Preprints
https://doi.org/10.5194/essd-2022-180
https://doi.org/10.5194/essd-2022-180
 
19 Aug 2022
19 Aug 2022

GWL_FCS30: global 30 m wetland map with fine classification system using multi-sourced and time-series remote sensing imagery in 2020

Xiao Zhang1,2, Liangyun Liu1,2,3, Tingting Zhao1,4, Xidong Chen5, Shangrong Lin6, Jinqing Wang1,2,3, Jun Mi1,2,3, and Wendi Liu1,2,3 Xiao Zhang et al.
  • 1International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China
  • 2Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
  • 3School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
  • 4College of Geomatics, Xi’an University of Science and Technology, Xi’an 710054, China
  • 5North China University of Water Resources and Electric Power, Zhengzhou 450046, China
  • 6School of Atmospheric Sciences, Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Sun Yat-sen University, Zhuhai 519082, Guangdong, China

Abstract. Wetlands, often called the “kidneys of the earth”, play an important role in maintaining ecological balance, conserving water resources, replenishing groundwater, and controlling soil erosion. Wetland mapping is very challenging because of its complicated temporal dynamics and large spatial and spectral heterogeneity. An accurate global 30-m wetland dataset that can simultaneously cover inland and coastal zones is lacking. This study proposes a novel method for wetland mapping by combining an automatic sample extraction method, multisource existing products, time-series satellite images, and a stratified classification strategy. This approach allowed for the generation of the first global 30-m wetland map with a fine classification system (GWL_FCS30), including four inland wetland sub-categories (swamp, marsh, flooded flat, and saline) and three coastal wetland sub-categories (mangrove, salt marsh, and tidal flats), which was developed using Google Earth Engine platform. We first combined existing multi-sourced global wetland products, expert knowledge, training sample refinement rules, and visual interpretation to generate a large and geographically distributed wetland training samples. Second, we integrated the time-series Landsat reflectance products and Sentinel-1 SAR imagery to generate water-level and phenological information to capture the complicated temporal dynamics and spectral heterogeneity of wetlands. Third, we applied a stratified classification strategy and the local adaptive random forest classification models to produce the wetland dataset with a fine classification system at each 5°×5° geographical tile in 2020. Lastly, the GWL_FCS30, mosaicked by 961 5°×5° regional wetland maps, was validated using 18,701 validation samples, which achieved an overall accuracy of 87.7 % and a kappa coefficient of 0.810. The cross-comparisons with other global wetland products demonstrated that the GWL_FCS30 dataset performed better in capturing the spatial patterns of wetlands and had significant advantages over the diversity of wetland subcategories. The statistical analysis showed that the global wetland area reached 3.57 million km2, including 3.10 million km2 of inland wetlands and 0.47 million km2 of coastal wetlands, approximately 62.3 % of which were distributed poleward of 40° N. Therefore, we can conclude that the proposed method is suitable for large-area wetland mapping and that the GWL_FCS30 dataset is an accurate wetland mapping product that has the potential to provide vital support for wetland management. The GWL_FCS30 dataset in 2020 is freely available at https://doi.org/10.5281/zenodo.6575731 (Liu et al. 2022).

Journal article(s) based on this preprint

Xiao Zhang et al.

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2022-180', Anonymous Referee #1, 01 Sep 2022
    • AC1: 'Reply on RC1', Liangyun Liu, 22 Nov 2022
  • RC2: 'Comment on essd-2022-180', Yangfan Li, 12 Sep 2022
    • AC2: 'Reply on RC2', Liangyun Liu, 22 Nov 2022
  • RC3: 'Comment on essd-2022-180', Anonymous Referee #3, 14 Sep 2022
    • AC3: 'Reply on RC3', Liangyun Liu, 22 Nov 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Liangyun Liu on behalf of the Authors (22 Nov 2022)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (22 Nov 2022) by Yuyu Zhou
RR by Anonymous Referee #1 (23 Nov 2022)
RR by Anonymous Referee #2 (02 Dec 2022)
ED: Publish subject to minor revisions (review by editor) (19 Dec 2022) by Yuyu Zhou
AR by Liangyun Liu on behalf of the Authors (21 Dec 2022)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (27 Dec 2022) by Yuyu Zhou

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2022-180', Anonymous Referee #1, 01 Sep 2022
    • AC1: 'Reply on RC1', Liangyun Liu, 22 Nov 2022
  • RC2: 'Comment on essd-2022-180', Yangfan Li, 12 Sep 2022
    • AC2: 'Reply on RC2', Liangyun Liu, 22 Nov 2022
  • RC3: 'Comment on essd-2022-180', Anonymous Referee #3, 14 Sep 2022
    • AC3: 'Reply on RC3', Liangyun Liu, 22 Nov 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Liangyun Liu on behalf of the Authors (22 Nov 2022)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (22 Nov 2022) by Yuyu Zhou
RR by Anonymous Referee #1 (23 Nov 2022)
RR by Anonymous Referee #2 (02 Dec 2022)
ED: Publish subject to minor revisions (review by editor) (19 Dec 2022) by Yuyu Zhou
AR by Liangyun Liu on behalf of the Authors (21 Dec 2022)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (27 Dec 2022) by Yuyu Zhou

Journal article(s) based on this preprint

Xiao Zhang et al.

Data sets

GWL_FCS30: global 30 m wetland map with fine classification system in 2020 Liangyun Liu, Xiao Zhang, Tingting Zhao, and Xidong Chen https://doi.org/10.5281/zenodo.6575731

Xiao Zhang et al.

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Short summary
An accurate global 30-m wetland dataset that can simultaneously cover inland and coastal zones is lacking. This study proposes a novel method for wetland mapping and generates the first global 30-m wetland map with a fine classification system (GWL_FCS30), including four inland wetland sub-categories (swamp, marsh, flooded flat, and saline) and three coastal wetland sub-categories (mangrove, salt marsh, and tidal flats).