AGTOC: A novel approach to winter wheat mapping by automatic generation of training samples and one-class classification on Google Earth Engine

Accurate and timely acquisition of crop spatial distribution is a prerequisite for growth monitoring and yield forecasting. Currently, the automatic acquisition of crop distribution at large scales is still a challenge due to the time-consuming processing of remotely sensed imagery and manual collection of sufficient training samples. Although the advent of cloud computing platforms has proved to improve the efficiency and automation of crop type classification, how to obtain sufficient training samples in an efficient and cost-effective way remains unclear. In this research, we developed a new approach integrating the automatic generation of training samples and one-class machine learning classification (AGTOC) for mapping winter wheat over Jiangsu Province, China on Google Earth Engine (GEE). After extracting spatial objects from Sentinel-2 imagery in the season of 2017–2018, this method performed recognition of winter wheat objects based on the unique phenology and spectral features of winter wheat. Then the generated winter wheat objects were further refined and regarded as training samples for provincial winter wheat classification with one-class support vector machine (OCSVM). Furthermore, the transferability of AGTOC was evaluated by applying the classification approach to different seasons (2016–2017 & 2019–2020) and a different sensor (Landsat-8 OLI). According to independent ground truth data, the winter wheat mapping with AGTOC achieved an overall accuracy (OA) of 92.61%. When compared with agricultural census data, the winter wheat area accounted for 99% and 90% of the variability at the municipal and county levels. Furthermore, the OA achieved 88.94% and 90.17% while transferring the AGTOC from 2017–2018 to 2016–2017 and 2019–2020. The transferability of the AGTOC model to Landsat-8 OLI imagery of the same season yielded an OA of 85.98%. These results demonstrated that AGTOC exhibited high efficiency and accuracy across the province, different seasons and sensors without the need of extensive field visits for training sample collection. This proposed approach has great potential in the automatic mapping of winter wheat on GEE at country or global levels.