Efficient Retrieval of Subpixel Cloud Fraction From Coarse Cloud Masks Using Dual‐Branch Deep Learning

Abstract Cloud detection is a critical procedure in satellite remote sensing. Most meteorological satellite products provide binary cloud masks, which identify whether a pixel in the satellite image is entirely cloudy or clear‐sky, and the subpixel cloud fraction (CF) of partly cloudy pixels is usually unavailable. In this work, we develop a deep‐learning neural network to estimate the subpixel cloud fraction of moderate‐resolution satellite images based on binary cloud masks, and train the neural network with high‐resolution satellite data. We aggregate 10 m Sentinel‐2 cloud masks into physically consistent 1 km binary cloud masks, smooth them with edge‐aware filtering to obtain collocated 1 km CF reference fields, and use these pairs to train DualCloudNet, a dual‐branch U‐Net that fuses complementary feature representations. The network takes the aggregated 1 km cloud mask as input and predicts the corresponding 1 km continuous CF, achieving a Pearson correlation of 0.92 and an RMSE of 0.103 on an independent test data set. The model outperforms a physically motivated statistical estimator, and its accuracy is slightly higher than two other deep‐learning network