IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing最新文献

{"title":"An Improved Spatiotemporal Fusion Framework for Land-Cover Temporal Harmonization of High-Resolution Remote Sensing Images","authors":"Kangning Li;Zilin Xie;Xiaojun Qiao;Jinzhong Yang;Jinbao Jiang","doi":"10.1109/JSTARS.2025.3561514","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3561514","url":null,"abstract":"High-resolution remote sensing images, with fine spatial detail but limited coverage and infrequent revisits, often exhibit temporal discrepancies that hinder large-scale Earth observations. While spatiotemporal fusion (STF) methods offer a solution, they often lead to reduced spatial resolution and struggle with multisource and multitemporal image processing. To address this issue, an improved STF framework for temporal harmonization (STF-TH) was proposed. Specifically, STF-TH first applies an existing STF method for initial temporal transformation. Second, spatial resolution is recovered through spatial texture correction, referencing the fine texture of the original image. Finally, temporal color correction leverages the consistency of coarse images to further reduce temporal discrepancies among results. STF-TH was evaluated across datasets collected from different satellites, regions, and times, and validated via both qualitative and quantitative analyses at global, local, and line profile levels. Compared with five STF methods, STF-TH demonstrated significant improvements, ranging from 12% to 261.28% across six image quality evaluation metrics. In addition, STF-TH achieved superior spatial texture preservation and temporal color transformation, with improvements of 51.85% and 59.07%, respectively. Furthermore, STF-TH significantly improved the subsequent classification accuracy, with the <italic>F</i>1-score and the overall accuracy improved to 89.88% and 93.87%, respectively. Notably, these STF-based improvements in STF-TH incurred negligible additional time consumption. Experimental results confirm that STF-TH is an efficient and effective model for temporal harmonization, considering potential problems of noise, patch effects, and spatial resolution degradation in traditional STF processing. STF-TH is expected to be applied to large-scale high-resolution annual land-cover monitoring.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"11731-11750"},"PeriodicalIF":4.7,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10966196","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144073189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Space-Frequency Fusion Dual-Branch Convolutional Neural Networks for Significant Wave Height Retrieval From GF-3 SAR Data","authors":"Xuan Jin;Yawei Zhao;Xin Zhang;Yanlei Du;Jinsong Chong","doi":"10.1109/JSTARS.2025.3561691","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3561691","url":null,"abstract":"Deep learning in synthetic aperture radar (SAR) sea state retrieval is becoming increasingly prevalent. In current studies, convolutional neural networks (CNNs) are widely employed to extract either deep space features from normalized radar cross section (NRCS) of SAR images or deep frequency features from SAR spectra, with some studies combining artificially designed scalar features to retrieve significant wave height (SWH). When the quality of ocean wave imaging is poor, it becomes challenging for CNN models to extract useful features from a single space or frequency domain, and the scalar features are insufficient to describe the complex relationships within the data, thereby limiting the retrieval accuracy of the models. To harness the space and frequency domain information in SAR data effectively and acquire more expressive fusion features, we propose a space-frequency fusion dual-branch CNN (DB-CNN) model. The model separately extracts deep space features from NRCS of SAR images and deep frequency features from SAR image spectra. By employing the space-frequency feature cross layer (SFFCL) and the gated feature fusion layer (GFFL), it enhances and fuses space-frequency features, thereby achieving more accurate SAR SWH retrieval. Most retrieval models based on GF-3 data primarily focus on wave mode data, with limited utilization of data from other imaging modes. To fully leverage the diverse imaging modes of GF-3 data, this study collects GF-3 data across various imaging modes and establishes matched datasets with the fifth-generation European Centre for Medium-Range Weather Forecasts reanalysis (ERA5) and buoy for model training and evaluation. Consequently, our model exhibits applicability across diverse imaging modes and superior performance under different sea states. In addition, ablation experiments are conducted to evaluate the importance of the SFFCL and GFFL modules.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"11416-11427"},"PeriodicalIF":4.7,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10966200","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144073120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing the Accuracy of Jason-3 PWV Products Over Coastal Areas Using the Back Propagation Neural Network","authors":"Yangzhao Gong;Zhizhao Liu","doi":"10.1109/JSTARS.2025.3559732","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3559732","url":null,"abstract":"The performance of microwave radiometers aboard altimetric satellites in measuring water vapor degrades significantly over coastal areas due to the mixing of land within its footprint. In this study, we propose using the back propagation neural network (BPNN) models to enhance the accuracy of Jason-3 precipitable water vapor (PWV) over coastal areas. PWV data from 2076 globally distributed coastal and island Global Navigation Satellite System (GNSS) stations and 237 radiosonde stations are used as the reference. Specifically, the GNSS PWV data in 2016 and 2017 are used to train the BPNN models, while the GNSS and radiosonde PWV observations from January 2018 to June 2023 are used to test the performances of the BPNN models proposed. Our results show that the proposed BPNN PWV models can considerably enhance the accuracy of Jason-3 PWV recorded in the coastal areas (within 25 km of land). Evaluated by the GNSS PWV, BPNN models can reduce the root mean square error (RMSE) of Jason-3 PWV in the coastal areas from 4.2 to 2.7 kg/m² (35.7% of RMSE reduction). Assessed by the radiosonde PWV, the results indicate that the RMSE of Jason-3 PWV in the coastal areas is decreased from 5.0 to 3.6 kg/m² (28.0% of RMSE reduction) after using the proposed BPNN models.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"10684-10693"},"PeriodicalIF":4.7,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10967237","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143896406","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Centripetal Intensive Deep Hashing for Remote Sensing Image Retrieval","authors":"Weigang Wang;Zhongwen Guo;Ziyuan Cui;Hailei Zhao;Lintao Xian","doi":"10.1109/JSTARS.2025.3561508","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3561508","url":null,"abstract":"With the breakthrough of convolutional neural networks, deep hashing methods have demonstrated remarkable performance in large-scale image retrieval tasks. However, existing deep supervised hashing methods, which rely on pairwise or triplet labels, typically learn the hash function via random or hardest sample mining within training batches. This strategy primarily captures local sample similarities, causing a distribution shift and limiting retrieval performance. Furthermore, most methods emphasize global features while overlooking structural information, which is essential for understanding spatial relationships in images. To solve these limitations, we propose a Centripetal Intensive Deep Hashing (CIDH) method for remote sensing image retrieval. Initially, we design a Hybrid-Attention Guided Multiscale Refinement Network that integrates channel and spatial attention to capture multiscale visual features and highlight salient regions at different scales. Subsequently, we introduce a central similarity loss via class-centered labels to optimize the spatial distribution of global samples, which can encourage hash codes with similar semantics to cluster around centroids and reduce distribution shift. Meanwhile, we incorporate a central intensive loss into the Hamming space to shorten intraclass Hamming distances, generating more compact and discriminative hash codes. Extensive experiments demonstrate the superiority of our CIDH method compared with current state-of-the-art deep hashing methods.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"12439-12453"},"PeriodicalIF":4.7,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10966211","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144125466","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unstable Slope Identification and Monitoring Using Polarization-Enhanced DS-InSAR: A Case Study in the Bailong River Basin","authors":"Yahui Qiu;Yuanjian Wang;Ximin Cui;Debao Yuan;Peixian Li","doi":"10.1109/JSTARS.2025.3561337","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3561337","url":null,"abstract":"Interferometry synthetic aperture radar (InSAR) technology has been widely applied to the identification and monitoring of unstable slopes. Recent studies have demonstrated that polarization information can enhance the quality of interferometric phase and increase the density of monitoring points. In this study, we propose a distributed scatterer InSAR (DS-InSAR) method centered on efficient polarization channel optimization and the construction of DS target covariance matrices to improve surface deformation monitoring in mountainous regions. This approach utilizes time-series InSAR based on polarimetric SAR data to enhance phase quality and monitoring point density. Specifically, the method first determines the optimal polarization channels for PS and DS points using the Broyden-Fletcher-Goldfarb-Shanno method with auxiliary land cover classification data, targeting amplitude dispersion and coherence. Next, the similarity-weighted approach is applied to estimate the sample covariance matrix for DS points. Finally, regularization parameters are introduced to further refine the optimal phase of DS points. Real-data experiments conducted in the Bailong River Basin of China, using Sentinel-1 ascending and descending data, demonstrate the effectiveness of the method through qualitative and quantitative analyses. Compared to traditional DS-InSAR techniques, the proposed method achieves a 10% improvement in monitoring point density and identifies 29 unstable slopes in the study area. In addition, incorporating polarimetric data enhances the accuracy of displacement evolution over time.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"11142-11154"},"PeriodicalIF":4.7,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10965906","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143913407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On Adversarial Robust Generalization of DNNs for Remote Sensing Image Classification","authors":"Wei Xue;Yonghao Wang;Shaoquan Zhang;Xiao Zheng;Ping Zhong","doi":"10.1109/JSTARS.2025.3561254","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3561254","url":null,"abstract":"Deep neural networks (DNNs)-based deep learning is an important technical support in the task of remote sensing image classification. But DNNs are susceptible to adversarial attacks. Adversarial training is one of the effective ways to obtain robust models that can against such attacks. However, many adversarial training strategies can only defend against known attacks and perform poorly when faced with unknown attacks, namely, although the adversarial robustness of the model is enhanced, the generalization is reduced. To alleviate this phenomenon, we propose a new method to improve the adversarial robust generalization of DNNs for remote sensing image classification within the framework of adversarial training. Specifically speaking, we impose a Euclidean regularization constraint on the gradient of the adversarial loss function in order to achieve a narrowing of the robust generalization gap by enhancing the gradient alignment between clean and adversarial examples. In addition, we incorporate a label smoothing strategy into the adversarial training process, aiming to further improve the adversarial robustness of the model by reducing its sensitivity to subtle fluctuations. The combination of the above two strategies can not only improve the adversarial robustness of the model but also improve its adversarial robust generalization. Finally, in the case of two classical attack approaches FGSM and PGD, we validate the effectiveness and feasibility of our method through extensive experiments on two commonly used remote sensing image classification datasets NWPU-RESSC45, RSSCN7, and WHU-RS19, demonstrating its superiority over previous methods, particularly in the context of improving the adversarial robust generalization of DNNs.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"11370-11385"},"PeriodicalIF":4.7,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10965860","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143913471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mixed Layer Depth Estimation From Multisource Remote Sensing Data Using Clustering-Machine Learning Method","authors":"Zengxin Guan;Kaijun Ren;Senliang Bao;Hengqian Yan;Huizan Wang;Yanlai Zhao;Jianbin Liu","doi":"10.1109/JSTARS.2025.3561207","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3561207","url":null,"abstract":"The oceanic mixed layer is essential for air–sea interactions, influencing energy exchanges, climate dynamics, and marine ecosystems through its depth, and seasonal variability. Currently, the mixed layer depth (MLD) is estimated using in-situ observations or model data, both of which are costly and resource-intensive. This study develops a clustering estimation model utilising multisource ocean data to enable faster and more accurate MLD estimation. The model accounts for the temperature and salinity characteristics of different oceanic regions. The K-means clustering method was employed to partition the Pacific Ocean, and the lightGBM model was applied to estimate the MLD in individual subregions. Alongside commonly used sea surface parameters, wind stress curl and precipitation were included as inputs. Feature analysis was conducted separately for the models in each partition. The estimated MLD was compared with that of the in-situ data, showing consistency with observed trends and effectively capturing the spatiotemporal characteristics of MLD across seasons and geographic locations. The estimation error (RMSE) was less than 11.2 m. To assess practical applicability, comparative experiments using remote sensing data were performed, highlighting the model's feasibility and utility. By integrating clustering analysis with advanced estimation models, this study provides a novel approach for accurately reproducing the Pacific Ocean's MLD, which is useful for better analyzing the changes in ocean heat flux and vertical dynamics of seawater.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"11183-11197"},"PeriodicalIF":4.7,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10965346","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143913564","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mapping Antarctic Blue Ice Areas With Sentinel-2A/B Images and LightGBM Model","authors":"Xiaolong Teng;Jiahui Xu;Xiangbin Cui;Guitao Shi;Zhengyi Hu;Qingyu Gu;Bailang Yu;Jianping Wu;Yan Huang","doi":"10.1109/JSTARS.2025.3560280","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3560280","url":null,"abstract":"Antarctic blue ice plays a crucial role in surface energy balance and paleoclimate research. A high-accuracy and comprehensive dataset of blue ice areas (BIAs) is essential for understanding climate dynamics and environmental changes in the region. While satellite remote sensing is effective in mapping BIAs, traditional methods rely on limited spectral bands and linear models with inherent limitations. This study integrated remote sensing techniques with ensemble learning algorithms to develop a high-resolution (10 m) Antarctic-wide BIA dataset using Sentinel-2 imagery based on the years 2017–2022. Random forest, XGBoost, and LightGBM integrated learning algorithms were used to model the extraction of Antarctic blue ice. The accuracy of the model was evaluated by confusion matrix with LightGBM achieving the highest overall accuracy (87.23%). We also used SHapley Additive exPlanations values to improve the interpretability of opaque system models by evaluating the contribution of each feature variable. Validation through visual interpretation of Sentinel-2A/B images further confirmed the model's reliability, with an accuracy of 90.61%. Based on these robust results, we generated detailed BIAs across Antarctica. Our findings estimate the total BIAs at 175 274 km², covering approximately 1.25% of the continent. The blue ice is mainly concentrated in low-elevation coastal areas and mountain slopes, especially in Dronning Maud Land, Amery Ice Shelf, Wilkes Land, Victoria Land, and Transantarctic Mountains. We further reveal that most of the blue ice is located at elevations below 500 m, with air temperatures between −5 and 0 °C, and ice velocity under 100 m/yr. Our high-resolution dataset provides crucial insights for future research in Antarctic glaciology, paleoclimate studies, and meteorite collection.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"11078-11092"},"PeriodicalIF":4.7,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10964008","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143900498","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Satellite Remote Sensing Time-Series Observation of Russia–Ukraine War: Ongoing Conflict Increased Crop Risk","authors":"Yin Zhang;Qingwu Hu;Shaohua Wang;Pengcheng Zhao;Mingyao Ai","doi":"10.1109/JSTARS.2025.3560935","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3560935","url":null,"abstract":"Assessing the impact of the Russia–Ukraine war on crop growth is of great significance for the formulation of humanitarian aid and food security strategies. Based on nighttime light remote sensing data, we proposed a war intensity nighttime light index (WarINLI). The WarINLI was able to assess the conflict intensity in various Ukrainian oblasts and military conflict incidents quantitatively and accurately. It has high generalization and applicability to support the accurate estimation of war intensity. We evaluated crop growth status based on changes in vegetation indices derived from satellite remote sensing images. The degradation areas of four vegetation indices all accounted for over 73% of total cultivated land area. Integrating nighttime and daytime RS data, we explored the impacts of Russia–Ukraine war on the change of crop growth status through coupling coordination degree (CCD) model. The CCD between WarINLI variation and change rates of four vegetation indices was all greater than 0.75, which demonstrated that Russia–Ukraine war deteriorated crop growth situation and intensified crop risk in Ukraine. Our study provides a reference for government to initiate policy and supply relief so as to mitigate global food security threat.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"10582-10594"},"PeriodicalIF":4.7,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10965469","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143913406","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficient Urban Tree Species Classification via Multirepresentation Fusion of Mobile Laser Scanning Data","authors":"Yinchi Ma;Peng Luan;Yujie Zhang;Bo Liu;Lijie Zhang","doi":"10.1109/JSTARS.2025.3560714","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3560714","url":null,"abstract":"Urban tree species identification is crucial for forest management and ecosystem assessment. Mobile laser scanning (MLS) provides significant advantages for this task through its flexibility in navigating complex urban environments with spatial constraints. However, MLS-based classification faces challenges, such as intricate canopy structures, incomplete point clouds from urban occlusions, and intraspecies variations. This study presents tree morphology multirepresentation fusion network, integrating 3-D point cloud data with 2-D projections for enhanced tree species classification. The framework employs a backpack-mounted MLS system to capture high-quality point cloud data. The core architecture features an adaptive hierarchical sampling module extracting multiscale geometric features, followed by a cross-view fusion module that implements stagewise fusion of 3-D structural information with 2-D representations. This fusion strategy not only leverages established 2-D feature extraction pipelines, but also addresses sparsity issues in point cloud projections. The method was validated on a diverse dataset of eight urban tree species (seven broadleaf and one coniferous species). Quantitative assessment yielded 98.57% F1-score and 98.77% overall accuracy with moderate computational resources (2.25M parameters, 1.11G FLOPs), demonstrating significant improvements over existing methods. The proposed workflow achieves a balance between classification accuracy and processing efficiency, making it suitable for large-scale urban tree inventory applications.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"11451-11468"},"PeriodicalIF":4.7,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10964520","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144073125","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}