected complex multi-cropping regimes or key economic crops (potato, oats, sesame) that are regionally important. In addition, recent efforts using deep learning [36] or transfer learning [34] achieved early-stage mapping with high accuracy but relied on extensive labeled samples and computational resources, which may limit scalability across large, diverse landscapes. By contrast, our approach balances performance and efficiency through the use of Random Forest classifiers, combined with interpretable feature selection and multi-temporal fusion.  
Our findings further demonstrate that crop spatial distribution strongly correlates with climatic and hydrological conditions: maize cultivation concentrates in irrigated lowlands (<520 m), while drought-adapted crops (oats, sesame) dominate water-limited uplands (470-600 m) with integrated short-season vegetable rotations. This altitudinal zonation is clearly discernible along a north-south topographic gradient, where cooler northern highlands preferentially support cold-tolerant crops and vegetables, whereas southern  
Figure 10: Random Forest variable importance (top 25). The vertical axis represents the feature bands, with the number suffix indicating the corresponding time phase of the band: _Jan–Mar for January–March, _Apr-Jun for April–June, _Jul-Sep for July–September, and _Oct–Dec for October–December.  
lowlands sustain intensive maize production through irrigation. These insights provide critical baselines for optimizing crop allocation under evolving climate regimes.