IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing最新文献_第10页

Omnibus Change Detection in Block Diagonal Covariance Matrix PolSAR Data Illustrated With Simulated and Sentinel-1 Data 用模拟数据和哨兵 1 号数据说明块对角线协方差矩阵 PolSAR 数据中的全方位变化检测

IF 4.7 2区地球科学

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2024-09-03 DOI: 10.1109/JSTARS.2024.3453442

Knut Conradsen;Henning Skriver;Allan Aasbjerg Nielsen

{"title":"Omnibus Change Detection in Block Diagonal Covariance Matrix PolSAR Data Illustrated With Simulated and Sentinel-1 Data","authors":"Knut Conradsen;Henning Skriver;Allan Aasbjerg Nielsen","doi":"10.1109/JSTARS.2024.3453442","DOIUrl":"10.1109/JSTARS.2024.3453442","url":null,"abstract":"This article describes the latest developments in our work on complex Wishart distribution-based detection of change in time series of multilook polarimetric synthetic aperture radar data in the covariance matrix representation. These developments include better approximations of the probability measures associated with the omnibus test statistics \u0000<inline-formula><tex-math>$bm {Q}$</tex-math></inline-formula>\u0000 and \u0000<inline-formula><tex-math>$bm {R}_{bm {j}}$</tex-math></inline-formula>\u0000 for block diagonal data in general, including the important cases with diagonal only Sentinel-1 data as obtained from Google Earth Engine and reflection symmetry data for full polarimetry. Additionally, the article introduces an omnibus version of the Loewner (or Löwner) order with visualization of change over time, where the omnibus change path shows significant difference. We also find the time point with the greatest change along the omnibus change path. The processing is illustrated with generated data and a series of 15 Sentinel-1A scenes covering Frankfurt Airport, Germany. Results show that the new and better approximations of the probability measures for the test statistics are important for the assignment of labels “change” or “no change” to a pixel or a patch, especially in “no change” regions. Furthermore, compared to the use of the full covariance matrix, the probability measures associated with the diagonal only test statistics incorrectly detect more change in these “no change” regions for the Sentinel-1 diagonal only data. Hence, the use of the full 2 \u0000<inline-formula><tex-math>$bm {times }$</tex-math></inline-formula>\u0000 2 covariance matrix if avalable is important. Finally, the omnibus Loewner order gives far fewer false detections than its pairwise counterpart.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10663868","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193240","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}

引用次数: 0

ResNeTS: A ResNet for Time Series Analysis of Sentinel-2 Data Applied to Grassland Plant-Biodiversity Prediction ResNeTS：应用于草地植物生物多样性预测的哨兵-2 数据时间序列分析 ResNet

IF 4.7 2区地球科学

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2024-09-03 DOI: 10.1109/JSTARS.2024.3454271

Álvaro G. Dieste;Francisco Argüello;Dora B. Heras;Paul Magdon;Anja Linstädter;Olena Dubovyk;Javier Muro

{"title":"ResNeTS: A ResNet for Time Series Analysis of Sentinel-2 Data Applied to Grassland Plant-Biodiversity Prediction","authors":"Álvaro G. Dieste;Francisco Argüello;Dora B. Heras;Paul Magdon;Anja Linstädter;Olena Dubovyk;Javier Muro","doi":"10.1109/JSTARS.2024.3454271","DOIUrl":"10.1109/JSTARS.2024.3454271","url":null,"abstract":"Analyzing time series from remote sensing data can aid in understanding spectral-temporal phenomena in ecosystems, such as the seasonal variation of plant components. Lately, deep learning has emerged as a strong method for mapping environmental variables from this data due to its exceptional predictive capabilities. This work studies the adaptation of the ResNet computer vision architecture for time series analysis of Sentinel-2 data. The resulting deep learning architecture, ResNeTS, stacks sequential convolutions to build a deep and narrow network, aligning with the design principles of leading convolutional architectures in computer vision. Experiments were carried out for predicting different plant-biodiversity indices, namely, species richness, and Shannon and Simpson indices, for temperate grassland ecosystems. The results show that ResNeTS can achieve moderate improvements in terms of accuracy compared to other state-of-the-art architectures, such as InceptionTime (up to +0.021 \u0000<inline-formula><tex-math>$r^{2}$</tex-math></inline-formula>\u0000), with reduced computational costs owing to its streamlined architecture.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10664042","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193245","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}

引用次数: 0

Multibranch Network for Addressing Intraclass Variation in Remote Sensing Building Detection 用于解决遥感建筑物探测中类内差异的多分支网络

IF 4.7 2区地球科学

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2024-09-03 DOI: 10.1109/JSTARS.2024.3454110

Ryuhei Hamaguchi

引用次数: 0

Spatial Downscaling of NPP/VIIRS DNB Nighttime Light Data Based on Deep Learning 基于深度学习的 NPP/VIIRS DNB 夜间光照数据空间降尺度技术

IF 4.7 2区地球科学

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2024-09-03 DOI: 10.1109/JSTARS.2024.3454093

Weixing Xu;Zhaocong Wu;Weihua Lin;Gang Xu

{"title":"Spatial Downscaling of NPP/VIIRS DNB Nighttime Light Data Based on Deep Learning","authors":"Weixing Xu;Zhaocong Wu;Weihua Lin;Gang Xu","doi":"10.1109/JSTARS.2024.3454093","DOIUrl":"10.1109/JSTARS.2024.3454093","url":null,"abstract":"Global-scale remotely sensed nighttime light (NTL) data, such as the Suomi National Polar-orbiting Partnership with the Visible Infrared Imaging Radiometer Suite (NPP/VIIRS) Day/Night Band (DNB) NTL data, has been widely applied across multiple disciplines. However, its broader application is still limited by its coarse spatial resolution. We proposed the NTL conditional multiscale downscaling model (NTL-CMDM) for downscaling NPP/VIIRS DNB. The model uses multisource scale factors as conditional constraints, progressively integrating NTL and scale factors to downscale NPP/VIIRS DNB from 500 to 130 m using data from 201 Chinese cities. The downscaled results were validated against the 130 m Loujia1-01 suggest that the NTL data quality was improved after downscaling, yielding higher the coefficient of determination (R: 0.407 versus 0.702) and lower root-mean-square error (RMSE: 7.020 versus 26.424 \u0000<italic>nWcm\u0000−2\u0000<italic>sr\u0000−1\u0000) values than those of the original NPP/VIIRS DNB. The downscaled results exhibit richer NTL feature details and show similarity to Luojia-1-01. More importantly, the downscaling enhances the accuracy of NTL statistical metrics, improving illuminated area by 10.23% and radiance estimation by 6.12%. Furthermore, the usability of the downscaled results was assessed by estimating county-level GDP. The GDP estimates based on the downscaled data were superior to those from the original NPP/VIIRS DNB data and consistent with the estimates obtained from Luojia1-01. Finally, generalization ability test using different algorithms in multiple cities demonstrate that NTL-CMDM is robust to cities with different NTL structures. The study verifies the practicability of employing deep learning methods to downscale NTL data, providing a feasible pathway for acquiring high-resolution NTL data over an expanded area.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10663836","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193242","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}

引用次数: 0

Deep Learning in Spaceborne GNSS Reflectometry: Correcting Precipitation Effects on Wind Speed Products 天基全球导航卫星系统反射测量中的深度学习：校正降水对风速产品的影响

IF 5.5 2区地球科学

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2024-09-03 DOI: 10.1109/jstars.2024.3453999

Tianqi Xiao, Caroline Arnold, Daixin Zhao, Lichao Mou, Jens Wickert, Milad Asgarimehr

引用次数: 0

A GNSS Terrestrial Water Storage Inversion Method Based on GRACE Spatial Constraints 基于 GRACE 空间约束的全球导航卫星系统陆地蓄水反演方法

IF 4.7 2区地球科学

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2024-09-03 DOI: 10.1109/JSTARS.2024.3454312

Bin Liu;Chenghao Shan;Weilong Rao;Xueming Xing;Jianbo Tan;Yabo Luo

{"title":"A GNSS Terrestrial Water Storage Inversion Method Based on GRACE Spatial Constraints","authors":"Bin Liu;Chenghao Shan;Weilong Rao;Xueming Xing;Jianbo Tan;Yabo Luo","doi":"10.1109/JSTARS.2024.3454312","DOIUrl":"10.1109/JSTARS.2024.3454312","url":null,"abstract":"The Laplace smoothing operator is used to constrain the relationship between the target grid and neighboring grids in the terrestrial water storage (TWS) inversion using global navigation satellite system (GNSS) observations. We propose an enhancement to the smoothing constraint matrix used in GNSS TWS inversion with prior spatial constraints from gravity recovery and climate experiment (GRACE) data and invert vertical GNSS displacement data for TWS changes in the Sichuan–Yunnan region. We focus on inverting multiyear seasonal TWS changes in the Sichuan–Yunnan region from January 2013 to June 2023, integrating GNSS, GRACE, and global land data assimilation system (GLDAS) data. Our findings demonstrate the consistency of spatiotemporal patterns between GNSS-inferred TWS and GRACE and GLDAS data. Comparing the estimated results with smoothing constraints, the proposed GNSS inversion utilizing the GRACE constraint enhances the capture of local TWS signals, improving spatial agreement with GRACE and GLDAS. The correlation coefficient with GRACE improves from 0.655 to 0.723, and with GLDAS, it improves from 0.730 to 0.779. We further integrate water balance equations for precipitation, runoff, and evapotranspiration in the Sichuan–Yunnan, validating our approach by aligning with established datasets and improving the spatial understanding of TWS dynamics. These enhancements underscore the effectiveness of our GNSS inversion strategy under the spatial constraint of GRACE and enable a more coherent and meaningful interpretation of GNSS-derived TWS changes.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10664049","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193239","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}

引用次数: 0

Estimation of Sea Surface Temperature From Landsat-8 Measurements via Neural Networks 通过神经网络从 Landsat-8 测量数据估算海面温度

IF 4.7 2区地球科学

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2024-09-03 DOI: 10.1109/JSTARS.2024.3453908

Jinyan Xie;Zhongping Lee;Xu Li;Daosheng Wang;Caiyun Zhang;Yufang Wu;Xiaolong Yu;Zhihuang Zheng

{"title":"Estimation of Sea Surface Temperature From Landsat-8 Measurements via Neural Networks","authors":"Jinyan Xie;Zhongping Lee;Xu Li;Daosheng Wang;Caiyun Zhang;Yufang Wu;Xiaolong Yu;Zhihuang Zheng","doi":"10.1109/JSTARS.2024.3453908","DOIUrl":"10.1109/JSTARS.2024.3453908","url":null,"abstract":"The Landsat-8 Collection 2 provides Level-2 surface temperature product (L8-L2ST) at a spatial resolution of 30 m, catering to various applications. However, discrepancies in the spatial resolution of certain parameters involved in L8-L2ST production often result in noticeable “checkerboard” patterns in images over oceanic waters. To enhance the accuracy of sea surface temperature (SST) products derived from the Landsat-8 measurements, this study introduces a neural network (NN) based algorithm for the estimation of SST. By sidestepping the conventional radiative-transfer-based method, which relies on numerous auxiliary data products, the SST generated by the NN-based algorithm could avoid the “checkerboard” issues encountered in the L8-L2ST products. Compared to the reference MODIS SST products, the root mean square error (RMSE) of NN-based SST is 0.7 °C, whereas the RMSE of L8-L2ST is 1.42 °C. In comparison to buoy data, the RMSE of this method is 1.18 °C, while the RMSE of L8-L2ST is 2 °C. This work thus presents a valuable framework for acquiring more consistent and better-quality SST products from Landsat-8 measurements.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10663844","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193237","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}

引用次数: 0

Spatiotemporal Dynamic Change and the Driving Mechanism of Desertification in the Yellow River Basin 黄河流域荒漠化的时空动态变化及其驱动机制

IF 4.7 2区地球科学

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2024-09-03 DOI: 10.1109/JSTARS.2024.3453295

Ling Ran;Lifeng Zhang;Yi He;Shengpeng Cao;Yujie Ding;Yan Guo;Xiao Wei;Mikalai Filonchyk

{"title":"Spatiotemporal Dynamic Change and the Driving Mechanism of Desertification in the Yellow River Basin","authors":"Ling Ran;Lifeng Zhang;Yi He;Shengpeng Cao;Yujie Ding;Yan Guo;Xiao Wei;Mikalai Filonchyk","doi":"10.1109/JSTARS.2024.3453295","DOIUrl":"10.1109/JSTARS.2024.3453295","url":null,"abstract":"The yellow river basin (YRB) plays a crucial role in maintaining national ecological security, and controlling desertification is a critical factor in strengthening the foundation for the high-quality development of the basin. However, there are fewer studies on the spatiotemporal patterns of desertification change in different regions of the YRB, and the mechanisms affecting desertification change are not yet clear. The objective of this article was to construct a desertification difference index (DDI) to characterize the degree of desertification in the YRB based on surface Albedo and normalized difference vegetation index. We analyzed the spatiotemporal patterns of desertification in different regions of the YRB. We used geographical detector and correlation analyses to screen out the main drivers affecting desertification change. We found that the DDI increased significantly at a rate of 0.5 × 10\u0000−3\u0000/a from 2001 to 2021, indicating a slowing down trend of desertification in the YRB. The spatial concentration of desertification was a high degree of agglomeration and significant spatial autocorrelation. Precipitation (PRE) and surface radiation were the primary factors influencing desertification in the YRB. The most important factor influencing desertification in the upstream of the YRB and midstream of the YRB areas was PRE. However, human footprint has exacerbated desertification in the downstream of the YRB. This article aims to reveal the driving mechanisms behind desertification in the YRB. The objective is to provide a reference for combating desertification and improving the ecological environment in the region.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10663976","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193279","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}

引用次数: 0

Multimodal Feature-Guided Pretraining for RGB-T Perception 多模态特征引导的 RGB-T 感知预训练

IF 4.7 2区地球科学

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2024-09-03 DOI: 10.1109/JSTARS.2024.3454054

Junlin Ouyang;Pengcheng Jin;Qingwang Wang

{"title":"Multimodal Feature-Guided Pretraining for RGB-T Perception","authors":"Junlin Ouyang;Pengcheng Jin;Qingwang Wang","doi":"10.1109/JSTARS.2024.3454054","DOIUrl":"10.1109/JSTARS.2024.3454054","url":null,"abstract":"Wide-range multiscale object detection for multispectral scene perception from a drone perspective is challenging. Previous RGB-T perception methods directly use backbone pretrained on RGB for thermal infrared feature extraction, leading to unexpected domain shift. We propose a novel multimodal feature-guided masked reconstruction pretraining method, named M2FP, aimed at learning transferable representations for drone-based RGB-T environmental perception tasks without domain bias. This article includes two key innovations as follows. 1) We design a cross-modal feature interaction module in M2FP, which encourages modality-specific backbones to actively learn cross-modal feature representations and avoid modality bias issues. 2) We design a global-aware feature interaction and fusion module suitable for various downstream tasks, which enhances the model's environmental perception from a global perspective in wide-range drone-based scenes. We fine-tune M2FP on the drone-based object detection dataset (DroneVehicle) and semantic segmentation dataset (Kust4K). On these two tasks, compared to the second-best methods, M2FP achieves state-of-the-art performance, with an improvement of 1.8% in mean average precision and 0.9% in mean intersection over union, respectively.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10663834","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193293","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}

引用次数: 0

Remotely Sensed Water Quality in Qatari Coastal Waters Between 2002 and 2022 2002 年至 2022 年卡塔尔沿海水域水质遥感情况

IF 4.7 2区地球科学

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2024-09-03 DOI: 10.1109/JSTARS.2024.3454092

Cheng Xue;Chuanmin Hu;Jennifer P. Cannizzaro;Brian B. Barnes;Lin Qi;Jing Shi;Yuyuan Xie;Benjamin D. Jaffe;David A. Palandro

{"title":"Remotely Sensed Water Quality in Qatari Coastal Waters Between 2002 and 2022","authors":"Cheng Xue;Chuanmin Hu;Jennifer P. Cannizzaro;Brian B. Barnes;Lin Qi;Jing Shi;Yuyuan Xie;Benjamin D. Jaffe;David A. Palandro","doi":"10.1109/JSTARS.2024.3454092","DOIUrl":"10.1109/JSTARS.2024.3454092","url":null,"abstract":"Over the past two decades, Qatar has undergone significant economic growth and development, yet little information is available on long-term trends in seawater quality around the Qatar Peninsula. This study analyzed spatiotemporal variations of remotely sensed optical water quality (OWQ) parameters in Qatari coastal waters between 2002 and 2022. These OWQ parameters, including chlorophyll-a concentration (Chla), turbidity (Turb), and Secchi disk depth (SDD), along with sea surface temperature, were derived from Moderate-resolution Imaging Spectroradiometer (MODIS)/Aquaobservations after applying an optically shallow-water mask. Additionally, changes in floating algae scum density, an indicator of harmful algal blooms (HABs), were derived from MultiSpectral Instrument (MSI)observations. Strong nearshore–offshore gradients were generally observed for all OWQ parameters (multiannual mean Chla ∼ 0.6–3 mg m\u0000−3\u0000; Turb ∼ 0.2–3 FNU; and SDD ∼ 5–12 m). SDD was typically greatest in late spring and summer when Chla and Turb were relatively low. OWQ variability in the main territorial sea was mainly driven by suspended sediments, while in the broader Exclusive Economic Zonewas driven by algal blooms. HABs dominated by \u0000<italic>Margalefidinium polykrikoides\u0000, \u0000<italic>Noctiluca scintillans\u0000, and \u0000<italic>Trichodesmium\u0000 spp. were frequently observed in deeper (>20 m) waters. Despite Qatar's massive economic development in recent years, declines in Chla and Turb and increased SDD were observed. Qatari coastal waters, however, are warming at a rate of 0.64 °C/decade, ∼2–3 times faster than neighboring Red Sea and Northern Arabian Sea waters, and ∼8 times faster than the global oceans. This thermal stress may pose future challenges for marine ecosystems and the services they provide.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10663846","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193241","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}

引用次数: 0