Abstract
The purpose of this study was to evaluate the feasibility and applicability of object-oriented crop classification using Sentinel-1 images in the Google Earth Engine (GEE). In this study, two study areas (Keshan farm and Tongnan town) with different average plot sizes in Heilongjiang Province, China, were selected. The research time was two consecutive years (2018 and 2019), which were used to verify the robustness of the method. Sentinel-1 images of the crop growth period (May to September) in each study area were composited with three time intervals (10 d, 15 d and 30 d). Then, the composite images were segmented by simple noniterative clustering (SNIC) according to different sizes and finally, the training samples and processed images were input into a random forest classifier for crop classification. The results showed the following: (1) the overall accuracy of using the object-oriented classification method combined composite Sentinel-1 image represented a great improvement compared with the pixel-based classification method in areas with large average plots (increase by 10%), the applicable scope of the method depends on the plot size of the study area; (2) the shorter time interval of the composite Sentinel-1 image was, the higher the crop classification accuracy was; (3) the features with high importance of composite Sentinel-1 images with different time intervals were mainly distributed in July, August and September, which was mainly due to the large differences in crop growth in these months; and (4) the optimal segmentation size of crop classification was closely related to image resolution and plot size. Previous studies usually emphasize the advantages of object-oriented classification. Our research not only emphasizes the advantages of object-oriented classification but also analyzes the constraints of using object-oriented classification, which is very important for the follow-up research of crop classification using object-oriented and synthetic aperture radar (SAR).
Highlights
With continuous global population growth, the problem of food security is becoming increasingly serious [1,2,3]
This study shows that the accuracy of crop classification can be improved to a very high level (OA > 90%) by using the method of object-oriented classification and time series Sentinel-1 in the case of a large plot size
The results emphasize the influence of the time interval and segmentation size of the composite image on the accuracy of crop classification when using sentinel-1 composite image combined with object-oriented classification
Summary
With continuous global population growth, the problem of food security is becoming increasingly serious [1,2,3]. To meet the increase in global demand for food in the future, improving the efficiency of food production so that it can be increased is the future governmental focus [4,5,6]. The accurate identification of the distribution of different crops on cultivated land is the premise of the rational distribution of food production; accurate identification of the distribution of different crops on cultivated land is the basic condition needed to achieve regional sustainable development and ensure food security [9,10]. Remote sensing technology is the most commonly used technology in crop classification [11,12]. Optical images have always been the main data used in crop classification
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