Pub Date : 2023-11-27DOI: 10.1080/2150704x.2023.2288068
Dunyue Cui, Zhichao Chen, Shidong Wang
Flexible spatio-temporal data fusion (FSDAF) is usually used to fuse high spatial resolution images with ordinary up-sampling methods processed low spatial resolution images. However, ordinary up-s...
{"title":"A novel spatio-temporal fusion approach combining deep learning downscaling and FSDAF method","authors":"Dunyue Cui, Zhichao Chen, Shidong Wang","doi":"10.1080/2150704x.2023.2288068","DOIUrl":"https://doi.org/10.1080/2150704x.2023.2288068","url":null,"abstract":"Flexible spatio-temporal data fusion (FSDAF) is usually used to fuse high spatial resolution images with ordinary up-sampling methods processed low spatial resolution images. However, ordinary up-s...","PeriodicalId":49132,"journal":{"name":"Remote Sensing Letters","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138519748","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-23DOI: 10.1080/2150704x.2023.2283901
Soon-Yong Park, Chang-Min Son, DongUk Seo, Seung-Hae Baek
With the increased availability of multi-view satellite images, the number of investigations on 3D urban scene reconstruction from multiple satellite images is also increasing. Conventional Multi-V...
{"title":"Fusion of monocular height maps for 3D urban scene reconstruction from uncalibrated satellite images","authors":"Soon-Yong Park, Chang-Min Son, DongUk Seo, Seung-Hae Baek","doi":"10.1080/2150704x.2023.2283901","DOIUrl":"https://doi.org/10.1080/2150704x.2023.2283901","url":null,"abstract":"With the increased availability of multi-view satellite images, the number of investigations on 3D urban scene reconstruction from multiple satellite images is also increasing. Conventional Multi-V...","PeriodicalId":49132,"journal":{"name":"Remote Sensing Letters","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2023-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138519734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-22DOI: 10.1080/2150704x.2023.2282400
Katarzyna Kubiak, Jan Kotlarz, Marcin Spiralski, Jakub Szymański
In this study, we are identifying differences in the UV/VIS/NIR (ultraviolet/visible/near-infrared) spectral signatures of maize leaves according to a range of fertilization rates from 0 to 240 kg/...
{"title":"Nitrogen fertilization assessment in maize (Zea mays L.) using hyperspectral UV/VIS/NIR data","authors":"Katarzyna Kubiak, Jan Kotlarz, Marcin Spiralski, Jakub Szymański","doi":"10.1080/2150704x.2023.2282400","DOIUrl":"https://doi.org/10.1080/2150704x.2023.2282400","url":null,"abstract":"In this study, we are identifying differences in the UV/VIS/NIR (ultraviolet/visible/near-infrared) spectral signatures of maize leaves according to a range of fertilization rates from 0 to 240 kg/...","PeriodicalId":49132,"journal":{"name":"Remote Sensing Letters","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2023-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138519744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-22DOI: 10.1080/2150704x.2023.2280464
Laurence C. Smith, Jessica V. Fayne, Bo Wang, Ethan D. Kyzivat, Colin J. Gleason, Merritt E. Harlan, Theodore Langhorst, Dongmei Feng, Tamlin M. Pavelsky, Daniel L. Peters
In late 2023 the Surface Water and Ocean Topography (SWOT) satellite mission will release unprecedented high-resolution measurements of water surface elevation (WSE) and water surface slope (WSS) g...
{"title":"Peace-Athabasca Delta water surface elevations and slopes mapped from AirSWOT Ka-band InSAR","authors":"Laurence C. Smith, Jessica V. Fayne, Bo Wang, Ethan D. Kyzivat, Colin J. Gleason, Merritt E. Harlan, Theodore Langhorst, Dongmei Feng, Tamlin M. Pavelsky, Daniel L. Peters","doi":"10.1080/2150704x.2023.2280464","DOIUrl":"https://doi.org/10.1080/2150704x.2023.2280464","url":null,"abstract":"In late\u00002023 the Surface Water and Ocean Topography (SWOT) satellite mission will release\u0000unprecedented high-resolution measurements of water surface elevation (WSE) and\u0000water surface slope (WSS) g...","PeriodicalId":49132,"journal":{"name":"Remote Sensing Letters","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2023-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138519746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-16DOI: 10.1080/2150704x.2023.2282402
Xiangchen Meng, Weihan Liu, Jie Cheng, Hao Guo
The National Oceanic and Atmospheric Administration (NOAA) Joint Polar Satellite System (JPSS) Enterprise algorithm with explicit path length correction (named as the adapted enterprise algorithm) ...
{"title":"An operational split-window algorithm for retrieving land surface temperature from FengYun-4A AGRI data","authors":"Xiangchen Meng, Weihan Liu, Jie Cheng, Hao Guo","doi":"10.1080/2150704x.2023.2282402","DOIUrl":"https://doi.org/10.1080/2150704x.2023.2282402","url":null,"abstract":"The National Oceanic and Atmospheric Administration (NOAA) Joint Polar Satellite System (JPSS) Enterprise algorithm with explicit path length correction (named as the adapted enterprise algorithm) ...","PeriodicalId":49132,"journal":{"name":"Remote Sensing Letters","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2023-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138519745","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-02DOI: 10.1080/2150704X.2023.2282399
Xiaobin Zhao, Mengmeng Zhang, Wei Li, Kun Gao, Ran Tao
ABSTRACT Hyperspectral target detection under complex background is a challenging and difficult task in remote-sensing earth observation. However, most existing algorithms assume that the background obeys the multivariate Gaussian model and ignores the complex spatial distribution. In this work, a hyperspectral target detection method based on sparse representation and Cauchy distance combined graph (SRCG) model is proposed. Firstly, pure dictionary sparse representation is used to obtain the similarity of the prior target pixel and test pixels. Secondly, the pixel-to-pixel Cauchy distance of the hyperspectral image is evaluated. Finally, the vertex edge graph pixel selection model is constructed to obtain the desired target pixels. The experimental results demonstrate the priority of the SRCG on six public and our collected hyperspectral datasets.
{"title":"A sparse representation and Cauchy distance combination graph for hyperspectral target detection","authors":"Xiaobin Zhao, Mengmeng Zhang, Wei Li, Kun Gao, Ran Tao","doi":"10.1080/2150704X.2023.2282399","DOIUrl":"https://doi.org/10.1080/2150704X.2023.2282399","url":null,"abstract":"ABSTRACT Hyperspectral target detection under complex background is a challenging and difficult task in remote-sensing earth observation. However, most existing algorithms assume that the background obeys the multivariate Gaussian model and ignores the complex spatial distribution. In this work, a hyperspectral target detection method based on sparse representation and Cauchy distance combined graph (SRCG) model is proposed. Firstly, pure dictionary sparse representation is used to obtain the similarity of the prior target pixel and test pixels. Secondly, the pixel-to-pixel Cauchy distance of the hyperspectral image is evaluated. Finally, the vertex edge graph pixel selection model is constructed to obtain the desired target pixels. The experimental results demonstrate the priority of the SRCG on six public and our collected hyperspectral datasets.","PeriodicalId":49132,"journal":{"name":"Remote Sensing Letters","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139290389","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-02DOI: 10.1080/2150704x.2023.2275549
Maryam Imani
ABSTRACTA modified version of the collaborative representation-based detector (CRD) is proposed for hyperspectral anomaly detection. In contrast to the conventional CRD, which uses a rectangular dual window, the shaped CRD (SCRD) selects the most appropriate neighbours from the dual window and discards the inappropriate ones. To this end, similarity of the neighbouring pixels to the centre is computed based on the cosine distance to utilize the local information. In addition, the low/high occurrence probability of anomalies/background exhibited in the histogram of the whole image is utilized as global information to find the closest neighbours to the background. The shaped dual window is used for linear approximation of pixels through the collaborative representation. SCRD improves the anomaly detection results with respect to some related works. Experiments on two hyperspectral images show that SCRD results in more accurate detection maps with a bit higher running time compared to CRD.KEYWORDS: collaborative representationdual windowhyperspectral imageanomaly detection Disclosure statementNo potential conflict of interest was reported by the author.
{"title":"A shaped collaborative representation-based detector for hyperspectral anomaly detection","authors":"Maryam Imani","doi":"10.1080/2150704x.2023.2275549","DOIUrl":"https://doi.org/10.1080/2150704x.2023.2275549","url":null,"abstract":"ABSTRACTA modified version of the collaborative representation-based detector (CRD) is proposed for hyperspectral anomaly detection. In contrast to the conventional CRD, which uses a rectangular dual window, the shaped CRD (SCRD) selects the most appropriate neighbours from the dual window and discards the inappropriate ones. To this end, similarity of the neighbouring pixels to the centre is computed based on the cosine distance to utilize the local information. In addition, the low/high occurrence probability of anomalies/background exhibited in the histogram of the whole image is utilized as global information to find the closest neighbours to the background. The shaped dual window is used for linear approximation of pixels through the collaborative representation. SCRD improves the anomaly detection results with respect to some related works. Experiments on two hyperspectral images show that SCRD results in more accurate detection maps with a bit higher running time compared to CRD.KEYWORDS: collaborative representationdual windowhyperspectral imageanomaly detection Disclosure statementNo potential conflict of interest was reported by the author.","PeriodicalId":49132,"journal":{"name":"Remote Sensing Letters","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135975580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-02DOI: 10.1080/2150704x.2023.2277157
Tingwei Zhang
ABSTRACTSynthetic aperture radar (SAR) provides high-resolution observations day and night, whose resulting images can be interpreted for different applications. For the SAR automatic target recognition (ATR) problem, this letter proposes a multi-view method based on adaptive decision fusion. The joint sparse representation (JSR) model is first employed to classify the multiple views. For the output decisions from different views, adaptive weights are determined based on Shannon entropy theory. The resulting weights are used for decision fusion to linearly combine the individual decisions from different SAR images to determine the target label. The MSTAR dataset is used for the experiments, on which both the standard operating condition (SOC) and two representative extended operating conditions (EOCs) are setup. By comparison with several state-of-the-art multi-view SAR ATR methods, the validity and robustness of the proposed method can be effectively confirmed.KEYWORDS: SARtarget recognitionjoint sparse representationadaptive weightsdecision fusion Disclosure statementNo potential conflict of interest was reported by the author.
{"title":"A Multi-view SAR target recognition method based on adaptive weighted decision fusion","authors":"Tingwei Zhang","doi":"10.1080/2150704x.2023.2277157","DOIUrl":"https://doi.org/10.1080/2150704x.2023.2277157","url":null,"abstract":"ABSTRACTSynthetic aperture radar (SAR) provides high-resolution observations day and night, whose resulting images can be interpreted for different applications. For the SAR automatic target recognition (ATR) problem, this letter proposes a multi-view method based on adaptive decision fusion. The joint sparse representation (JSR) model is first employed to classify the multiple views. For the output decisions from different views, adaptive weights are determined based on Shannon entropy theory. The resulting weights are used for decision fusion to linearly combine the individual decisions from different SAR images to determine the target label. The MSTAR dataset is used for the experiments, on which both the standard operating condition (SOC) and two representative extended operating conditions (EOCs) are setup. By comparison with several state-of-the-art multi-view SAR ATR methods, the validity and robustness of the proposed method can be effectively confirmed.KEYWORDS: SARtarget recognitionjoint sparse representationadaptive weightsdecision fusion Disclosure statementNo potential conflict of interest was reported by the author.","PeriodicalId":49132,"journal":{"name":"Remote Sensing Letters","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136017633","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-02DOI: 10.1080/2150704x.2023.2277155
Jongho Woo, Noh-Hun Seong, Daeseong Jung, Suyoung Sim, Nayeon Kim, Sungwoo Park, Sungwon Choi, Eunha Sohn, Ki-Hong Park, Kyung-Soo Han
ABSTRACTSatellite-based surface albedo data are widely used to monitor and analyse the global climate and environmental changes. Korea continuously retrieves surface albedo from the Communication, Ocean and Meteorological Satellite (COMS)/Meteorological Imager sensor (MI) and GEO-KOMPSAT-2A (GK-2A)/Advanced Meteorological Imager sensor (AMI). However, the quality of these surface albedo outputs differs due to differences in the algorithms, input data and resolution, which limits their long-term use as climate data. By analyzing errors in the surface albedo data from COMS/MI and GK-2A/AMI and applying corrections, continuous climate monitoring can be enhanced. This study developed a correction model based on machine learning using multiple linear regression (MLR), random forest (RF) and deep neural network (DNN) models to consider the albedo data error characteristics of each satellite. The best performing RF model was used for correction. The errors of the corrected RF COMS/MI data were reduced; when validated with in-situ data, the Root Mean Square Error (RMSE) of the COMS/MI improved from 0.056 to 0.023, similar to the RMSE of 0.019 of GK-2A/AMI. It also showed stability in the time series validation with GLASS satellite data, with a consistent mean RMSE of 0.036.KEYWORDS: Surface AlbedoGK-2A/AMICOMS/MIAERONETGLASSCorrectionMachine learning Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1. The English in this document has been checked by at least two professional editors, both native speakers of English. For a certificate, please see: (http://www.textcheck.com/certificate/iFW2k3)Additional informationFundingThis work was funded by the Korea Meteorological Administration’s Research and Development Program “Technical Development on Weather Forecast Support and Convergence Service using Meteorological Satellites” under Grant (KMA2020-00120).
{"title":"An ai approach to ensuring consistency of albedo products from COMS/MI and GK-2A/AMI","authors":"Jongho Woo, Noh-Hun Seong, Daeseong Jung, Suyoung Sim, Nayeon Kim, Sungwoo Park, Sungwon Choi, Eunha Sohn, Ki-Hong Park, Kyung-Soo Han","doi":"10.1080/2150704x.2023.2277155","DOIUrl":"https://doi.org/10.1080/2150704x.2023.2277155","url":null,"abstract":"ABSTRACTSatellite-based surface albedo data are widely used to monitor and analyse the global climate and environmental changes. Korea continuously retrieves surface albedo from the Communication, Ocean and Meteorological Satellite (COMS)/Meteorological Imager sensor (MI) and GEO-KOMPSAT-2A (GK-2A)/Advanced Meteorological Imager sensor (AMI). However, the quality of these surface albedo outputs differs due to differences in the algorithms, input data and resolution, which limits their long-term use as climate data. By analyzing errors in the surface albedo data from COMS/MI and GK-2A/AMI and applying corrections, continuous climate monitoring can be enhanced. This study developed a correction model based on machine learning using multiple linear regression (MLR), random forest (RF) and deep neural network (DNN) models to consider the albedo data error characteristics of each satellite. The best performing RF model was used for correction. The errors of the corrected RF COMS/MI data were reduced; when validated with in-situ data, the Root Mean Square Error (RMSE) of the COMS/MI improved from 0.056 to 0.023, similar to the RMSE of 0.019 of GK-2A/AMI. It also showed stability in the time series validation with GLASS satellite data, with a consistent mean RMSE of 0.036.KEYWORDS: Surface AlbedoGK-2A/AMICOMS/MIAERONETGLASSCorrectionMachine learning Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1. The English in this document has been checked by at least two professional editors, both native speakers of English. For a certificate, please see: (http://www.textcheck.com/certificate/iFW2k3)Additional informationFundingThis work was funded by the Korea Meteorological Administration’s Research and Development Program “Technical Development on Weather Forecast Support and Convergence Service using Meteorological Satellites” under Grant (KMA2020-00120).","PeriodicalId":49132,"journal":{"name":"Remote Sensing Letters","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135975020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-02DOI: 10.1080/2150704x.2023.2275550
Muhammad Usman, Ahmad Ali Gul, Sawaid Abbas, Umair Rabbani, Syed Muhammad Irteza
ABSTRACTLimited access to observe in-situ sediment changes requires viable means for quantifying sediment transport in large rivers for effective management of changes in river channels. This study developed a remote sensing-based framework to identify erosion hotspots by magnifying sediment concentration from Sentinel-2 and Landsat-8/9 multispectral images of the Brahmaputra River and the Indus River. First, uncorrelated independent bands were produced to boost the spectral information using the Principal Component Analysis (PCA). The optimal band composite was then identified by applying the Optimum Index Factor (OIF) on the Principal Components (PCs). This approach determined a 3-PCs composite having the highest variance with the least correlation to highlight active morphological changes during flood times. The results of the study reaffirm the significance of the minor PCs (PC4, PC5 and PC6) to characterize the small variation in the data, whereas the main PCs depict the majority of the brightness values around means. The approach was applied to Sentinel-2 imagery acquired on September 2018 in the Brahmaputra River, and Landsat-8/9 images of 2015 and 2022 in the Indus River during flood time to enhance and identify active riverbank erosion hotspots. Precise and timely monitoring of erosion-prone areas can support the control of riverbank erosion and improve soil conservation practices. AcknowledgmentsThe authors are grateful for the contributions of Prof. Dr Atsuhiro Yorozuya, Mr Hiroshi Koseki, Prof. Dr Shoji Okada and Dr Tanjir Ahmed for the turbidity measurements carried out in the Brahmaputra River in 2018 which have been used in this study. This research was supported in part by a grant (University Research Projects Grants F.Y. 2021-22, 2022-23) from the University of the Punjab, Lahore, Pakistan.Disclosure statementNo potential conflict of interest was reported by the author(s).
{"title":"Identifying morphological hotspots in large rivers by optimizing image enhancement","authors":"Muhammad Usman, Ahmad Ali Gul, Sawaid Abbas, Umair Rabbani, Syed Muhammad Irteza","doi":"10.1080/2150704x.2023.2275550","DOIUrl":"https://doi.org/10.1080/2150704x.2023.2275550","url":null,"abstract":"ABSTRACTLimited access to observe in-situ sediment changes requires viable means for quantifying sediment transport in large rivers for effective management of changes in river channels. This study developed a remote sensing-based framework to identify erosion hotspots by magnifying sediment concentration from Sentinel-2 and Landsat-8/9 multispectral images of the Brahmaputra River and the Indus River. First, uncorrelated independent bands were produced to boost the spectral information using the Principal Component Analysis (PCA). The optimal band composite was then identified by applying the Optimum Index Factor (OIF) on the Principal Components (PCs). This approach determined a 3-PCs composite having the highest variance with the least correlation to highlight active morphological changes during flood times. The results of the study reaffirm the significance of the minor PCs (PC4, PC5 and PC6) to characterize the small variation in the data, whereas the main PCs depict the majority of the brightness values around means. The approach was applied to Sentinel-2 imagery acquired on September 2018 in the Brahmaputra River, and Landsat-8/9 images of 2015 and 2022 in the Indus River during flood time to enhance and identify active riverbank erosion hotspots. Precise and timely monitoring of erosion-prone areas can support the control of riverbank erosion and improve soil conservation practices. AcknowledgmentsThe authors are grateful for the contributions of Prof. Dr Atsuhiro Yorozuya, Mr Hiroshi Koseki, Prof. Dr Shoji Okada and Dr Tanjir Ahmed for the turbidity measurements carried out in the Brahmaputra River in 2018 which have been used in this study. This research was supported in part by a grant (University Research Projects Grants F.Y. 2021-22, 2022-23) from the University of the Punjab, Lahore, Pakistan.Disclosure statementNo potential conflict of interest was reported by the author(s).","PeriodicalId":49132,"journal":{"name":"Remote Sensing Letters","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135875380","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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