Remote Sensing

21 pages, 20519 KiB

Open AccessArticle

Volume Estimation of Land Surface Change Based on GaoFen-7

by Chen Yin, Qingke Wen, Shuo Liu, Yixin Yuan, Dong Yang and Xiankun Shi

Remote Sens. 2025, 17(7), 1310; https://doi.org/10.3390/rs17071310 (registering DOI) - 6 Apr 2025

Volume of change provides a comprehensive and objective reflection of land surface transformation, meeting the emerging demand for feature change monitoring in the era of big data. However, existing land surface monitoring methods often focus on a single dimension, either horizontal or vertical, [...] Read more.

Volume of change provides a comprehensive and objective reflection of land surface transformation, meeting the emerging demand for feature change monitoring in the era of big data. However, existing land surface monitoring methods often focus on a single dimension, either horizontal or vertical, making it challenging to achieve quantitative volumetric change monitoring. Accurate volumetric change measurements are indispensable in many fields, such as monitoring open-pit coal mines. Therefore, the main content and conclusions of this paper are as follows: (1) A method for Automatic Control Points Extraction from ICESat-2/ATL08 products was developed, integrating Land cover types and Phenological information (ACPELP), achieving a mean absolute error (MAE) of 1.05 m in the horizontal direction and 1.99 m in the vertical direction for stereo change measurements. This method helps correct image positioning errors, enabling the acquisition of geospatially aligned GaoFen-7 (GF-7) imagery. (2) A function-based classification system for open-pit coal mines was established, enabling precise extraction of stereoscopic change region to support accurate volumetric calculations. (3) A method for calculating the mining and stripping volume of open-pit coal mines based on GF-7 imagery is proposed. The method utilizes photogrammetry to extract elevation features and combines spectral features with elevation data to estimate stripping volumes, achieving an excellent error rate (ER) of 0.26%. The results indicate that our method is cost-effective and highly practical, filling the gap in accurate and comprehensive monitoring of land surface changes. Full article

(This article belongs to the Special Issue Advances in Remote Sensing for Land Subsidence Monitoring)

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19 pages, 13012 KiB

Open AccessArticle

Neural Network-Based Temporal Ensembling of Water Depth Estimates Derived from SuperDove Images

by Milad Niroumand-Jadidi, Carl J. Legleiter and Francesca Bovolo

Remote Sens. 2025, 17(7), 1309; https://doi.org/10.3390/rs17071309 (registering DOI) - 6 Apr 2025

Abstract

CubeSats provide a wealth of high-frequency observations at a meter-scale spatial resolution. However, most current methods of inferring water depth from satellite data consider only a single image. This approach is sensitive to the radiometric quality of the data acquired at that particular [...] Read more.

CubeSats provide a wealth of high-frequency observations at a meter-scale spatial resolution. However, most current methods of inferring water depth from satellite data consider only a single image. This approach is sensitive to the radiometric quality of the data acquired at that particular instant in time, which could be degraded by various confounding factors, such as sun glint or atmospheric effects. Moreover, using single images in isolation fails to exploit recent improvements in the frequency of satellite image acquisition. This study aims to leverage the dense image time series from the SuperDove constellation via an ensembling framework that helps to improve empirical (regression-based) bathymetry retrieval. Unlike previous studies that only ensembled the original spectral data, we introduce a neural network-based method that instead ensembles the water depths derived from multi-temporal imagery, provided the data are acquired under steady flow conditions. We refer to this new approach as NN-depth ensembling. First, every image is treated individually to derive multitemporal depth estimates. Then, we use another NN regressor to ensemble the temporal water depths. This step serves to automatically weight the contribution of the bathymetric estimates from each time instance to the final bathymetry product. Unlike methods that ensemble spectral data, NN-depth ensembling mitigates against propagation of uncertainties in spectral data (e.g., noise due to sun glint) to the final bathymetric product. The proposed NN-depth ensembling is applied to temporal SuperDove imagery of reaches from the American, Potomac, and Colorado rivers with depths of up to 10 m and evaluated against in situ measurements. The proposed method provided more accurate and robust bathymetry retrieval than single-image analyses and other ensembling approaches. Full article

(This article belongs to the Special Issue Advances in Remote Sensing of the Inland and Coastal Water Zones II)

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25 pages, 19515 KiB

Open AccessArticle

Extensive Feature-Inferring Deep Network for Hyperspectral and Multispectral Image Fusion

by Abdolraheem Khader, Jingxiang Yang, Sara Abdelwahab Ghorashi, Ali Ahmed, Zeinab Dehghan and Liang Xiao

Remote Sens. 2025, 17(7), 1308; https://doi.org/10.3390/rs17071308 (registering DOI) - 5 Apr 2025

Abstract

Hyperspectral (HS) and multispectral (MS) image fusion is the most favorable way to obtain a hyperspectral image that has high resolution in terms of spatial and spectral information. This fusion problem can be accomplished by formulating a mathematical model and solving it either [...] Read more.

Hyperspectral (HS) and multispectral (MS) image fusion is the most favorable way to obtain a hyperspectral image that has high resolution in terms of spatial and spectral information. This fusion problem can be accomplished by formulating a mathematical model and solving it either analytically or iteratively. The mathematical solutions class has serious challenges, e.g., computation cost, manually tuning parameters, and the absence of imaging models that laboriously affect the fusion process. With the revolution of deep learning, the recent HS-MS image fusion techniques gained good outcomes by utilizing the power of the convolutional neural network (CNN) for feature extraction. Moreover, extracting intrinsic information, e.g., non-local spatial and global spectral features, is the most critical issue faced by deep learning methods. Therefore, this paper proposes an Extensive Feature-Inferring Deep Network (EFINet) with extensive-scale feature-interacting and global correlation refinement modules to improve the effectiveness of HS-MS image fusion. The proposed network retains the most vital information through the extensive-scale feature-interacting module in various feature scales. Moreover, the global semantic information is achieved by utilizing the global correlation refinement module. The proposed network is validated through rich experiments conducted on two popular datasets, the Houston and Chikusei datasets, and it attains good performance compared to the state-of-the-art HS-MS image fusion techniques. Full article

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23 pages, 56521 KiB

Open AccessArticle

Multi-Source SAR-Based Surface Deformation Analysis of Edgecumbe Volcano, Alaska, and Its Relationship with Earthquakes

by Shuangcheng Zhang, Ziheng Ju, Yufen Niu, Zhong Lu, Qianyou Fan, Jinqi Zhao, Zhengpei Zhou, Jinzhao Si, Xuhao Li and Yiyao Li

Remote Sens. 2025, 17(7), 1307; https://doi.org/10.3390/rs17071307 (registering DOI) - 5 Apr 2025

Abstract

Edgecumbe, a dormant volcano located on Kruzof Island in the southeastern part of Alaska, USA, west of the Sitka Strait, has exhibited increased volcanic activity since 2018. To assess the historical and current intensity of this activity and explore its relationship with seismic [...] Read more.

Edgecumbe, a dormant volcano located on Kruzof Island in the southeastern part of Alaska, USA, west of the Sitka Strait, has exhibited increased volcanic activity since 2018. To assess the historical and current intensity of this activity and explore its relationship with seismic events in the surrounding region, this study utilized data from the ERS-1/2, ALOS-1, and Sentinel-1 satellites. The Permanent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) and Small Baseline Subset InSAR (SBAS-InSAR) techniques were employed to obtain surface deformation data spanning nearly 30 years. Based on the acquired deformation field, the point-source Mogi model was applied to invert the position and temporal volume changes in the volcanic source. Then, by integrating seismic activity data from the surrounding area, the correlation between volcanic activity and earthquake occurrences was analyzed. The results indicate the following: (1) the coherence of interferograms is influenced by seasonal variations, with snow accumulation during the winter months negatively impacting interferometric coherence. (2) Between 1992 and 2000, the surface of the volcano remained relatively stable. From 2007 to 2010, the frequency of seismic events increased, leading to significant surface deformation, with the maximum Line-of-Sight (LOS) deformation rate during this period reaching −26 mm/yr. Between 2015 and 2023, the volcano entered a phase of accelerated uplift, with surface deformation rates increasing to 68 mm/yr after August 2018. (3) The inversion results for the period from 2015 to 2023 show that the volcanic source, located at a depth of 5.4 km, experienced expansion in its magma chamber, with a volumetric increase of 57.8 × 10⁶ m³. These inversion results are consistent with surface deformation fields obtained from both ascending and descending orbits, with cumulative LOS displacement reaching approximately 210 mm and 250 mm in the ascending and descending tracks, respectively. (4) Long-term volcanic surface deformation, changes in magma source volume, and seismic activity suggest that the earthquakes occurring after 2018 have facilitated the expansion of the volcanic magma source and intensified surface deformation. The uplift rate around the volcano has significantly increased. Full article

(This article belongs to the Special Issue Applications of Remote Sensing Technology in Volcano Hazard Monitoring)

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32 pages, 23634 KiB

Open AccessArticle

Predictive Archaeological Risk Assessment at Reservoirs with Multitemporal LiDAR and Machine Learning (XGBoost): The Case of Valdecañas Reservoir (Spain)

by Enrique Cerrillo-Cuenca and Primitiva Bueno-Ramírez

Remote Sens. 2025, 17(7), 1306; https://doi.org/10.3390/rs17071306 (registering DOI) - 5 Apr 2025

Abstract

The conservation and monitoring of archaeological sites submerged in water reservoirs have become increasingly necessary in a climatic context where water management policies are possibly accelerating erosion and sedimentation processes. This study assesses the potential of using multitemporal LiDAR data and Machine Learning [...] Read more.

The conservation and monitoring of archaeological sites submerged in water reservoirs have become increasingly necessary in a climatic context where water management policies are possibly accelerating erosion and sedimentation processes. This study assesses the potential of using multitemporal LiDAR data and Machine Learning (ML)—specifically the XGBoost algorithm—to predict erosional and sedimentary processes affecting archaeological sites in the Valdecañas Reservoir (Spain). Using data from 2010 to 2023, topographic variations were calculated through a robust workflow that included the co-registration of LiDAR point clouds and the generation of high-resolution DEMs. Hydrological variables, topographic descriptors, and water dynamics-related factors were extracted and used to train models based on the detected measurement errors and the temporal ranges of the DEMs. The model trained with 2018–2023 data exhibited the highest predictive performance (R² = 0.685), suggesting that sedimentary and erosional patterns are partially predictable. Finally, a multicriteria approach was applied using a DEM generated from 1957 aerial photographs to estimate past variations based on historical terrain conditions. The results indicate that areas exposed to fluctuating water levels and different topographic orientations suffer greater damage. This study highlights the value of LiDAR and ML in assessing the vulnerability of archaeological sites in highly dynamic environments. Full article

(This article belongs to the Special Issue Multi-Data Integration in Near-Surface Geophysics and Close Range Remote Sensing Applied to Cultural Heritage)

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36 pages, 8348 KiB

Open AccessArticle

Classical vs. Machine Learning-Based Inpainting for Enhanced Classification of Remote Sensing Image

by Aleksandra Sekrecka and Kinga Karwowska

Remote Sens. 2025, 17(7), 1305; https://doi.org/10.3390/rs17071305 (registering DOI) - 5 Apr 2025

Abstract

Inpainting is a technique that allows for the reconstruction of images and the removal of unnecessary elements. In our research, we employed inpainting to eliminate erroneous lines in the images and examined its abilities in improving classification quality. To reduce the erroneous lines, [...] Read more.

Inpainting is a technique that allows for the reconstruction of images and the removal of unnecessary elements. In our research, we employed inpainting to eliminate erroneous lines in the images and examined its abilities in improving classification quality. To reduce the erroneous lines, we designed ResGMCNN, whose multi-column generator model uses residual blocks. For our studies, we used data from the COWC and DOTA datasets. The GMCNN model with residual connections outperformed most classical inpainting methods, including the Telea and Navier–Stokes methods, achieving a maximum structural similarity index measure (SSIM) of 0.93. However, despite the improvement in filling quality, these results still lag behind the Criminisi method, which achieved the highest SSIM values (up to 0.99). We investigated the improvement in classification quality by removing vehicles from the road class in images acquired by UAVs. For vehicle removal, we used Criminisi inpainting, as well as Navier–Stokes and Telea for comparison. Classification was performed using eight classifiers, six of which were based on machine learning, where we proposed our solutions. The results showed that classification quality could be improved by several to over a dozen percent, depending on the metric, image, and classification method. The F1-score and Cohen Kappa metrics indicated an improvement in classification quality of up to 13% in comparison to the classification of the original image. Nevertheless, each of the classical inpainting methods examined improved the road classification. Full article

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28 pages, 13800 KiB

Open AccessArticle

MMTSCNet: Multimodal Tree Species Classification Network for Classification of Multi-Source, Single-Tree LiDAR Point Clouds

by Jan Richard Vahrenhold, Melanie Brandmeier and Markus Sebastian Müller

Remote Sens. 2025, 17(7), 1304; https://doi.org/10.3390/rs17071304 (registering DOI) - 5 Apr 2025

Abstract

Trees play a critical role in climate regulation, biodiversity, and carbon storage as they cover approximately 30% of the global land area. Nowadays, ml is key to automating large-scale tree species classification based on active and passive sensing systems, with a recent trend [...] Read more.

Trees play a critical role in climate regulation, biodiversity, and carbon storage as they cover approximately 30% of the global land area. Nowadays, ml is key to automating large-scale tree species classification based on active and passive sensing systems, with a recent trend favoring data fusion approaches for higher accuracy. The use of 3D dl models has improved tree species classification by capturing structural and geometric data directly from point clouds. We propose a fully mmtscnet that processes lidar point clouds, fwf data, derived features, and bidirectional, color-coded depth images in their native data formats without any modality transformation. We conduct several experiments as well as an ablation study to assess the impact of data fusion. Classification performance on the combination of als data with fwf data scored the highest, achieving an oa of nearly 97%, a Mean Average F1-score (MAF) of nearly 97%, and a Kappa Coefficient of 0.96. Results for the other data subsets show that the als data in combination with or even without fwf data produced the best results, which was closely followed by the uls data. Additionally, it is evident that the inclusion of fwf data provided significant benefits to the classification performance, resulting in an increase in the MAF of +4.66% for the als data, +4.69% for the uls data under leaf-on conditions, and +2.59% for the uls data under leaf-off conditions. The proposed model is also compared to a state-of-the-art unimodal 3D-dl model (PointNet++) as well as a feature-based unimodal dl architecture (DSTCN). The mmtscnet architecture outperformed the other models by several percentage points, depending on the characteristics of the input data. Full article

23 pages, 9632 KiB

Open AccessArticle

Adaptive Barrage Jamming Against SAR Based on Prior Information and Scene Segmentation

by Zhengwei Guo, Longyuan Wang, Zhenchang Liu, Zewen Fu, Ning Li and Xuebo Zhang

Remote Sens. 2025, 17(7), 1303; https://doi.org/10.3390/rs17071303 (registering DOI) - 5 Apr 2025

Abstract

Due to the advantages of easy implementation and fine jamming effect, barrage jamming against synthetic aperture radar (SAR) has received extensive attention in the field of electronic countermeasures. However, most methods of barrage jamming still have limitations, such as uncontrollable jamming position and [...] Read more.

Due to the advantages of easy implementation and fine jamming effect, barrage jamming against synthetic aperture radar (SAR) has received extensive attention in the field of electronic countermeasures. However, most methods of barrage jamming still have limitations, such as uncontrollable jamming position and coverage and high-power requirements. To address these issues, an improved barrage jamming method is proposed in this paper. The proposed method fully combines the prior information of the region of interest (ROI), and the precise jamming with controllable position, coverage, and power is realized. For the proposed method, the ROI is firstly divided into several sub-scenes according to the obtained prior information, and the signal is intercepted. Then the frequency response function of the jammer for each sub-scene is generated. The frequency response function of the jammer, which consists of position modulation function and jamming coverage function, is decomposed into slow-time-dependent parts and slow-time-independent parts. The slow-time-independent parts are generated offline in advance, and the real-time performance of the proposed method is guaranteed through this way. Finally, the intercepted signal is modulated by the frequency response function to generate the two-dimensional controllable jamming effect. Theoretical analysis and simulation results show that the proposed method can produce jamming effects with controllable position and coverage, and the utilization efficiency of jamming power is improved. Full article

(This article belongs to the Special Issue Ocean Remote Sensing Based on Radar, Sonar and Optical Techniques (Second Edition))

25 pages, 4826 KiB

Open AccessArticle

Enhancing Cross-Domain Remote Sensing Scene Classification by Multi-Source Subdomain Distribution Alignment Network

by Yong Wang, Zhehao Shu, Yinzhi Feng, Rui Liu, Qiusheng Cao, Danping Li and Lei Wang

Remote Sens. 2025, 17(7), 1302; https://doi.org/10.3390/rs17071302 (registering DOI) - 5 Apr 2025

Abstract

Multi-source domain adaptation (MSDA) in remote sensing (RS) scene classification has recently gained significant attention in the visual recognition community. It leverages multiple well-labeled source domains to train a model capable of achieving strong generalization on the target domain with little to no [...] Read more.

Multi-source domain adaptation (MSDA) in remote sensing (RS) scene classification has recently gained significant attention in the visual recognition community. It leverages multiple well-labeled source domains to train a model capable of achieving strong generalization on the target domain with little to no labeled data from the target domain. However, the distribution shifts among multiple source domains make it more challenging to align the distributions between the target domain and all source domains concurrently. Moreover, relying solely on global alignment risks losing fine-grained information for each class, especially in the task of RS scene classification. To alleviate these issues, we present a Multi-Source Subdomain Distribution Alignment Network (MSSDANet), which introduces novel network structures and loss functions for subdomain-oriented MSDA. By adopting a two-level feature extraction strategy, this model attains better global alignment between the target domain and multiple source domains, as well as alignment at the subdomain level. First, it includes a pre-trained convolutional neural network (CNN) as a common feature extractor to fully exploit the shared invariant features across one target and multiple source domains. Secondly, a dual-domain feature extractor is used after the common feature extractor, which maps the data from each pair of target and source domains to a specific dual-domain feature space and performs subdomain alignment. Finally, a dual-domain feature classifier is employed to make predictions by averaging the outputs from multiple classifiers. Accompanied by the above network, two novel loss functions are proposed to boost the classification performance. Discriminant Semantic Transfer (DST) loss is exploited to force the model to effectively extract semantic information among target and source domain samples, while Class Correlation (CC) loss is introduced to reduce the feature confusion from different classes within the target domain. It is noteworthy that our MSSDANet is developed in an unsupervised manner for domain adaptation, indicating that no label information from the target domain is required during training. Extensive experiments on four common RS image datasets demonstrate that the proposed method achieves state-of-the-art performance for cross-domain RS scene classification. Specifically, in the dual-source and three-source settings, MSSDANet outperforms the second-best algorithm in terms of overall accuracy (OA) by 2.2% and 1.6%, respectively. Full article

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70 pages, 53621 KiB

Open AccessArticle

Absolute Vicarious Calibration, Extended PICS (EPICS) Based De-Trending and Validation of Hyperspectral Hyperion, DESIS, and EMIT

by Harshitha Byregowda, Larry Leigh, Morakot Kaewmanee, Dinithi Siriwardana Pathiranage, Juliana Fajardo Rueda, David Aaron and Cibele Teixeira Pinto

Remote Sens. 2025, 17(7), 1301; https://doi.org/10.3390/rs17071301 (registering DOI) - 5 Apr 2025

Abstract

This study addresses the critical need for radiometrically accurate and consistent hyperspectral data as the remote sensing community moves towards a hyperspectral world. Previous calibration efforts on Hyperion, the first on-orbit hyperspectral sensors, have exhibited temporal stability and absolute accuracy limitations. This work [...] Read more.

This study addresses the critical need for radiometrically accurate and consistent hyperspectral data as the remote sensing community moves towards a hyperspectral world. Previous calibration efforts on Hyperion, the first on-orbit hyperspectral sensors, have exhibited temporal stability and absolute accuracy limitations. This work has developed and validated a novel cross-calibration methodology to address these challenges. Also, this work adds two other hyperspectral sensors, DLR Earth Sensing Imaging Spectrometer (DESIS) and Earth Surface mineral Dust Source Investigation instrument (EMIT), to maintain temporal continuity and enhance spatial coverage along with spectral resolution. The study established a robust approach for calibrating Hyperion using DESIS and EMIT. The methodology involves several key processes. First is a temporal stability assessment on Extended Pseudo Invariant Calibration Sites (EPICS) Cluster13–Global Temporal Stable (GTS) over North Africa (Cluster13–GTS) using Landsat Sensors Landsat 7 (ETM+), Landsat 8 (OLI). Second, a temporal trend correction model was developed for DESIS and Hyperion using statistically selected models. Third, absolute calibration was developed for DESIS and EMIT using multiple vicarious calibration sites, resulting in an overall absolute calibration uncertainty of 2.7–5.4% across the DESIS spectrum and 3.1–6% on non-absorption bands for EMIT. Finally, Hyperion was cross-calibrated using calibrated DESIS and EMIT as reference (with traceability to ground reference) with a calibration uncertainty within the range of 7.9–12.9% across non-absorption bands. The study also validates these calibration coefficients using OLI over Cluster13–GTS. The validation provided results suggesting a statistical similarity between the absolute calibrated hyperspectral sensors mean TOA (top-of-atmosphere) reflectance with that of OLI. This study offers a valuable contribution to the community by fulfilling the above-mentioned needs, enabling more reliable intercomparison, and combining multiple hyperspectral datasets for various applications. Full article

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27 pages, 49665 KiB

Open AccessArticle

ETQ-Matcher: Efficient Quadtree-Attention-Guided Transformer for Detector-Free Aerial–Ground Image Matching

by Chuan Xu, Beikang Wang, Zhiwei Ye and Liye Mei

Remote Sens. 2025, 17(7), 1300; https://doi.org/10.3390/rs17071300 (registering DOI) - 5 Apr 2025

Abstract

UAV aerial–ground feature matching is used for remote sensing applications, such as urban mapping, disaster management, and surveillance. However, current semi-dense detectors are sparse and inadequate for comprehensively addressing problems like scale variations from inherent viewpoint differences, occlusions, illumination changes, and repeated textures. [...] Read more.

UAV aerial–ground feature matching is used for remote sensing applications, such as urban mapping, disaster management, and surveillance. However, current semi-dense detectors are sparse and inadequate for comprehensively addressing problems like scale variations from inherent viewpoint differences, occlusions, illumination changes, and repeated textures. To address these issues, we propose an efficient quadtree-attention-guided transformer (ETQ-Matcher) based on efficient LoFTR, which integrates the multi-layer transformer with channel attention (MTCA) to capture global features. Specifically, to tackle various complex urban building scenarios, we propose quadtree-attention feature fusion (QAFF), which implements alternating self- and cross-attention operations to capture the context of global images and establish correlations between image pairs. We collect 12 pairs of UAV remote sensing images using drones and handheld devices, and we further utilize representative multi-source remote sensing images along with MegaDepth datasets to demonstrate their strong generalization ability. We compare ETQ-Matcher to classic algorithms, and our experimental results demonstrate its superior performance in challenging aerial–ground urban scenes and multi-source remote sensing scenarios. Full article

(This article belongs to the Special Issue Application of Spatial Information Science and Cartography in the Big Remotely Sensed Data Era)

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22 pages, 3000 KiB

Open AccessArticle

Estimating Chlorophyll-a Concentrations in Optically Shallow Waters Using Gaofen-1 Wide-Field-of-View (GF-1 WFV) Datasets from Lake Taihu, China

by Fuli Yan, Yuzhuo Li, Xiangtao Fan, Hongdeng Jian and Yun Li

Remote Sens. 2025, 17(7), 1299; https://doi.org/10.3390/rs17071299 (registering DOI) - 5 Apr 2025

Abstract

Lake Taihu has highly turbid inland waters with complex optical properties. Due to the bottom effect of submerged aquatic plants in optically shallow waters, currently available phytoplankton chlorophyll-a retrieval algorithms tend to overestimate chlorophyll-a concentrations in the eastern part of Lake Taihu. This [...] Read more.

Lake Taihu has highly turbid inland waters with complex optical properties. Due to the bottom effect of submerged aquatic plants in optically shallow waters, currently available phytoplankton chlorophyll-a retrieval algorithms tend to overestimate chlorophyll-a concentrations in the eastern part of Lake Taihu. This overestimation can distort the eutrophication evaluation of the entire lake. This paper identifies submerged and emergent plants, determines the retrieval models for the upwelling (K_u) and downwelling (K_d) irradiance attenuation coefficients, and proposes a phytoplankton chlorophyll-a retrieval model using a water depth optimization-based method to remove the bottom effect. The results show the following: (1) The normalized difference vegetation index (NDVI) method can distinguish the bottom mud (NDVI < −0.46) and submerged aquatic plants (−0.46 ≤ NDVI < 0.52) from the emergent plants (NDVI ≥ 0.52) with 90% accuracy. (2) The downwelling and upwelling irradiance attenuation coefficients are highly correlated with the suspended sediments, and retrieval models for these coefficients in three visible bands with high accuracy are presented. (3) Compared to traditional algorithms without bottom effect removal, the proposed chlorophyll-a concentration estimation algorithm based on the water depth-optimized bottom effect removal method efficiently reduces the bottom effect of the submerged aquatic plants. The root mean square error (RMSE) for the obtained chlorophyll-a concentrations decreases from 45.61 to 8.69 , and the mean absolute percentage error (MAPE) is reduced from 245.12% to 19.58%. In the validation step, the obtained RMSE of 10.89 and MAPE of 17.52% are consistent with the proposed algorithm. This research provides a good reference for the determination of chlorophyll-a concentrations in phytoplankton in complex inland water bodies. The findings are potentially useful for the operational monitoring of harmful algal blooms in the future. Full article

16 pages, 4488 KiB

Open AccessTechnical Note

Land Use and Land Cover Classification with Deep Learning-Based Fusion of SAR and Optical Data

by Ayesha Irfan, Yu Li, Xinhua E and Guangmin Sun

Remote Sens. 2025, 17(7), 1298; https://doi.org/10.3390/rs17071298 (registering DOI) - 5 Apr 2025

Abstract

Land use and land cover (LULC) classification through remote sensing imagery serves as a cornerstone for environmental monitoring, resource management, and evidence-based urban planning. While Synthetic Aperture Radar (SAR) and optical sensors individually capture distinct aspects of Earth’s surface, their complementary nature SAR [...] Read more.

Land use and land cover (LULC) classification through remote sensing imagery serves as a cornerstone for environmental monitoring, resource management, and evidence-based urban planning. While Synthetic Aperture Radar (SAR) and optical sensors individually capture distinct aspects of Earth’s surface, their complementary nature SAR excelling in structural and all-weather observation and optical sensors providing rich spectral information—offers untapped potential for improving classification robustness. However, the intrinsic differences in their imaging mechanisms (e.g., SAR’s coherent scattering versus optical’s reflectance properties) pose significant challenges in achieving effective multimodal fusion for LULC analysis. To address this gap, we propose a multimodal deep-learning framework that systematically integrates SAR and optical imagery. Our approach employs a dual-branch neural network, with two fusion paradigms being rigorously compared: the Early Fusion strategy and the Late Fusion strategy. Experiments on the SEN12MS dataset—a benchmark containing globally diverse land cover categories—demonstrate the framework’s efficacy. Our Early Fusion strategy achieved 88% accuracy (F1 score: 87%), outperforming the Late Fusion approach (84% accuracy, F1 score: 82%). The results indicate that optical data provide detailed spectral signatures useful for identifying vegetation, water bodies, and urban areas, whereas SAR data contribute valuable texture and structural details. Early Fusion’s superiority stems from synergistic low-level feature extraction, capturing cross-modal correlations lost in late-stage fusion. Compared to state-of-the-art baselines, our proposed methods show a significant improvement in classification accuracy, demonstrating that multimodal fusion mitigates single-sensor limitations (e.g., optical cloud obstruction and SAR speckle noise). This study advances remote sensing technology by providing a precise and effective method for LULC classification. Full article

(This article belongs to the Special Issue Multi-platform and Multi-modal Remote Sensing Data Fusion with Advanced Deep Learning Techniques (Second Edition))

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17 pages, 7128 KiB

Open AccessArticle

Application of Deep Learning on Global Spaceborne Radar and Multispectral Imagery for the Estimation of Urban Surface Height Distribution

by Vivaldi Rinaldi and Masoud Ghandehari

Remote Sens. 2025, 17(7), 1297; https://doi.org/10.3390/rs17071297 (registering DOI) - 5 Apr 2025

Abstract

Digital Surface Models (DSMs) have a wide range of applications, including the spatial and temporal analysis of human habitation. Traditionally, DSMs are generated by rasterizing Light Detection and Ranging (LiDAR) point clouds. While LiDAR provides high-resolution details, the acquisition of required data is [...] Read more.

Digital Surface Models (DSMs) have a wide range of applications, including the spatial and temporal analysis of human habitation. Traditionally, DSMs are generated by rasterizing Light Detection and Ranging (LiDAR) point clouds. While LiDAR provides high-resolution details, the acquisition of required data is logistically challenging and costly, leading to limited spatial coverage and temporal frequency. Satellite imagery, such as Synthetic Aperture Radar (SAR), contains information on surface height variations in the scene within the reflected signal. Transforming satellite imagery data into a global DSM is challenging but would be of great value if those challenges were overcome. This study explores the application of a U-Net architecture to generate DSMs by coupling Sentinel-1 SAR and Sentinel-2 optical imagery. The model is trained on surface height data from multiple U.S. cities to produce a normalized DSM (NDSM) and assess its ability to generalize inferences for cities outside the training dataset. The analysis of the results shows that the model performs moderately well when inferring test cities but its performance remains well below that of the training cities. Further examination, through the comparison of height distributions and cross-sectional analysis, reveals that estimation bias is influenced by the input image resolution and the presence of geometric distortion within the SAR image. These findings highlight the need for refinement in preprocessing techniques as well as advanced training approaches and model architecture that can better handle the complexities of urban landscapes encoded in satellite imagery. Full article

(This article belongs to the Section AI Remote Sensing)

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22 pages, 11910 KiB

Open AccessArticle

Comprehensive Assessment of Nine Fine-Resolution Global Forest Cover Products in the Three-North Shelter Forest Program Region

by Chengfei Wang, Xiao Zhang, Tingting Zhao and Liangyun Liu

Remote Sens. 2025, 17(7), 1296; https://doi.org/10.3390/rs17071296 (registering DOI) - 5 Apr 2025

Abstract

Accurate forest cover maps are essential for forest conservation and sustainable development. Numerous global forest cover products have emerged in recent years; however, most tend to neglect sparsely forested arid and semi-arid areas, such as the Three-North Shelter Forest (TNSF) Program Region in [...] Read more.

Accurate forest cover maps are essential for forest conservation and sustainable development. Numerous global forest cover products have emerged in recent years; however, most tend to neglect sparsely forested arid and semi-arid areas, such as the Three-North Shelter Forest (TNSF) Program Region in China. Despite their sparse distribution, forests in these areas play a vital role in maintaining global ecological balance and biodiversity. Therefore, a comprehensive evaluation of these products is necessary. In this study, the performance of nine global forest cover products was systematically investigated at a 10–30 m resolution (GlobeLand30, GLC_FCS30D, FROM-GLC30, FROM-GLC10, ESA World Cover, ESRI Land Cover, GFC30, GFC 2020, and GFC) in the TNSF region around 2020. Specifically, a novel and comprehensive validation dataset was first generated by integrating all available open-access validation datasets in the TNSF region after visual interpretation. Second, the consistency and accuracy of nine forest cover products were evaluated, and their discrepancies with government statistical data were analyzed. The results indicate that GFC2020 provides the highest overall accuracy (OA) of 90.49%, followed by ESA World Cover, while GlobeLand30 had the lowest accuracy of 84.78%. Meanwhile, compared with statistical data, all nine products underestimated forest areas, especially in these hyper-arid zones (aridity index < 0.03). Notably, 31.04% of the area is identified as forest by only one product, attributable to differences in forest definitions and remote sensing data among the products. Therefore, this study provides a detailed assessment and analysis of nine global forest cover products from multiple perspectives, offering valuable insights for users in selecting appropriate forest cover products and supporting forest management. Full article

(This article belongs to the Section Forest Remote Sensing)

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37 pages, 9633 KiB

Open AccessArticle

Analysis and Modeling of Statistical Distribution Characteristics for Multi-Aspect SAR Images

by Rui Zhu, Fei Teng and Wen Hong

Remote Sens. 2025, 17(7), 1295; https://doi.org/10.3390/rs17071295 (registering DOI) - 4 Apr 2025

Abstract

Multi-aspect synthetic aperture radar (SAR) is an emerging observation mode in SAR. Through multi-aspect observations, along with coherent and incoherent image processing, multi-aspect SAR effectively addresses issues like layovers, shadows, and foreshortening in conventional SAR. It can obtain multi-aspect scattering images of the [...] Read more.

Multi-aspect synthetic aperture radar (SAR) is an emerging observation mode in SAR. Through multi-aspect observations, along with coherent and incoherent image processing, multi-aspect SAR effectively addresses issues like layovers, shadows, and foreshortening in conventional SAR. It can obtain multi-aspect scattering images of the observed scene. Modeling the statistical distribution characteristics of multi-aspect SAR images is crucial for its processing and applications. Currently, there is no comprehensive and systematic study on the statistical distribution characteristics of multi-aspect SAR images. Therefore, this paper conducts qualitative and quantitative analyses of these characteristics. Furthermore, we investigate the applicability and limitations of five single-parametric models commonly used in conventional SAR for modeling the statistical distribution characteristics of multi-aspect SAR images. The experimental results show that none of these models could accurately model the multi-aspect SAR images. To address this issue, we propose a finite mixture model (FMM) and evaluate its feasibility to accurately model the statistical distribution characteristics of multi-aspect SAR on X-band GOTCHA data and C-band Zhuhai data. The experimental results demonstrate that, compared with the single-parametric models, our method can accurately model the statistical distribution characteristics of various types of targets in multi-aspect SAR images from different observation aspects and aperture angles in various bands. Full article

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22 pages, 1677 KiB

Open AccessArticle

Multi-Dimensional Parameter-Estimation Method for a Spatial Target Based on the Micro-Range Decomposition of a High-Resolution Range Profile

by Xing Wang, Degui Yang and Zhichen Zhao

Remote Sens. 2025, 17(7), 1294; https://doi.org/10.3390/rs17071294 (registering DOI) - 4 Apr 2025

Abstract

The high-precision estimation of multi-dimensional parameters for spatial targets based on high-resolution range profiles is crucial for target recognition. However, existing estimation methods face difficulties in resolving the strong coupling between the target shape and the micro-motion parameters, as well as in fully [...] Read more.

The high-precision estimation of multi-dimensional parameters for spatial targets based on high-resolution range profiles is crucial for target recognition. However, existing estimation methods face difficulties in resolving the strong coupling between the target shape and the micro-motion parameters, as well as in fully utilizing micro-motion information under complex modulation characteristics. To address these challenges, this paper proposes a multi-dimensional parameter-estimation method for spatial targets based on micro-range decomposition. A micro-range model of the target is first constructed, and the micro-range modulation characteristics are analyzed. Then, micro-range coefficients are selected based on their Cramér–Rao lower bound (CRLB), and the correlation between these coefficients and target parameters is exploited for scattering center matching. An optimization model is further built for multi-dimensional parameter estimation, enabling the accurate estimation of parameters such as precession frequency, precession angle, and structural dimensions under both single-view and multi-view conditions. The experimental results show that in the dual-view case, all parameters are estimated with relative errors (REs) below 1.15% and root mean square error (RMSE) values below 0.05. In the single-view case, key parameters are estimated with REs under 15%. Compared with conventional methods, the proposed method achieves lower RMSE and significantly improved robustness and stability. These results demonstrate the effectiveness and practical potential of the proposed method for spatial target parameter estimation. Full article

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17 pages, 3980 KiB

Open AccessArticle

Landscape Spatiotemporal Heterogeneity Decreased the Resistance of Alpine Grassland to Soil Droughts

by Yuxin Wang, Hu Liu, Wenzhi Zhao, Jiachang Jiang and Zhibin He

Remote Sens. 2025, 17(7), 1293; https://doi.org/10.3390/rs17071293 (registering DOI) - 4 Apr 2025

Abstract

Alpine grasslands face increasing threats from soil droughts due to climate change. While extensive research has focused on the direct impacts of drought on vegetation, the role of landscape fragmentation and spatiotemporal heterogeneity in shaping the response of these ecosystems to drought remains [...] Read more.

Alpine grasslands face increasing threats from soil droughts due to climate change. While extensive research has focused on the direct impacts of drought on vegetation, the role of landscape fragmentation and spatiotemporal heterogeneity in shaping the response of these ecosystems to drought remains inadequately explored. This study aims to fill this gap by examining the Gannan alpine grassland in the northeastern Qinghai-Tibet Plateau. Using remote sensing data, indicators of spatial and temporal heterogeneity were derived, including spatial variance (SCV), spatial autocorrelation (SAC), and temporal autocorrelation (TAC). Two soil drought thresholds (Tr: threshold of rapid resistance loss and Tc: threshold of complete resistance loss) representing percentile-based drought intensities were identified to assess NDVI decline under drought conditions. Our findings indicate that the grassland has low resistance to soil droughts, with mean Tr and Tc of 8.93th and 7.36th percentile, respectively. Both increasing and decreasing spatiotemporal heterogeneity reduced vegetation resistance, with increasing SCV having a more pronounced effect. Specifically, increasing SCV increased Tr and Tc 1.4 times faster and 2.6 time slower than decreasing SCV, respectively. These results underscore the critical role of landscape heterogeneity in modulating grassland responses to drought, suggesting that managing vegetation patches could enhance ecosystem resilience. Full article

(This article belongs to the Special Issue Root-Zone Soil Moisture Retrieval and Applications from Remote Sensing Measurements)

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18 pages, 6034 KiB

Open AccessArticle

How Urban Expansion and Climatic Regimes Affect Groundwater Storage in China’s Major River Basins: A Comparative Analysis of the Humid Yangtze and Semi-Arid Yellow River Basins

by Weijing Zhou and Lu Hao

Remote Sens. 2025, 17(7), 1292; https://doi.org/10.3390/rs17071292 - 4 Apr 2025

Abstract

This study investigated and compared the spatiotemporal evolution and driving factors of groundwater storage anomalies (GWSAs) under the dual pressures of climate change and urban expansion in two contrasting river basins of China. Integrating GRACE and GLDAS data with multi-source remote sensing data [...] Read more.

This study investigated and compared the spatiotemporal evolution and driving factors of groundwater storage anomalies (GWSAs) under the dual pressures of climate change and urban expansion in two contrasting river basins of China. Integrating GRACE and GLDAS data with multi-source remote sensing data and using attribution analysis, we reveal divergent urban GWSA dynamics between the humid Yangtze River Basin (YZB) and semi-arid Yellow River Basin (YRB). The GWSAs in YZB urban grids showed a marked increasing trend at 3.47 mm/yr (p < 0.05) during 2002–2020, aligning with the upward patterns observed in agricultural land types including dryland and paddy fields, rather than exhibiting the anticipated decline. Conversely, GWSAs in YRB urban grids experienced a pronounced decline (−5.59 mm/yr, p < 0.05), exceeding those observed in adjacent dryland regions (−5.00 mm/yr). The contrasting climatic regimes form the fundamental drivers. YZB’s humid climate (1074 mm/yr mean precipitation) with balanced seasonality amplified groundwater recharge through enhanced surface runoff (+6.1%) driven by precipitation increases (+7.4 mm/yr). In contrast, semi-arid YRB’s water deficit intensified, despite marginal precipitation gains (+3.5 mm/yr), as amplified evapotranspiration (+4.1 mm/yr) exacerbated moisture scarcity. Human interventions further differentiated trajectories: YZB’s urban clusters demonstrated GWSA growth across all city types, highlighting the synergistic effects of urban expansion under humid climates through optimized drainage infrastructure and reduced evapotranspiration from impervious surfaces. Conversely, YRB’s over-exploitation due to rapid urbanization coupled with irrigation intensification drove cross-sector GWSA depletion. Quantitative attribution revealed climate change dominated YZB’s GWSA dynamics (86% contribution), while anthropogenic pressures accounted for 72% of YRB’s depletion. These findings provide critical insights for developing basin-specific management strategies, emphasizing climate-adaptive urban planning in water-rich regions versus demand-side controls in water-stressed basins. Full article

(This article belongs to the Special Issue Analyzing the Influence of Environmental Change on Water and Terrestrial Vegetation Using Satellite Data)

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