Articles | Volume 23, issue 7
https://doi.org/10.5194/hess-23-3037-2019
© Author(s) 2019. 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-23-3037-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
17 Jul 2019
Research article |
| 17 Jul 2019
| 17 Jul 2019
A new dense 18-year time series of surface water fraction estimates from MODIS for the Mediterranean region
Linlin Li
CORRESPONDING AUTHOR
Faculty of Geo-information Science and Earth Observation, University
of Twente, Enschede, the Netherlands
Andrew Skidmore
Faculty of Geo-information Science and Earth Observation, University
of Twente, Enschede, the Netherlands
Department of Environmental Sciences, Macquarie University, NSW,
Australia
Anton Vrieling
Faculty of Geo-information Science and Earth Observation, University
of Twente, Enschede, the Netherlands
Tiejun Wang
Faculty of Geo-information Science and Earth Observation, University
of Twente, Enschede, the Netherlands
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Cited
21 citations as recorded by crossref.
- Combining Multi-Sensor Satellite Imagery to Improve Long-Term Monitoring of Temporary Surface Water Bodies in the Senegal River Floodplain A. Ogilvie et al. 10.3390/rs12193157
- Construction of High Spatial-Temporal Water Body Dataset in China Based on Sentinel-1 Archives and GEE Y. Li et al. 10.3390/rs12152413
- AHSWFM: Automated and Hierarchical Surface Water Fraction Mapping for Small Water Bodies Using Sentinel-2 Images Y. Wang et al. 10.3390/rs14071615
- Development of the global dataset of Wetland Area and Dynamics for Methane Modeling (WAD2M) Z. Zhang et al. 10.5194/essd-13-2001-2021
- Regression-based surface water fraction mapping using a synthetic spectral library for monitoring small water bodies Y. Wang et al. 10.1080/15481603.2023.2217573
- Determining Temporal Uncertainty of a Global Inland Surface Water Time Series S. Mayr et al. 10.3390/rs13173454
- Systematic Water Fraction Estimation for a Global and Daily Surface Water Time-Series S. Mayr et al. 10.3390/rs13142675
- Evaluation of a new 18-year MODIS-derived surface water fraction dataset for constructing Mediterranean wetland open surface water dynamics L. Li et al. 10.1016/j.jhydrol.2020.124956
- A Novel Workflow for Seasonal Wetland Identification Using Bi-Weekly Multiple Remote Sensing Data L. Xing et al. 10.3390/rs14041037
- Automatic mapping of 500 m daily open water body fraction in the American continent using GOES-16 ABI imagery X. Wang et al. 10.1016/j.rse.2024.114040
- Remote Sensing of Surface Water Dynamics in the Context of Global Change—A Review P. Sogno et al. 10.3390/rs14102475
- Monitoring high spatiotemporal water dynamics by fusing MODIS, Landsat, water occurrence data and DEM X. Li et al. 10.1016/j.rse.2021.112680
- Image similarity-based gap filling method can effectively enrich surface water mapping information W. Huang et al. 10.1016/j.isprsjprs.2023.07.011
- A new dense 18-year time series of surface water fraction estimates from MODIS for the Mediterranean region L. Li et al. 10.5194/hess-23-3037-2019
- Monitoring surface water area variations of reservoirs using daily MODIS images by exploring sub-pixel information F. Ling et al. 10.1016/j.isprsjprs.2020.08.008
- DeepWaterFraction: A globally applicable, self-training deep learning approach for percent surface water area estimation from Landsat mission imagery Z. Hao et al. 10.1016/j.jhydrol.2024.131512
- The Performance of Landsat-8 and Landsat-9 Data for Water Body Extraction Based on Various Water Indices: A Comparative Analysis J. Chen et al. 10.3390/rs16111984
- Developing a High-Resolution Seamless Surface Water Extent Time-Series over Lake Victoria by Integrating MODIS and Landsat Data G. Wu et al. 10.3390/rs15143500
- A review of freely accessible global datasets for the study of floods, droughts and their interactions with human societies S. Lindersson et al. 10.1002/wat2.1424
- Effects of Climate and Anthropogenic Drivers on Surface Water Area in the Southeastern United States M. Gaines et al. 10.1029/2021WR031484
- A surface water mapping framework combining optical and radar remote sensing and its application in China Y. Yang et al. 10.1080/10106049.2022.2129836
21 citations as recorded by crossref.
- Combining Multi-Sensor Satellite Imagery to Improve Long-Term Monitoring of Temporary Surface Water Bodies in the Senegal River Floodplain A. Ogilvie et al. 10.3390/rs12193157
- Construction of High Spatial-Temporal Water Body Dataset in China Based on Sentinel-1 Archives and GEE Y. Li et al. 10.3390/rs12152413
- AHSWFM: Automated and Hierarchical Surface Water Fraction Mapping for Small Water Bodies Using Sentinel-2 Images Y. Wang et al. 10.3390/rs14071615
- Development of the global dataset of Wetland Area and Dynamics for Methane Modeling (WAD2M) Z. Zhang et al. 10.5194/essd-13-2001-2021
- Regression-based surface water fraction mapping using a synthetic spectral library for monitoring small water bodies Y. Wang et al. 10.1080/15481603.2023.2217573
- Determining Temporal Uncertainty of a Global Inland Surface Water Time Series S. Mayr et al. 10.3390/rs13173454
- Systematic Water Fraction Estimation for a Global and Daily Surface Water Time-Series S. Mayr et al. 10.3390/rs13142675
- Evaluation of a new 18-year MODIS-derived surface water fraction dataset for constructing Mediterranean wetland open surface water dynamics L. Li et al. 10.1016/j.jhydrol.2020.124956
- A Novel Workflow for Seasonal Wetland Identification Using Bi-Weekly Multiple Remote Sensing Data L. Xing et al. 10.3390/rs14041037
- Automatic mapping of 500 m daily open water body fraction in the American continent using GOES-16 ABI imagery X. Wang et al. 10.1016/j.rse.2024.114040
- Remote Sensing of Surface Water Dynamics in the Context of Global Change—A Review P. Sogno et al. 10.3390/rs14102475
- Monitoring high spatiotemporal water dynamics by fusing MODIS, Landsat, water occurrence data and DEM X. Li et al. 10.1016/j.rse.2021.112680
- Image similarity-based gap filling method can effectively enrich surface water mapping information W. Huang et al. 10.1016/j.isprsjprs.2023.07.011
- A new dense 18-year time series of surface water fraction estimates from MODIS for the Mediterranean region L. Li et al. 10.5194/hess-23-3037-2019
- Monitoring surface water area variations of reservoirs using daily MODIS images by exploring sub-pixel information F. Ling et al. 10.1016/j.isprsjprs.2020.08.008
- DeepWaterFraction: A globally applicable, self-training deep learning approach for percent surface water area estimation from Landsat mission imagery Z. Hao et al. 10.1016/j.jhydrol.2024.131512
- The Performance of Landsat-8 and Landsat-9 Data for Water Body Extraction Based on Various Water Indices: A Comparative Analysis J. Chen et al. 10.3390/rs16111984
- Developing a High-Resolution Seamless Surface Water Extent Time-Series over Lake Victoria by Integrating MODIS and Landsat Data G. Wu et al. 10.3390/rs15143500
- A review of freely accessible global datasets for the study of floods, droughts and their interactions with human societies S. Lindersson et al. 10.1002/wat2.1424
- Effects of Climate and Anthropogenic Drivers on Surface Water Area in the Southeastern United States M. Gaines et al. 10.1029/2021WR031484
- A surface water mapping framework combining optical and radar remote sensing and its application in China Y. Yang et al. 10.1080/10106049.2022.2129836
Latest update: 13 Jun 2025
Short summary
We derived an 8 d, 500 m resolution surface water fraction product over the Mediterranean region for 2000–2017 based on MODIS data. This dataset complements existing surface water/wetland datasets by adding more temporal detail. It allows for the seasonal, inter-annual, and long-term dynamics of the surface water extent to be monitored, inclusive of small-sized and highly dynamic water bodies; it can also contribute to biodiversity and climate change assessment.
We derived an 8 d, 500 m resolution surface water fraction product over the Mediterranean region...
