Abstract: 【Objective】Using multi-source remote sensing data for crop extraction in Hulun Buir of Inner Mongolia,it aimed to provide solutions for large-scale crop extraction applications under cloudy and rainy conditions,across climatic regions and alpine dry farming areas.【Methods】Based on the Google Earth Engine platform,and used Sentinel-1 and Sentinel-2 as the main source of multi-source remote sensing data. Using Boxcar filtered Sentinel-1 multi-polarization data to construct a time series data set,and acquire Sentinel-2 TOA growth season data and synthesized it into a minimum cloud cover image and calculate multiple remote sensing index features;using topographic factors and land cover as auxiliary data for classification,combined ground sample data and apply it to a random forest classifier to obtain a 10 m precision crop distribution map. By evaluating the classification accuracy and classification contribution level,the data set after feature optimization was used to extract the crop distribution in the study area,and analyze the distribution characteristics of crops.【Results】By combining radar data and optical data(VV_B+VH_B+M+I+T+L),87.41% of crop classification accuracy was obtained,which was higher than the extraction results of a separate combination(VV_B+VH_B,M+I+T+L)7.58% and 7.88%;500 trees classification had the highest accuracy,once the number of decision trees exceeds or less than 500,the classification accuracy decreased,and excessive decision trees did not improve the performance of the classifier;the overall accuracy of crops extracted from the data set composed of the top 24 important bands of multi-source remote sensing data was 87.76%,and the Kappa coefficient was 0.86.【Conclusion】The results of this study had obtained a relatively ideal crop distribution map,which could realize routine operational monitoring and provide effective means for crop remote sensing surveys.

Key words: Multi-source remote sensing data, Dry farming area, Crop mapping, Machine Learning