Abstract: Accurate monitoring of crop spatial distribution in topographically complex areas is of great significance for guiding agricultural management production and reasonable allocation of resources. However, scattered crop distribution and high spatial heterogeneity pose challenges for precise classification. The objective of this study was to explore a method for simultaneous precise identification of multiple crops with multi-temporal high-resolution satellite data under complex terrain conditions, thus further providing a theoretical and practical basis for crop identification in Chongqing city area and the complex low hills area in southwest China. The western Chongqing was selected as the study area. First, a multi-scale segmentation algorithm was adopted to construct homogeneous units from fields of the same feature type. This approach helped to achieve high accuracy classification by avoiding overly fragmented classification results based on pixel classification. Secondly, the multi-temporal Sentinel-2/MSI remote sensing images were used to explore the weathering patterns and characteristic parameters during the fertility period of major food crops. The spectral indices, such as NDVI, RVI and NDWI, were constructed to compare the differences in remote sensing spectral information of different crop types at various fertility periods based on ground sample points. Such information was then combined with the topographic features to determine the optimal combination for crop identification. Finally, an object-oriented decision tree logical classification rule set was established to extract major grain crop growing areas. The results showed that, (1) the multi-scale segmentation method was an effective approach that can make crop identification on a field basis. In addition, the vegetation edge segmentation was optimal at a segmentation scale of 30 and a compactness and shape factor of 0.5. (2) The target feature discrimination threshold was established by combining the spectral feature indices of NDWI in April, RVI in June, NDVI in August and NDVI in August with the topographic features such as altitude and slope. The overall accuracy of the classification reached 90.8%, being 85.7%, 83.3% and 80.7%, for paddy rice, maize, and sweet potato, respectively. This paper showed that the crop planting area identification method based on multiple spectral indices combined with topographic features can achieve high recognition accuracy and has practical significance.

Key words: Complex terrain, Sentinel-2, Crops, Object-oriented, Decision trees