Multichannel synthetic aperture radar (MC-SAR) allows for high-resolution imaging of a wide swath (HRWS), at the cost of acquiring and downlinking a significantly larger amount of data, compared with conventional SAR systems. In this letter, we discuss the potential of efficient data volume reduction (DVR) for MC-SAR. Specifically, we focus on methods based on transform coding (TC) and linear predictive coding (LPC), which exploit the redundancy introduced in the raw data by the finer azimuth sampling peculiar to the MC system. The proposed approaches, in combination with a variable-bit quantization, allow for the optimization of the resulting performance and data rate. We consider three exemplary yet realistic MC-SAR systems, and we conduct simulations and analyses on synthetic SAR data considering different radar backscatter distributions, which demonstrate the effectiveness of the proposed methods.
{"title":"Coding-Based Data Compression for Multichannel SAR","authors":"Michele Martone;Nicola Gollin;Gerhard Krieger;Ernesto Imbembo;Paola Rizzoli","doi":"10.1109/LGRS.2024.3510433","DOIUrl":"https://doi.org/10.1109/LGRS.2024.3510433","url":null,"abstract":"Multichannel synthetic aperture radar (MC-SAR) allows for high-resolution imaging of a wide swath (HRWS), at the cost of acquiring and downlinking a significantly larger amount of data, compared with conventional SAR systems. In this letter, we discuss the potential of efficient data volume reduction (DVR) for MC-SAR. Specifically, we focus on methods based on transform coding (TC) and linear predictive coding (LPC), which exploit the redundancy introduced in the raw data by the finer azimuth sampling peculiar to the MC system. The proposed approaches, in combination with a variable-bit quantization, allow for the optimization of the resulting performance and data rate. We consider three exemplary yet realistic MC-SAR systems, and we conduct simulations and analyses on synthetic SAR data considering different radar backscatter distributions, which demonstrate the effectiveness of the proposed methods.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10772623","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142821182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The first experiment was conducted to monitor vertical distributions of carbon dioxide (CO2) and aerosols in the low troposphere (below 1 km) using a self-developed mobile, integrated, and 3-D scannable ground-based Mie-Raman lidar in Nanyang, Henan Province, China. The mean volume ratio of CO2 at low altitude (below 200 m) ranged from 418 to 419 ppm, with an average fluctuation of 7–9 ppm, and the mean volume ratio of CO2 was primarily distributed between 406 and 420 ppm during experimental periods. The vertical distribution of CO2 volume ratios exhibited a gradually decrease trends with increasing altitude integrally indicating that observation site belonged to carbon source regions. The quality of the CO2 echo signal was improved with decreasing daylight intensity. Specifically, the stratification of CO2 with time is gradually evident in the observations. The high aerosol extinction coefficients in Nanyang were mainly concentrated below an altitude of approximately 300 m, indicating that pollution near the ground was heavy. The vertical distribution of aerosol extinction coefficients was characterized by the phenomenon that altitudes of high value declined at night due to atmospheric dry deposition. This study demonstrated that our Mie-Raman scattering lidar can successfully obtain the vertical distribution of CO2 volume ratio and aerosol extinction coefficient, which can provide new datasets and technological support for local environmental department and “carbon neutrality” scientific research.
{"title":"Vertical Distributions of CO2 Volume Ratio and Aerosol Extinction Coefficients in Low-Altitude Utilizing Mie–Raman Lidar at Nanyang City, China","authors":"Miao Zhang;Yanli Yang;Jiawen Wu;Renjie Lan;Jun Zhang;Xiaoge Chang","doi":"10.1109/LGRS.2024.3509974","DOIUrl":"https://doi.org/10.1109/LGRS.2024.3509974","url":null,"abstract":"The first experiment was conducted to monitor vertical distributions of carbon dioxide (CO2) and aerosols in the low troposphere (below 1 km) using a self-developed mobile, integrated, and 3-D scannable ground-based Mie-Raman lidar in Nanyang, Henan Province, China. The mean volume ratio of CO2 at low altitude (below 200 m) ranged from 418 to 419 ppm, with an average fluctuation of 7–9 ppm, and the mean volume ratio of CO2 was primarily distributed between 406 and 420 ppm during experimental periods. The vertical distribution of CO2 volume ratios exhibited a gradually decrease trends with increasing altitude integrally indicating that observation site belonged to carbon source regions. The quality of the CO2 echo signal was improved with decreasing daylight intensity. Specifically, the stratification of CO2 with time is gradually evident in the observations. The high aerosol extinction coefficients in Nanyang were mainly concentrated below an altitude of approximately 300 m, indicating that pollution near the ground was heavy. The vertical distribution of aerosol extinction coefficients was characterized by the phenomenon that altitudes of high value declined at night due to atmospheric dry deposition. This study demonstrated that our Mie-Raman scattering lidar can successfully obtain the vertical distribution of CO2 volume ratio and aerosol extinction coefficient, which can provide new datasets and technological support for local environmental department and “carbon neutrality” scientific research.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142821191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-29DOI: 10.1109/LGRS.2024.3509393
Yingjie Kong;Xuquan Wang;Kai Zhang;Hong Li;Wenbo Wan;Jiande Sun
In this letter, we proposed a multiscale integration network with quaternion convolution (MQ-Net) for the fusion of low spatial resolution multispectral (LRMS) and panchromatic (PAN) images. In this network, LRMS and PAN images are resampled at different scales and fed into feature fusion modules (FFMs) to merge the spatial and spectral information among them. Then, multiscale feature enhancement modules (MFEMs) are designed to sufficiently learn the spatial and spectral information at different scales. Meanwhile, we employ a quaternion convolution module (QCM) to better capture the dependencies within spectral bands of LRMS images. Then, the quaternion features are introduced into MFEMs for efficient feature enhancement. Finally, all information from different scales is integrated for the reconstruction of high LRMS images. Reduced- and full-resolution experiments are performed on GeoEye-1 and WorldView-2 satellite datasets. Compared to some state-of-the-art pansharpening methods, the proposed MQ-Net obtains better results in terms of qualitative and quantitative evaluations. The code is available at https://github.com/RSMagneto/MQ-Net .
{"title":"Multiscale Integration Network With Quaternion Convolution for Pansharpening","authors":"Yingjie Kong;Xuquan Wang;Kai Zhang;Hong Li;Wenbo Wan;Jiande Sun","doi":"10.1109/LGRS.2024.3509393","DOIUrl":"https://doi.org/10.1109/LGRS.2024.3509393","url":null,"abstract":"In this letter, we proposed a multiscale integration network with quaternion convolution (MQ-Net) for the fusion of low spatial resolution multispectral (LRMS) and panchromatic (PAN) images. In this network, LRMS and PAN images are resampled at different scales and fed into feature fusion modules (FFMs) to merge the spatial and spectral information among them. Then, multiscale feature enhancement modules (MFEMs) are designed to sufficiently learn the spatial and spectral information at different scales. Meanwhile, we employ a quaternion convolution module (QCM) to better capture the dependencies within spectral bands of LRMS images. Then, the quaternion features are introduced into MFEMs for efficient feature enhancement. Finally, all information from different scales is integrated for the reconstruction of high LRMS images. Reduced- and full-resolution experiments are performed on GeoEye-1 and WorldView-2 satellite datasets. Compared to some state-of-the-art pansharpening methods, the proposed MQ-Net obtains better results in terms of qualitative and quantitative evaluations. The code is available at \u0000<uri>https://github.com/RSMagneto/MQ-Net</uri>\u0000.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-29DOI: 10.1109/LGRS.2024.3509377
Yulin He;Yue Chen;Yu Mao Wu;Peng Liu
The two-scale model (TSM) has been widely used to calculate scattering from surfaces with multiple roughness scales. In our study, we derived an improved TSM based on the angular composite model (ACM), which expresses the calculation of the scattering coefficient as an integral over the surface angles, making it possible to accelerate the process by combining with Monte Carlo integration. In addition, any method can be employed to calculate the small-scale scattering. We use the first-order small-slope approximation model (SSA1) with wider applicability instead of the small perturbation model (SPM) to calculate the scattering contribution from small-scale rough surfaces. We analyze the roughness of short waves on anisotropic wind-driven sea surfaces, give numerical examples of horizontally polarized backscattering and bistatic scattering, taking into account the L-band and different wind speeds, and compare them with the measurement data, classic TSM (KA + SPM), facet-based TSM (KA + SSA1), closed-form two-scale model [bistatic anisotropic polarimetric TSM (BA-PTSM)], and second-order small-slope approximation model (SSA2) algorithms. The proposed bistatic scattering model is in general agreement with the classic model and is demonstrated to be more consistent with measurements at larger scattering angles and more insensitive to the cutoff wavenumber.
{"title":"An Improved Two-Scale Analytical Model for Bistatic Scattering of Wind-Driven Sea Surfaces","authors":"Yulin He;Yue Chen;Yu Mao Wu;Peng Liu","doi":"10.1109/LGRS.2024.3509377","DOIUrl":"https://doi.org/10.1109/LGRS.2024.3509377","url":null,"abstract":"The two-scale model (TSM) has been widely used to calculate scattering from surfaces with multiple roughness scales. In our study, we derived an improved TSM based on the angular composite model (ACM), which expresses the calculation of the scattering coefficient as an integral over the surface angles, making it possible to accelerate the process by combining with Monte Carlo integration. In addition, any method can be employed to calculate the small-scale scattering. We use the first-order small-slope approximation model (SSA1) with wider applicability instead of the small perturbation model (SPM) to calculate the scattering contribution from small-scale rough surfaces. We analyze the roughness of short waves on anisotropic wind-driven sea surfaces, give numerical examples of horizontally polarized backscattering and bistatic scattering, taking into account the L-band and different wind speeds, and compare them with the measurement data, classic TSM (KA + SPM), facet-based TSM (KA + SSA1), closed-form two-scale model [bistatic anisotropic polarimetric TSM (BA-PTSM)], and second-order small-slope approximation model (SSA2) algorithms. The proposed bistatic scattering model is in general agreement with the classic model and is demonstrated to be more consistent with measurements at larger scattering angles and more insensitive to the cutoff wavenumber.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-29DOI: 10.1109/LGRS.2024.3509134
Shengpei Xia;Xiaokai Wang;Wenchao Chen;Xinyue Pan;Jingrui Luo
The moving high-speed train (HST) generates strong vibrations in the railway roadbed, causing seismic waves to propagate through the subsurface medium. Consequently, moving HSTs can be considered as a novel seismic source for probing subsurface structures near high-speed railways (HSRs). An HST has several carriages, making it a typical combined moving source that induces a complex interference wavefield. Seismic interferometry (SI) is a commonly used method for generating virtual shot gathers based on background noise, and the phase-shifting method (PS) is commonly used to generate a dispersion spectrum based on the constructed virtual shot gathers. Therefore, SI and PS have been used for constructing virtual shot gathers and further generating the dispersion spectrum in HST-induced seismic signal processing. Although the HST-induced seismic wavefield exhibits complex interference features, it still maintains stable and strong amplitude characteristics. Therefore, we propose a method for directly extracting the dispersion spectrum from the HST-induced seismic signal through time-frequency decomposition and similarity-based velocity scanning. Compared to the commonly used procedure (SI + PS), the proposed method avoids the virtual shot gather construction procedure. The synthetic data example and real data example have shown the proposed method’s effectiveness.
{"title":"Extracting Dispersion Spectrum Directly From the High-Speed Train-Induced Seismic Signal","authors":"Shengpei Xia;Xiaokai Wang;Wenchao Chen;Xinyue Pan;Jingrui Luo","doi":"10.1109/LGRS.2024.3509134","DOIUrl":"https://doi.org/10.1109/LGRS.2024.3509134","url":null,"abstract":"The moving high-speed train (HST) generates strong vibrations in the railway roadbed, causing seismic waves to propagate through the subsurface medium. Consequently, moving HSTs can be considered as a novel seismic source for probing subsurface structures near high-speed railways (HSRs). An HST has several carriages, making it a typical combined moving source that induces a complex interference wavefield. Seismic interferometry (SI) is a commonly used method for generating virtual shot gathers based on background noise, and the phase-shifting method (PS) is commonly used to generate a dispersion spectrum based on the constructed virtual shot gathers. Therefore, SI and PS have been used for constructing virtual shot gathers and further generating the dispersion spectrum in HST-induced seismic signal processing. Although the HST-induced seismic wavefield exhibits complex interference features, it still maintains stable and strong amplitude characteristics. Therefore, we propose a method for directly extracting the dispersion spectrum from the HST-induced seismic signal through time-frequency decomposition and similarity-based velocity scanning. Compared to the commonly used procedure (SI + PS), the proposed method avoids the virtual shot gather construction procedure. The synthetic data example and real data example have shown the proposed method’s effectiveness.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810450","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Broadband emissivity (BBE) is an important variable in the evaluation of the energy budget and can be provided by the remote sensing products. As one of the commonly used BBE products, global land surface satellite (GLASS) AVHRR BBE and MODIS BBE are quite different. In this study, a new framework of AVHRR BEE estimation based on GLASS MODIS BBE is developed by introducing the more detailed soil datasets, the consideration of hemisphere and season, and the global selection of sampling points into the modeling. After our modification of the original GLASS AVHRR BBE, the modified BBE significantly eliminates the discrepancies between GLASS AVHRR and MODIS BEEs during 2001–2019, especially in summer and winter (0.004 decline of discrepancies), in the extreme arid and moist region [0.002 decline of discrepancies when aridity index (AI) <1>4], in the high altitude area (0.01 decline of discrepancies when DEM >5000 m) and in some desert regions (0.005 decline of discrepancies when albedo >0.5). In addition, the application of our framework can significantly improve the performance of the original GLASS AVHRR BBE before 2000 when the GLASS MODIS BEEs is unavailable. Our framework is helpful for the reliable application of GLASS BBE and can provide a more satisfactory BBE product in a long time series (near to 40 years).
{"title":"Improving GLASS AVHRR-Derived Broadband Thermal-Infrared Emissivity (BBE) Using GLASS MODIS-Derived BBE: A Global Long-Term Study","authors":"Hongjun Zhu;Jie Yuan;Xin Pan;Zhanchuan Wang;Zi Yang;Xu Ding;Suyi Liu;Yuqian Li;Yulong Zhou;Wenqing Ma;Yingbao Yang","doi":"10.1109/LGRS.2024.3508745","DOIUrl":"https://doi.org/10.1109/LGRS.2024.3508745","url":null,"abstract":"Broadband emissivity (BBE) is an important variable in the evaluation of the energy budget and can be provided by the remote sensing products. As one of the commonly used BBE products, global land surface satellite (GLASS) AVHRR BBE and MODIS BBE are quite different. In this study, a new framework of AVHRR BEE estimation based on GLASS MODIS BBE is developed by introducing the more detailed soil datasets, the consideration of hemisphere and season, and the global selection of sampling points into the modeling. After our modification of the original GLASS AVHRR BBE, the modified BBE significantly eliminates the discrepancies between GLASS AVHRR and MODIS BEEs during 2001–2019, especially in summer and winter (0.004 decline of discrepancies), in the extreme arid and moist region [0.002 decline of discrepancies when aridity index (AI) <1>4], in the high altitude area (0.01 decline of discrepancies when DEM >5000 m) and in some desert regions (0.005 decline of discrepancies when albedo >0.5). In addition, the application of our framework can significantly improve the performance of the original GLASS AVHRR BBE before 2000 when the GLASS MODIS BEEs is unavailable. Our framework is helpful for the reliable application of GLASS BBE and can provide a more satisfactory BBE product in a long time series (near to 40 years).","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-28DOI: 10.1109/LGRS.2024.3508080
Xiaoyu Ma;Jinsheng Zhang;Shouyi Liao;Ting Li;Zehao Li
Aeromagnetic interference is one of the important factors limiting the application of aeromagnetic data on aircraft platforms. Therefore, magnetic compensation is necessary for aeromagnetic data processing, which is of great significance to improve the accuracy of geomagnetic navigation. In recent years, aeromagnetic compensation methods can be mainly divided into two categories: linear regression methods based on the Tolles–Lawson (T–L) model and data-driven methods based on machine learning. However, the accuracy of linear regression methods is subject to the complexity of the model and the problem of multicollinearity, while data-driven methods require the quantity and quality of aeromagnetic measurement data. To solve this problem, we proposed an aeromagnetic compensation method taking advantage of both the T–L model and neural network. The T–L model parameters are trained through our network, while the attention mechanism is applied in the hidden layer to enhance the feature extraction ability of the model for time series. We validate our method by applying it to an open-access dataset. The Experimental results demonstrate that our method has higher compensation accuracy and generalization performance than the classical algorithms.
{"title":"An Aeromagnetic Compensation Method Based on Attention Mechanism","authors":"Xiaoyu Ma;Jinsheng Zhang;Shouyi Liao;Ting Li;Zehao Li","doi":"10.1109/LGRS.2024.3508080","DOIUrl":"https://doi.org/10.1109/LGRS.2024.3508080","url":null,"abstract":"Aeromagnetic interference is one of the important factors limiting the application of aeromagnetic data on aircraft platforms. Therefore, magnetic compensation is necessary for aeromagnetic data processing, which is of great significance to improve the accuracy of geomagnetic navigation. In recent years, aeromagnetic compensation methods can be mainly divided into two categories: linear regression methods based on the Tolles–Lawson (T–L) model and data-driven methods based on machine learning. However, the accuracy of linear regression methods is subject to the complexity of the model and the problem of multicollinearity, while data-driven methods require the quantity and quality of aeromagnetic measurement data. To solve this problem, we proposed an aeromagnetic compensation method taking advantage of both the T–L model and neural network. The T–L model parameters are trained through our network, while the attention mechanism is applied in the hidden layer to enhance the feature extraction ability of the model for time series. We validate our method by applying it to an open-access dataset. The Experimental results demonstrate that our method has higher compensation accuracy and generalization performance than the classical algorithms.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Accurately measuring and inverting air parameters, such as air temperature and humidity, is crucial for weather forecasting, climate research, and environmental monitoring. In this letter, we propose an inversion method based on the transformer model to accurately estimate the spatial distribution of air temperature and humidity. Compared with traditional methods, the transformer model demonstrates superior ability in capturing nonlinear relationships and spatial dependencies in observational data, thereby improving inversion accuracy. Experiments conducted on real observational data have shown that compared to traditional techniques, the proposed method achieves a reduction of over 4.8% in the root mean square error (RMSE) of air temperature and over 14.2% in humidity estimation, demonstrating its high accuracy and reliability in inverting air temperature and humidity. This method provides a new approach for advancing air parameter inversion technology.
{"title":"A Transformer Network Air Temperature and Humidity Inversion Method Based on ATMS Brightness Temperature Data","authors":"Chengwang Xiao;Jian Dong;Haofeng Dou;Yinan Li;Wenjing Wang;Fengchao Ren","doi":"10.1109/LGRS.2024.3507938","DOIUrl":"https://doi.org/10.1109/LGRS.2024.3507938","url":null,"abstract":"Accurately measuring and inverting air parameters, such as air temperature and humidity, is crucial for weather forecasting, climate research, and environmental monitoring. In this letter, we propose an inversion method based on the transformer model to accurately estimate the spatial distribution of air temperature and humidity. Compared with traditional methods, the transformer model demonstrates superior ability in capturing nonlinear relationships and spatial dependencies in observational data, thereby improving inversion accuracy. Experiments conducted on real observational data have shown that compared to traditional techniques, the proposed method achieves a reduction of over 4.8% in the root mean square error (RMSE) of air temperature and over 14.2% in humidity estimation, demonstrating its high accuracy and reliability in inverting air temperature and humidity. This method provides a new approach for advancing air parameter inversion technology.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844387","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ocean velocities are essential for understanding how the ocean influences and responds to climate dynamics, making their accurate reconstruction crucial for both climate modeling and predictions. However, reconstructing interior ocean velocities remains a significant challenge due to the sparse distribution of velocity observations and the ocean’s complex dynamics. In this study, we introduce an efficient methodology for reconstructing interior ocean velocities by combining sea surface satellite data—including sea surface height (SSH), temperature, wind, and current—with Argo velocity observations, using the dynamic mode decomposition (DMD) technique. DMD offers the advantage of reducing the dimensionality of interior velocity fields, helping to address the limitations caused by sparse observations. The reconstructed velocity for the Southern Pacific Ocean (SPO) was validated against Argo and acoustic Doppler current profiler (ADCP) velocities, showing a strong correlation than GLORYS12V1 velocities. In particular, the reconstructed velocities have a mean correlation coefficient of 0.78 for the zonal component and 0.74 for the meridional component above 1000 m. Additionally, the reconstructed flow field exhibits a coherent pattern that closely aligns with the eddies observed in SSH. This research significantly contributes to the Global Ocean Monitoring and Observing Program by enhancing both the accuracy and resolution of ocean velocity measurements.
{"title":"Reconstruction of Interior Velocity in the Southern Pacific Ocean Using Satellite and Argo Data","authors":"Liang Xiang;Yongsheng Xu;Haiwei Sun;Qingjun Zhang;Weiya Kong;Lin Zhang;Xiangguang Zhang;Chao Huang;Dandan Zhao","doi":"10.1109/LGRS.2024.3508023","DOIUrl":"https://doi.org/10.1109/LGRS.2024.3508023","url":null,"abstract":"Ocean velocities are essential for understanding how the ocean influences and responds to climate dynamics, making their accurate reconstruction crucial for both climate modeling and predictions. However, reconstructing interior ocean velocities remains a significant challenge due to the sparse distribution of velocity observations and the ocean’s complex dynamics. In this study, we introduce an efficient methodology for reconstructing interior ocean velocities by combining sea surface satellite data—including sea surface height (SSH), temperature, wind, and current—with Argo velocity observations, using the dynamic mode decomposition (DMD) technique. DMD offers the advantage of reducing the dimensionality of interior velocity fields, helping to address the limitations caused by sparse observations. The reconstructed velocity for the Southern Pacific Ocean (SPO) was validated against Argo and acoustic Doppler current profiler (ADCP) velocities, showing a strong correlation than GLORYS12V1 velocities. In particular, the reconstructed velocities have a mean correlation coefficient of 0.78 for the zonal component and 0.74 for the meridional component above 1000 m. Additionally, the reconstructed flow field exhibits a coherent pattern that closely aligns with the eddies observed in SSH. This research significantly contributes to the Global Ocean Monitoring and Observing Program by enhancing both the accuracy and resolution of ocean velocity measurements.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142821183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-27DOI: 10.1109/LGRS.2024.3507760
Kangning Du;Chang Liu;Xian Sun;Lin Cao;Shu Tian
Remote sensing change detection (RSCD) has achieved creditable success in recent years. However, the challenge of identifying changed objects with shape details persists in RSCD. In this letter, we proposed a cross-temporal interaction with difference refinement network (CTIDRNet) to solve interference-caused fake change and incomplete irregular change shape in RSCD tasks. Specifically, by combining cross-attention and self-attention to steer the temporal feature interaction of each input, we design a temporal feature attention (TFA) module to excavate the potential relation of change areas and suppress the unchanged object interference. Afterward, a deformable convolution is used to design a difference feature refinement (DFR) architecture to capture temporal difference information at diverse feature levels. At last, we proposed a multiscale-guided fusion (MGF) module to fuse pyramid features, thereby dealing with scaling changes. Experimental results on three datasets show that CTIDRNet can extract irregularly changed areas effectively, and the evaluation result outperforms other SOTA methods, with an improvement of 1.79%–19.82%, 2.9%–11.07%, and 0.97%–8.91% in terms of F1 for CDD, SYSU, and LEVIR datasets, respectively. The demo code of this work is publicly available at https://github.com/lucyjiong/CTIDR .
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