Rice is one of the most important food crops for human beings. The timely and accurate understanding of the distribution of rice can provide an important scientific basis for food security, agricultural policy formulation, and regional development planning. As an active remote sensing system, polarimetric synthetic aperture radar (PolSAR) has the advantage of working both day and night and in all weather conditions and hence plays an important role in rice growing area identification. This paper focuses on the topic of rice planting area identification using multi-temporal PolSAR images and a deep learning method. A rice planting area identification attention U-Net (RIAU-Net) model is proposed, which is trained by multi-temporal Sentinel-1 dual-polarimetric images acquired in different periods of rice growth. In addition, considering the diversity of the rice growth period in different years caused by the different climatic conditions and other factors, a transfer mechanism is investigated to apply the well-trained model to monitor the rice planting areas in different years. The experimental results show that the proposed method can significantly improve the classification accuracy, with 11–14% F1-score improvement compared with the traditional methods and a pleasing generalization ability in different years. Moreover, the classified rice planting regions are continuous. For reproducibility, the source codes of the well-trained RIAU-Net model are provided.

Rapid and accurate monitoring of algal blooms using remote sensing techniques is an effective means for the prevention and control of algal blooms. Traditional methods often have difficulty achieving the balance between interpretative accuracy and efficiency. The advantages of a deep learning method bring new possibilities to the rapid and precise identification of algal blooms using images. In this paper, taking Chaohu Lake as the study area, a dual U-Net model (including a U-Net network for spring and winter and a U-Net network for summer and autumn) is proposed for the identification of algal blooms using remote sensing images according to the different traits of the algae in different seasons. First, the spectral reflection characteristics of the algae in Chaohu Lake in different seasons are analyzed, and sufficient samples are selected for the training of the proposed model. Then, by adding an attention gate architecture to the classical U-Net framework, which can enhance the capability of the network on feature extraction, the dual U-Net model is constructed and trained for the identification of algal blooms in different seasons. Finally, the identification results are obtained by inputting remote sensing data into the model. The experimental results show that the interpretation accuracy of the proposed deep learning model is higher than 90% in most cases with the fastest processing time being less than 10 s, which achieves much better performance than the traditional supervised classification method and also outperforms the single U-Net model using data of whole year as the training samples. Furthermore, the profiles of algal blooms are well-captured.