Articles | Volume 25, issue 5
https://doi.org/10.5194/hess-25-2373-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-25-2373-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
06 May 2021
Research article |
| 06 May 2021
| 06 May 2021
A simple cloud-filling approach for remote sensing water cover assessments
Connor Mullen
Department of Civil and Environmental Engineering and Earth Sciences, University of Notre Dame, Notre Dame, IN 46556, USA
Gopal Penny
Department of Civil and Environmental Engineering and Earth Sciences, University of Notre Dame, Notre Dame, IN 46556, USA
Department of Civil and Environmental Engineering and Earth Sciences, University of Notre Dame, Notre Dame, IN 46556, USA
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Cited
15 citations as recorded by crossref.
- Surface water expansion due to increasing water demand on the Loess Plateau Y. Liu et al. 10.1016/j.ejrh.2023.101485
- Improving on mapping long-term surface water with a novel framework based on the Landsat imagery series L. Lan et al. 10.1016/j.jenvman.2024.120202
- High-resolution surface water dynamics in Earth’s small and medium-sized reservoirs G. Donchyts et al. 10.1038/s41598-022-17074-6
- Image similarity-based gap filling method can effectively enrich surface water mapping information W. Huang et al. 10.1016/j.isprsjprs.2023.07.011
- Monitoring Surface Water Inundation of Poyang Lake and Dongting Lake in China Using Sentinel-1 SAR Images Z. Wang et al. 10.3390/rs14143473
- Resolving data gaps in global surface water monthly records through a self-supervised deep learning strategy Z. Hao et al. 10.1016/j.jhydrol.2024.131673
- Unveiling the hidden dynamics of intermittent surface water: A remote sensing framework Z. Xiao et al. 10.1016/j.rse.2024.114285
- Enhancing surface water mapping and monthly dynamics monitoring with a stepwise gap-filling method Z. Liu et al. 10.1080/17538947.2024.2413882
- Intra- and inter-annual spatiotemporal variations and climatic driving factors of surface water area in the Irtysh River Basin during 1985–2022 E. Zhu et al. 10.1016/j.rsase.2025.101455
- From wetlands to wetlandscapes: Remote sensing calibration of process‐based hydrological models in heterogeneous landscapes C. Mullen et al. 10.1002/hyp.14739
- Time-series surface water gap filling based on spatiotemporal neighbourhood similarity B. Bai et al. 10.1016/j.jag.2022.102882
- Beyond clouds: Seamless flood mapping using Harmonized Landsat and Sentinel-2 time series imagery and water occurrence data Z. Li et al. 10.1016/j.isprsjprs.2024.07.022
- Reconstruction of long-term high-resolution lake variability: Algorithm improvement and applications in China L. Feng et al. 10.1016/j.rse.2023.113775
- Catchment-scale thawing and greening decreases long-term nitrogen export in NE Greenland S. Speir et al. 10.1088/1748-9326/ad3e8e
- Mapping irrigation regimes in Chinese paddy lands through multi-source data assimilation Y. Wang et al. 10.1016/j.agwat.2024.109083
15 citations as recorded by crossref.
- Surface water expansion due to increasing water demand on the Loess Plateau Y. Liu et al. 10.1016/j.ejrh.2023.101485
- Improving on mapping long-term surface water with a novel framework based on the Landsat imagery series L. Lan et al. 10.1016/j.jenvman.2024.120202
- High-resolution surface water dynamics in Earth’s small and medium-sized reservoirs G. Donchyts et al. 10.1038/s41598-022-17074-6
- Image similarity-based gap filling method can effectively enrich surface water mapping information W. Huang et al. 10.1016/j.isprsjprs.2023.07.011
- Monitoring Surface Water Inundation of Poyang Lake and Dongting Lake in China Using Sentinel-1 SAR Images Z. Wang et al. 10.3390/rs14143473
- Resolving data gaps in global surface water monthly records through a self-supervised deep learning strategy Z. Hao et al. 10.1016/j.jhydrol.2024.131673
- Unveiling the hidden dynamics of intermittent surface water: A remote sensing framework Z. Xiao et al. 10.1016/j.rse.2024.114285
- Enhancing surface water mapping and monthly dynamics monitoring with a stepwise gap-filling method Z. Liu et al. 10.1080/17538947.2024.2413882
- Intra- and inter-annual spatiotemporal variations and climatic driving factors of surface water area in the Irtysh River Basin during 1985–2022 E. Zhu et al. 10.1016/j.rsase.2025.101455
- From wetlands to wetlandscapes: Remote sensing calibration of process‐based hydrological models in heterogeneous landscapes C. Mullen et al. 10.1002/hyp.14739
- Time-series surface water gap filling based on spatiotemporal neighbourhood similarity B. Bai et al. 10.1016/j.jag.2022.102882
- Beyond clouds: Seamless flood mapping using Harmonized Landsat and Sentinel-2 time series imagery and water occurrence data Z. Li et al. 10.1016/j.isprsjprs.2024.07.022
- Reconstruction of long-term high-resolution lake variability: Algorithm improvement and applications in China L. Feng et al. 10.1016/j.rse.2023.113775
- Catchment-scale thawing and greening decreases long-term nitrogen export in NE Greenland S. Speir et al. 10.1088/1748-9326/ad3e8e
- Mapping irrigation regimes in Chinese paddy lands through multi-source data assimilation Y. Wang et al. 10.1016/j.agwat.2024.109083
Latest update: 04 Mar 2025
Short summary
The level of lake water is rapidly changing globally, and long-term, consistent observations of lake water extents are essential for ascertaining and attributing these changes. These data are rarely collected and challenging to obtain from satellite imagery. The proposed method addresses these challenges without any local data, and it was successfully validated against lakes with and without ground data. The algorithm is a valuable tool for the reliable historical water extent of changing lakes.
The level of lake water is rapidly changing globally, and long-term, consistent observations of...
