In the context of the continuous degradation of the global environment, ecological restoration has become a primary task in global environmental governance. In this process, remote sensing technology, as an advanced monitoring and analysis tool, plays a key role in monitoring ecological restoration. This article reviews the application of remote sensing technology in ecological restoration monitoring. Based on a comprehensive analysis of the literature in the field of ecological remote sensing, it systematically summarizes the major in-orbit spaceborne and airborne sensors and their related products. This article further proposes a series of evaluation indicators for ecological restoration from four aspects: forests, soil, water, and the atmosphere, and elaborates on the calculation methods for these indicators. In addition, this paper also summarizes the methods for evaluating the effectiveness of ecological restoration, including subjective evaluation, objective evaluation, and comprehensive evaluation methods. Finally, we analyze the challenges faced by remote sensing technology in evaluating ecological restoration effectiveness, such as issues with the precision of indicators extraction, the limitations of spatial resolution, and the diversity of evaluation methods. This review also looks forward to future ecological restoration technologies, such as the potential applications of integrated aerospace and terrestrial remote sensing, multi-data fusion, and machine learning technologies. This study reveals the effectiveness of remote sensing technology in ecological restoration monitoring, aiming to provide efficient tools and innovative strategies for future remote sensing monitoring and assessment of ecological restoration.

Road extraction from remote sensing imagery plays a critical role in applications such as autonomous driving, urban planning, and infrastructure development. Although deep learning methods have achieved notable progress, current approaches still struggle with complex backgrounds, varying road widths, and strong texture interference, often leading to fragmented road predictions or the misclassification of background regions. Given that roads typically exhibit smooth low-frequency characteristics while background clutter tends to manifest in mid- and high-frequency ranges, incorporating frequency-domain information can enhance the model’s structural perception and discrimination capabilities. To address these challenges, we propose a novel frequency-aware road extraction network, termed PWFNet, which combines frequency-domain modeling with multi-scale feature enhancement. PWFNet comprises two key modules. First, the Pyramidal Wavelet Convolution (PWC) module employs multi-scale wavelet decomposition fused with localized convolution to accurately capture road structures across various spatial resolutions. Second, the Frequency-aware Adjustment Module (FAM) partitions the Fourier spectrum into multiple frequency bands and incorporates a spatial attention mechanism to strengthen low-frequency road responses while suppressing mid- and high-frequency background noise. By integrating complementary modeling from both spatial and frequency domains, PWFNet significantly improves road continuity, edge clarity, and robustness under complex conditions. Experiments on the DeepGlobe and CHN6-CUG road datasets demonstrate that PWFNet achieves IoU improvements of 3.8% and 1.25% over the best-performing baseline methods, respectively. In addition, we conducted cross-region transfer experiments by directly applying the trained model to remote sensing images from different geographic regions and at varying resolutions to assess its generalization capability. The results demonstrate that PWFNet maintains the continuity of main and branch roads and preserves edge details in these transfer scenarios, effectively reducing false positives and missed detections. This further validates its practicality and robustness in diverse real-world environments.

The Surface Water and Ocean Topography (SWOT) mission represents a major advance in satellite hydrology by extending conventional nadir altimetry to two-dimensional wide-swath observations of the inland-water surface elevation, extent, width, and slope. This review combines a bibliometric analysis of 556 publications indexed in the Web of Science Core Collection from January 2019 to April 2026 with a thematic synthesis of representative post-launch studies. In contrast to earlier reviews centered largely on mission preparation or individual application domains, we examine the evolution of SWOT research together with study-level evidence on data products, water-surface-elevation retrieval, river-discharge estimation, hydrological applications, uncertainty, and multi-mission integration. The literature shows a clear transition from pre-launch algorithm development and simulation toward post-launch validation and application. Reported water-surface-elevation performance is strongly context dependent: representative studies include a mean RMSE of 0.29 m across Yangtze River stations, MAE below 0.10 m for Tibetan Plateau lakes against ICESat-2, and MAE of 6.7 cm for 1 km²-averaged elevations in herbaceous wetlands. These values should be interpreted as study-specific evidence rather than as single mission-wide accuracy because the validation design, spatial support, environmental conditions, and processing strategies differ substantially among studies. River-discharge estimation remains more uncertain because the bathymetry, roughness, channel geometry, slope estimation, and temporal sampling introduce additional uncertainty. Major remaining challenges concern observation constraints, processing and product uncertainty, transferability, operational implementation, and uneven validation evidence. Future progress will depend on standardized and reproducible processing, uncertainty-aware validation, cross-regional benchmarking, and the integration of SWOT with complementary satellite observations and hydrological or hydraulic models.

Estuarine wetlands are highly dynamic ecosystems, and the vegetation serves as a critical indicator of ecological health. Accurate mapping of different vegetation types remains challenging due to spectral similarities and the high dimensionality of time-series data. This research introduces a Google Earth Engine (GEE)-based hierarchical framework for mapping eight typical vegetation types in the Liaohe Estuary using Sentinel-2 imagery from 2023 to 2025. To address data redundancy, a novel feature selection algorithm based on Mahalanobis distance and class separability (FSMD-CS) was developed, reducing 225 dimensions to seven optimal variables. Integrated with a hierarchical decision tree calibrated using the SEaTH approach, the framework achieved an overall accuracy of 86.39% (kappa = 0.832), surpassing single-temporal spectral imagery classification and unoptimized MPS feature classification, which achieved OAs of 72.31% and 82.68%, respectively. The majority of selected features originate from the early green-up and late senescence stages, indicating that seasonal phenological metrics offer superior discrimination of vegetation types compared to peak-summer spectral data. Overall, the proposed framework provides an efficient and interpretable solution for fine-scale estuarine vegetation mapping.