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

{"title":"Adaptive Gaussian-PSO XGBoost Model for Alpine Forests Aboveground Biomass Estimation Using Spaceborne PolSAR and LiDAR Data","authors":"Fu-Gen Jiang;Ming-Dian Li;Si-Wei Chen","doi":"10.1109/JSTARS.2025.3559233","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3559233","url":null,"abstract":"Accurate estimation of forest aboveground biomass (AGB) is fundamental to forest management and ecosystem monitoring. Natural forest ecosystems are an important guarantee to maintain the global ecological balance and carbon cycle, but the complex climate, dramatic topographic relief, and saturation effects make it difficult to achieve reasonable AGB estimation of alpine forests with commonly used optical data. In this study, spaceborne dual-polarimetric synthetic aperture radar and light detection and ranging data were combined to break through the limitation of optical data, and the information on the vertical structure inside the forests was extracted, to achieve high-precision forest AGB estimation and reveal the distribution pattern of forest AGB. An adaptive Gaussian-particle swarm algorithm XGBoost model (AGP-XGBOOST) was proposed to improve the forest AGB estimation, which adjusted the PSO through the built-in adaptive parameter of the Gaussian function to achieve the hyperparameter optimization for the XGBoost model. The proposed method was validated with the forest survey data, and classic machine-learning models were constructed for comparison. The comparative analysis was carried out using natural forests in the eastern Tibetan Plateau as an example, and the results showed that the proposed AGP-XGBOOST model consistently maintained the best performance across all models, and the AGB estimation errors caused by the combined data source decreased by 30.8%, 24.4%, and 10.1% compared to the independent data sources. In addition, the forest AGB mapping showed that the distribution pattern of forest AGB on the eastern Tibetan Plateau was significantly affected by terrain fluctuations.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"10157-10171"},"PeriodicalIF":4.7,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10959714","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143883495","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":"HTC-HAD: A Hybrid Transformer-CNN Approach for Hyperspectral Anomaly Detection","authors":"Minghua Zhao;Wen Zheng;Jing Hu","doi":"10.1109/JSTARS.2025.3559079","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3559079","url":null,"abstract":"Hyperspectral anomaly detection (HAD) identifies anomalies by analyzing differences between anomalies and background pixels without prior information, presenting a significant challenge. Most existing studies leverage the high correlation in spectral and spatial dimensions, primarily focusing on local spectral and spatial information for background reconstruction while neglecting long-range dependencies. This local perception constrains models from fully capturing intrinsic spatial–spectral connections. To address this, we propose a novel hybrid transformer-CNN network for HAD (HTC-HAD). Specifically, HTC-HAD combines CNNs with transformers, where the CNN focuses on local modeling, and the transformer addresses long-range modeling. This dual approach ensures the accurate reconstruction of backgrounds by capturing both local and long-range dependencies. Meanwhile, to reduce model complexity and redundancy among neighboring bands, we incorporate a simplified and effective band selection strategy as preprocessing. In addition, to prevent anomalies from being reconstructed during background estimation, we employ an adaptive weight loss function to suppress them. Experimental results on several real datasets, both qualitatively and quantitatively, demonstrate that our HTC-HAD achieves satisfying detection performance.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"10144-10156"},"PeriodicalIF":4.7,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10959089","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143883496","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":"DSMF-Net: A One-Stage SAR Ship Detection Network Based on Deformable Strip Convolution and Multiscale Feature Refinement and Fusion","authors":"Xingyu Liu;Jun Pan;Rong Hu;Wenli Huang;Jiawei Lin;Jiarui Hu","doi":"10.1109/JSTARS.2025.3559414","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3559414","url":null,"abstract":"Synthetic aperture radar (SAR), an all-weather and day-and-night remote sensing imaging technology, is crucial for ship detection. However, SAR images are hampered by speckle noise and coastal clutter, and ship targets exhibit multiscale and small-scale characteristics. To tackle these challenges, we introduce the DSMF-Net, a SAR ship detection network leveraging deformable strip convolution and multiscale feature refinement and fusion. First, to counter interference from complex backgrounds, such as nearshore ports and speckle noise, the deformable strip convolution (DSConv) is introduced and incorporated into the backbone network for SAR ship feature extraction, named SSFEBackbone. DSConv adaptively adjusts convolution sampling positions based on ship target characteristics, precisely extracting features with directional and strip structures. Second, the dual-stream self-attention feature refinement module is utilized to refine high-level semantic features. Through the mixing spatial and channel attention (MSCA) mechanism, differences and correlations between complex backgrounds and ship entities are further captured, enhancing feature expression. Finally, the adaptive selective feature pyramid network is proposed. By leveraging MSCA attention, high-level semantic and low-level spatial features are flexibly matched, enabling better key information retention during fusion and background clutter suppression, thus improving detection performance for complex backgrounds and multiscale ship targets. Experimental results demonstrate that DSMF-Net performs significantly better in ship detection in SAR images. It outperforms existing state-of-the-art methods on the SAR-Ship-Dataset, high-resolution SAR images dataset, and SAR ship detection dataset, achieving an AP<inline-formula><tex-math>$_{text{50}}$</tex-math></inline-formula> of 96.8%, 93.1%, and 97.4%, respectively.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"10694-10710"},"PeriodicalIF":4.7,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10960366","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143888369","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":"CD-STMamba: Toward Remote Sensing Image Change Detection With Spatio-Temporal Interaction Mamba Model","authors":"Shanwei Liu;Shuaipeng Wang;Wei Zhang;Tao Zhang;Mingming Xu;Muhammad Yasir;Shiqing Wei","doi":"10.1109/JSTARS.2025.3559085","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3559085","url":null,"abstract":"Change detection (CD) is a critical Earth observation task. Convolutional neural network (CNN) and Transformer have demonstrated their superior performance in CD tasks. However, the limitations of the limited receptive field of CNN and the high-computational complexity of Transformer remain. Recently, the Mamba architecture, based on state-space models, has demonstrated strong global receptive field capabilities and implements linear time complexity in computational processes. While some researchers have incorporated it into CD tasks, most have neglected the effective application of the Mamba selective scanning algorithm for modeling bitemporal image dependencies, resulting in suboptimal feature learning from bitemporal images. In this article, we propose a CD Mamba model (CD-STMamba), which can efficiently encode and decode bitemporal images interactively from multiple dimensions, thus enabling more accurate CD. Specifically, we propose a spatio-temporal interaction module (STIM), which can interact with bitemporal image features in multiple dimensions and fit with the Mamba architecture, allowing it to fully learn the global contextual information of the bitemporal input image. We also introduce a decoding block called the CD block, which can be fully decoded to learn multiple spatio-temporal relationships based on the characteristics of STIM. This block employs multiple change visual state space blocks internally to decode different spatio-temporal interactions and utilizes the change attention module to capture change features comprehensively for more accurate CD. The proposed CD-STMamba achieved state-of-the-art intersection over union (IoU) on three datasets, Wuhan University Building Change Detection Dataset (91.29% ), Sun Yat-Sen University Change Detection (73.45% ), and Change Detection Dataset (95.56% ).","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"10471-10485"},"PeriodicalIF":4.7,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10959091","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143888317","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 Image Inpainting With Edge-Conditional Expectation Attention","authors":"Dazhi Zhou;Yanjun Chen;Yuhong Zhang;Jing Niu","doi":"10.1109/JSTARS.2025.3559203","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3559203","url":null,"abstract":"Satellite images often suffer from data loss and corruption due to various factors, including sensor malfunctions and atmospheric interference, leading to incomplete and degraded imagery. In satellite images, long-range dependencies are particularly significant due to irregular and widely distributed geomorphological edges, such as rivers, mountains, and urban structures. Traditional convolutional neural network-based inpainting methods face challenges due to their fixed receptive fields and parameter sharing, limiting their ability to effectively capture long-range dependencies and differentiate between corrupted and uncorrupted areas. To address these limitations, we propose a deep learning approach based on an edge-conditional expectation attention module, which conditions the attention mechanism on edge information to enhance the model's focus on high-frequency edge details. This enables the network to capture critical structures within the image better. In addition, we apply Chebyshev’s inequality within the attention mechanism to constrain the expectation of attention outputs, reducing excessive deviations and stabilizing the reconstruction process. Experimental results demonstrate that our approach outperforms several state-of-the-art methods in restoring missing regions and reconnecting geomorphological features.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"10830-10845"},"PeriodicalIF":4.7,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10959709","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143888324","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":"A Framework for Flood Disaster Detection From Remote Sensing Images Using Spatiotemporal Fusion With Digital Twin Technology","authors":"Se-Jung Lim;K. Sakthidasan Sankaran;Anandakumar Haldorai","doi":"10.1109/JSTARS.2025.3559205","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3559205","url":null,"abstract":"Flood is regarded as common disaster which could cause serious devastation in any country. Typically, it is caused due to precipitation & river runoffs, specifically at the time of excessive rainfall season. The technology of sensor network has been used to monitor changes in landcovers and water level fluctuations. Moreover, effective disaster monitoring & notification system in real-time becomes a crucial part which needs to be overcome. For this reason, the proposed methodology is designed aiming at developing natural disaster prediction and monitoring system for alerting that aids in offering right decision at right time. At first, remote sensing image data are collected and preprocessed using Frequency Ratio and Multi-collinearity test (MCT) to ensure noise removal and image augmentation by enhancing their quality. A feature extraction process is carried with the use of Deep Convolution VGGNet-16 from which optimal features are selected using Improved Harris Hawks Optimization algorithm (IHHOA). Then, a Flexible Spatio-temporal image fusion (F-SPTF) approach is employed to fuse images. After this, Deep cascaded RNN classifier is employed for predicting flood occurrence and to map flood susceptibility areas. This, in turn, classifies the normal and abnormal condition of flood occurrence thus giving alerts in case of natural disaster occurrences which could be visualized through digital twin technologies. The suggested scheme offers an accuracy rate of about (99.89%), precision (99.37%), recall (99.82%), and <italic>F</i>1-score (99.74%). The error rates estimated like RMSE (0.784), MAE (0.764), and MAPE (0.102) also seems to be lower than other existing models compared.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"11547-11560"},"PeriodicalIF":4.7,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10959713","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143937948","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":"Screening and Artifact Detection of RFI in Sentinel-1A Time-Series Images Combining Change Detection Techniques With Structural Similarity Index","authors":"Zhizheng Zhang;Gaofeng Shu;Yabo Huang;Lin Wu;Ning Li","doi":"10.1109/JSTARS.2025.3559171","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3559171","url":null,"abstract":"As a wideband radar system, spaceborne synthetic aperture radar (SAR) is susceptible from other high-power radiation sources, which can cause radio frequency interference (RFI) artifacts in the acquired images. Given that RFI significantly impacts data processing and image interpretation, screening and artifact detection of RFI have become top priorities for spaceborne SAR systems, which generate massive data daily. However, current image-level methods mostly rely on the acquisition of relevant prior knowledge, which makes efficient screening of large-scale products difficult when the scenario changes. In this article, a novel screening and detection method based on change detection techniques is proposed. Due to the time-varying feature of RFI artifacts in time-series SAR images, local areas affected by RFI can be masked through change detection operator. The screening step is performed by quantifying and analyzing the feature differences between RFI artifacts and ground-truth information in local areas. Therefore, an RFI-free background is constructed based on the results, enabling effective artifacts detection of RFI-containing images. Experimental results of Sentinel-1A Level-2 products verify the effectiveness and robustness of the proposed method.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"10864-10881"},"PeriodicalIF":4.7,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10959716","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143888325","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":"Infrared Satellite Detection Limits for Monitoring Atmospheric Ammonia","authors":"Mark W. Shephard;Shailesh K. Kharol;Enrico Dammers;Christopher E. Sioris;Andrew Bell;Rik Jansen;Jérôme Caron;Ralph Snel;Emanuela Palombo;Karen E. Cady-Pereira;Chris A. McLinden;Erik Lutsch;Robert O. Knuteson","doi":"10.1109/JSTARS.2025.3557240","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3557240","url":null,"abstract":"This study investigates single pixel nadir viewing detection limit of atmospheric ammonia for a range of area flux mapping satellite infrared sensor spectral resolutions (0.05–2.0 cm⁻¹) and measurement noise levels. The detection level of ammonia is computed directly from simulated satellite ammonia spectral signatures, which has the advantage of being independent of retrieval methodologies. Information on the frequency of a given detection limit, and the cumulative probability of detection, are provided as a function of instrument spectral resolution and noise. For example, a Cross-track Infrared Sounder-like instrument with a modest spectral resolution of 0.625 cm⁻¹ and excellent signal-to-noise ratio of ∼1600 would be able to detect ammonia on average ∼70% of the time; these same instrument specifications will have a detection limit of 0.2 ppbv (surface) or 1.6×10¹⁵ molec cm⁻² (total column) that can be achieved at a detection rate of 10% as it requires favorable infrared remote sensing conditions (large thermal contrast). Under more typical atmospheric states a detection limit of 0.5 ppbv (3.5 × 10¹⁵ molec cm⁻²) is achieved at a 50% detection rate. This detection limit information is valuable for applications that incorporate remote sensing data in conditions when the atmospheric ammonia amounts are below the detection limit of the satellite sensor (e.g., nongrowing season in crop fertilizer source regions). As the simulations use real-world atmospheric state observations as inputs the results can be used to provide general guidance on the detection limits of past, current, and potential new environmental flux mapping instruments used for ammonia monitoring covering large geographical regions.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"10272-10291"},"PeriodicalIF":4.7,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10960508","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143888405","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":"Detailed PV Monitor: A Highly Generalized Photovoltaic Panels Segmentation Network Integrating Context-Aware and Deep Feature Reconstruction","authors":"Xiaopu Zhang;Huayi Wu;Kunlun Qi;Yuehui Qian;Yongxian Zhang;Ligang Wang;Jianxun Wang","doi":"10.1109/JSTARS.2025.3558471","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3558471","url":null,"abstract":"The urgency of global climate change has driven the rapid expansion of photovoltaic (PV) energy systems. However, accurately identifying PV panels remains a major challenge due to complex environmental backgrounds, spectral confusion, and the lack of high-quality annotated datasets. These factors significantly impact the generalization ability of deep learning models in large-scale high-resolution remote sensing applications, thereby limiting the effective monitoring and planning of PV power stations. To address these challenges, this article proposes a highly adaptable PV panel segmentation network, detailed PV monitoring (DPVM), specifically designed to enhance PV panel recognition in high-resolution imagery. DPVM integrates an adaptive context-aware module (ACAM) and a deep feature reconstruction decoder (DFRD). ACAM improves segmentation accuracy by leveraging multiscale feature fusion and spatial attention mechanisms. DFRD employs multistage decoding and feature synthesis to achieve high-quality image reconstruction. We trained DPVM on our self-constructed Northwest China PV dataset to ensure comprehensive learning of PV panel characteristics. Subsequently, we conducted generalization tests on other publicly available datasets, including AIR-PV and PVP. Experimental results demonstrate that DPVM exhibits outstanding robustness and broad adaptability, ensuring stable performance across diverse scenarios. Specifically, DPVM excels in complex backgrounds, significantly reducing PV panel missed detections, improving edge delineation, and outperforming classical and state-of-the-art segmentation models in key metrics.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"10131-10143"},"PeriodicalIF":4.7,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10955288","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143875182","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":"Integrated Water Vapor Estimation During Clear Skies Using a Ground-Based Infrared Radiometer and the Light Gradient Boosting Machine Method","authors":"Wenyue Wang;Catalina Medina Porcile;Wenzhi Fan;Klemens Hocke","doi":"10.1109/JSTARS.2025.3558761","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3558761","url":null,"abstract":"New algorithms of retrieving atmospheric integrated water vapor (IWV) under clear-sky conditions for the infrared radiometer using linear regression, quadratic regression (QR), and light gradient boosting machine (LightGBM) methods are developed in this work. IWV data estimated using a physical method from ground-based microwave radiometer measurements of 23 days of clear sky over the Swiss Plateau from 2022 to 2023 serve as truth references. In addition to infrared brightness temperature, the input features also include various surface meteorological measurements. To capture the temporal dynamics of water vapor, the algorithms are trained with features and parameters adjusted not only through tenfold cross-validation but also by considering the time series. The validation shows that the linear and QR algorithms performed similarly with R<inline-formula><tex-math>$^{2}$</tex-math></inline-formula> of 0.64, mean squared errors of 7.99 mm and 7.85 mm, and mean absolute error (MAE) of 2.24 mm and 2.25 mm, respectively. The LightGBM-based algorithm outperforms the two regression algorithms in retrieving IWV, with R<inline-formula><tex-math>$^{2}$</tex-math></inline-formula> of 0.83, mean square error of 3.81 mm, and MAE of 1.53 mm. The IWV time series obtained from the three algorithms closely align with the measurements from the microwave radiometer. These proposed algorithms offer accurate and reliable IWV estimation for the infrared radiometer with high temporal resolution (7 s) in complex terrain, with potential for application in broader infrared radiometer networks.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"10724-10732"},"PeriodicalIF":4.7,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10956133","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143896282","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}