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

{"title":"UTSFANet: Unsupervised Two-Stage Fine Adjustment Network for Infrared Remote Sensing Image Stitching","authors":"Pengfei Zhang;Jinnan Gong;Tianjun Shi;Guangzhen Bao;Zhile Wang;Shikai Jiang","doi":"10.1109/JSTARS.2025.3584788","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3584788","url":null,"abstract":"Image stitching aims to align two images from different perspectives. For infrared remote sensing images, the low resolution, lack of strong-feature points, and the presence of large textureless regions make it difficult to achieve effective feature matching and high-quality image stitching results. In the field of remote sensing stitching, the primary challenge is how to effectively extract features, reduce the influence of parallax, and improve the registration accuracy. To improve the image stitching performance and obtain parallax-tolerant fine registration results, we propose a two-stage image stitching method based on unsupervised learning. First, in the first stage, we use a multilevel feature extraction network to effectively extract image correlation features, progressively refining the registration from coarse to fine, thus ensuring performance under large-baseline conditions. Second, by utilizing a discrete-feature detection module in the multilevel network, we remove anomalous feature regions and recombine effective local feature regions, enabling the fusion of detailed features with global features and improving registration accuracy. Finally, in the second stage, an image fine adjustment module is applied to process the image background and foreground, further eliminating parallax artifacts and improving registration accuracy. Compared with the existing methods, our method has advantages in both registration accuracy and parallax tolerance. Extensive experiments demonstrate that our method effectively registers and stitches infrared remote sensing images on both the self-built infrared remote sensing dataset and the publicly available UDIS-D dataset, outperforming current state-of-the-art methods in terms of performance.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"17476-17489"},"PeriodicalIF":4.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11062333","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144680828","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":"The Spatial Variation of Lake Surface Water Temperature of Poyang Lake in Summer and Its Impact on Regional Precipitation","authors":"Miaoxia Tian;Haibo Zou;Jing Zheng;Anning Huang;Qi Huang","doi":"10.1109/JSTARS.2025.3584852","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3584852","url":null,"abstract":"With moderate resolution imaging spectroradiometer (MODIS) land surface temperature products, the spatial distribution of lake surface water temperature (LSWT) in Poyang Lake (PL) during summer are explored. Results show that the high-LSWT areas at noon [13:30 local solar time (LST)] are mainly located in the southwest, south, and east of PL where the water depth is relatively shallow, while the low-LSWT regions are situated in the middle and north of PL where the water depth is relatively deep, and the spatial difference can reach 1.5 °C. This distribution is obviously different from the previous finding (i.e., high LSWT in middle of north of PL, and low LSWT in southwest of PL). By night (01:30 LST), the distribution is roughly consistent with that at noon, rather than opposite. This is mainly induced by the relatively high air temperature over the PL region at night, which cannot only increase the downward longwave radiation of atmosphere, but also decrease the heat transport from lake to air, favorably reducing the heat loss in PL and slowing down the cooling of PL. To reveal the effects of the spatial variations of LSWT on regional precipitation, two sets of experiments, i.e., nonupdate experiment with default LSWT and update experiment with LSWT originated from MODIS data, are conducted, and results show that UP experiment obviously improves precipitation simulation in PL region. Diagnoses indicate that the effects of LSWT on local precipitation are accomplished through adjusting the low-level upward motion and atmospheric instability.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"16462-16472"},"PeriodicalIF":4.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11061776","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144646628","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 Maritime Situational Awareness Through End-to-End Onboard Raw Data Analysis","authors":"Roberto Del Prete;Manuel Salvoldi;Domenico Barretta;Nicolas Longépé;Gabriele Meoni;Arnon Karnieli;Maria Daniela Graziano;Alfredo Renga","doi":"10.1109/JSTARS.2025.3584999","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3584999","url":null,"abstract":"Satellite-based onboard data processing is crucial for time-sensitive applications requiring timely and efficient rapid response. Advances in edge artificial intelligence are shifting computational power from ground-based centres to on-orbit platforms, transforming the “sensing-communication-decision-feedback” cycle and reducing latency from acquisition to delivery. The current research presents a framework addressing the strict bandwidth, energy, and latency constraints of small satellites, focusing on maritime monitoring. The study contributes three main innovations. First, it investigates the application of deep learning techniques for direct ship detection and classification from raw satellite imagery. By simplifying the onboard processing chain, our approach facilitates direct analyzes without requiring computationally intensive steps such as calibration and ortho-rectification. Second, to address the scarcity of raw satellite data, we introduce two novel datasets, VDS2Raw and VDV2Raw, which are derived from raw data from Sentinel-2 and Vegetation and Environment Monitoring New Micro Satellite (VEN <inline-formula><tex-math>$mu$</tex-math></inline-formula>S) missions, respectively, and enriched with automatic identification system records. Third, we characterize the tasks’ optimal single and multiple spectral band combinations through statistical and feature-based analyzes validated on both datasets. In sum, we demonstrate the feasibility of the proposed method through a proof-of-concept on CubeSat-like hardware, confirming the models’ potential for operational satellite-based maritime monitoring.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"16997-17018"},"PeriodicalIF":4.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11061780","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144646632","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":"Spatial Multifeature and Dual-Layer Multihop Graph Convolution Networks for Hyperspectral Image Classification","authors":"Xiangyue Yu;Ning Li;Di Wu;Zheng Li;Zhenyuan Wu;Ximing Ma","doi":"10.1109/JSTARS.2025.3584970","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3584970","url":null,"abstract":"Hyperspectral image (HSI) classification constitutes a crucial research direction within the domain of remote sensing. Convolutional neural networks (CNNs) and graph convolutional network (GCN) have exhibited outstanding classification performance in this field, emerging as current research focuses. Nevertheless, GCN possesses certain limitations in capturing the neighborhood features of images, while traditional 2-D CNNs are incapable of fully extracting the spatial information of HSI. To address these problems, we propose a novel architecture dubbed spatial multifeature and dual-layer multihop graph convolutional network (SMTGCN). This network is capable of concurrently extracting pixel-level spatial features and superpixel-level spectral features. Specifically, a dual-layer multihop graph convolutional network is constructed within the GCN branch, which can take the features of superpixel at different segmentation scales as network nodes to effectively capture and fuse the superpixel features in HSI. In the CNN branch, a multiscale spatial structure is constructed for feature extraction and fusion, and a hybrid attention mechanism model is proposed to enhance the feature capture ability, a multilayer pooling structure is added to retain more detailed information while suppressing excessive redundant data. Finally, the features extracted by the GCN branch and the CNN branch are fused to realize HSI classification. Experimental results conducted on four benchmark HSI datasets indicate that, in comparison with existing classification methods, SMTGCN achieves remarkable improvements in classification performance when using a small number of training samples.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"18391-18410"},"PeriodicalIF":5.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11062325","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144750956","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":"Exploring the Annual Dynamics of China’s Rivers From 2016 to 2023 Based on Sentinel-Derived Datasets","authors":"Kaifeng Peng;Beibei Si;Weiguo Jiang;Meihong Ma;Xuejun Wang","doi":"10.1109/JSTARS.2025.3584770","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3584770","url":null,"abstract":"Rivers play import roles in ecological biodiversity, shipping trade, and carbon cycle. In our study, we developed an effective, robust, and accurate algorithm for national-scale river mapping, and produced the annual China river extent dataset (CRED) from 2016 to 2023. We assessed the reliability of the CRED based on test samples and data intercomparison. The results indicated that the overall accuracies of the CRED were greater than 88.4% from 2016 to 2023. The rivers of the CRED from 2017 to 2023 achieved good accuracy, with the user accuracies, producer accuracies and F1-score of rivers exceeding 80.4%, 85.0%, and 83.7%, respectively. In 2016, rivers of the CRED achieved medium accuracy, with F1-score of 78.4%. A further data comparison indicated that our CRED had good consistency with existing river-related datasets, with correlation coefficient (R) greater than 0.75. The area statistics indicated that the river area in China were 44948.78 km² in 2023. From 2016 to 2023, the river areas were characterized by an initial increase, followed by a decrease, and then a slight increase. Spatially, the decreased rivers were located mainly in Southeast China, whereas the increased rivers were distributed mainly in Central China and Northeast China. In general, the CRED explicitly delineated river extents and dynamics in China, which could provide a good foundation for improving river ecology and management.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"16694-16706"},"PeriodicalIF":4.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11061783","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144646496","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":"ICESat-2 Satellite LiDAR Bathymetry Extraction Algorithm Based on Cubic Function Fitting Prediction Interval Along Track Segments","authors":"Junyuan Chen;Yi Ma;Yang Jiang;Kun Jia;Aijun Cui;Xuechun Zhang;Shaohui Li;Jingyu Zhang","doi":"10.1109/JSTARS.2025.3584760","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3584760","url":null,"abstract":"The 532-nm laser pulses emitted by the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) demonstrate significant potential for shallow water depth measurement. However, due to factors such as atmospheric scattering and absorption, and solar background noise, ICESat-2 data inevitably contains many noise photons. Based on the continuously changing characteristics of the seafloor topography, this article proposes an ICESat-2 bathymetry extraction algorithm based on cubic function fitting prediction interval along track segments. The key step of this algorithm is that the underwater photons are selected using the range of the mean plus or minus five times the standard deviation of the Gaussian function fitted of the photon height histogram, after which cubic function fitting is performed. When the coefficient of determination (<italic>R</i>²) of the fitting is larger than a threshold, the 95% prediction interval is used for denoising; if it is smaller than the threshold, denoising is performed using the Gaussian function fitting of the histogram. Five research areas are selected to conduct relevant experiments. The denoising results are evaluated by in-situ bathymetry data and the manually extracted signal photons, respectively. The results show that ICESat-2 bathymetry data and in-situ bathymetry data exhibit a highly consistent trend, with both <italic>R</i>² greater than 0.97 and RMSE less than 0.5 m. The evaluation metrics calculated based on the manually selected signal photons all exceed 93%, with <italic>F</i>₁ scores above 96%. Compared with adaptive elevation difference thresholding algorithm, density-based spatial clustering of applications with noise, and ordering points to identify the clustering structure, the proposed algorithm exhibits the best denoising effect, and the extracted seafloor photons show good continuity without obvious noise photons.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"17181-17196"},"PeriodicalIF":4.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11062332","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144646627","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":"Lightweight Band-Adaptive Hyperspectral Image Compression With Feature Decouple and Recurrent Model","authors":"Jiahui Liu;Lili Zhang;Jingang Wang;Lele Qu","doi":"10.1109/JSTARS.2025.3584931","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3584931","url":null,"abstract":"Advanced deep-learning methodologies have led to notable improvements in hyperspectral image compression. While most existing deep learning approaches primarily concentrate on reducing spatial redundancy, the challenge of addressing spectral redundancy remains unresolved. Furthermore, the implementation of current models in resource-limited settings is often impeded by their high parameter counts and computational demands. To address these challenges, we propose a lightweight band-adaptive hyperspectral image compression model (LBA-HIM) aimed at enhancing compression efficiency while ensuring low computational overhead. LBA-HIM incorporates a feature decoupling mechanism that effectively separates hyperspectral images into fundamental and detailed features, thereby facilitating the reduction of spatial redundancy while preserving critical image details. In addition, a recurrent structure is integrated into the band encoding and decoding processes, enabling the utilization of prior information from previously processed bands in subsequent operations. This strategy contributes to more efficient data compression by minimizing spectral redundancy. An adaptive weighted fusion mechanism is also employed to optimize the integration of multilevel features. Evaluation across six hyperspectral datasets indicates that the LBA-HIM model significantly enhances both compression efficiency and image quality while simultaneously lowering computational costs. At a compression rate of 0.25 bits per pixel per band, LBA-HIM achieves an average peak signal-to-noise ratio of 38.25 dB, which represents an improvement of approximately 2.5 dB over the current state-of-the-art techniques.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"16733-16749"},"PeriodicalIF":4.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11061775","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144646539","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":"Multisensor Data Fusion for Coastal Boundary Detection by Res-U-Net Implementation Using High-Resolution UAV Imagery","authors":"Qin Wang;Nyasha J. Kavhiza;Fakhrul Islam;Ilyas Ahmad Huqqani;Mohsin Abbas;Sanjoy Barman","doi":"10.1109/JSTARS.2025.3584853","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3584853","url":null,"abstract":"Environmental planning, hazard monitoring, and coastal management depend critically on accurate shoreline definition. This work utilizes high-resolution UAV data to develop a deep learning framework based on a Residual U-Net architecture for shoreline semantic segmentation. The proposed model integrates residual learning blocks into the conventional U-Net architecture to enhance gradient flow, improve feature extraction, and preserve fine boundary details in challenging coastal settings. Under a supervised learning framework, the model has been trained and validated using a dataset including UAV-acquired photographs and manually annotated shoreline masks. The preprocessed input data has been reinforced by geometric adjustments and contrast normalizing to improve resilience and generalization. The Adam optimizer and binary cross-entropy loss helped the model be trained across 150 epochs. F1-score and intersection over union (IoU) measures have been used in quantitative performance evaluation. With a peak validation F1-score of 0.9483 and an IoU of 0.9018, the findings demonstrate that the Residual U-Net achieves great segmentation accuracy, showing robust spatial alignment with ground truth annotations. Visual analysis of the expected masks confirmed the approach’s applicability to real-world situations by revealing consistent coastline localization throughout diverse environmental circumstances. This work presents a scalable and accurate method for operational shoreline monitoring, demonstrating the potential of deep residual structures for coastal boundary mapping using UAV platforms. Large-scale geospatial analytics and real-time coastal change detection can both benefit from the framework’s extension to multitemporal and multisensor data.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"16722-16732"},"PeriodicalIF":4.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11061779","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144646493","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":"Multiscale Edge Enhancement and Progressive Change-Aware Network for Remote Sensing Change Detection","authors":"Yan Xing;Jiali Hu;Yunan Jia;Rui Huang","doi":"10.1109/JSTARS.2025.3584959","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3584959","url":null,"abstract":"Change detection (CD) in remote sensing (RS) images serves as a vital method for identifying changes on the Earth’s surface. Recent advancements in deep learning (DL)-based CD methods have shown considerable progress. However, there is still significant room for further improvement of CD performance, particularly in fine-grained detection, such as enhancing edge details and reducing pseudochanges. To this end, a novel multiscale edge enhancement and progressive change-aware network (MEPNet) is proposed to improve the ability of feature representation for changed objects. Specifically, we introduce an edge enhancement module (EEM) to capture the long-range dependency, explicitly emphasizing high-frequency feature, and strengthening edge information to improve the accuracy of change regions. In addition, we propose a progressive change-aware module that progressively applies depthwise separable convolutions with kernels of decreasing size to localize changes at different scales, enabling precise refinement of change objects and reducing pseudochanges. These two components work together to advance the performance of MEPNet. Experimental results demonstrate that our method outperforms 11 SOTA methods on the LEVIR-CD, SYSU-CD, and CDD datasets, achieving superior accuracy and efficiency. The source code can be found at <uri>https://github.com/take-off-xyz/MEPNet</uri>","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"18197-18208"},"PeriodicalIF":5.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11060886","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144725178","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 County-Level GDP Estimation Accuracy With Downscaled NPP-VIIRS Nighttime Light Data","authors":"Weihua Lin;Weixing Xu;Zhaocong Wu;Jiaheng Cao","doi":"10.1109/JSTARS.2025.3584188","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3584188","url":null,"abstract":"Nighttime light (NTL) data have provided invaluable support for estimating gross domestic product (GDP). However, commonly used global-scale NTL data acquired by the visible infrared imaging radiometer suite (VIIRS) aboard the Suomi National Polar-orbiting Partnership (NPP) satellite suffer from relatively coarse spatial resolution (15 arcsec), limiting their potential for fine-scale applications. In this article, we employed a deep-learning-based NTL conditional multiscale downscaling model (NTL-CMDM), incorporating multisource scale factors as conditional constraints, to downscale NPP-VIIRS NTL data (500 m) to a finer spatial scale of 130 m. Furthermore, the effectiveness of downscaled NTL data for county-level GDP estimation was evaluated through comparison with NPP-VIIRS and Luojia1-01 NTL data in 205 Chinese county-level cities with varying economic development levels in the Beijing, Shanghai, and Guangzhou regions. The results show that regressions between GDP and both Total Nighttime Light (TNL) and Nighttime Light Area (NLA) using the downscaled NTL data (<italic>R</i> > 0.782 and <italic>R</i> > 0.634) achieve higher fitting accuracy than those using NPP-VIIRS NTL data (<italic>R</i> > 0.716 and <italic>R</i> > 0.110), and approach the performance of Luojia1-01 NTL data (<italic>R</i> > 0.796 and <italic>R</i> > 0.267). Additionally, the downscaled NTL data improve the accuracy of GDP estimates by reducing the relative error between estimated and statistical GDP compared to NPP-VIIRS NTL data. More importantly, the spatial distribution of GDP estimates derived from the downscaled NTL data more closely aligns with statistical GDP data, reflecting a more realistic geographic pattern. This article demonstrates that the downscaled NTL data generated by NTL-CMDM offer a promising data source for more accurate and fine-scale socioeconomic analysis.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"17552-17564"},"PeriodicalIF":4.7,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11059305","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144687822","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}