## 6 Conclusions  
This study demonstrates that integrating Sentinel-1, Sentinel-2, and Landsat-8/9 effectively captures structural and phenological variations in fragmented agricultural landscapes within the farming-pastoral ecotone of China. The method achieved high classification accuracy, with overall accuracy and the Kappa coefficient exceeding 0.90 from 2021 to 2023, and strong agreement between mapped and statistical crop areas at the county level (R\({}^{2}\): 0.85-0.88; RMSE: 0.42-0.58 \(\times\) 10\({}^{4}\) ha). Spatiotemporal analyses revealed distinct crop distribution patterns: oats, potato, sesame, and vegetables were predominantly cultivated in the northern Zhangjiakou, while maize dominated southern regions. Significant annual variations in crop area proportions were observed, along with specific altitudinal preferences: maize, potato, and sesame were primarily grown at 480-520 m, while oats and other crops were concentrated at 520-600 m. Most crops were cultivated on gentle slopes of 0-5\({}^{\circ}\), with sesame extending to 4-10\({}^{\circ}\) slopes. Temporal analysis from 2019 to 2023 indicated that sesame, oats, and potato predominantly followed rotation patterns, while maize cultivation largely followed monoculture practices. These patterns were shaped by multiple interacting factors, including water availability, economic incentives, and the constraints of continuous cropping.  
While the proposed framework proves robust and transferable for smallholder-dominated agroecosystems, certain limitations remain. Intercropping, short-cycle crops,and asynchronous planting schedules introduce spectral mixing, particularly for underrepresented crops such as sesame. These patterns are difficult to distinguish using pixel-based majority labels, potentially leading to rotation misclassification. Future efforts should explore sub-annual classification, object-based approaches, and the integration of field survey data to better capture local management practices. In addition, integrating UAV observations, hyperspectral imagery, and deep learning models could further enhance mapping precision and address spectral confusion in heterogeneous plots. Overall, these findings offer critical insights for optimizing crop rotation strategies, promoting agricultural sustainability, and enhancing land-use efficiency within ecologically vulnerable regions.  
Conceptualization, Z.Z. and B.C.; methodology, Z.H. and Y.L.; software, Z.H., Y.Y. and H.Z.; validation, Z.H., H.Z. and J.G.; formal analysis, Z.H., Y.L. and K.M.; investigation, Z.H. and Y.L.; resources, F.C., J.G. and B.C.; writing--original draft preparation, Z.H.; writing--review and editing, Z.Z., B.C., Y.L., H.Z. and K.M.; visualization, Y.Y.; project administration, Z.Z. and B.C.; funding acquisition, Z.Z. and B.C. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Science and Technology Key Program of Inner Mongolia (2021 ZD0002), and the earmarked fund for the China Agriculture Research System (CARS-07-B-5, and CARS-07-A-6).  
All satellite data used in this study are freely available. Other data and codes generated or analyzed during this study are available from the corresponding author upon reasonable request.  
The authors declare no competing financial interests or personal relationships that could have influenced the work reported in this paper.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} FPEC & the farming-pastoral ecotone of China \\ SAR & Synthetic Aperture Radar \\ GEE & Google Earth Engine \\ RF & Random Forest \\ \end{tabular}