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

{"title":"2025 Index IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Vol. 18","authors":"","doi":"10.1109/JSTARS.2026.3671141","DOIUrl":"https://doi.org/10.1109/JSTARS.2026.3671141","url":null,"abstract":"","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"29531-29837"},"PeriodicalIF":5.3,"publicationDate":"2026-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11422996","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147362282","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":"Stability Assessment of Spire and PlanetiQ Receiver Clocks and Its Implications for GNSS-RO Atmospheric Profiles","authors":"Zhe Li;Pengyue Sun;Xiaoming Wang;Dingyi Liu;Haobo Li;Ying Xu;Jinglei Zhang;Sizhe Shen;Hongxin Zhang","doi":"10.1109/JSTARS.2026.3668804","DOIUrl":"https://doi.org/10.1109/JSTARS.2026.3668804","url":null,"abstract":"Commercial SmallSats offer cost-effective alternatives to traditional GNSS radio occultation (GNSS-RO) missions through scalable constellation deployments. In GNSS-RO processing, the short-term stability of the LEO receiver clock is a key constraint on the feasibility and accuracy of undifferenced (UD) retrievals. Specifically, limited stability in compact receivers can introduce clock noise that degrades retrieval profiles and increases uncertainty. This study evaluated Spire and PlanetiQ onboard clock stability and quantified the impact on bending-angle and refractivity retrievals. Spire exhibited lower short-term clock stability, with 1-s clock stability exceeding <inline-formula><tex-math>$10^{-6}$</tex-math></inline-formula>, making UD infeasible, while its single-differenced (SD)-derived profiles remained consistent with UCAR and ECMWF reference datasets. In contrast, PlanetiQ exhibited better short-term stability, with the 1-s clock stability typically better than <inline-formula><tex-math>$10^{-9}$</tex-math></inline-formula>. Subsequent analyses were confined to clock segments with 1-s clock stability better than <inline-formula><tex-math>$10^{-12}$</tex-math></inline-formula>, sufficient for accurate SD and UD processing. For PlanetiQ, refractivity derived from both methods was in agreement between 10 and 25 km (mean bias <inline-formula><tex-math>$&lt; 0.05%$</tex-math></inline-formula>, STD <inline-formula><tex-math>$&lt; 1%$</tex-math></inline-formula>); above 25 km, SD showed slightly larger deviations due to reference-link noise. Across constellations, GPS showed the lowest deviations while GLONASS had the highest. Sensitivity tests with injected clock noise targeting 1-s clock stability over the range of <inline-formula><tex-math>$10^{-12}$</tex-math></inline-formula> to <inline-formula><tex-math>$10^{-10}$</tex-math></inline-formula> showed that UD and SD were statistically comparable when 1-s clock stability was about <inline-formula><tex-math>$text{3.0}times text{10}^{-11}$</tex-math></inline-formula>, and the retrieval deviations increased with both altitude and noise amplitude. These results confirm that PlanetiQ’s high clock stability supports accurate SD and UD retrievals and provide valuable insights for oscillator selection, quality control, and processing strategy in cost-effective GNSS-RO missions.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"19 ","pages":"8756-8772"},"PeriodicalIF":5.3,"publicationDate":"2026-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11415667","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147440545","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 Probabilistic STA-Bayesian Algorithm for GNSS-R Retrieval of Arctic Soil Freeze–Thaw States","authors":"Xiaofeng Meng;Cong Yin;Junming Xia;Feixiong Huang;Weihua Bai;Qifei Du;Dongmei Song;Lichang Duan;Yunlong Du;Guanyi Wang;Rui Liu","doi":"10.1109/JSTARS.2026.3668267","DOIUrl":"https://doi.org/10.1109/JSTARS.2026.3668267","url":null,"abstract":"Soil freeze–thaw (F/T) states are a key indicator of the Arctic climate, highlighting the need for their accurate retrieval. Global navigation satellite system-reflectometry (GNSS-R) offers a promising approach for retrieving soil F/T states, as F/T states significantly affect soil dielectric properties and reflectivity. Traditional reflectivity-based retrieval algorithms, such as the seasonal threshold algorithm (STA), are highly susceptible to surface interference, including vegetation attenuation and terrain effects, which limits their retrieval accuracy when using a fixed classification threshold. This study proposes an STA-Bayesian algorithm that formulates soil F/T retrieval as a probabilistic inference task. By combining seasonal prior information with GNSS-R observation likelihoods under frozen and thawed states, the algorithm derives posterior probabilities for Bayesian-optimal F/T state decisions, replacing fixed-threshold retrieval with a probabilistic approach that is more robust to surface heterogeneity. The algorithm is applied to GNSS-R data from the Fengyun-3E (FY-3E) satellite and Tianmu-1 (TM-1) constellation. In validation against ERA5-Land soil temperature-derived F/T states, the STA-Bayesian F/T product attains an average accuracy of 92.97%, outperforming both the STA (75.35%), and the soil moisture active passive (SMAP) (85.22%). The STA-Bayesian algorithm shows substantial improvements over both the STA and SMAP F/T products in densely vegetated regions (e.g., forests and shrublands) and demonstrates greater robustness to terrain roughness effects. The results demonstrate the STA-Bayesian algorithm's promise for soil F/T monitoring over heterogeneous surfaces and its adaptability to retrieval in the complex environmental conditions of the Arctic.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"19 ","pages":"8448-8462"},"PeriodicalIF":5.3,"publicationDate":"2026-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11414093","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147440518","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 Characteristics and Controlling Factors of Permafrost Deformation in the Qinghai–Tibet Plateau Revealed Through InSAR Measurements","authors":"Zhida Xu;Liming Jiang;Zhiping Jiao;Rui Guo;Zhiwei Zhou;Ronggang Huang","doi":"10.1109/JSTARS.2026.3664223","DOIUrl":"https://doi.org/10.1109/JSTARS.2026.3664223","url":null,"abstract":"Accelerated climate warming has intensified permafrost degradation across the Qinghai–Tibet Plateau (QTP), profoundly impacting regional hydrological cycles and carbon fluxes. Surface deformation observed in permafrost areas offers valuable insights into the extent of this degradation. However, the spatial patterns and controlling factors of permafrost deformation across the entire QTP remain poorly understood. This study reveals the long-term and seasonal deformation characteristics of permafrost regions across the entire QTP using Sentinel-1 SAR imagery (primarily from 2019 to 2021) through time-series InSAR combined with a permafrost deformation model. Notably, it provides the first plateau-wide characterization of the spatial distribution of seasonal deformation. In addition, the dominant factors controlling both long-term and seasonal deformation were identified with the geographic detector method. The results demonstrate that permafrost regions experience significantly higher surface subsidence rates (−2.46 mm/year) than seasonally frozen areas (−0.54 mm/year). Spatially, ice-rich permafrost regions with lower thermal stability exhibit higher subsidence rates. The average seasonal deformation amplitude in permafrost areas is 3.85 mm, with larger values concentrated in flat basins and areas surrounding lakes and rivers. Moreover, spatial variability of long-term deformation is mainly controlled by solar radiation and precipitation, and both are positively correlated with deformation rates. Slope is the primary driver of spatial variation in seasonal deformation amplitude. These findings highlight the critical role of surface energy, moisture, and terrain in permafrost dynamics. The study demonstrates the potential of integrating time-series InSAR with geospatial analysis to support large-scale permafrost monitoring and estimation of ground-ice meltwater across the QTP.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"19 ","pages":"8005-8017"},"PeriodicalIF":5.3,"publicationDate":"2026-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11415417","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147362385","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 Dense Ship Detection in SAR Images Through Cluster-Region-Based Super-Resolution","authors":"Yilei Shi;Hecheng Jia;Shangru Teng;Haipeng Wang","doi":"10.1109/JSTARS.2026.3667933","DOIUrl":"https://doi.org/10.1109/JSTARS.2026.3667933","url":null,"abstract":"Synthetic aperture radar (SAR), with its all-weather and all-day imaging capabilities, has become an essential tool in ship monitoring. In recent years, deep learning has significantly enhanced the performance of ship detection in SAR images. However, in complex coastal scenarios, ships are often densely clustered with arbitrary orientations, and the speckle noise and scattering properties make it challenging to distinguish adjacent objects. To address these issues, this article proposes an integrated ship detection method in SAR images incorporating cluster-guided super-resolution. First, a ship cluster detection pipeline is designed, which supports automatic cluster region labeling, training, and prediction. Second, a super-resolution module is developed to reconstruct and enhance the object features within cluster regions. In addition, an oriented ship detection network is employed for fine-grained ship detection within cluster regions, and an ensemble learning strategy is utilized to optimize the detection results. Experimental results on the SSDD and RSDD-SAR datasets demonstrate that the proposed method significantly improves detection accuracy in nearshore scenarios, especially for densely arranged ships.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"19 ","pages":"8478-8492"},"PeriodicalIF":5.3,"publicationDate":"2026-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11410573","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147440517","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":"Hyperspectral Sparse Unmixing via Joint Robust Spatial Priors and Weighted Total Variation Regularization","authors":"Fan Li;Xiyu Chen;Shaoquan Zhang;Jiaqiang Zhou;Huasheng Zhu;Yuyang Liu;Hongyu Zhang;Chengzhi Deng;Shengqian Wang","doi":"10.1109/JSTARS.2026.3668120","DOIUrl":"https://doi.org/10.1109/JSTARS.2026.3668120","url":null,"abstract":"Sparse unmixing, which introduces a large spectral library to transform the problem of mixed-pixel decomposition into the search for an optimal subset of endmembers that best represents the spectral characteristics of an image, has become a prominent research focus. Building upon this, the integration of spatial information from the image is an effective approach to improve unmixing accuracy. Although numerous existing methods have explored spatial information from various perspectives, they often become ineffective in scenarios characterized by abundant outliers and complex noise, leading to loss of detail in abundance maps. To address these limitations, this article proposes an unmixing method integrating robust spatial priors and weighted total variation sparse regularization (RSPWTV). The objective is to fully exploit the spatial information from multiple angles, aiming to enhance unmixing accuracy while increasing robustness against noise and outliers, and effectively preserving edge structures. Specifically, building upon the robust sparse unmixing framework, the proposed method introduces two key innovations. First, a weighted total variation regularization model incorporating an adaptive weighting mechanism is constructed. By combining spectral angular similarity and spatial proximity information within local neighborhoods, discriminative adaptive weighting factors are generated to dynamically adjust the smoothing intensity across different regions. This strategy strengthens smoothing in homogeneous areas to effectively suppress noise while reducing smoothing in edge regions to preserve geometric structures and detail. Second, a superpixel-guided preprocessing strategy is employed. This involves performing coarse unmixing to obtain initial abundance estimates, which are then used to construct a filter-guided weighted sparse prior. This prior facilitates image smoothing, thereby mitigating the interference of noise in the unmixing process and significantly improving the overall stability and accuracy of the results. The proposed model is efficiently solved using the alternating direction method of multipliers. Experimental validation on both simulated data and real hyperspectral images demonstrates that the proposed RSPWTV method outperforms existing unmixing methods in terms of outlier resistance, noise robustness, and edge preservation capability, exhibiting superior unmixing performance.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"19 ","pages":"8410-8427"},"PeriodicalIF":5.3,"publicationDate":"2026-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11410541","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147440562","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":"Surface Water Extraction by Multidimensional Feature Fusion of Sentinel-1/2 and Random Forest: A Five-Year Analysis in Jiangsu, China","authors":"Mingxing Wu;Fengcheng Guo;Jing Zhang;Li Zhao;Wensong Liu;Chongchong Zhou","doi":"10.1109/JSTARS.2026.3667561","DOIUrl":"https://doi.org/10.1109/JSTARS.2026.3667561","url":null,"abstract":"Surface water extraction in regions with complex terrain is often hindered by low accuracy and frequent misclassification. To mitigate these challenges, this article proposes a high-precision water extraction framework based on the Google Earth Engine platform. The framework centers on the Random Forest classifier and integrates multipolarization scattering features derived from Sentinel-1 SAR data, multispectral water indices obtained from Sentinel-2 imagery, and slope constraints extracted from the digital elevation model (DEM). The joint use of multisource features and topographic constraints improves the delineation of surface water boundaries, particularly in complex terrain and under various interference conditions. Jiangsu Province was selected as the experimental study area. Experimental results indicate that the proposed framework achieves a Kappa above 0.9 and a producer's accuracy exceeding 90%, reflecting stable and reliable surface water extraction performance. In addition, the framework is capable of characterizing the spatiotemporal variations of surface water in Jiangsu, thereby supporting large-scale and high-frequency monitoring of surface water resources in complex environments. Further experiments suggest that the framework can be generalized and applied to other regions.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"19 ","pages":"8302-8318"},"PeriodicalIF":5.3,"publicationDate":"2026-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11408840","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147362338","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":"Learning to See More: A Spectral Extension Super-Resolution Framework for Harmonized Satellite-UAS Imagery","authors":"Arif Masrur;Peder A. Olsen;Carlan Jackson;Paul R. Adler","doi":"10.1109/JSTARS.2026.3667863","DOIUrl":"https://doi.org/10.1109/JSTARS.2026.3667863","url":null,"abstract":"Super-resolution (SR) in remote sensing is generally effective only when the resolution gap between inputs and targets is less than 10×, limiting its applicability for producing field-scale, high-resolution data from sensors, such as Sentinel-2. Classical pansharpening, which relies on a high-resolution panchromatic band from the same platform, degrades sharply at large-scale factors. We propose a spectral extension SR framework that enables >80× upscaling (10–20 m to 12.5 cm) of Sentinel-2 VNIR bands using co-registered UAS-RGB imagery as high-resolution side information. The framework has two stages. First, UAS hyperspectral data (400–1000 nm) are spectrally harmonized via nonnegative regression to simulate high-resolution Sentinel-2 bands free of atmospheric noise. Second, these harmonized bands serve as ground truth to train a lightweight super-resolution convolutional neural network (SRCNN)-based regression model that maps Sentinel-2 spectral content onto UAS spatial textures. The method succeeds at extreme scale factors because the hyperspectral data exhibit a low-rank structure, with local spectra dominated by soil and vegetation components. UAS-RGB bands capture these dominant components, enabling—unlike pansharpening—spectral extension into red-edge and near-infrared bands. Despite the use of an SR model the problem is closer to a guided colorization model. Applied to precision farming sites across Maryland and Pennsylvania, the approach achieves a spectral fidelity greater than 30 dB on unseen crops. The harmonized data improve cover crop nitrogen prediction by <inline-formula><tex-math>$approx 31%$</tex-math></inline-formula> over Sentinel-2 and UAS-RGB baselines, delivering multispectral UAS performance without specialized sensors. The SRCNN model generalizes across crops and seasons and supports flexible configurations, including RGB-only training, spatial resolution invariance (4–12.5 cm) and advancing scalable, low-cost multimodal remote sensing.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"19 ","pages":"8740-8755"},"PeriodicalIF":5.3,"publicationDate":"2026-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11410010","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147440500","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 Change Detection in Heterogeneous Remote Sensing Images With Online All-Integer Pruning Training","authors":"Chengyang Zhang;Xueqian Wang;Weiming Li;Gang Li;Huina Song;Zhaohui Song;Jie Zhao;Antonio Plaza","doi":"10.1109/JSTARS.2026.3667547","DOIUrl":"https://doi.org/10.1109/JSTARS.2026.3667547","url":null,"abstract":"Detection of changes in heterogeneous remote sensing images is vital, especially in response to emergencies like earthquakes and floods. Current homogenous transformation-based change detection (CD) methods often suffer from high computation and memory costs, which are not friendly to edge-computation devices like onboard CD devices at satellites. To address this issue, this article proposes a new lightweight CD method for heterogeneous remote sensing images that employs the online all-integer pruning (OAIP) training strategy to efficiently fine-tune the CD network using the current test data. The proposed CD network consists of two visual geometry group subnetworks as the backbone architecture. In the OAIP-based training process, all the weights, gradients, and intermediate data are quantized to integers to speed up training and reduce memory usage, where the per-layer block exponentiation scaling scheme is utilized to reduce the computation errors of network parameters by dynamically adjusting the integer representation range. Second, an adaptive filter-level pruning method based on the L1-norm criterion is employed to further lighten the fine-tuning process of the CD network. Experimental results show that the proposed OAIP-based method attains similar detection performance (but with significantly reduced computation complexity and memory usage) in comparison with state-of-the-art CD methods.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"19 ","pages":"8719-8739"},"PeriodicalIF":5.3,"publicationDate":"2026-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11408850","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147440558","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":"An Estimate of Effective Resolution for CYGNSS Surface Water Detection Revealed by Comparison With Multisensor Optical Data","authors":"Matthew Bonnema;Cédric H. David;Brandi Downs;Eric Loria;Mary Morris;Hai Nguyen","doi":"10.1109/JSTARS.2026.3666143","DOIUrl":"https://doi.org/10.1109/JSTARS.2026.3666143","url":null,"abstract":"The location of water on the Earth’s surface is critical for a wide range of science and application uses. Global navigation satellite system reflectometry (GNSS-R) is a remote sensing technique with demonstrated potential for identifying areas of the Earth’s surface that are inundated, given that GNSS-R signals are sensitive to smooth surfaces, allowing for the detection of surface water. However, the signal is known to saturate even at relatively low quantities of surface water within an observational footprint, complicating the interpretation of GNSS-R observations. To date, the minimal detectable size of a water body within the GNSS-R signal footprint has remained elusive. This manuscript examines this sensitivity of one GNSS-R mission, the cyclone global navigation satellite system (CYGNSS), by using a high resolution surface water extent product as truth (dynamic surface water extent). This analysis reveals that for the Tonle Sap Lake region in Cambodia, the lower limit of water area detectable by CYGNSS was 0.62 <inline-formula><tex-math>$text{km}^{2}$</tex-math></inline-formula> of water surface area within a 0.05<inline-formula><tex-math>$^{circ }$</tex-math></inline-formula> grid cell, or 2% surface water cover. This finding indicates that at least for this region, a probabilistic interpretation of CYGNSS observations may be more appropriate than a binary classification. Such an interpretation establishes a foundation for synergistic use of CYGNSS GNSS-R with other surface water remote sensing technologies.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"19 ","pages":"8080-8091"},"PeriodicalIF":5.3,"publicationDate":"2026-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11407967","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147362262","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}