The frequent occurrence of sandstorms on the Mongolian Plateau has become a critical factor influencing the stability of regional ecosystems and social activities. In this study, a deep learning framework was developed for predicting sandstorm paths on the Mongolian Plateau. A spatio-temporal feature dataset was established using remote sensing imagery and meteorological observations. Spatial features were extracted through a convolutional neural network (CNN), while the temporal evolution of sandstorms was modeled using a bidirectional long short-term memory (BiLSTM) network. A random forest algorithm was employed to assess the relative importance of meteorological and geographical factors. The results indicate that the proposed CNN-BiLSTM model achieved strong performance at prediction intervals of 1, 6, 12, 18, and 24 h, with overall accuracy, F1-score, and AUC all exceeding 0.80. The 24 h prediction yielded the best results, with evaluation metrics of 0.861, 0.878, and 0.898, respectively. Compared with the individual CNN and BiLSTM models, the CNN-BiLSTM model demonstrated superior performance. The findings suggest that the model provides high predictive accuracy and stability across different time steps, thereby offering strong support for dust storm path prediction on the Mongolian Plateau and contributing to the reduction of disaster-related risks and losses.

Frequent grassland fires on the Mongolian Plateau endanger the regional environment, human safety, and property, creating a demand for near-real-time active-fire detection with high spatiotemporal resolution. While remote sensing serves as the primary detection method, current fire products struggle to balance high temporal and spatial resolutions for immediate monitoring. Furthermore, although deep learning models may sometimes outperform traditional threshold-based algorithms in generalization, their heavy reliance on extensive, manually annotated datasets severely restricts their application in small-sample, data-scarce scenarios. To address the dual challenges of near-real-time detection and limited sample availability, this study proposes FIRE-BYOL, an active-fire detection framework integrating BYOL self-supervised learning with supervised fine-tuning. Leveraging high-frequency multispectral AHI data from Himawari-8/9 as the primary input, the framework effectively trains on a small sample of high-confidence labels generated from VIIRS fire products. Robust feature extraction is achieved through self-supervised pre-training on large volumes of unlabeled data to mitigate label dependency, followed by supervised fine-tuning for precise active-fire classification. Our experimental results demonstrate that FIRE-BYOL excels under small-sample conditions, achieving F1 scores of 0.8746 during the day and 0.9764 at night. Outperforming Random Forest, XGBoost, FireCNN, and the WLF product, the model exhibits exceptional near-real-time monitoring capabilities for early-stage and entire fire events. By delivering accurate detection at a 10 min temporal and 2 km spatial resolution, FIRE-BYOL offers a highly effective, data-efficient technical solution for high-frequency regional fire monitoring on the Mongolian Plateau and similar environments.