Search Results (741)

Pansharpening plays an important role in remote sensing image processing. Its purpose is to fuse a high-spatial-resolution panchromatic (PAN) image and a low-spatial-resolution multispectral (LRMS) image, thereby reconstructing a high-resolution multispectral (HRMS) image with both high spatial clarity and high spectral fidelity. In recent years, diffusion models have shown great potential in image generation. However, existing diffusion-based pansharpening methods usually adopt a fixed denoising strategy, making it difficult to adapt to the stage-wise changes in the denoising process and complex degradation distributions. Based on this, we propose PanDiM, an efficient generative framework for pansharpening. Specifically, we reformulate pansharpening as a high-frequency residual restoration process constrained by multimodal conditions. To improve the response accuracy of the model in complex regions, we design a Degradation-Posterior Guidance Module (DPGM), which extracts dual-scale physical detail priors from the PAN image, explicitly infers the degradation posterior, and converts it into dynamic control variables to adaptively regulate the state evolution of Mamba. In addition, we propose a time-aware mechanism, which allows temporal information to directly intervene in posterior estimation and state-space modeling, so as to accurately match the modeling requirements of different denoising stages. Considering the characteristics of residual reconstruction, we further propose a frequency-decoupled loss (FDL), which separates low- and high-frequency components in the frequency domain and applies targeted constraints. This significantly enhances the model’s ability to represent textures and achieves more robust spectral fidelity. Extensive experiments on three benchmark datasets, including WorldView-3, GaoFen-2, and QuickBird, show that PanDiM significantly outperforms existing mainstream methods in both reduced-resolution and full-resolution evaluations, providing a new solution for high-fidelity pansharpening in complex scenarios. Full article

Soil moisture (SM) plays a pivotal role in regulating terrestrial energy-water exchanges and exerts substantial influence on agricultural productivity. In this study, a high-resolution soil moisture (HRSM) dataset (16 m) was generated by integrating multi-source remote sensing data from SMAP, HJ-2, Sentinel-2, and Gaofen-6, together with the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model. The data assimilation (DA) method was implemented for assimilating HRSM within the Ensemble Kalman Filter (EnKF) framework using the Noah-MP model at a spatial resolution of 1 km. To enhance the spatial detail of SM, HRSM and its relative uncertainties derived from the three-cornered hat (TCH) method were used to update the observation error and Kalman gain in the EnKF framework, thereby improving SM profile estimates at a 16 m resolution. The performance of the DA method was evaluated against in situ measurements during the spring drought period in central Yunnan Province, China. The results show that assimilating HRSM (DA_HRSM) significantly improves surface and root-zone SM estimates in the Noah-MP model. The simulated SM from the DA_HRSM method demonstrates lower relative uncertainty. Compared to the assimilation of SMAP SM, the DA_HRSM method provides higher-resolution spatial features of SM and enhances spatial heterogeneity across 20 irrigation districts. The DA_HRSM method effectively captured the spring drought in central Yunnan, demonstrating good agreement with the Palmer Drought Severity Index (PDSI). The result highlights the advantages of incorporating high-resolution SM data into agricultural and drought monitoring systems. Full article

In optical remote sensing 3D reconstruction, high-resolution satellite stereo matching is a critical task, yet it is challenged by extreme imaging geometries, texture-less and repetitive patterns, occlusions, and scene variations caused by spatio-temporal heterogeneity. To address these issues, we propose IFMA-Stereo, an innovative binocular disparity estimation method that leverages a monocular depth foundation model. Our approach constructs a multi-scale spatial information pyramid to jointly integrate the foundation model with a disparity extraction network. At the feature level, an attention interaction mechanism captures multi-dimensional contextual dependencies and transforms general scene understanding priors into long-range associative features suitable for stereo cost volume construction. At the pixel level, a cyclic iterative refinement module embeds depth information from the foundation model throughout the iteration process and performs joint optimization, enhancing the model’s adaptability in geometrically complex regions. Experiments on the US3D and GaoFen-7 datasets demonstrate that IFMA-Stereo achieves superior performance in challenging areas (texture-less regions, disparity discontinuities, repetitive patterns) and effectively mitigates prediction errors caused by spatio-temporal heterogeneity, albeit at the cost of increased inference time compared to baseline methods. Quantitatively, the method achieves an end-point error (EPE) of 1.347 and a D1 error of 7.26% on the US3D dataset, and an EPE of 1.585 and a D1 error of 13.41% on the GaoFen-7 dataset. Notably, the method also yields precise predictions for unseen urban areas, indicating strong generalization. These results confirm that IFMA-Stereo achieves state-of-the-art accuracy in remote sensing disparity estimation. Full article

This study investigated vegetation frontline dynamics, fractional vegetation cover (FVC), and community succession in the tidal-flat wetlands of the Liaohe Estuary. The eastern bank of the Liaohe River within the Shuangtaihe National Nature Reserve was selected as the study area, and six periods of Landsat and Gaofen-1 (GF-1) imagery from 2000 to 2025 were used. Remote-sensing preprocessing, normalized difference vegetation index (NDVI)-based FVC inversion, vegetation frontline extraction, Digital Shoreline Analysis System (DSAS)-based rate calculation, land-cover classification, and spatial correlation analysis were integrated to characterize wetland spatiotemporal dynamics and succession patterns. The results showed that the linear regression rate (LRR) and end point rate (EPR) effectively captured the long-term trend and five short-term fluctuations in vegetation frontline migration. FVC fluctuated markedly over the 25-year period, whereas the weighted average (WA) of the five FVC classes remained generally stable and effectively summarized overall vegetation growth. Vegetation frontline migration was spatially associated with annual FVC change (ΔFVC); both LRR and ΔFVC showed significant positive spatial autocorrelation and evident spatial clustering. In addition, the conversion among mudflats, Suaeda salsa, Phragmites australis, and water bodies was closely coupled with frontline migration. These findings provide a scientific basis for quantifying coastal wetland sustainability and for designing spatially targeted restoration strategies in the Liaohe Estuary. The proposed coupling analysis framework also offers a transferable remote sensing approach for monitoring wetland sustainability under changing environmental conditions. Full article

Accurately mapping surface freshwater bodies (e.g., ponds, reservoirs, and small lakes) is vital for managing insular ecosystems and communities. However, satellite-based extraction in coastal settings is challenged by seawater intrusion, complex topography, and cloud cover. Focusing on bedrock islands outside China’s Pearl River Estuary, this study developed a robust method to address these issues. We used both Gaofen-1 (GF-1) optical and Gaofen-3 (GF-3) Synthetic Aperture Radar (SAR) imagery, supported by field-collected water quality samples from surface freshwater body shorelines for model training and validation. The performance of two index-based methods (the Normalized Difference Water Index, NDWI, and the Normalized Difference Vegetation Index, NDVI), two machine learning algorithms (Random Forest, RF, and Support Vector Machine, SVM), and a U-Net convolutional neural network (U-Net) deep learning model was compared. The U-Net model achieved the highest accuracy, with Area Under the Curve (AUC) values of 0.881 (GF-1) and 0.840 (GF-3). It effectively discriminated freshwater from seawater and mitigated cloud interference, demonstrating superior precision and robustness over traditional methods. This work establishes a high-precision framework for monitoring island freshwater resources, supporting sustainable water management. The proposed framework provides a practical tool for tracking freshwater availability under climate variability and anthropogenic pressures, contributing to the monitoring of Sustainable Development Goal (SDG) indicator 6.3.2 on ambient water quality. Full article

Urban canopy height is an essential indicator for characterizing vegetation structure and carbon sequestration, yet satellite LiDAR often lacks sufficient spatial resolution, airborne LiDAR is costly, and SAR has limited sensitivity to vegetation structure. This study proposes a canopy height inversion framework using high-resolution stereo pairs from the Gaofen-7 (GF-7) satellite. A 0.65 m Digital Surface Model (DSM) was generated from GF-7 data, and a relative surface height was derived by differencing the GF-7 DSM from a coarse 30 m DSM reference. Key features were selected via Boruta and Random Forest Recursive Feature Elimination (RF-RFE), and six models—linear, polynomial, support vector machine, backpropagation neural network, XGBoost, and RF—were compared. The results showed that the Boruta feature set improved average R² by 8.2%. Among all models, RF performed best (test set R² = 0.71, RMSE = 1.70 m) and exhibited the strongest resistance to overfitting. Canopy heights within Beijing’s Fifth Ring Road showed an “outer-high, inner-low” pattern: large parks exceeded 30 m, while the Central Business District remained below 3 m. GF-7 stereo pairs enable efficient and cost-effective retrieval of canopy height in fragmented urban green spaces, supporting ecological parameter quantification and urban green-space management. Full article

River ecological restoration in lowland plain basins is often constrained by fragmented river networks, degraded riparian zones, eutrophication risk, and intensive human disturbance. Conventional monitoring approaches rarely connect watershed-scale dynamics with responses from typical restoration units, limiting quantitative evaluation and the separation of direct project outcomes from broader environmental variability. To address this gap, this study developed a collaborative satellite–unmanned aerial vehicle (UAV)–unmanned surface vehicle (USV) monitoring framework and applied it to the Nihe River Basin, China, a lowland plain river undergoing systematic restoration under the Shan-shui Initiative. The framework combines Sentinel-2 time-series imagery, high-resolution Gaofen-1, Gaofen-2, and Jilin-1 imagery, UAV orthophotos, USV observations, and auxiliary environmental datasets. Unlike single-scale monitoring approaches, it links watershed-scale indicators, including water-body dynamics, chlorophyll-related eutrophication risk, riparian ecological background, and soil-water conservation capacity, with unit-scale diagnosis of riparian buffer and riverine wetland restoration. Results showed that river water-body area increased from 37.78 km² to 40.59 km² during 2021–2024, while normalized difference chlorophyll index (NDCI)-based eutrophication risk improved in 9.12% of the monitored river area and degraded in only 0.47%. Riparian vegetation cover remained high, whereas regional soil-water conservation capacity declined due to climatic factors, revealing asynchronous responses between local recovery and regional background conditions. At the unit scale, riparian buffer restoration enhanced buffer continuity and near-bank water quality, as reflected by decreased chemical oxygen demand (COD), increased dissolved oxygen (DO), and limited ammonia nitrogen (NH₃-N) improvement. Riverine wetland restoration promoted land-use adjustment and ecological spatial reorganization. This cross-scale evidence chain supports adaptive management of inland river and wetland restoration projects. Full article

Satellites become critical to space exploration, global communication, Earth observation, and navigation. There is a growing need for satellite avionics that are highly integrated, reliable, and low-cost, which is essential for mass production and reliable on-orbit operation. This work demonstrates integrated satellite avionics with high reliability and low cost based on a monolithic programmable system-on-chip (SoPC) through highly synergistic hardware–software co-design, with successful on-orbit validation. The system highly integrates satellite management, attitude and orbit control, power management, telecontrol and telecommand (TC&TM), and data storage into a monolithic PolarFire^® SoC (System-on-Chip), and leverages an asymmetric multiprocessing (AMP) architecture. It achieves significant reductions in size, weight, power, and cost (SWaP-C) while ensuring comprehensive functionality and operational reliability. The Jilin-1 Gaofen-05A mission verified the proposed SoPC-based satellite avionics for low Earth orbit (LEO) commercial satellites. Long-term telemetry data confirms its stable operation, with a bus voltage ranging from 11.4 to 12.3 V, an average power consumption of 33.4 W, and a solar array output current of 6.2–6.5 A, all of which meet the design expectations. This work offers a feasible technical approach and engineering reference for commercial integrated satellite avionics featuring high reliability and cost efficiency. Full article

Ground control point (GCP) selection is a critical step in the automated high-precision geometric correction of remote sensing imagery. While the quantity, quality, and distribution of GCPs are three factors which may affect the accuracy of geometric correction, traditional automated selection methods predominantly focus on optimizing spatial distribution, often neglecting the inherent quality heterogeneity within matched point sets. This paper proposes a Reliability-weighted Spatial Coverage Sampling (SCS+R) method, which integrates matching reliability into the spatial coverage sampling framework via an adaptive weight factor (α). Experiments using Gaofen-2 (GF-2) imagery demonstrate that with 58 GCPs selected by SCS+R, the relative geometric consistency with the reference imagery is improved to a sub-pixel level (1.55–2.23 m) for multispectral images and within two pixels (0.99–1.81 m) for panchromatic images. Compared to the standard SCS, Voronoi, and weighted Voronoi methods, SCS+R improves the average accuracy by approximately 25%, 16%, and 8%, respectively. These results verify the enhanced stability and robustness of the proposed method in complex environments. Moreover, the optimal adaptive reliability weight α consistently stabilizes in a low range of 0.1–0.3, quantitatively revealing a key principle for small-sample GCP selection: spatial uniformity provides the foundation, while point reliability is the key to achieving high precision. Full article

Accurate simulation of hyperspectral cloud radiance remains challenging under optically thick cloud conditions, where conventional layered radiative transfer (RT) models tend to underestimate cloud-induced backscattering and return radiance in the visible to shortwave infrared (VIS–SWIR) range. In this study, we propose an extinction-dependent interlayer reflective augmentation within a Curtis–Godson (CG)-based layered RT framework. While the cloud-top and cloud-bottom heights are still used to define the cloudy layer in the radiative transfer simulation, the proposed method does not impose a single bulk reflective boundary at the cloud scale. Instead, it adds an extinction-dependent reflective coupling term at discretized sublayer interfaces to compensate for the underrepresented backward radiative contribution in standard layered solvers. The proposed approach is designed for optically thick, plane-parallel cloud conditions and aims to improve forward radiance simulation rather than detailed microphysical retrieval. The formulation is constructed so that the reflective augmentation vanishes as the local extinction decreases, although the present experiments focus on optically thick liquid-cloud cases. The numerical evaluation is conducted over the 0.8–2.5 μm range, corresponding to the valid unsaturated Gaofen-5A (GF-5A) bands used for comparison. Validation using GF-5A hyperspectral observations indicates that the proposed method improves the spectral fidelity of simulated thick-cloud radiance under the adopted representative cloud-parameter setting and scene-level anchoring strategy. Relative to the baseline compact layered RT formulation, the proposed method provides a favorable balance between computational efficiency and spectral accuracy, making it suitable as a fast forward module for hyperspectral cloud radiance simulation of optically thick liquid-cloud scenes. Full article

Cloud detection in high-resolution Gaofen-1 (GF-1) imagery is challenging due to the absence of short-wave infrared (SWIR) bands, which prevents the use of physically interpretable indices such as the Normalized Difference Snow Index (NDSI) and often leads to severe cloud–snow confusion. To address this limitation, we propose a unified framework, termed the Cross-Modality Spectral Expansion and Dual-Prior Network (CMSE-DPNet), that integrates cross-modality spectral expansion with physical–semantic dual priors. First, an improved CycleGAN reconstructs 13-band pseudo-Sentinel-2 spectra from four-band GF-1 imagery, enabling the computation of snow-sensitive physical indices. Second, a Snow-Aware Feature Attention Guidance Module (SAFAGM) introduces pixel-level physical priors derived from NDSI, while a Label-Guided Channel Attention Module (LG-CAM) injects scene-level semantic priors inferred from geographic metadata using a large language model. These complementary priors guide the network to better distinguish clouds from spectrally similar backgrounds. Experiments on the GF-1 dataset show that the proposed method achieves an F1-score of 94.41% and an Intersection over Union (IoU) of 89.40%, outperforming several state-of-the-art cloud detection methods. The results indicate that cross-modality spectral expansion combined with physical–semantic prior guidance effectively improves cloud detection performance in complex cloud–snow coexistence scenarios. Full article

Recent advances in satellite missions, particularly the Landsat, Sentinel, and Gaofen series, have led to the rapid accumulation of high-quality remote sensing data with frequent revisits. As these data have become more widely available, Satellite Image Time Series (SITS) have become an important tool for monitoring Earth surface dynamics. SITS now supports a wide range of applications, including precision agriculture, Land Use/Cover Change (LULCC) monitoring, environmental management, and disaster response. This growth has also promoted the development of advanced SITS classification datasets. However, existing reviews have mainly focused on SITS classification algorithms or specific applications, while systematic comparisons of public SITS benchmark datasets remain limited. This lack of synthesis makes it difficult for researchers to navigate fragmented resources and select datasets that match specific scientific or operational tasks. To address this gap, this paper provides a comprehensive review and analysis of 29 publicly available medium-to-high-resolution SITS classification benchmark datasets released between 2017 and 2025. These datasets are intended for training, testing, and validating land-cover classification algorithms, rather than for direct use as operational map products. We conduct a detailed statistical and comparative analysis of these datasets, focusing on their key characteristics across spectral, temporal, and spatial dimensions, as well as their labeling systems. In addition, this review summarizes the SITS classification algorithms that have been developed and benchmarked using these datasets. Finally, we identify the main challenges in constructing and applying SITS classification datasets and discuss future research directions, particularly in data reconstruction, multimodal fusion, change analysis, and advanced model architectures. This survey provides the research community with a systematic overview of SITS classification benchmark datasets and aims to support continued progress in this rapidly developing field. Full article

Forest above-ground biomass (AGB) estimation is crucial for evaluating carbon dynamics. Although optical and synthetic aperture radar (SAR) data provide complementary spectral and structural information, limitations in existing fusion approaches restrict AGB estimation accuracy. This study proposes a multi-source data fusion framework comparing two image fusion strategies—the conventional Hue-Intensity-Saturation Wavelet (HIS-Wavelet) method and a deep learning-based HIS-Non-Subsampled Shearlet Transform combined with Pulse Coupled Neural Network (HIS-NSST + PCNN) approach—for forest AGB estimation using Gaofen-1 (GF-1), Gaofen-2 (GF-2), and Gaofen-3 (GF-3) satellite imagery in a subtropical forest area of Yunnan Province, China. Three regression models—Multiple Linear Stepwise Regression (MLSR), K-Nearest Neighbor (KNN), and KNN with Fast Iterative Feature Selection (KNN-FIFS)—were systematically compared to evaluate estimation performance and justify model selection. Results indicate that the HIS-NSST + PCNN method outperforms HIS-Wavelet in fusion quality metrics, with the GF-2 Red-Near-infrared-Blue (RNB) band and GF-3 combination using HH co-polarization achieving the highest image quality. The optimal AGB retrieval was achieved with the GF-1RNB and GF-3 combination under HIS-NSST + PCNN (coefficient of determination (R²) = 0.80, root mean square error (RMSE) = 14.79 t/ha), improving R² by 0.07 and RMSE by 2.35 t/ha over HIS-Wavelet. However, for GF-2 + GF-3, HIS-Wavelet achieved marginally better inversion accuracy (R² = 0.71) than HIS-NSST + PCNN (R² = 0.69), indicating that superior fusion quality does not directly translate to higher inversion accuracy. Bootstrap resampling analysis (1000 iterations) confirmed the statistical robustness, with the optimal KNN-FIFS yielding R² = 0.800 (95% confidence interval (CI): 0.678–0.924) and RMSE = 14.79 t/ha (95% CI: 12.51–17.22 t/ha), showing non-overlapping confidence intervals with both benchmark models. These findings demonstrate that spectral complementarity between optical and SAR data plays a more critical role than spatial resolution alone in fusion-based AGB estimation, and that adaptive feature selection is essential for maximizing inversion accuracy. Full article

Safe low-altitude flight requires precise perception of obstacles like widespread transmission towers. Traditional inspection is often costly and inefficient. While satellite remote sensing enables automated detection, transmission towers exhibit small scales, slender structures, and random orientations, causing feature loss and receptive field mismatch. This study constructs HRS-PTD, a multi-source, multi-resolution satellite optical dataset, and analyzes target morphology. We then propose an optimized composite YOLO model using a streamlined three-stage baseline with C3k2 and SPPF modules. To enhance small object feature reconstruction, CARAFE is integrated into the upsampling path for content-aware dynamic kernels. Furthermore, a direction-aware C_DCA module, incorporating deformable convolutions, utilizes multi-directional strip branches and adaptive attention to improve slender target representation. Ablation experiments show the model achieves 92.28% mAP, with precision and recall increasing by 1.53 and 12.12 percentage points over the baseline. Comparative experiments against representative classical detectors further demonstrate that the proposed model achieves superior overall performance in both detection accuracy and inference efficiency. Tests on Google Earth and Gaofen-7 imagery yield 88% and 76% accuracy, confirming real-world feasibility. Full article

Cloud detection is essential for quantitative land-surface remote sensing and cloud-climate research. However, existing methods often prioritize spatial features over spectral features, which limits thin-cloud detection. To address this issue, this paper proposes a Thin-Cloud-Sensitive Network (TCSNet) for hyperspectral imagery. TCSNet employs an encoder–decoder architecture with a dual-branch design: a convolutional neural network (CNN) extracts multi-scale local features, while a PVTv2-B2 Transformer captures long-range spectral dependencies. To effectively integrate the complementary representations from both branches, a Cross-Modal Fusion (CMF) module with a lightweight single-channel gate is introduced at each stage, followed by a channel attention mechanism (SE) for feature recalibration. Subsequently, a Multi-Scale Fusion (MSF) module is used to integrate multi-level features through a top-down pathway, enabling deep semantic information to guide shallow feature expression. Furthermore, to enhance the decoder’s feature representation capability, a Combined Attention Mechanism (CAM) is incorporated at each decoder stage. This design enables the network to simultaneously focus on important channels, salient regions, and cloud boundaries, effectively alleviating spectral confusion between thin clouds and the underlying surface. Experimental results on Gaofen-5 01 hyperspectral data demonstrate that TCSNet achieves the highest recall (92.98%), ${\mathrm{R}\mathrm{e}\mathrm{c}\mathrm{a}\mathrm{l}\mathrm{l}}_{thin}$ (85.59%), and ${\mathrm{R}\mathrm{e}\mathrm{c}\mathrm{a}\mathrm{l}\mathrm{l}}_{thick}$ (99.75%), thereby validating its superiority for thin-cloud detection. Full article