Carbon dioxide is one of the most influential greenhouse gases affecting human life. CO₂ data can be obtained through three methods: ground-based, airborne, and satellite-based observations. However, ground-based monitoring is typically composed of sparsely distributed stations, while airborne monitoring has limited coverage and spatial resolution; they cannot fully reflect the spatiotemporal distribution of CO₂. Satellite remote sensing plays a crucial role in monitoring the global distribution of atmospheric CO₂, offering high observation accuracy and wide coverage. However, satellite remote sensing still faces spatiotemporal constraints, such as interference from clouds (or aerosols) and limitations from satellite orbits, which can lead to significant data loss. Therefore, the reconstruction of satellite-based CO₂ data becomes particularly important. This article summarizes methods for the reconstruction of satellite-based CO₂ data, including interpolation, data fusion, and super-resolution reconstruction techniques, and their advantages and disadvantages, it also provides a comprehensive overview of the classification and applications of super-resolution reconstruction techniques. Finally, the article offers future perspectives, suggesting that ideas like image super-resolution reconstruction represent the future trend in the field of satellite-based CO₂ data reconstruction.

The combined use of synthetic aperture radar (SAR) and optical images for surface observation is gaining increasing attention. Optical images, with their distinct edge features, can accurately classify different objects, while SAR images reveal deeper internal variations. To address the challenge of differing feature distributions in multi-source images, we propose an edge enhancement network, OSNet (network for optical and SAR images), designed to jointly extract features from optical and SAR images and enhance edge feature representation. OSNet consists of three core modules: a dual-branch backbone, a synergistic attention integration module, and a global-guided local fusion module. These modules, respectively, handle modality-independent feature extraction, feature sharing, and global-local feature fusion. In the backbone module, we introduce a differentiable Lee filter and a Laplacian edge detection operator in the SAR branch to suppress noise and enhance edge features. Additionally, we designed a multi-source attention fusion module to facilitate cross-modal information exchange between the two branches. We validated OSNet’s performance on segmentation tasks (WHU-OPT-SAR) and regression tasks (SNOW-OPT-SAR). The results show that OSNet improved PA and MIoU by 2.31% and 2.58%, respectively, in the segmentation task, and reduced MAE and RMSE by 3.14% and 4.22%, respectively, in the regression task.

Snow plays a significant role in the global energy balance, climate change, hydrological cycles, and other areas. However, traditional surface observation methods are limited in capturing the spatiotemporal dynamics of snow. This paper systematically reviews Machine Learning algorithms applicable to snow cover recognition. It highlights traditional Machine Learning methods such as Support Vector Machines and Random Forests, as well as more semantically oriented Deep Learning methods, including CNNs, attention mechanisms, and Transformers. These methods have shown robust performance in the domain of snow identification. Lastly, the paper discusses the strengths and weaknesses of different approaches and suggests directions for future research. Through this paper, readers will gain a comprehensive understanding of Machine Learning-based snow recognition algorithms and how these algorithms can be leveraged better.