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Google Earth Engine (GEE) provides a convenient platform that enables the use of a diverse array of applications based on optical satellite images covering long time periods and large areas. However, cloud contamination often causes optical satellite images to lose land surface information, which can greatly affect their spatial and temporal availability in areas with frequent cloud cover. Reconstructing the missing values is very important when researchers attempt to improve the potential for using optical satellite images. Recently, convolutional neural network (CNN)-based methods have shown great potential in cloud removal. Unfortunately, current CNN-based cloud removal methods can only be applied locally, which would result in inefficient data downloading and storage problems. To address these problems, this paper proposes a method that integrates GEE data and a multi-level feature connected CNN (DeepGEE-S2CR) to remove clouds in Sentinel-2 imagery using Sentinel-1 Synthetic Aperture Radar imagery as auxiliary data. Specifically, the training process of the multi-level feature connected CNN was conducted locally, so that cloud removal could then be performed on the client of GEE using online data with the trained CNN model. An experiment using a set of globally distributed Sentinel-2 images was conducted to validate the proposed method; the results showed the process could generate results comparable to a classic deep Sentinel-2 cloud removal method (DSen2-CR) that should be run locally (the average root mean square errors of DeepGEE-S2CR and DSen2-CR were 0.045 and 0.042, respectively). The proposed method is especially convenient for removing clouds in Sentinel-2 imagery covering a large area.

Removing Cloud Cover Interference from Sentinel-2 imagery in Google Earth Engine by fusing Sentinel-1 SAR data with a CNN model