Pub Date : 2022-12-01DOI: 10.1109/MGRS.2022.3219778
Adriano Camps, J. F. Muñoz-Martín, J. A. Ruiz-de-Azua, L. Fernández, A. Pérez-Portero, David Llavería, C. Herbert, M. Pablos, A. Golkar, A. Gutierrez, Carlos Antonio, J. Bandeiras, Jorge Andrade, D. Cordeiro, Simone Briatore, Nicola Garzaniti, F. Nichele, R. Mozzillo, A. Piumatti, Margherita Cardi, Marco Esposito, C. van Dijk, N. Vercruyssen, J. Barbosa, John R. Hefele, R. Koeleman, B. C. Domínguez, M. Pastena, G. Filippazzo, A. Reagan
The Federated Satellite Systems/3Cat-5 (FSSCat) mission was the winner of the European Space Agency (ESA) Sentinel Small Satellite (S3) Challenge and overall winner of the 2017 Copernicus Masters competition. It consisted of two six-unit CubeSats. The Earth observation payloads were 1) the Flexible Microwave Payload 2 (FMPL-2) onboard 3Cat-5/A, an L-band microwave radiometer and GNSS reflectometer (GNSS-R) implemented using a software-defined radio (SDR), and 2) the HyperScout-2 onboard 3Cat-5/B, a hyperspectral camera, with the first experiment using artificial intelligence to discard cloudy images. FSSCat was launched on 3 September 2020 and injected into a 535-km synchronous orbit. 3Cat-5/A was operated for three months until the payload was probably damaged by a solar flare and coronal mass ejection. During this time, all scientific requirements were met, including the generation of coarse-resolution and downscaled soil moisture (SM) maps, sea ice extent (SIE) maps, concentration and thickness maps, and even wind speed (WS) and sea surface salinity (SSS) maps, which were not originally foreseen. 3Cat-5/B was operated a few more months until the number of images acquired met the requirements. This article briefly describes the FSSCat mission and the FMPL-2 payload and summarizes the main scientific results.
{"title":"FSSCat: The Federated Satellite Systems 3Cat Mission: Demonstrating the capabilities of CubeSats to monitor essential climate variables of the water cycle [Instruments and Missions]","authors":"Adriano Camps, J. F. Muñoz-Martín, J. A. Ruiz-de-Azua, L. Fernández, A. Pérez-Portero, David Llavería, C. Herbert, M. Pablos, A. Golkar, A. Gutierrez, Carlos Antonio, J. Bandeiras, Jorge Andrade, D. Cordeiro, Simone Briatore, Nicola Garzaniti, F. Nichele, R. Mozzillo, A. Piumatti, Margherita Cardi, Marco Esposito, C. van Dijk, N. Vercruyssen, J. Barbosa, John R. Hefele, R. Koeleman, B. C. Domínguez, M. Pastena, G. Filippazzo, A. Reagan","doi":"10.1109/MGRS.2022.3219778","DOIUrl":"https://doi.org/10.1109/MGRS.2022.3219778","url":null,"abstract":"The Federated Satellite Systems/3Cat-5 (FSSCat) mission was the winner of the European Space Agency (ESA) Sentinel Small Satellite (S3) Challenge and overall winner of the 2017 Copernicus Masters competition. It consisted of two six-unit CubeSats. The Earth observation payloads were 1) the Flexible Microwave Payload 2 (FMPL-2) onboard 3Cat-5/A, an L-band microwave radiometer and GNSS reflectometer (GNSS-R) implemented using a software-defined radio (SDR), and 2) the HyperScout-2 onboard 3Cat-5/B, a hyperspectral camera, with the first experiment using artificial intelligence to discard cloudy images. FSSCat was launched on 3 September 2020 and injected into a 535-km synchronous orbit. 3Cat-5/A was operated for three months until the payload was probably damaged by a solar flare and coronal mass ejection. During this time, all scientific requirements were met, including the generation of coarse-resolution and downscaled soil moisture (SM) maps, sea ice extent (SIE) maps, concentration and thickness maps, and even wind speed (WS) and sea surface salinity (SSS) maps, which were not originally foreseen. 3Cat-5/B was operated a few more months until the number of images acquired met the requirements. This article briefly describes the FSSCat mission and the FMPL-2 payload and summarizes the main scientific results.","PeriodicalId":48660,"journal":{"name":"IEEE Geoscience and Remote Sensing Magazine","volume":"10 1","pages":"260-269"},"PeriodicalIF":14.6,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44884764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2022-09-01DOI: 10.1109/MGRS.2022.3189005
D. Liang, Heng Zhang, Kaiyu Liu, Dacheng Liu, Robert Wang
Bistatic synthetic aperture radar (BiSAR) and multistatic (MuSAR) systems with a separated transmitter and receiver have been widely used for remote sensing. However, frequency deviation among different oscillators will cause a modulated phase error on the echo signal. Therefore, phase synchronization is one of the most critical problems to be addressed in BiSAR/MuSAR systems. In this article, we review synchronization techniques, which include synchronization by direct signal, synchronization by synchronization module, and synchronization by autonomous estimation. Furthermore, the future development of synchronization technology is prospected.
{"title":"Phase Synchronization Techniques for Bistatic and Multistatic Synthetic Aperture Radar: Accounting for frequency offset","authors":"D. Liang, Heng Zhang, Kaiyu Liu, Dacheng Liu, Robert Wang","doi":"10.1109/MGRS.2022.3189005","DOIUrl":"https://doi.org/10.1109/MGRS.2022.3189005","url":null,"abstract":"Bistatic synthetic aperture radar (BiSAR) and multistatic (MuSAR) systems with a separated transmitter and receiver have been widely used for remote sensing. However, frequency deviation among different oscillators will cause a modulated phase error on the echo signal. Therefore, phase synchronization is one of the most critical problems to be addressed in BiSAR/MuSAR systems. In this article, we review synchronization techniques, which include synchronization by direct signal, synchronization by synchronization module, and synchronization by autonomous estimation. Furthermore, the future development of synchronization technology is prospected.","PeriodicalId":48660,"journal":{"name":"IEEE Geoscience and Remote Sensing Magazine","volume":"10 1","pages":"153-167"},"PeriodicalIF":14.6,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44052862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2022-09-01DOI: 10.1109/MGRS.2022.3169947
R. Thoreau, V. Achard, L. Risser, B. Berthelot, X. Briottet
Machine learning algorithms have demonstrated impressive results for land cover mapping from hyperspectral data. To enhance generalization capabilities of statistical models, active learning (AL) methods guide the annotation of the training data set by querying the most informative samples. The training of the classifier can then be performed on an optimal training data set. We bring under the same framework uncertainty, representativeness, and performance-based AL techniques; conduct a benchmark on state-of-the-art methods and release a toolbox (https://github.com/Romain3Ch216/AL4EO) to allow experimentation with these approaches. The experiments are conducted on various data sets: a toy data set, classic hyperspectral benchmark data sets, and a complex hyperspectral scene. We evaluate the methods with usual accuracy metrics as well as complementary metrics, which allow us to provide guidelines when choosing a relevant AL strategy in a real use case.
{"title":"Active Learning for Hyperspectral Image Classification: A comparative review","authors":"R. Thoreau, V. Achard, L. Risser, B. Berthelot, X. Briottet","doi":"10.1109/MGRS.2022.3169947","DOIUrl":"https://doi.org/10.1109/MGRS.2022.3169947","url":null,"abstract":"Machine learning algorithms have demonstrated impressive results for land cover mapping from hyperspectral data. To enhance generalization capabilities of statistical models, active learning (AL) methods guide the annotation of the training data set by querying the most informative samples. The training of the classifier can then be performed on an optimal training data set. We bring under the same framework uncertainty, representativeness, and performance-based AL techniques; conduct a benchmark on state-of-the-art methods and release a toolbox (https://github.com/Romain3Ch216/AL4EO) to allow experimentation with these approaches. The experiments are conducted on various data sets: a toy data set, classic hyperspectral benchmark data sets, and a complex hyperspectral scene. We evaluate the methods with usual accuracy metrics as well as complementary metrics, which allow us to provide guidelines when choosing a relevant AL strategy in a real use case.","PeriodicalId":48660,"journal":{"name":"IEEE Geoscience and Remote Sensing Magazine","volume":"10 1","pages":"256-278"},"PeriodicalIF":14.6,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43877473","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2022-09-01DOI: 10.1109/mgrs.2022.3203865
R. Ramachandran, M. Maskey, C. Lynnes, Aruni John, Tathagata Mukherjee
{"title":"Investigating Different Data-Traceability Approaches to Prevent Data Swamps [Perspectives]","authors":"R. Ramachandran, M. Maskey, C. Lynnes, Aruni John, Tathagata Mukherjee","doi":"10.1109/mgrs.2022.3203865","DOIUrl":"https://doi.org/10.1109/mgrs.2022.3203865","url":null,"abstract":"","PeriodicalId":48660,"journal":{"name":"IEEE Geoscience and Remote Sensing Magazine","volume":"1 1","pages":""},"PeriodicalIF":14.6,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41519126","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2022-09-01DOI: 10.1109/MGRS.2022.3168135
Xinlian Liang, A. Kukko, Ivan Balenovic, N. Saarinen, S. Junttila, V. Kankare, M. Holopainen, M. Mokroš, P. Surový, H. Kaartinen, Luka Jurjevic, E. Honkavaara, R. Näsi, Jingbin Liu, M. Hollaus, Jiaojiao Tian, Xiaowei Yu, Jie Pan, Shangshu Cai, Juho-Pekka Virtanen, Yunshen Wang, J. Hyyppä
Remote sensing-based forest investigation and monitoring have become more affordable and applicable in the past few decades. The current bottleneck limiting practical use of the vast volume of remote sensing data lies in the lack of affordable, reliable, and detailed field references, which are required for necessary calibrations of satellite and aerial data and calibrations of relevant allometric models. Conventional field investigations are mostly limited to a small scale, using a small quantity of observations. Rapid development in close-range remote sensing has been witnessed during the past two decades, i.e., in the constant decrease of the costs, size, and weight of sensors; steady improvements in the availability, mobility, and reliability of platforms; and progress in computational capacity and data science. These advances have paved the way for turning conventional expensive and inefficient manual forest in situ data collections into affordable and efficient autonomous observations.
{"title":"Close-Range Remote Sensing of Forests: The state of the art, challenges, and opportunities for systems and data acquisitions","authors":"Xinlian Liang, A. Kukko, Ivan Balenovic, N. Saarinen, S. Junttila, V. Kankare, M. Holopainen, M. Mokroš, P. Surový, H. Kaartinen, Luka Jurjevic, E. Honkavaara, R. Näsi, Jingbin Liu, M. Hollaus, Jiaojiao Tian, Xiaowei Yu, Jie Pan, Shangshu Cai, Juho-Pekka Virtanen, Yunshen Wang, J. Hyyppä","doi":"10.1109/MGRS.2022.3168135","DOIUrl":"https://doi.org/10.1109/MGRS.2022.3168135","url":null,"abstract":"Remote sensing-based forest investigation and monitoring have become more affordable and applicable in the past few decades. The current bottleneck limiting practical use of the vast volume of remote sensing data lies in the lack of affordable, reliable, and detailed field references, which are required for necessary calibrations of satellite and aerial data and calibrations of relevant allometric models. Conventional field investigations are mostly limited to a small scale, using a small quantity of observations. Rapid development in close-range remote sensing has been witnessed during the past two decades, i.e., in the constant decrease of the costs, size, and weight of sensors; steady improvements in the availability, mobility, and reliability of platforms; and progress in computational capacity and data science. These advances have paved the way for turning conventional expensive and inefficient manual forest in situ data collections into affordable and efficient autonomous observations.","PeriodicalId":48660,"journal":{"name":"IEEE Geoscience and Remote Sensing Magazine","volume":"10 1","pages":"32-71"},"PeriodicalIF":14.6,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45376520","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2022-09-01DOI: 10.1109/MGRS.2022.3184951
Lingsheng Meng, Xiaohui Yan
The oceans are an important component of Earth’s system and play a crucial role in climate change through the coupled atmosphere–ocean process. Observations are fundamental for studying and understanding the oceans. While in situ measurements are limited, satellites can remotely monitor oceans continuously for extended periods, with broad spatial coverages. These sustained in situ and remotely sensed observations are available for longer time periods; however, the later are limited to the surface ocean. Owing to the unavailability of subsurface observations, the limited studies could focus on understanding subsurface oceanic processes [e.g., subsurface flows and eddies, internal waves (IWs) and tides, undercurrents, and so on] and conducting comprehensive studies of the oceans, such as the recent warming of oceans.
{"title":"Remote Sensing for Subsurface and Deeper Oceans: An overview and a future outlook","authors":"Lingsheng Meng, Xiaohui Yan","doi":"10.1109/MGRS.2022.3184951","DOIUrl":"https://doi.org/10.1109/MGRS.2022.3184951","url":null,"abstract":"The oceans are an important component of Earth’s system and play a crucial role in climate change through the coupled atmosphere–ocean process. Observations are fundamental for studying and understanding the oceans. While in situ measurements are limited, satellites can remotely monitor oceans continuously for extended periods, with broad spatial coverages. These sustained in situ and remotely sensed observations are available for longer time periods; however, the later are limited to the surface ocean. Owing to the unavailability of subsurface observations, the limited studies could focus on understanding subsurface oceanic processes [e.g., subsurface flows and eddies, internal waves (IWs) and tides, undercurrents, and so on] and conducting comprehensive studies of the oceans, such as the recent warming of oceans.","PeriodicalId":48660,"journal":{"name":"IEEE Geoscience and Remote Sensing Magazine","volume":"10 1","pages":"72-92"},"PeriodicalIF":14.6,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43458358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2022-09-01DOI: 10.1109/MGRS.2022.3175159
Guiping Wu, Xiangming Xiao, Yuanbo Liu
Surface water, which refers to water stored in rivers, streams, lakes, reservoirs, ponds, and wetlands, is a precious resource in terms of biodiversity, ecology, water management, and economics. As a significant hydrological parameter, surface water storage (SWS) influences the exchange of water and energy between the land/water surface and atmosphere. The quantification of SWS and its dynamics is crucial for a better understanding of global hydrological and biogeochemical processes. For more than 30 years, Earth observation (EO) technology has shown that SWS can be measured to some degree, and a variety of techniques have been proposed to facilitate this purpose.
{"title":"Satellite-Based Surface Water Storage Estimation: Its history, current status, and future prospects","authors":"Guiping Wu, Xiangming Xiao, Yuanbo Liu","doi":"10.1109/MGRS.2022.3175159","DOIUrl":"https://doi.org/10.1109/MGRS.2022.3175159","url":null,"abstract":"Surface water, which refers to water stored in rivers, streams, lakes, reservoirs, ponds, and wetlands, is a precious resource in terms of biodiversity, ecology, water management, and economics. As a significant hydrological parameter, surface water storage (SWS) influences the exchange of water and energy between the land/water surface and atmosphere. The quantification of SWS and its dynamics is crucial for a better understanding of global hydrological and biogeochemical processes. For more than 30 years, Earth observation (EO) technology has shown that SWS can be measured to some degree, and a variety of techniques have been proposed to facilitate this purpose.","PeriodicalId":48660,"journal":{"name":"IEEE Geoscience and Remote Sensing Magazine","volume":" ","pages":"10-31"},"PeriodicalIF":14.6,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47523459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2022-09-01DOI: 10.1109/MGRS.2022.3170092
A. Arienzo, G. Vivone, A. Garzelli, L. Alparone, J. Chanussot
Panchromatic (Pan) sharpening, or pansharpening, refers to the combination of a multispectral (MS) image and Pan data with a finer spatial resolution. Since the early days of this research topic, the issue of quality assessment has played a central role in the related literature, pushing investigators toward extensive research. The solution to this problem is nontrivial because of its ill-posed nature. Indeed, no reference image is available to compare with the outcome of the fusion process.
{"title":"Full-Resolution Quality Assessment of Pansharpening: Theoretical and hands-on approaches","authors":"A. Arienzo, G. Vivone, A. Garzelli, L. Alparone, J. Chanussot","doi":"10.1109/MGRS.2022.3170092","DOIUrl":"https://doi.org/10.1109/MGRS.2022.3170092","url":null,"abstract":"<italic>Panchromatic</italic> (<italic>Pan</italic>) <italic>sharpening</italic>, or <italic>pansharpening</italic>, refers to the combination of a multispectral (MS) image and Pan data with a finer spatial resolution. Since the early days of this research topic, the issue of quality assessment has played a central role in the related literature, pushing investigators toward extensive research. The solution to this problem is nontrivial because of its ill-posed nature. Indeed, no reference image is available to compare with the outcome of the fusion process.","PeriodicalId":48660,"journal":{"name":"IEEE Geoscience and Remote Sensing Magazine","volume":"10 1","pages":"168-201"},"PeriodicalIF":14.6,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45805251","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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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