## 1 Introduction  
The accurate spatiotemporal mapping of crop types and rotation patterns constitutes a scientific foundation for advancing agricultural sustainability, optimizing land-use management, and safeguarding global food security [1; 2]. As a pivotal component of precision agriculture, the reliable identification of crop rotation patterns not only facilitates the equilibrium between agricultural production and ecological conservation but also delivers actionable insights for policymakers to design adaptive strategies in soil management, resource allocation, and productivity enhancement [3]. These requirements are particularly critical in the farming-pastoral ecotones, transitional zones between semi-arid and arid climates, where rainfed agriculture coexists with livestock grazing systems [4; 5]. Globally distributed across ecologically vulnerable regions, such as the Eurasian steppe belt, East Asian loess plateaus, African Sahel, and South American Altiplano, these ecotones exhibit heightened sensitivity to anthropogenic disturbances due to their fragmented landscapes, climatic variability, and delicate soil-water balances [6; 7]. In such contexts, agricultural intensification directly modulates regional ecological stability, necessitating high-precision crop mapping to reconcile productivity goals with environmental sustainability [8]. Consequently, the spatiotemporally explicit monitoring of agricultural land use emerges as both a scientific imperative and technical challenge for implementing the United Nations Sustainable Development Goals in these marginal environments. The evolution of satellite remote sensing has revolutionized agricultural monitoring by enabling synoptic, repetitive, and cost-effective earth observation [9; 10]. While moderate-resolution sensors (e.g., MODIS at 250-500 m) suffice for continental-scale crop surveillance, their limited spatial fidelity impedes smallholder field delineation in fragmented farmlands [11]. Although Landsat's 30 m imagery (freely accessible since 2008) improved spatial granularity, its 16-day revisit cycle often fails to capture critical phenophases essential for crop discrimination [12; 13; 14]. For instance, mixed pixel effects persist when field sizes fall below sensor resolution thresholds, while temporal gaps may obscure rapid vegetation changes during key growth stages. Recent advancements in satellite constellations have partially addressed these limitations: Sentinel-2's enhanced capabilities (10-20 m resolution; 5-day revisit) enable improved crop phenology tracking and operational crop mapping, as evidenced by successful implementations in European agricultural systems [15; 16; 17; 18]. Complementary Sentinel-1 Synthetic Aperture Radar (SAR) data provide all-weather observation capacity, yet their standalone utility remains constrained by speckle noise and complex backscatter interpretation challenges [19; 20]. Despite the synergistic use of optical-SAR fusion in crop classification, current methodologies predominantly focus on dominant monocultures in homogeneous landscapes, leaving critical gaps in characterizing heterogeneous agroecosystems with intricate crop mixtures and rotations [21]. This technological shortcoming is acutely manifested in the farming-pastoral ecotones, where smallholder farming systems exhibit high spatiotemporal heterogeneity in cropping patterns, necessitating integrated multi-sensor approaches for accurate land-use characterization. Previous investigations into crop pattern dynamics have established foundational methodologies but continue to exhibit notable limitations in scope and generalizability across complex landscapes [17; 22]. In China, existing studies have primarily concentrated on staple cereals such as maize, wheat, and rice, while regionally significant cash crops and intricate multi-crop rotation systems remain underexplored [23; 24; 25; 26; 27; 28].