Our project is done in close collaboration with the Technical University of Munich. In particular with the TUM Data Science in Earth Observation (Sipeo) group. The complete list of associated publications might be also interesting for you and is available here.
Publications
Publications
2023 |
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Chen, Sining; Shi, Yilei; Xiong, Zhitong; Zhu, Xiao Xiang HTC-DC Net: Monocular Height Estimation From Single Remote Sensing Images Journal Article IEEE Transactions on Geoscience and Remote Sensing, 61 , pp. 1-18, 2023, ISSN: 0196-2892. @article{RN263, title = {HTC-DC Net: Monocular Height Estimation From Single Remote Sensing Images}, author = {Sining Chen and Yilei Shi and Zhitong Xiong and Xiao Xiang Zhu}, url = {https://ieeexplore.ieee.org/ielx7/36/10006360/10294289.pdf?tp=&arnumber=10294289&isnumber=10006360&ref=}, doi = {10.1109/tgrs.2023.3321255}, issn = {0196-2892}, year = {2023}, date = {2023-01-01}, journal = {IEEE Transactions on Geoscience and Remote Sensing}, volume = {61}, pages = {1-18}, keywords = {}, pubstate = {published}, tppubtype = {article} } | |
Daixin, Zhao; Konrad, Heidler; Milad, Asgarimehr; Caroline, Arnold; Tianqi, Xiao; Jens, Wickert; Xiang, Zhu Xiao; Lichao, Mou DDM-Former: Transformer networks for GNSS reflectometry global ocean wind speed estimation Journal Article Remote Sensing of Environment, 294 , pp. 113629, 2023, ISSN: 0034-4257. Links | BibTeX | Tags: Deep learning, GNSS reflectometry, Ocean wind speed, Transformer networks @article{RN264, title = {DDM-Former: Transformer networks for GNSS reflectometry global ocean wind speed estimation}, author = {Zhao Daixin and Heidler Konrad and Asgarimehr Milad and Arnold Caroline and Xiao Tianqi and Wickert Jens and Zhu Xiao Xiang and Mou Lichao}, url = {https://www.sciencedirect.com/science/article/pii/S0034425723001803}, doi = {https://doi.org/10.1016/j.rse.2023.113629}, issn = {0034-4257}, year = {2023}, date = {2023-01-01}, journal = {Remote Sensing of Environment}, volume = {294}, pages = {113629}, keywords = {Deep learning, GNSS reflectometry, Ocean wind speed, Transformer networks}, pubstate = {published}, tppubtype = {article} } | |
de Gélis, Iris; Saha, Sudipan; Shahzad, Muhammad; Corpetti, Thomas; Lefèvre, Sébastien; Zhu, Xiao Xiang Deep Unsupervised Learning for 3D ALS Point Clouds Change Detection Journal Article Computer Methods and Programs in Biomedicine, 240 , pp. 107721, 2023, ISSN: 0169-2607. Links | BibTeX | Tags: Band selection, Data gravitation, Human brain, Medical hyperspectral images @article{RN265, title = {Deep Unsupervised Learning for 3D ALS Point Clouds Change Detection}, author = {Iris de Gélis and Sudipan Saha and Muhammad Shahzad and Thomas Corpetti and Sébastien Lefèvre and Xiao Xiang Zhu}, url = {https://www.sciencedirect.com/science/article/pii/S0169260723003875}, doi = {https://doi.org/10.1016/j.cmpb.2023.107721}, issn = {0169-2607}, year = {2023}, date = {2023-01-01}, journal = {Computer Methods and Programs in Biomedicine}, volume = {240}, pages = {107721}, keywords = {Band selection, Data gravitation, Human brain, Medical hyperspectral images}, pubstate = {published}, tppubtype = {article} } | |
Jens, Hoffmann Eike; Karam, Abdulahhad; Xiang, Zhu Xiao Using social media images for building function classification Journal Article Cities, 133 , pp. 104107, 2023, ISSN: 0264-2751. Links | BibTeX | Tags: Big data analytics, Building function classification, Social media image analysis, Urban land use @article{RN266, title = {Using social media images for building function classification}, author = {Hoffmann Eike Jens and Abdulahhad Karam and Zhu Xiao Xiang}, url = {https://www.sciencedirect.com/science/article/pii/S0264275122005467}, doi = {https://doi.org/10.1016/j.cities.2022.104107}, issn = {0264-2751}, year = {2023}, date = {2023-01-01}, journal = {Cities}, volume = {133}, pages = {104107}, keywords = {Big data analytics, Building function classification, Social media image analysis, Urban land use}, pubstate = {published}, tppubtype = {article} } | |
Fan, Fan; Shi, Yilei; Guggemos, Tobias; Zhu, Xiao Xiang Hybrid Quantum-Classical Convolutional Neural Network Model for Image Classification Journal Article IEEE Transactions on Neural Networks and Learning Systems, pp. 1-15, 2023, ISSN: 2162-237X. @article{RN267, title = {Hybrid Quantum-Classical Convolutional Neural Network Model for Image Classification}, author = {Fan Fan and Yilei Shi and Tobias Guggemos and Xiao Xiang Zhu}, doi = {10.1109/tnnls.2023.3312170}, issn = {2162-237X}, year = {2023}, date = {2023-01-01}, journal = {IEEE Transactions on Neural Networks and Learning Systems}, pages = {1-15}, keywords = {}, pubstate = {published}, tppubtype = {article} } | |
Gawlikowski, Jakob; Saha, Sudipan; Niebling, Julia; Zhu, Xiao Xiang Handling unexpected inputs: incorporating source-wise out-of-distribution detection into SAR-optical data fusion for scene classification Journal Article EURASIP Journal on Advances in Signal Processing, 2023 (1), 2023, ISSN: 1687-6180. @article{RN268, title = {Handling unexpected inputs: incorporating source-wise out-of-distribution detection into SAR-optical data fusion for scene classification}, author = {Jakob Gawlikowski and Sudipan Saha and Julia Niebling and Xiao Xiang Zhu}, doi = {10.1186/s13634-023-01008-z}, issn = {1687-6180}, year = {2023}, date = {2023-01-01}, journal = {EURASIP Journal on Advances in Signal Processing}, volume = {2023}, number = {1}, keywords = {}, pubstate = {published}, tppubtype = {article} } | |
Gawlikowski, Jakob; Tassi, Cedrique Rovile Njieutcheu; Ali, Mohsin; Lee, Jongseok; Humt, Matthias; Feng, Jianxiang; Kruspe, Anna; Triebel, Rudolph; Jung, Peter; Roscher, Ribana; Shahzad, Muhammad; Yang, Wen; Bamler, Richard; Zhu, Xiao Xiang A survey of uncertainty in deep neural networks Journal Article Artificial Intelligence Review, 56 (S1), pp. 1513-1589, 2023, ISSN: 0269-2821. @article{RN269, title = {A survey of uncertainty in deep neural networks}, author = {Jakob Gawlikowski and Cedrique Rovile Njieutcheu Tassi and Mohsin Ali and Jongseok Lee and Matthias Humt and Jianxiang Feng and Anna Kruspe and Rudolph Triebel and Peter Jung and Ribana Roscher and Muhammad Shahzad and Wen Yang and Richard Bamler and Xiao Xiang Zhu}, doi = {10.1007/s10462-023-10562-9}, issn = {0269-2821}, year = {2023}, date = {2023-01-01}, journal = {Artificial Intelligence Review}, volume = {56}, number = {S1}, pages = {1513-1589}, keywords = {}, pubstate = {published}, tppubtype = {article} } | |
Guo, Jianhua; Liu, Zhiheng; Zhu, Xiao Xiang Assessing the macro-scale patterns of urban tree canopy cover in Brazil using high-resolution remote sensing images Journal Article Sustainable Cities and Society, 100 , 2023, ISSN: 22106707. @article{RN270, title = {Assessing the macro-scale patterns of urban tree canopy cover in Brazil using high-resolution remote sensing images}, author = {Jianhua Guo and Zhiheng Liu and Xiao Xiang Zhu}, doi = {10.1016/j.scs.2023.105003}, issn = {22106707}, year = {2023}, date = {2023-01-01}, journal = {Sustainable Cities and Society}, volume = {100}, keywords = {}, pubstate = {published}, tppubtype = {article} } | |
Heidler, Konrad; Mou, Lichao; Hu, Di; Jin, Pu; Li, Guangyao; Gan, Chuang; Wen, Ji-Rong; Zhu, Xiao Xiang Self-supervised audiovisual representation learning for remote sensing data Journal Article International Journal of Applied Earth Observation and Geoinformation, 116 , pp. 103130, 2023, ISSN: 1569-8432. Links | BibTeX | Tags: Self-supervised learning Multi-modal learning Representation learning Audiovisual dataset @article{RN271, title = {Self-supervised audiovisual representation learning for remote sensing data}, author = {Konrad Heidler and Lichao Mou and Di Hu and Pu Jin and Guangyao Li and Chuang Gan and Ji-Rong Wen and Xiao Xiang Zhu}, url = {https://www.sciencedirect.com/science/article/pii/S1569843222003181}, doi = {https://doi.org/10.1016/j.jag.2022.103130}, issn = {1569-8432}, year = {2023}, date = {2023-01-01}, journal = {International Journal of Applied Earth Observation and Geoinformation}, volume = {116}, pages = {103130}, keywords = {Self-supervised learning Multi-modal learning Representation learning Audiovisual dataset}, pubstate = {published}, tppubtype = {article} } | |
Heidler, Konrad; Mou, Lichao; Loebel, Erik; Scheinert, Mirko; Lefèvre, Sébastien; Zhu, Xiao A Deep Active Contour Model for Delineating Glacier Calving Fronts Journal Article IEEE Transactions on Geoscience and Remote Sensing, 61 (5615912), 2023, ISSN: 1558-0644. @article{RN272, title = {A Deep Active Contour Model for Delineating Glacier Calving Fronts}, author = {Konrad Heidler and Lichao Mou and Erik Loebel and Mirko Scheinert and Sébastien Lefèvre and Xiao Zhu}, doi = {10.1109/TGRS.2023.3296539}, issn = {1558-0644}, year = {2023}, date = {2023-01-01}, journal = { IEEE Transactions on Geoscience and Remote Sensing}, volume = {61}, number = {5615912}, keywords = {}, pubstate = {published}, tppubtype = {article} } | |
Hermann, Martin; Saha, Sudipan; Zhu, Xiao Xiang Filtering Specialized Change in a Few-Shot Setting Journal Article IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 16 , pp. 1185-1196, 2023, ISSN: 1939-1404. @article{RN273, title = {Filtering Specialized Change in a Few-Shot Setting}, author = {Martin Hermann and Sudipan Saha and Xiao Xiang Zhu}, doi = {10.1109/jstars.2022.3231915}, issn = {1939-1404}, year = {2023}, date = {2023-01-01}, journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing}, volume = {16}, pages = {1185-1196}, keywords = {}, pubstate = {published}, tppubtype = {article} } | |
Hu, Jingliang; Liu, Rong; Hong, Danfeng; Camero, Andrés; Yao, Jing; Schneider, Mathias; Kurz, Franz; Segl, Karl; Zhu, Xiao Xiang MDAS: a new multimodal benchmark dataset for remote sensing Journal Article Earth System Science Data, 15 (1), pp. 113-131, 2023, ISSN: 1866-3516. @article{RN274, title = {MDAS: a new multimodal benchmark dataset for remote sensing}, author = {Jingliang Hu and Rong Liu and Danfeng Hong and Andrés Camero and Jing Yao and Mathias Schneider and Franz Kurz and Karl Segl and Xiao Xiang Zhu}, doi = {10.5194/essd-15-113-2023}, issn = {1866-3516}, year = {2023}, date = {2023-01-01}, journal = {Earth System Science Data}, volume = {15}, number = {1}, pages = {113-131}, keywords = {}, pubstate = {published}, tppubtype = {article} } | |
Huang, Wei; Shi, Yilei; Xiong, Zhitong; Zhu, Xiao AdaptMatch: Adaptive Matching for Semi-supervised Binary Segmentation of Remote Sensing Images Journal Article IEEE Transactions on Geoscience and Remote Sensing, PP , pp. 1-1, 2023. @article{RN275, title = {AdaptMatch: Adaptive Matching for Semi-supervised Binary Segmentation of Remote Sensing Images}, author = {Wei Huang and Yilei Shi and Zhitong Xiong and Xiao Zhu}, doi = {10.1109/TGRS.2023.3332490}, year = {2023}, date = {2023-01-01}, journal = {IEEE Transactions on Geoscience and Remote Sensing}, volume = {PP}, pages = {1-1}, keywords = {}, pubstate = {published}, tppubtype = {article} } | |
Jianhua, Guo; Qingsong, Xu; Yue, Zeng; Zhiheng, Liu; Xiang, Zhu Xiao Nationwide urban tree canopy mapping and coverage assessment in Brazil from high-resolution remote sensing images using deep learning Journal Article ISPRS Journal of Photogrammetry and Remote Sensing, 198 , pp. 1-15, 2023, ISSN: 0924-2716. Links | BibTeX | Tags: Brazil, Remote sensing, Semi-supervised learning, Urban ecosystem services, Urban tree canopy @article{RN276, title = {Nationwide urban tree canopy mapping and coverage assessment in Brazil from high-resolution remote sensing images using deep learning}, author = {Guo Jianhua and Xu Qingsong and Zeng Yue and Liu Zhiheng and Zhu Xiao Xiang}, url = {https://www.sciencedirect.com/science/article/pii/S0924271623000461}, doi = {https://doi.org/10.1016/j.isprsjprs.2023.02.007}, issn = {0924-2716}, year = {2023}, date = {2023-01-01}, journal = {ISPRS Journal of Photogrammetry and Remote Sensing}, volume = {198}, pages = {1-15}, keywords = {Brazil, Remote sensing, Semi-supervised learning, Urban ecosystem services, Urban tree canopy}, pubstate = {published}, tppubtype = {article} } | |
Kondmann, Lukas; Saha, Sudipan; Zhu, Xiao Xiang SemiSiROC: Semisupervised Change Detection With Optical Imagery and an Unsupervised Teacher Model Journal Article IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 16 , pp. 3879-3891, 2023. Links | BibTeX | Tags: Earth Training Optical sensors Optical imaging Data models Optical filters Uncertainty Change detection (CD) multitemporal optical images semisupervised unsupervised @article{RN277, title = {SemiSiROC: Semisupervised Change Detection With Optical Imagery and an Unsupervised Teacher Model}, author = {Lukas Kondmann and Sudipan Saha and Xiao Xiang Zhu}, doi = {10.1109/JSTARS.2023.3268104}, year = {2023}, date = {2023-01-01}, journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing}, volume = {16}, pages = {3879-3891}, keywords = {Earth Training Optical sensors Optical imaging Data models Optical filters Uncertainty Change detection (CD) multitemporal optical images semisupervised unsupervised}, pubstate = {published}, tppubtype = {article} } | |
Li, Tian; Dawson, Geoffrey J; Chuter, Stephen J; Bamber, Jonathan L Grounding line retreat and tide-modulated ocean channels at Moscow University and Totten Glacier ice shelves, East Antarctica Journal Article The Cryosphere, 17 (2), pp. 1003-1022, 2023, ISSN: 1994-0424. @article{RN278, title = {Grounding line retreat and tide-modulated ocean channels at Moscow University and Totten Glacier ice shelves, East Antarctica}, author = {Tian Li and Geoffrey J Dawson and Stephen J Chuter and Jonathan L Bamber}, doi = {10.5194/tc-17-1003-2023}, issn = {1994-0424}, year = {2023}, date = {2023-01-01}, journal = {The Cryosphere}, volume = {17}, number = {2}, pages = {1003-1022}, keywords = {}, pubstate = {published}, tppubtype = {article} } | |
Qingyu, Li; Lichao, Mou; Yuansheng, Hua; Yilei, Shi; Sining, Chen; Yao, Sun; Xiang, Zhu Xiao 3DCentripetalNet: Building height retrieval from monocular remote sensing imagery Journal Article International Journal of Applied Earth Observation and Geoinformation, 120 , pp. 103311, 2023, ISSN: 1569-8432. Links | BibTeX | Tags: Building footprint generation, Building height retrieval, Height estimation, Monocular imagery @article{RN279, title = {3DCentripetalNet: Building height retrieval from monocular remote sensing imagery}, author = {Li Qingyu and Mou Lichao and Hua Yuansheng and Shi Yilei and Chen Sining and Sun Yao and Zhu Xiao Xiang}, url = {https://www.sciencedirect.com/science/article/pii/S1569843223001334}, doi = {https://doi.org/10.1016/j.jag.2023.103311}, issn = {1569-8432}, year = {2023}, date = {2023-01-01}, journal = {International Journal of Applied Earth Observation and Geoinformation}, volume = {120}, pages = {103311}, keywords = {Building footprint generation, Building height retrieval, Height estimation, Monocular imagery}, pubstate = {published}, tppubtype = {article} } | |
Qingyu, Li; Sebastian, Krapf; Yilei, Shi; Xiang, Zhu Xiao SolarNet: A convolutional neural network-based framework for rooftop solar potential estimation from aerial imagery Journal Article International Journal of Applied Earth Observation and Geoinformation, 116 , pp. 103098, 2023, ISSN: 1569-8432. Links | BibTeX | Tags: Convolutional neural network, Remote sensing, Renewable energy, Roof segments and orientations, Solar potential @article{RN280, title = {SolarNet: A convolutional neural network-based framework for rooftop solar potential estimation from aerial imagery}, author = {Li Qingyu and Krapf Sebastian and Shi Yilei and Zhu Xiao Xiang}, url = {https://www.sciencedirect.com/science/article/pii/S1569843222002862}, doi = {https://doi.org/10.1016/j.jag.2022.103098}, issn = {1569-8432}, year = {2023}, date = {2023-01-01}, journal = {International Journal of Applied Earth Observation and Geoinformation}, volume = {116}, pages = {103098}, keywords = {Convolutional neural network, Remote sensing, Renewable energy, Roof segments and orientations, Solar potential}, pubstate = {published}, tppubtype = {article} } | |
Runmin, Dong; Lixian, Zhang; Weijia, Li; Shuai, Yuan; Lin, Gan; Juepeng, Zheng; Haohuan, Fu; Lichao, Mou; Xiang, Zhu Xiao An adaptive image fusion method for Sentinel-2 images and high-resolution images with long-time intervals Journal Article International Journal of Applied Earth Observation and Geoinformation, 121 , pp. 103381, 2023, ISSN: 1569-8432. Links | BibTeX | Tags: Deep learning, High-resolution remote sensing, Multi-source image, Spatial resolution, Super-resolution @article{RN281, title = {An adaptive image fusion method for Sentinel-2 images and high-resolution images with long-time intervals}, author = {Dong Runmin and Zhang Lixian and Li Weijia and Yuan Shuai and Gan Lin and Zheng Juepeng and Fu Haohuan and Mou Lichao and Zhu Xiao Xiang}, url = {https://www.sciencedirect.com/science/article/pii/S1569843223002054}, doi = {https://doi.org/10.1016/j.jag.2023.103381}, issn = {1569-8432}, year = {2023}, date = {2023-01-01}, journal = {International Journal of Applied Earth Observation and Geoinformation}, volume = {121}, pages = {103381}, keywords = {Deep learning, High-resolution remote sensing, Multi-source image, Spatial resolution, Super-resolution}, pubstate = {published}, tppubtype = {article} } | |
Shanyu, Zhou; Lichao, Mou; Yuansheng, Hua; Lixian, Zhang; Hermann, Kaufmann; Xiang, Zhu Xiao Can we use deep learning models to identify the functionality of plastics from space? Journal Article International Journal of Applied Earth Observation and Geoinformation, 123 , pp. 103491, 2023, ISSN: 1569-8432. Links | BibTeX | Tags: Deep learning, Environmental management, Plastic detection, Plastic functionality, Sentinel-2 @article{RN282, title = {Can we use deep learning models to identify the functionality of plastics from space?}, author = {Zhou Shanyu and Mou Lichao and Hua Yuansheng and Zhang Lixian and Kaufmann Hermann and Zhu Xiao Xiang}, url = {https://www.sciencedirect.com/science/article/pii/S1569843223003151}, doi = {https://doi.org/10.1016/j.jag.2023.103491}, issn = {1569-8432}, year = {2023}, date = {2023-01-01}, journal = {International Journal of Applied Earth Observation and Geoinformation}, volume = {123}, pages = {103491}, keywords = {Deep learning, Environmental management, Plastic detection, Plastic functionality, Sentinel-2}, pubstate = {published}, tppubtype = {article} } | |
Song, Qian; Albrecht, Conrad; Xiong, Zhitong; Zhu, Xiao Biomass Estimation and Uncertainty Quantification From Tree Height Journal Article IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, PP , pp. 1-14, 2023, ( ). @article{RN283, title = {Biomass Estimation and Uncertainty Quantification From Tree Height}, author = {Qian Song and Conrad Albrecht and Zhitong Xiong and Xiao Zhu}, doi = {10.1109/JSTARS.2023.3271186}, year = {2023}, date = {2023-01-01}, journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing}, volume = {PP}, pages = {1-14}, note = { }, keywords = {}, pubstate = {published}, tppubtype = {article} } | |
Traoré, Kalifou René; Camero, Andrés; Zhu, Xiao Xiang A data-driven approach to neural architecture search initialization Journal Article Annals of Mathematics and Artificial Intelligence, 2023, ISSN: 1012-2443. @article{RN284, title = {A data-driven approach to neural architecture search initialization}, author = {Kalifou René Traoré and Andrés Camero and Xiao Xiang Zhu}, doi = {10.1007/s10472-022-09823-0}, issn = {1012-2443}, year = {2023}, date = {2023-01-01}, journal = {Annals of Mathematics and Artificial Intelligence}, keywords = {}, pubstate = {published}, tppubtype = {article} } | |
Wei, Huang; Yilei, Shi; Zhitong, Xiong; Qi, Wang; Xiang, Zhu Xiao Semi-supervised bidirectional alignment for Remote Sensing cross-domain scene classification Journal Article ISPRS Journal of Photogrammetry and Remote Sensing, 195 , pp. 192-203, 2023, ISSN: 0924-2716. Links | BibTeX | Tags: Bidirectional sample-class alignment, Cross-domain classification, Remote sensing, Semi-supervised domain adaptation @article{RN285, title = {Semi-supervised bidirectional alignment for Remote Sensing cross-domain scene classification}, author = {Huang Wei and Shi Yilei and Xiong Zhitong and Wang Qi and Zhu Xiao Xiang}, url = {https://www.sciencedirect.com/science/article/pii/S0924271622003069}, doi = {https://doi.org/10.1016/j.isprsjprs.2022.11.013}, issn = {0924-2716}, year = {2023}, date = {2023-01-01}, journal = {ISPRS Journal of Photogrammetry and Remote Sensing}, volume = {195}, pages = {192-203}, keywords = {Bidirectional sample-class alignment, Cross-domain classification, Remote sensing, Semi-supervised domain adaptation}, pubstate = {published}, tppubtype = {article} } | |
Xiangyu, Zhao; Jingliang, Hu; Lichao, Mou; Zhitong, Xiong; Xiang, Zhu Xiao Cross-city Landuse classification of remote sensing images via deep transfer learning Journal Article International Journal of Applied Earth Observation and Geoinformation, 122 , pp. 103358, 2023, ISSN: 1569-8432. Links | BibTeX | Tags: Cross-city classification, Deep learning, Domain adaptation, Local climate zone classification, Sentinel-1, Sentinel-2, Transfer learning @article{RN286, title = {Cross-city Landuse classification of remote sensing images via deep transfer learning}, author = {Zhao Xiangyu and Hu Jingliang and Mou Lichao and Xiong Zhitong and Zhu Xiao Xiang}, url = {https://www.sciencedirect.com/science/article/pii/S1569843223001826}, doi = {https://doi.org/10.1016/j.jag.2023.103358}, issn = {1569-8432}, year = {2023}, date = {2023-01-01}, journal = {International Journal of Applied Earth Observation and Geoinformation}, volume = {122}, pages = {103358}, keywords = {Cross-city classification, Deep learning, Domain adaptation, Local climate zone classification, Sentinel-1, Sentinel-2, Transfer learning}, pubstate = {published}, tppubtype = {article} } | |
Xin-Yi, Tong; Gui-Song, Xia; Xiang, Zhu Xiao Enabling country-scale land cover mapping with meter-resolution satellite imagery Journal Article ISPRS Journal of Photogrammetry and Remote Sensing, 196 , pp. 178-196, 2023, ISSN: 0924-2716. Links | BibTeX | Tags: Classification, Dataset, Deep learning, Domain adaptation, Gaofen-1, Gaofen-2, High-spatial resolution, Land cover mapping, Megacity, PlanetScope, Sentinel-2, Transfer learning @article{RN287, title = {Enabling country-scale land cover mapping with meter-resolution satellite imagery}, author = {Tong Xin-Yi and Xia Gui-Song and Zhu Xiao Xiang}, url = {https://www.sciencedirect.com/science/article/pii/S0924271622003264}, doi = {https://doi.org/10.1016/j.isprsjprs.2022.12.011}, issn = {0924-2716}, year = {2023}, date = {2023-01-01}, journal = {ISPRS Journal of Photogrammetry and Remote Sensing}, volume = {196}, pages = {178-196}, keywords = {Classification, Dataset, Deep learning, Domain adaptation, Gaofen-1, Gaofen-2, High-spatial resolution, Land cover mapping, Megacity, PlanetScope, Sentinel-2, Transfer learning}, pubstate = {published}, tppubtype = {article} } | |
Xiong, Zhitong; Huang, Wei; Hu, Jingtao; Zhu, Xiao THE Benchmark: Transferable Representation Learning for Monocular Height Estimation Journal Article IEEE Transactions on Geoscience and Remote Sensing, PP , pp. 1-1, 2023. @article{RN288, title = {THE Benchmark: Transferable Representation Learning for Monocular Height Estimation}, author = {Zhitong Xiong and Wei Huang and Jingtao Hu and Xiao Zhu}, doi = {10.1109/TGRS.2023.3311764}, year = {2023}, date = {2023-01-01}, journal = {IEEE Transactions on Geoscience and Remote Sensing}, volume = {PP}, pages = {1-1}, keywords = {}, pubstate = {published}, tppubtype = {article} } | |
Xu, Fang; Shi, Yilei; Ebel, Patrick; Yang, Wen; Zhu, Xiao High-Resolution Cloud Removal with Multi-Modal and Multi-Resolution Data Fusion: A New Baseline and Benchmark Journal Article 2023. BibTeX | Tags: @article{RN289, title = {High-Resolution Cloud Removal with Multi-Modal and Multi-Resolution Data Fusion: A New Baseline and Benchmark}, author = {Fang Xu and Yilei Shi and Patrick Ebel and Wen Yang and Xiao Zhu}, year = {2023}, date = {2023-01-01}, keywords = {}, pubstate = {published}, tppubtype = {article} } | |
Xu, Qingsong; Shi, Yilei; Guo, Jianhua; Ouyang, Chaojun; Zhu, Xiao UCDFormer: Unsupervised Change Detection Using a Transformer-driven Image Translation Journal Article Joint Urban Remote Sensing Event (JURSE), 2023 , 2023, ISBN: 978-1-6654-9374-1. @article{RN290, title = {UCDFormer: Unsupervised Change Detection Using a Transformer-driven Image Translation}, author = {Qingsong Xu and Yilei Shi and Jianhua Guo and Chaojun Ouyang and Xiao Zhu}, doi = {10.1109/JURSE57346.2023.10144178}, isbn = {978-1-6654-9374-1}, year = {2023}, date = {2023-01-01}, journal = {Joint Urban Remote Sensing Event (JURSE)}, volume = {2023}, keywords = {}, pubstate = {published}, tppubtype = {article} } | |
Xu, Qingsong; Shi, Yilei; Yuan, Xin; Zhu, Xiao Xiang Universal Domain Adaptation for Remote Sensing Image Scene Classification Journal Article IEEE Transactions on Geoscience and Remote Sensing, 61 , pp. 1-15, 2023, ( ). Links | BibTeX | Tags: Remote sensing Adaptation models Data models Uncertainty Image classification Entropy Earth Remote sensing image classification source data generation (SDG) transferable weight universal domain adaptation (DA) @article{RN291, title = {Universal Domain Adaptation for Remote Sensing Image Scene Classification}, author = {Qingsong Xu and Yilei Shi and Xin Yuan and Xiao Xiang Zhu}, doi = {10.1109/TGRS.2023.3235988}, year = {2023}, date = {2023-01-01}, journal = {IEEE Transactions on Geoscience and Remote Sensing}, volume = {61}, pages = {1-15}, note = { }, keywords = {Remote sensing Adaptation models Data models Uncertainty Image classification Entropy Earth Remote sensing image classification source data generation (SDG) transferable weight universal domain adaptation (DA)}, pubstate = {published}, tppubtype = {article} } | |
Zhang, Fahong; Shi, Yilei; Xiong, Zhitong; Huang, Wei; Zhu, Xiao Pseudo Features-Guided Self-Training for Domain Adaptive Semantic Segmentation of Satellite Images Journal Article IEEE Transactions on Geoscience and Remote Sensing, PP , pp. 1-1, 2023. @article{RN292, title = {Pseudo Features-Guided Self-Training for Domain Adaptive Semantic Segmentation of Satellite Images}, author = {Fahong Zhang and Yilei Shi and Zhitong Xiong and Wei Huang and Xiao Zhu}, doi = {10.1109/TGRS.2023.3281503}, year = {2023}, date = {2023-01-01}, journal = {IEEE Transactions on Geoscience and Remote Sensing}, volume = {PP}, pages = {1-1}, keywords = {}, pubstate = {published}, tppubtype = {article} } | |
Zhou, Fei; Sun, Xin; Sun, Chengze; Dong, Junyu; Zhu, Xiao Xiang Adaptive Morphology Filter: A Lightweight Module for Deep Hyperspectral Image Classification Journal Article IEEE Transactions on Geoscience and Remote Sensing, 61 , pp. 1-16, 2023. Links | BibTeX | Tags: Convolution Kernel Morphology Feature extraction Training Hyperspectral imaging Information filters Deep learning (DL) hyperspectral image (HSI) classification morphology filter structural reparameterization (SRP) @article{RN293, title = {Adaptive Morphology Filter: A Lightweight Module for Deep Hyperspectral Image Classification}, author = {Fei Zhou and Xin Sun and Chengze Sun and Junyu Dong and Xiao Xiang Zhu}, doi = {10.1109/TGRS.2023.3327418}, year = {2023}, date = {2023-01-01}, journal = {IEEE Transactions on Geoscience and Remote Sensing}, volume = {61}, pages = {1-16}, keywords = {Convolution Kernel Morphology Feature extraction Training Hyperspectral imaging Information filters Deep learning (DL) hyperspectral image (HSI) classification morphology filter structural reparameterization (SRP)}, pubstate = {published}, tppubtype = {article} } | |
Dong, Runmin; Mou, Lichao; Chen, Mengxuan; Li, Weijia; Tong, Xin-Yi; Yuan, Shuai; Zhang, Lixian; Zheng, Juepeng; Zhu, Xiao Xiang; Fu, Haohuan Large-Scale Land Cover Mapping with Fine-Grained Classes via Class-Aware Semi-Supervised Semantic Segmentation Miscellaneous Proceedings of ICCV 2023, Paris, France, 2023. @misc{RN37, title = {Large-Scale Land Cover Mapping with Fine-Grained Classes via Class-Aware Semi-Supervised Semantic Segmentation}, author = {Runmin Dong and Lichao Mou and Mengxuan Chen and Weijia Li and Xin-Yi Tong and Shuai Yuan and Lixian Zhang and Juepeng Zheng and Xiao Xiang Zhu and Haohuan Fu}, url = {https://doi.org/10.1109/ICCV51070.2023.01539}, doi = {10.1109/ICCV51070.2023.01539}, year = {2023}, date = {2023-01-01}, pages = {16737-16747}, howpublished = {Proceedings of ICCV 2023, Paris, France}, keywords = {}, pubstate = {published}, tppubtype = {misc} } | |
Ebel, Patrick; Garnot, Vivien Sainte Fare; Schmitt, Michael; Wegner, Jan; Zhu, Xiao UnCRtainTS: Uncertainty Quantification for Cloud Removal in Optical Satellite Time Series Book 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2023, ISBN: 979-8-3503-0250-9. @book{RN38, title = {UnCRtainTS: Uncertainty Quantification for Cloud Removal in Optical Satellite Time Series}, author = {Patrick Ebel and Vivien Sainte Fare Garnot and Michael Schmitt and Jan Wegner and Xiao Zhu}, doi = {10.1109/CVPRW59228.2023.00202}, isbn = {979-8-3503-0250-9}, year = {2023}, date = {2023-01-01}, pages = {2086-2096}, publisher = {2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)}, keywords = {}, pubstate = {published}, tppubtype = {book} } | |
2022 |
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Bamber J. L., Oppenheimer Kopp Aspinall M R E W P; Cooke, R M Climate Processes Driving the Uncertainty in Projections of Future Sea Level Rise: Findings From a Structured Expert Judgement Approach Journal Article Earth's Future, 10 (10), 2022. Abstract | Links | BibTeX | Tags: . @article{Bamber2022, title = {Climate Processes Driving the Uncertainty in Projections of Future Sea Level Rise: Findings From a Structured Expert Judgement Approach}, author = {Bamber, J. L., Oppenheimer, M., Kopp, R. E., Aspinall, W. P., and Cooke, R. M. }, url = {https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2022EF002772}, doi = {2022EF002772}, year = {2022}, date = {2022-10-03}, journal = {Earth's Future}, volume = {10}, number = {10}, abstract = {The ice sheets covering Antarctica and Greenland present the greatest uncertainty in, and largest potential contribution to, future sea level rise. The uncertainty arises from a paucity of suitable observations covering the full range of ice sheet behaviors, incomplete understanding of the influences of diverse processes, and limitations in defining key boundary conditions for the numerical models. To investigate the impact of these uncertainties on ice sheet projections we undertook a structured expert judgement study. Here, we interrogate the findings of that study to identify the dominant drivers of uncertainty in projections and their relative importance as a function of ice sheet and time. We find that for the 21st century, Greenland surface melting, in particular the role of surface albedo effects, and West Antarctic ice dynamics, specifically the role of ice shelf buttressing, dominate the uncertainty. The importance of these effects holds under both a high-end 5°C global warming scenario and another that limits global warming to 2°C. During the 22nd century the dominant drivers of uncertainty shift. Under the 5°C scenario, East Antarctic ice dynamics dominate the uncertainty in projections, driven by the possible role of ice flow instabilities. These dynamic effects only become dominant, however, for a temperature scenario above the Paris Agreement 2°C target and beyond 2100. Our findings identify key processes and factors that need to be addressed in future modeling and observational studies in order to reduce uncertainties in ice sheet projections.}, keywords = {.}, pubstate = {published}, tppubtype = {article} } The ice sheets covering Antarctica and Greenland present the greatest uncertainty in, and largest potential contribution to, future sea level rise. The uncertainty arises from a paucity of suitable observations covering the full range of ice sheet behaviors, incomplete understanding of the influences of diverse processes, and limitations in defining key boundary conditions for the numerical models. To investigate the impact of these uncertainties on ice sheet projections we undertook a structured expert judgement study. Here, we interrogate the findings of that study to identify the dominant drivers of uncertainty in projections and their relative importance as a function of ice sheet and time. We find that for the 21st century, Greenland surface melting, in particular the role of surface albedo effects, and West Antarctic ice dynamics, specifically the role of ice shelf buttressing, dominate the uncertainty. The importance of these effects holds under both a high-end 5°C global warming scenario and another that limits global warming to 2°C. During the 22nd century the dominant drivers of uncertainty shift. Under the 5°C scenario, East Antarctic ice dynamics dominate the uncertainty in projections, driven by the possible role of ice flow instabilities. These dynamic effects only become dominant, however, for a temperature scenario above the Paris Agreement 2°C target and beyond 2100. Our findings identify key processes and factors that need to be addressed in future modeling and observational studies in order to reduce uncertainties in ice sheet projections. | |
Yann Ziegler Bramha Dutt Vishwakarma, Aoibheann Brady Stephen Chuter Sam Royston Richard Westaway Jonathan Bamber M L Can GPS and GRACE data be used to separate past and present-day surface loading in a data-driven approach? Journal Article Geophysical Journal International, 232 (2), pp. 884-901, 2022. Abstract | Links | BibTeX | Tags: . @article{Ziegler2022, title = {Can GPS and GRACE data be used to separate past and present-day surface loading in a data-driven approach?}, author = {Yann Ziegler, Bramha Dutt Vishwakarma, Aoibheann Brady, Stephen Chuter, Sam Royston, Richard M Westaway, Jonathan L Bamber}, url = {https://doi.org/10.1093/gji/ggac365}, doi = {doi:10.1093/gji/ggac365}, year = {2022}, date = {2022-09-17}, journal = {Geophysical Journal International}, volume = {232}, number = {2}, pages = {884-901}, abstract = {Glacial isostatic adjustment (GIA) and the hydrological cycle are both associated with mass changes and vertical land motion (VLM), which are observed by GRACE and GPS, respectively. Hydrology-related VLM results from the instantaneous response of the elastic solid Earth to surface loading by freshwater, whereas GIA-related VLM reveals the long-term response of the viscoelastic Earth mantle to past ice loading history. Thus, observations of mass changes and VLM are interrelated, making GIA and hydrology difficult to quantify and study independently. In this work, we investigate the feasibility of separating these processes based on GRACE and GPS observations, in a fully data-driven and physically consistent approach. We take advantage of the differences in the spatio-temporal characteristics of the GIA and hydrology fields to estimate the respective contributions of each component using a Bayesian hierarchical modelling framework. A closed-loop synthetic test confirms that our method successfully solves this source separation problem. However, there are significant challenges when applying the same approach with actual observations and the answer to the main question of this study is more nuanced. In particular, in regions where GPS station coverage is sparse, the lack of informative data becomes a limiting factor.}, keywords = {.}, pubstate = {published}, tppubtype = {article} } Glacial isostatic adjustment (GIA) and the hydrological cycle are both associated with mass changes and vertical land motion (VLM), which are observed by GRACE and GPS, respectively. Hydrology-related VLM results from the instantaneous response of the elastic solid Earth to surface loading by freshwater, whereas GIA-related VLM reveals the long-term response of the viscoelastic Earth mantle to past ice loading history. Thus, observations of mass changes and VLM are interrelated, making GIA and hydrology difficult to quantify and study independently. In this work, we investigate the feasibility of separating these processes based on GRACE and GPS observations, in a fully data-driven and physically consistent approach. We take advantage of the differences in the spatio-temporal characteristics of the GIA and hydrology fields to estimate the respective contributions of each component using a Bayesian hierarchical modelling framework. A closed-loop synthetic test confirms that our method successfully solves this source separation problem. However, there are significant challenges when applying the same approach with actual observations and the answer to the main question of this study is more nuanced. In particular, in regions where GPS station coverage is sparse, the lack of informative data becomes a limiting factor. | |
Vishwakarma B. D., Ziegler Bamber Y J L; Royston, S Separating GIA signal from surface mass change using GPS and GRACE data Journal Article Geophysical Journal International, 232 (1), pp. 537-547, 2022. Abstract | Links | BibTeX | Tags: . @article{Vishwakarma2022, title = {Separating GIA signal from surface mass change using GPS and GRACE data}, author = {Vishwakarma, B. D., Ziegler, Y., Bamber, J. L., and Royston, S.}, url = {https://doi.org/10.1093/gji/ggac336}, doi = {doi:10.1093/gji/ggac336}, year = {2022}, date = {2022-08-23}, journal = {Geophysical Journal International}, volume = {232}, number = {1}, pages = {537-547}, abstract = {The visco-elastic response of the solid Earth to the past glacial cycles and the present-day surface mass change (PDSMC) are detected by the geodetic observation systems such as global navigation satellite system and satellite gravimetry. Majority of the contemporary PDSMC is driven by climate change and in order to better understand them using the aforementioned geodetic observations, glacial isostatic adjustment (GIA) signal should be accounted first. The default approach is to use forward GIA models that use uncertain ice-load history and approximate Earth rheology to predict GIA, yielding large uncertainties. The proliferation of contemporary, global, geodetic observations and their coverage have therefore enabled estimation of data-driven GIA solutions. A novel framework is presented that uses geophysical relations between the vertical land motion (VLM) and geopotential anomaly due to GIA and PDSMC to express GPS VLM trends and GRACE geopotential trends as a function of either GIA or PDSMC, which can be easily solved using least-squares regression. The GIA estimates are data-driven and differ significantly from forward models over Alaska and Greenland.}, keywords = {.}, pubstate = {published}, tppubtype = {article} } The visco-elastic response of the solid Earth to the past glacial cycles and the present-day surface mass change (PDSMC) are detected by the geodetic observation systems such as global navigation satellite system and satellite gravimetry. Majority of the contemporary PDSMC is driven by climate change and in order to better understand them using the aforementioned geodetic observations, glacial isostatic adjustment (GIA) signal should be accounted first. The default approach is to use forward GIA models that use uncertain ice-load history and approximate Earth rheology to predict GIA, yielding large uncertainties. The proliferation of contemporary, global, geodetic observations and their coverage have therefore enabled estimation of data-driven GIA solutions. A novel framework is presented that uses geophysical relations between the vertical land motion (VLM) and geopotential anomaly due to GIA and PDSMC to express GPS VLM trends and GRACE geopotential trends as a function of either GIA or PDSMC, which can be easily solved using least-squares regression. The GIA estimates are data-driven and differ significantly from forward models over Alaska and Greenland. | |
Fang Xu Yilei Shi, Patrick Ebel Lei Yu Gui-Song Xia Wen Yang ; Zhu, Xiao Xiang GLF-CR: SAR-Enhanced Cloud Removal with Global-Local Fusion Journal Article ISPRS Journal of Photogrammetry and Remote Sensing, 2022. @article{xu2022, title = {GLF-CR: SAR-Enhanced Cloud Removal with Global-Local Fusion}, author = {Fang Xu, Yilei Shi, Patrick Ebel,Lei Yu, Gui-Song Xia, Wen Yang, and Xiao Xiang Zhu}, year = {2022}, date = {2022-08-10}, journal = {ISPRS Journal of Photogrammetry and Remote Sensing}, keywords = {.}, pubstate = {published}, tppubtype = {article} } | |
Mrinalini Kochupillai Matthias Kahl, Michael Schmitt Hannes Taubenböck Xiao Xiang Zhu Artificial Intelligence for Earth Observation: Understanding Emerging Ethical Issues and Opportunities Journal Article IEEE Geoscience and Remote Sensing Magazine, 2022. @article{kochupillai2022, title = {Artificial Intelligence for Earth Observation: Understanding Emerging Ethical Issues and Opportunities}, author = {Mrinalini Kochupillai, Matthias Kahl, Michael Schmitt, Hannes Taubenböck, Xiao Xiang Zhu}, year = {2022}, date = {2022-08-10}, journal = {IEEE Geoscience and Remote Sensing Magazine}, keywords = {.}, pubstate = {published}, tppubtype = {article} } | |
Grinsted A., Bamber Bingham Buzzard Nias Ng J R S I K; Weeks, J The Transient Sea Level response to external forcing in CMIP6 models Journal Article Earth's Future, 10 (10), 2022. Abstract | Links | BibTeX | Tags: . @article{Grinsted2022, title = {The Transient Sea Level response to external forcing in CMIP6 models}, author = {Grinsted, A., Bamber, J., Bingham, R., Buzzard, S., Nias, I., Ng, K., and Weeks, J.}, url = {https://doi.org/10.1029/2022EF002696}, doi = {2022EF002696}, year = {2022}, date = {2022-08-02}, journal = {Earth's Future}, volume = {10}, number = {10}, abstract = {Earth is warming and sea levels are rising as land-based ice is lost to melt, and oceans expand due to accumulation of heat. The pace of ice loss and steric expansion is linked to the intensity of warming. How much faster sea level will rise as climate warms is, however, highly uncertain and difficult to model. Here, we quantify the transient sea level sensitivity of the sea level budget in both models and observations. Models show little change in sensitivity to warming between the first and second half of the twenty-first century for most contributors. The exception is glaciers and ice caps (GIC) that have a greater sensitivity pre-2050 (2.8 ± 0.4 mm/yr/K) compared to later (0.7 ± 0.1 mm/yr/K). We attribute this change to the short response time of glaciers and their changing area over time. Model sensitivities of steric expansion (1.5 ± 0.2 mm/yr/K), and Greenland Ice Sheet mass loss (0.8 ± 0.2 mm/yr/K) are greater than, but still compatible with, corresponding estimates from historical data (1.4 ± 0.5 and 0.4 ± 0.2 mm/yr/K). Antarctic Ice Sheet (AIS) models tends to show lower rates of sea level rise (SLR) with warming (−0.0 ± 0.3 mm/yr/K) in contrast to historical estimates (0.4 ± 0.2 mm/yr/K). This apparent low bias in AIS sensitivity is only partly able to account for a similar low bias identified in the sensitivity of global mean sea level excluding GIC (3.1 ± 0.4 vs. 2.3 ± 0.4 mm/yr/K). The balance temperature, where SLR is zero, lies close to the pre-industrial value, implying that SLR can only be mitigated by substantial global cooling.}, keywords = {.}, pubstate = {published}, tppubtype = {article} } Earth is warming and sea levels are rising as land-based ice is lost to melt, and oceans expand due to accumulation of heat. The pace of ice loss and steric expansion is linked to the intensity of warming. How much faster sea level will rise as climate warms is, however, highly uncertain and difficult to model. Here, we quantify the transient sea level sensitivity of the sea level budget in both models and observations. Models show little change in sensitivity to warming between the first and second half of the twenty-first century for most contributors. The exception is glaciers and ice caps (GIC) that have a greater sensitivity pre-2050 (2.8 ± 0.4 mm/yr/K) compared to later (0.7 ± 0.1 mm/yr/K). We attribute this change to the short response time of glaciers and their changing area over time. Model sensitivities of steric expansion (1.5 ± 0.2 mm/yr/K), and Greenland Ice Sheet mass loss (0.8 ± 0.2 mm/yr/K) are greater than, but still compatible with, corresponding estimates from historical data (1.4 ± 0.5 and 0.4 ± 0.2 mm/yr/K). Antarctic Ice Sheet (AIS) models tends to show lower rates of sea level rise (SLR) with warming (−0.0 ± 0.3 mm/yr/K) in contrast to historical estimates (0.4 ± 0.2 mm/yr/K). This apparent low bias in AIS sensitivity is only partly able to account for a similar low bias identified in the sensitivity of global mean sea level excluding GIC (3.1 ± 0.4 vs. 2.3 ± 0.4 mm/yr/K). The balance temperature, where SLR is zero, lies close to the pre-industrial value, implying that SLR can only be mitigated by substantial global cooling. | |
Vishwakarma BD Ramsankaran R, Azam MF Bolch Mandal Srivastava Kumar Sahu Navinkumar PJ Tanniru SR Javed Soheb Dimri AP Yadav Devaraju Chinnasamy Reddy MJ Murugesan GP Arora Jain SK Ojha CSP Harrison T A S P R A M M B P M S; J, Bamber Challenges in Understanding the Variability of the Cryosphere in the Himalaya and Its Impact on Regional Water Resources Journal Article Frontiers in Water, 4 , 2022. Abstract | Links | BibTeX | Tags: . @article{BD2022, title = {Challenges in Understanding the Variability of the Cryosphere in the Himalaya and Its Impact on Regional Water Resources}, author = {Vishwakarma BD, Ramsankaran R, Azam MF, Bolch T, Mandal A, Srivastava S, Kumar P, Sahu R, Navinkumar PJ, Tanniru SR, Javed A, Soheb M, Dimri AP, Yadav M, Devaraju B, Chinnasamy P, Reddy MJ, Murugesan GP, Arora M, Jain SK, Ojha CSP, Harrison S and Bamber J }, url = { https://doi.org/10.3389/frwa.2022.909246}, year = {2022}, date = {2022-07-28}, journal = {Frontiers in Water}, volume = {4}, abstract = {The Himalaya plays a vital role in regulating the freshwater availability for nearly a billion people living in the Indus, Ganga, and Brahmaputra River basins. Due to climate change and constantly evolving human-hydrosphere interactions, including land use/cover changes, groundwater extraction, reservoir or dam construction, water availability has undergone significant change, and is expected to change further in the future. Therefore, understanding the spatiotemporal evolution of the hydrological cycle over the Himalaya and its river basins has been one of the most critical exercises toward ensuring regional water security. However, due to the lack of extensive in-situ measurements, complex hydro-climatic environment, and limited collaborative efforts, large gaps in our understanding exist. Moreover, there are several significant issues with available studies, such as lack of consistent hydro-meteorological datasets, very few attempts at integrating different data types, limited spatiotemporal sampling of hydro-meteorological measurements, lack of open access to in-situ datasets, poorly accounted anthropogenic climate feedbacks, and limited understanding of the hydro-meteorological drivers over the region. These factors result in large uncertainties in our estimates of current and future water availability over the Himalaya, which constraints the development of sustainable water management strategies for its river catchments hampering our preparedness for the current and future changes in hydro-climate. To address these issues, a partnership development workshop entitled “Water sEcurity assessment in rIvers oriGinating from Himalaya (WEIGH),” was conducted between the 07th and 11th September 2020. Based on the intense discussions and deliberations among the participants, the most important and urgent research questions were identified. This white paper synthesizes the current understanding, highlights, and the most significant research gaps and research priorities for studying water availability in the Himalaya.}, keywords = {.}, pubstate = {published}, tppubtype = {article} } The Himalaya plays a vital role in regulating the freshwater availability for nearly a billion people living in the Indus, Ganga, and Brahmaputra River basins. Due to climate change and constantly evolving human-hydrosphere interactions, including land use/cover changes, groundwater extraction, reservoir or dam construction, water availability has undergone significant change, and is expected to change further in the future. Therefore, understanding the spatiotemporal evolution of the hydrological cycle over the Himalaya and its river basins has been one of the most critical exercises toward ensuring regional water security. However, due to the lack of extensive in-situ measurements, complex hydro-climatic environment, and limited collaborative efforts, large gaps in our understanding exist. Moreover, there are several significant issues with available studies, such as lack of consistent hydro-meteorological datasets, very few attempts at integrating different data types, limited spatiotemporal sampling of hydro-meteorological measurements, lack of open access to in-situ datasets, poorly accounted anthropogenic climate feedbacks, and limited understanding of the hydro-meteorological drivers over the region. These factors result in large uncertainties in our estimates of current and future water availability over the Himalaya, which constraints the development of sustainable water management strategies for its river catchments hampering our preparedness for the current and future changes in hydro-climate. To address these issues, a partnership development workshop entitled “Water sEcurity assessment in rIvers oriGinating from Himalaya (WEIGH),” was conducted between the 07th and 11th September 2020. Based on the intense discussions and deliberations among the participants, the most important and urgent research questions were identified. This white paper synthesizes the current understanding, highlights, and the most significant research gaps and research priorities for studying water availability in the Himalaya. | |
Sam Royston Rory J. Bingham, ; Bamber, Jonathan L Attributing decadal climate variability in coastal sea-level trends Journal Article Ocean Science, 18 (4), pp. 1093–1107, 2022. Abstract | Links | BibTeX | Tags: . @article{Royston2022, title = {Attributing decadal climate variability in coastal sea-level trends}, author = { Sam Royston, Rory J. Bingham, and Jonathan L. Bamber }, url = {https://doi.org/10.5194/os-18-1093-2022}, year = {2022}, date = {2022-07-27}, journal = {Ocean Science}, volume = {18}, number = {4}, pages = {1093–1107}, abstract = {Decadal sea-level variability masks longer-term changes due to natural and anthropogenic drivers in short-duration records and increases uncertainty in trend and acceleration estimates. When making regional coastal management and adaptation decisions, it is important to understand the drivers of these changes to account for periods of reduced or enhanced sea-level change. The variance in decadal sea-level trends about the global mean is quantified and mapped around the global coastlines of the Atlantic, Pacific, and Indian oceans from historical CMIP6 runs and a high-resolution ocean model forced by reanalysis data. We reconstruct coastal, sea-level trends via linear relationships with climate mode and oceanographic indices. Using this approach, more than one-third of the variability in decadal sea-level trends can be explained by climate indices at 24.6 % to 73.1 % of grid cells located within 25 km of a coast in the Atlantic, Pacific, and Indian oceans. At 10.9 % of the world's coastline, climate variability explains over two-thirds of the decadal sea-level trend. By investigating the steric, manometric, and gravitational components of sea-level trend independently, it is apparent that much of the coastal ocean variability is dominated by the manometric signal, the consequence of the open-ocean steric signal propagating onto the continental shelf. Additionally, decadal variability in the gravitational, rotational, and solid-Earth deformation (GRD) signal should not be ignored in the total. There are locations such as the Persian Gulf and African west coast where decadal sea-level variability is historically small that are susceptible to future changes in hydrology and/or ice mass changes that drive intensified regional GRD sea-level change above the global mean. The magnitude of variance explainable by climate modes quantified in this study indicates an enhanced uncertainty in projections of short- to mid-term regional sea-level trend.}, keywords = {.}, pubstate = {published}, tppubtype = {article} } Decadal sea-level variability masks longer-term changes due to natural and anthropogenic drivers in short-duration records and increases uncertainty in trend and acceleration estimates. When making regional coastal management and adaptation decisions, it is important to understand the drivers of these changes to account for periods of reduced or enhanced sea-level change. The variance in decadal sea-level trends about the global mean is quantified and mapped around the global coastlines of the Atlantic, Pacific, and Indian oceans from historical CMIP6 runs and a high-resolution ocean model forced by reanalysis data. We reconstruct coastal, sea-level trends via linear relationships with climate mode and oceanographic indices. Using this approach, more than one-third of the variability in decadal sea-level trends can be explained by climate indices at 24.6 % to 73.1 % of grid cells located within 25 km of a coast in the Atlantic, Pacific, and Indian oceans. At 10.9 % of the world's coastline, climate variability explains over two-thirds of the decadal sea-level trend. By investigating the steric, manometric, and gravitational components of sea-level trend independently, it is apparent that much of the coastal ocean variability is dominated by the manometric signal, the consequence of the open-ocean steric signal propagating onto the continental shelf. Additionally, decadal variability in the gravitational, rotational, and solid-Earth deformation (GRD) signal should not be ignored in the total. There are locations such as the Persian Gulf and African west coast where decadal sea-level variability is historically small that are susceptible to future changes in hydrology and/or ice mass changes that drive intensified regional GRD sea-level change above the global mean. The magnitude of variance explainable by climate modes quantified in this study indicates an enhanced uncertainty in projections of short- to mid-term regional sea-level trend. | |
van den Khan S. A., Colgan Neumann Broeke Brunt Noël Bamber Hassan W T A M R K M B J L J; Bjørk, A A Accelerating Ice Loss From Peripheral Glaciers in North Greenland Journal Article Geophysical Research Letters, 49 (12), 2022. Abstract | Links | BibTeX | Tags: . @article{Khan2022c, title = {Accelerating Ice Loss From Peripheral Glaciers in North Greenland}, author = {Khan, S. A., Colgan, W., Neumann, T. A., van den Broeke, M. R., Brunt, K. M., Noël, B., Bamber, J. L., Hassan, J., and Bjørk, A. A. }, url = {https://doi.org/10.1029/2022GL098915}, doi = {2022GL098915}, year = {2022}, date = {2022-06-16}, journal = {Geophysical Research Letters}, volume = {49}, number = {12}, abstract = {In recent decades, Greenland's peripheral glaciers have experienced large-scale mass loss, resulting in a substantial contribution to sea level rise. While their total area of Greenland ice cover is relatively small (4%), their mass loss is disproportionally large compared to the Greenland ice sheet. Satellite altimetry from Ice, Cloud, and land Elevation Satellite (ICESat) and ICESat-2 shows that mass loss from Greenland's peripheral glaciers increased from 27.2 ± 6.2 Gt/yr (February 2003–October 2009) to 42.3 ± 6.2 Gt/yr (October 2018–December 2021). These relatively small glaciers now constitute 11 ± 2% of Greenland's ice loss and contribute to global sea level rise. In the period October 2018–December 2021, mass loss increased by a factor of four for peripheral glaciers in North Greenland. While peripheral glacier mass loss is widespread, we also observe a complex regional pattern where increases in precipitation at high altitudes have partially counteracted increases in melt at low altitude.}, keywords = {.}, pubstate = {published}, tppubtype = {article} } In recent decades, Greenland's peripheral glaciers have experienced large-scale mass loss, resulting in a substantial contribution to sea level rise. While their total area of Greenland ice cover is relatively small (4%), their mass loss is disproportionally large compared to the Greenland ice sheet. Satellite altimetry from Ice, Cloud, and land Elevation Satellite (ICESat) and ICESat-2 shows that mass loss from Greenland's peripheral glaciers increased from 27.2 ± 6.2 Gt/yr (February 2003–October 2009) to 42.3 ± 6.2 Gt/yr (October 2018–December 2021). These relatively small glaciers now constitute 11 ± 2% of Greenland's ice loss and contribute to global sea level rise. In the period October 2018–December 2021, mass loss increased by a factor of four for peripheral glaciers in North Greenland. While peripheral glacier mass loss is widespread, we also observe a complex regional pattern where increases in precipitation at high altitudes have partially counteracted increases in melt at low altitude. | |
Rosa, Laura Elena Cué La; Oliveira, Dário Augusto Borges Learning from Label Proportions with Prototypical Contrastive Clustering Journal Article Proceedings of the AAAI Conference on Artificial Intelligence, 36 (2), pp. 2153-2161, 2022. @article{Rosa_Oliveira_2022, title = {Learning from Label Proportions with Prototypical Contrastive Clustering}, author = {Laura Elena Cué La Rosa and Dário Augusto Borges Oliveira}, url = {https://ojs.aaai.org/index.php/AAAI/article/view/20112}, doi = {10.1609/aaai.v36i2.20112}, year = {2022}, date = {2022-06-01}, journal = {Proceedings of the AAAI Conference on Artificial Intelligence}, volume = {36}, number = {2}, pages = {2153-2161}, keywords = {}, pubstate = {published}, tppubtype = {article} } | |
Chuter S. J., Zammit-Mangion Rougier Dawson A J G; Bamber, J L Mass evolution of the Antarctic Peninsula over the last 2 decades from a joint Bayesian inversion Journal Article The Cryosphere, 16 (4), pp. 1349-1367, 2022. Abstract | Links | BibTeX | Tags: . @article{Chuter2022, title = {Mass evolution of the Antarctic Peninsula over the last 2 decades from a joint Bayesian inversion}, author = {Chuter, S. J., Zammit-Mangion, A., Rougier, J., Dawson, G., and Bamber, J. L. }, url = {https://tc.copernicus.org/articles/16/1349/2022/}, doi = {tc-16-1349-2022}, year = {2022}, date = {2022-04-12}, journal = {The Cryosphere}, volume = {16}, number = {4}, pages = {1349-1367}, abstract = {The Antarctic Peninsula has become an increasingly important component of the Antarctic Ice Sheet mass budget over the last 2 decades, with mass losses generally increasing. However, due to the challenges presented by the topography and geometry of the region, there remain large variations in mass balance estimates from conventional approaches and in assessing the relative contribution of individual ice sheet processes. Here, we use a regionally optimized Bayesian hierarchical model joint inversion approach that combines data from multiple altimetry studies (ENVISAT, ICESat, CryoSat-2 swath), gravimetry (GRACE and GRACE-FO), and localized DEM differencing observations to solve for annual mass trends and their attribution to individual driving processes for the period 2003–2019. This is first time that such localized observations have been assimilated directly to estimate mass balance as part of a wider-scale regional assessment. The region experienced a mass imbalance rate of Gt yr−1 between 2003 and 2019, predominantly driven by accelerations in ice dynamic mass losses in the first decade and sustained thereafter. Inter-annual variability is driven by surface processes, particularly in 2016 due to increased precipitation driven by an extreme El Niño, which temporarily returned the sector back to a state of positive mass balance. In the West Palmer Land and the English Coast regions, surface processes are a greater contributor to mass loss than ice dynamics in the early part of the 2010s. Our results show good agreement with conventional and other combination approaches, improving confidence in the robustness of mass trend estimates, and in turn, understanding of the region's response to changes in external forcing.}, keywords = {.}, pubstate = {published}, tppubtype = {article} } The Antarctic Peninsula has become an increasingly important component of the Antarctic Ice Sheet mass budget over the last 2 decades, with mass losses generally increasing. However, due to the challenges presented by the topography and geometry of the region, there remain large variations in mass balance estimates from conventional approaches and in assessing the relative contribution of individual ice sheet processes. Here, we use a regionally optimized Bayesian hierarchical model joint inversion approach that combines data from multiple altimetry studies (ENVISAT, ICESat, CryoSat-2 swath), gravimetry (GRACE and GRACE-FO), and localized DEM differencing observations to solve for annual mass trends and their attribution to individual driving processes for the period 2003–2019. This is first time that such localized observations have been assimilated directly to estimate mass balance as part of a wider-scale regional assessment. The region experienced a mass imbalance rate of Gt yr−1 between 2003 and 2019, predominantly driven by accelerations in ice dynamic mass losses in the first decade and sustained thereafter. Inter-annual variability is driven by surface processes, particularly in 2016 due to increased precipitation driven by an extreme El Niño, which temporarily returned the sector back to a state of positive mass balance. In the West Palmer Land and the English Coast regions, surface processes are a greater contributor to mass loss than ice dynamics in the early part of the 2010s. Our results show good agreement with conventional and other combination approaches, improving confidence in the robustness of mass trend estimates, and in turn, understanding of the region's response to changes in external forcing. | |
Qian, Kun; Wang, Yuanyuan; Shi, Yilei; Zhu, Xiao Xiang γ-Net: Superresolving SAR Tomographic Inversion via Deep Learning Journal Article IEEE Transactions on Geoscience and Remote Sensing, 2022. @article{, title = {γ-Net: Superresolving SAR Tomographic Inversion via Deep Learning}, author = {Kun Qian and Yuanyuan Wang and Yilei Shi and Xiao Xiang Zhu}, year = {2022}, date = {2022-03-30}, booktitle = {IEEE Transactions on Geoscience and Remote Sensing}, journal = {IEEE Transactions on Geoscience and Remote Sensing}, abstract = {(This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.) Synthetic aperture radar tomography (TomoSAR) has been extensively employed in 3-D reconstruction in dense urban areas using high-resolution SAR acquisitions. Compressive sensing (CS)-based algorithms are generally considered as the state of the art in super-resolving TomoSAR, in particular in the single look case. This superior performance comes at the cost of extra computational burdens, because of the sparse reconstruction, which cannot be solved analytically and we need to employ computationally expensive iterative solvers. In this paper, we propose a novel deep learning-based super-resolving TomoSAR inversion approach, γ-Net, to tackle this challenge. γ-Net adopts advanced complex-valued learned iterative shrinkage thresholding algorithm (CV-LISTA) to mimic the iterative optimization step in sparse reconstruction. Simulations show the height estimate from a well-trained γ-Net approaches the Cramér-Rao lower bound while improving the computational efficiency by 1 to 2 orders of magnitude comparing to the first-order CS-based methods. It also shows no degradation in the super-resolution power comparing to the state-of-the-art second-order TomoSAR solvers, which are much more computationally expensive than the first-order methods. Specifically, γ-Net reaches more than 90% detection rate in moderate super-resolving cases at 25 measurements at 6dB SNR. Moreover, simulation at limited baselines demonstrates that the proposed algorithm outperforms the second-order CS-based method by a fair margin. Test on real TerraSAR-X data with just 6 interferograms also shows high-quality 3-D reconstruction with high-density detected double scatterers.}, keywords = {}, pubstate = {published}, tppubtype = {article} } (This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.) Synthetic aperture radar tomography (TomoSAR) has been extensively employed in 3-D reconstruction in dense urban areas using high-resolution SAR acquisitions. Compressive sensing (CS)-based algorithms are generally considered as the state of the art in super-resolving TomoSAR, in particular in the single look case. This superior performance comes at the cost of extra computational burdens, because of the sparse reconstruction, which cannot be solved analytically and we need to employ computationally expensive iterative solvers. In this paper, we propose a novel deep learning-based super-resolving TomoSAR inversion approach, γ-Net, to tackle this challenge. γ-Net adopts advanced complex-valued learned iterative shrinkage thresholding algorithm (CV-LISTA) to mimic the iterative optimization step in sparse reconstruction. Simulations show the height estimate from a well-trained γ-Net approaches the Cramér-Rao lower bound while improving the computational efficiency by 1 to 2 orders of magnitude comparing to the first-order CS-based methods. It also shows no degradation in the super-resolution power comparing to the state-of-the-art second-order TomoSAR solvers, which are much more computationally expensive than the first-order methods. Specifically, γ-Net reaches more than 90% detection rate in moderate super-resolving cases at 25 measurements at 6dB SNR. Moreover, simulation at limited baselines demonstrates that the proposed algorithm outperforms the second-order CS-based method by a fair margin. Test on real TerraSAR-X data with just 6 interferograms also shows high-quality 3-D reconstruction with high-density detected double scatterers. | |
van den Shfaqat A. Khan Jonathan L. Bamber, Eric Rignot Veit Helm Andy Aschwanden David Holland Michiel Broeke Michalea King Brice Noël Martin Truffer Angelika Humbert William Colgan Saurabh Vijay Peter Kuipers Munneke M Greenland Mass Trends From Airborne and Satellite Altimetry During 2011–2020 Journal Article Journal of Geophysical Research: Earth Surface, 127 (4), 2022. Abstract | Links | BibTeX | Tags: . @article{Khan2022b, title = {Greenland Mass Trends From Airborne and Satellite Altimetry During 2011–2020}, author = {Shfaqat A. Khan, Jonathan L. Bamber, Eric Rignot, Veit Helm, Andy Aschwanden, David M. Holland, Michiel van den Broeke, Michalea King, Brice Noël, Martin Truffer, Angelika Humbert, William Colgan, Saurabh Vijay, Peter Kuipers Munneke }, url = {https://doi.org/10.1029/2021JF006505}, doi = {e2021JF006505}, year = {2022}, date = {2022-03-21}, journal = {Journal of Geophysical Research: Earth Surface}, volume = {127}, number = {4}, abstract = {We use satellite and airborne altimetry to estimate annual mass changes of the Greenland Ice Sheet. We estimate ice loss corresponding to a sea-level rise of 6.9 ± 0.4 mm from April 2011 to April 2020, with a highest annual ice loss rate of 1.4 mm/yr sea-level equivalent from April 2019 to April 2020. On a regional scale, our annual mass loss timeseries reveals 10–15 m/yr dynamic thickening at the terminus of Jakobshavn Isbræ from April 2016 to April 2018, followed by a return to dynamic thinning. We observe contrasting patterns of mass loss acceleration in different basins across the ice sheet and suggest that these spatiotemporal trends could be useful for calibrating and validating prognostic ice sheet models. In addition to resolving the spatial and temporal fingerprint of Greenland's recent ice loss, these mass loss grids are key for partitioning contemporary elastic vertical land motion from longer-term glacial isostatic adjustment (GIA) trends at GPS stations around the ice sheet. Our ice-loss product results in a significantly different GIA interpretation from a previous ice-loss product.}, keywords = {.}, pubstate = {published}, tppubtype = {article} } We use satellite and airborne altimetry to estimate annual mass changes of the Greenland Ice Sheet. We estimate ice loss corresponding to a sea-level rise of 6.9 ± 0.4 mm from April 2011 to April 2020, with a highest annual ice loss rate of 1.4 mm/yr sea-level equivalent from April 2019 to April 2020. On a regional scale, our annual mass loss timeseries reveals 10–15 m/yr dynamic thickening at the terminus of Jakobshavn Isbræ from April 2016 to April 2018, followed by a return to dynamic thinning. We observe contrasting patterns of mass loss acceleration in different basins across the ice sheet and suggest that these spatiotemporal trends could be useful for calibrating and validating prognostic ice sheet models. In addition to resolving the spatial and temporal fingerprint of Greenland's recent ice loss, these mass loss grids are key for partitioning contemporary elastic vertical land motion from longer-term glacial isostatic adjustment (GIA) trends at GPS stations around the ice sheet. Our ice-loss product results in a significantly different GIA interpretation from a previous ice-loss product. | |
Tom Mitcham G. Hilmar Gudmundsson, ; Bamber, Jonathan L The instantaneous impact of calving and thinning on the Larsen C Ice Shelf Journal Article The Cryosphere, 16 (3), pp. 883–901, 2022. Abstract | Links | BibTeX | Tags: . @article{Mitcham2022, title = {The instantaneous impact of calving and thinning on the Larsen C Ice Shelf}, author = {Tom Mitcham, G. Hilmar Gudmundsson, and Jonathan L. Bamber }, url = {https://doi.org/10.5194/tc-16-883-2022}, doi = {tc-16-883-2022}, year = {2022}, date = {2022-03-11}, journal = {The Cryosphere}, volume = {16}, number = {3}, pages = {883–901}, abstract = {The Antarctic Peninsula has seen rapid and widespread changes in the extent of its ice shelves in recent decades, including the collapse of the Larsen A and B ice shelves in 1995 and 2002, respectively. In 2017 the Larsen C Ice Shelf (LCIS) lost around 10 % of its area by calving one of the largest icebergs ever recorded (A68). This has raised questions about the structural integrity of the shelf and the impact of any changes in its extent on the flow of its tributary glaciers. In this work, we used an ice flow model to study the instantaneous impact of changes in the thickness and extent of the LCIS on ice dynamics and in particular on changes in the grounding line flux (GLF). We initialised the model to a pre-A68 calving state and first replicated the calving of the A68 iceberg. We found that there was a limited instantaneous impact on upstream flow – with speeds increasing by less than 10 % across almost all of the shelf – and a 0.28 % increase in GLF. This result is supported by observations of ice velocity made before and after the calving event. We then perturbed the ice-shelf geometry through a series of instantaneous, idealised calving and thinning experiments of increasing magnitude. We found that significant changes to the geometry of the ice shelf, through both calving and thinning, resulted in limited instantaneous changes in GLF. For example, to produce a doubling of GLF from calving, the new calving front needed to be moved to 5 km from the grounding line, removing almost the entire ice shelf. For thinning, over 200 m of the ice-shelf thickness had to be removed across the whole shelf to produce a doubling of GLF. Calculating the instantaneous increase in GLF (607 %) after removing the entire ice shelf allowed us to quantify the total amount of buttressing provided by the LCIS. From this, we identified that the region of the ice shelf in the first 5 km downstream of the grounding line provided over 80 % of the buttressing capacity of the shelf. This is due to the large resistive stresses generated in the narrow, local embayments downstream of the largest tributary glaciers.}, keywords = {.}, pubstate = {published}, tppubtype = {article} } The Antarctic Peninsula has seen rapid and widespread changes in the extent of its ice shelves in recent decades, including the collapse of the Larsen A and B ice shelves in 1995 and 2002, respectively. In 2017 the Larsen C Ice Shelf (LCIS) lost around 10 % of its area by calving one of the largest icebergs ever recorded (A68). This has raised questions about the structural integrity of the shelf and the impact of any changes in its extent on the flow of its tributary glaciers. In this work, we used an ice flow model to study the instantaneous impact of changes in the thickness and extent of the LCIS on ice dynamics and in particular on changes in the grounding line flux (GLF). We initialised the model to a pre-A68 calving state and first replicated the calving of the A68 iceberg. We found that there was a limited instantaneous impact on upstream flow – with speeds increasing by less than 10 % across almost all of the shelf – and a 0.28 % increase in GLF. This result is supported by observations of ice velocity made before and after the calving event. We then perturbed the ice-shelf geometry through a series of instantaneous, idealised calving and thinning experiments of increasing magnitude. We found that significant changes to the geometry of the ice shelf, through both calving and thinning, resulted in limited instantaneous changes in GLF. For example, to produce a doubling of GLF from calving, the new calving front needed to be moved to 5 km from the grounding line, removing almost the entire ice shelf. For thinning, over 200 m of the ice-shelf thickness had to be removed across the whole shelf to produce a doubling of GLF. Calculating the instantaneous increase in GLF (607 %) after removing the entire ice shelf allowed us to quantify the total amount of buttressing provided by the LCIS. From this, we identified that the region of the ice shelf in the first 5 km downstream of the grounding line provided over 80 % of the buttressing capacity of the shelf. This is due to the large resistive stresses generated in the narrow, local embayments downstream of the largest tributary glaciers. | |
Li T., Dawson Chuter G J S J; Bamber, J L A high-resolution Antarctic grounding zone product from ICESat-2 laser altimetry Journal Article Earth System Science Data, 14 (2), pp. 535–557, 2022. Abstract | Links | BibTeX | Tags: . @article{Li2022c, title = {A high-resolution Antarctic grounding zone product from ICESat-2 laser altimetry}, author = {Li, T., Dawson, G. J., Chuter, S. J., and Bamber, J. L. }, url = {https://doi.org/10.5194/essd-14-535-2022}, doi = {essd-14-535-2022}, year = {2022}, date = {2022-02-08}, journal = {Earth System Science Data}, volume = {14}, number = {2}, pages = {535–557}, abstract = {The Antarctic grounding zone, which is the transition between the fully grounded ice sheet to freely floating ice shelf, plays a critical role in ice sheet stability, mass budget calculations, and ice sheet model projections. It is therefore important to continuously monitor its location and migration over time. Here we present the first ICESat-2-derived high-resolution grounding zone product of the Antarctic Ice Sheet, including three important boundaries: the inland limit of tidal flexure (Point F), inshore limit of hydrostatic equilibrium (Point H), and the break in slope (Point Ib). This dataset was derived from automated techniques developed in this study, using ICESat-2 laser altimetry repeat tracks between 30 March 2019 and 30 September 2020. The new grounding zone product has a near-complete coverage of the Antarctic Ice Sheet with a total of 21 346 Point F, 18 149 Point H, and 36 765 Point Ib locations identified, including the difficult-to-survey grounding zones, such as the fast-flowing glaciers draining into the Amundsen Sea embayment. The locations of newly derived ICESat-2 landward limit of tidal flexure agree well with the most recent differential synthetic aperture radar interferometry (DInSAR) observations in 2018, with a mean absolute separation and standard deviation of 0.02 and 0.02 km, respectively. By comparing the ICESat-2-derived grounding zone with the previous grounding zone products, we find a grounding line retreat of up to 15 km on the Crary Ice Rise of Ross Ice Shelf and a pervasive landward grounding line migration along the Amundsen Sea embayment during the past 2 decades. We also identify the presence of ice plains on the Filchner–Ronne Ice Shelf and the influence of oscillating ocean tides on grounding zone migration. The product derived from this study is available at https://doi.org/10.5523/bris.bnqqyngt89eo26qk8keckglww (Li et al., 2021) and is archived and maintained at the National Snow and Ice Data Center.}, keywords = {.}, pubstate = {published}, tppubtype = {article} } The Antarctic grounding zone, which is the transition between the fully grounded ice sheet to freely floating ice shelf, plays a critical role in ice sheet stability, mass budget calculations, and ice sheet model projections. It is therefore important to continuously monitor its location and migration over time. Here we present the first ICESat-2-derived high-resolution grounding zone product of the Antarctic Ice Sheet, including three important boundaries: the inland limit of tidal flexure (Point F), inshore limit of hydrostatic equilibrium (Point H), and the break in slope (Point Ib). This dataset was derived from automated techniques developed in this study, using ICESat-2 laser altimetry repeat tracks between 30 March 2019 and 30 September 2020. The new grounding zone product has a near-complete coverage of the Antarctic Ice Sheet with a total of 21 346 Point F, 18 149 Point H, and 36 765 Point Ib locations identified, including the difficult-to-survey grounding zones, such as the fast-flowing glaciers draining into the Amundsen Sea embayment. The locations of newly derived ICESat-2 landward limit of tidal flexure agree well with the most recent differential synthetic aperture radar interferometry (DInSAR) observations in 2018, with a mean absolute separation and standard deviation of 0.02 and 0.02 km, respectively. By comparing the ICESat-2-derived grounding zone with the previous grounding zone products, we find a grounding line retreat of up to 15 km on the Crary Ice Rise of Ross Ice Shelf and a pervasive landward grounding line migration along the Amundsen Sea embayment during the past 2 decades. We also identify the presence of ice plains on the Filchner–Ronne Ice Shelf and the influence of oscillating ocean tides on grounding zone migration. The product derived from this study is available at https://doi.org/10.5523/bris.bnqqyngt89eo26qk8keckglww (Li et al., 2021) and is archived and maintained at the National Snow and Ice Data Center. | |
Li, Tian; Dawson, Geoffrey J; Chuter, Stephen J; Bamber, Jonathan L A high-resolution Antarctic grounding zone product from ICESat-2 laser altimetry Journal Article Earth System Science Data, 14 , pp. 535-557, 2022, ISSN: 1866-3516. Abstract | Links | BibTeX | Tags: @article{Li2022, title = {A high-resolution Antarctic grounding zone product from ICESat-2 laser altimetry}, author = {Tian Li and Geoffrey J Dawson and Stephen J Chuter and Jonathan L Bamber}, url = {https://essd.copernicus.org/articles/14/535/2022/}, doi = {10.5194/essd-14-535-2022}, issn = {1866-3516}, year = {2022}, date = {2022-01-01}, journal = {Earth System Science Data}, volume = {14}, pages = {535-557}, abstract = { }, <p><![CDATA[Abstract. The Antarctic grounding zone, which is the transition between the fully grounded ice sheet to freely floating ice shelf, plays a critical role in ice sheet stability, mass budget calculations, and ice sheet model projections. It is therefore important to continuously monitor its location and migration over time. Here we present the first ICESat-2-derived high-resolution grounding zone product of the Antarctic Ice Sheet, including three important boundaries: the inland limit of tidal flexure (Point F), inshore limit of hydrostatic equilibrium (Point H), and the break in slope (Point Ib). This dataset was derived from automated techniques developed in this study, using ICESat-2 laser altimetry repeat tracks between 30 March 2019 and 30 September 2020. The new grounding zone product has a near-complete coverage of the Antarctic Ice Sheet with a total of 21 346 Point F, 18 149 Point H, and 36 765 Point Ib locations identified, including the difficult-to-survey grounding zones, such as the fast-flowing glaciers draining into the Amundsen Sea embayment. The locations of newly derived ICESat-2 landward limit of tidal flexure agree well with the most recent differential synthetic aperture radar interferometry (DInSAR) observations in 2018, with a mean absolute separation and standard deviation of 0.02 and 0.02 km, respectively. By comparing the ICESat-2-derived grounding zone with the previous grounding zone products, we find a grounding line retreat of up to 15 km on the Crary Ice Rise of Ross Ice Shelf and a pervasive landward grounding line migration along the Amundsen Sea embayment during the past 2 decades. We also identify the presence of ice plains on the Filchner–Ronne Ice Shelf and the influence of oscillating ocean tides on grounding zone migration. The product derived from this study is available at https://doi.org/10.5523/bris.bnqqyngt89eo26qk8keckglww (Li et al., 2021) and is archived and maintained at the National Snow and Ice Data Center.]]></p> | |
Lehmann, Fanny; Vishwakarma, Bramha Dutt; Bamber, Jonathan How well are we able to close the water budget at the global scale? Journal Article Hydrology and Earth System Sciences, 26 , pp. 35-54, 2022, ISSN: 1607-7938. Abstract | Links | BibTeX | Tags: @article{Lehmann2022, title = {How well are we able to close the water budget at the global scale?}, author = {Fanny Lehmann and Bramha Dutt Vishwakarma and Jonathan Bamber}, url = {https://hess.copernicus.org/articles/26/35/2022/}, doi = {10.5194/hess-26-35-2022}, issn = {1607-7938}, year = {2022}, date = {2022-01-01}, journal = {Hydrology and Earth System Sciences}, volume = {26}, pages = {35-54}, abstract = { Abstract. The water budget equation describes the exchange of water between the land, ocean, and atmosphere. Being able to adequately close the water budget gives confidence in our ability to model and/or observe the spatio-temporal variations in the water cycle and its components. Due to advances in observation techniques, satellite sensors, and modelling, a number of data products are available that represent the components of water budget in both space and time. Despite these advances, closure of the water budget at the global scale has been elusive. In this study, we attempt to close the global water budget using precipitation, evapotranspiration, and runoff data at the catchment scale. The large number of recent state-of-the-art datasets provides a new evaluation of well-used datasets. These estimates are compared to terrestrial water storage (TWS) changes as measured by the Gravity Recovery And Climate Experiment (GRACE) satellite mission. We investigated 189 river basins covering more than 90 % of the continental land area. TWS changes derived from the water balance equation were compared against GRACE data using two metrics: the Nash–Sutcliffe efficiency (NSE) and the cyclostationary NSE. These metrics were used to assess the performance of more than 1600 combinations of the various datasets considered. We found a positive NSE and cyclostationary NSE in 99 % and 62 % of the basins examined respectively. This means that TWS changes reconstructed from the water balance equation were more accurate than the long-term (NSE) and monthly (cyclostationary NSE) mean of GRACE time series in the corresponding basins. By analysing different combinations of the datasets that make up the water balance, we identified data products that performed well in certain regions based on, for example, climatic zone. We identified that some of the good results were obtained due to the cancellation of errors in poor estimates of water budget components. Therefore, we used coefficients of variation to determine the relative quality of a data product, which helped us to identify bad combinations giving us good results. In general, water budget components from ERA5-Land and the Catchment Land Surface Model (CLSM) performed better than other products for most climatic zones. Conversely, the latest version of CLSM, v2.2, performed poorly for evapotranspiration in snow-dominated catchments compared, for example, with its predecessor and other datasets available. Thus, the nature of the catchment dynamics and balance between components affects the optimum combination of datasets. For regional studies, the combination of datasets that provides the most realistic TWS for a basin will depend on its climatic conditions and factors that cannot be determined a priori. We believe that the results of this study provide a road map for studying the water budget at catchment scale. },keywords = {}, pubstate = {published}, tppubtype = {article} } <p>Abstract. The water budget equation describes the exchange of water between the land, ocean, and atmosphere. Being able to adequately close the water budget gives confidence in our ability to model and/or observe the spatio-temporal variations in the water cycle and its components. Due to advances in observation techniques, satellite sensors, and modelling, a number of data products are available that represent the components of water budget in both space and time. Despite these advances, closure of the water budget at the global scale has been elusive. In this study, we attempt to close the global water budget using precipitation, evapotranspiration, and runoff data at the catchment scale. The large number of recent state-of-the-art datasets provides a new evaluation of well-used datasets. These estimates are compared to terrestrial water storage (TWS) changes as measured by the Gravity Recovery And Climate Experiment (GRACE) satellite mission. We investigated 189 river basins covering more than 90 % of the continental land area. TWS changes derived from the water balance equation were compared against GRACE data using two metrics: the Nash–Sutcliffe efficiency (NSE) and the cyclostationary NSE. These metrics were used to assess the performance of more than 1600 combinations of the various datasets considered. We found a positive NSE and cyclostationary NSE in 99 % and 62 % of the basins examined respectively. This means that TWS changes reconstructed from the water balance equation were more accurate than the long-term (NSE) and monthly (cyclostationary NSE) mean of GRACE time series in the corresponding basins. By analysing different combinations of the datasets that make up the water balance, we identified data products that performed well in certain regions based on, for example, climatic zone. We identified that some of the good results were obtained due to the cancellation of errors in poor estimates of water budget components. Therefore, we used coefficients of variation to determine the relative quality of a data product, which helped us to identify bad combinations giving us good results. In general, water budget components from ERA5-Land and the Catchment Land Surface Model (CLSM) performed better than other products for most climatic zones. Conversely, the latest version of CLSM, v2.2, performed poorly for evapotranspiration in snow-dominated catchments compared, for example, with its predecessor and other datasets available. Thus, the nature of the catchment dynamics and balance between components affects the optimum combination of datasets. For regional studies, the combination of datasets that provides the most realistic TWS for a basin will depend on its climatic conditions and factors that cannot be determined a priori. We believe that the results of this study provide a road map for studying the water budget at catchment scale.</p> |