Journal of Remote Sensing / 2021 / Article / Tab 10

Review Article

Finer-Resolution Mapping of Global Land Cover: Recent Developments, Consistency Analysis, and Prospects

Table 10

Definitions and details of the six global 30 m inland water products.

Dataset namePeriodDefinitionMethodLiterature

GLC_FCS302015, 2020Divided into artificial water bodies and natural water bodies: artificial water bodies consist of areas that are covered by water due to the construction of artefacts such as reservoirs, canals, and artificial lakes; the latter type consists of areas that are naturally covered by water, such as lakes and rivers.
Dataset link: https://zenodo.org/record/3986872, https://zenodo.org/record/4280923
Local adaptive random forest models, Global Spatial Temporal Spectral Library (GSPECLib), and time series of Landsat imagery are used to automatically generate water body products for each geographical cell.Zhang et al. [22]
Liu et al. [15]
FROM_GLC2010, 2015, 2017All inland water pixels in width or pixels (6 ha) in area. Fish ponds are included in this category. Spectral characteristics vary widely, and water bodies change in area with the season.
Dataset link: http://data.ess.tsinghua.edu.cn/fromglc2015_v1.html
Water is treated as an independent layer and classified using 91,433 training samples and multitemporal Landsat imagery.Gong et al. [12]
GlobeLand302000, 2010, 2017Water bodies include clear water, green water, and turbid water and display spectral diversity. Clear water has a lower reflectance in all bands, while turbid water has a higher reflectance due to the blend of mud and sand; green water is caused by eutrophication and to some extent displays spectral features similar to green vegetation.
Dataset link: http://www.globallandcover.com/GLC30Download/index.aspx
A combination of supervised Maximum Likelihood Classification (MLC) and a prior knowledge-based decision tree classifier are employed to classify water pixels; an object-based method is then used to refine the water extraction results.Chen et al. [14]
G1WBM1990, 2000, 2005, 2010In this study, water is any stretch of inland water larger than open to the sky, including fresh and brackish water, and is divided into two categories: (1) permanent water, defined as water bodies (e.g., shallow-water river channels and lakes) that are detected with a , and (2) temporary water, defined as water bodies (e.g., floodplains, wetlands, and paddy fields) with .
Dataset link: http://hydro.iis.u-tokyo.ac.jp/~yamadai/G3WBM/
Multiple classification criteria with globally uniform thresholds are used together with the Landsat Global Land Survey database to classify inland water pixels; the results are then refined using Shuttle Radar Topography Mission Water Body Data (SWBD).Yamazaki et al. [24]
JRC_GSW1984–2015 (annual, monthly)A pixel that is detected as water throughout the year is defined as permanent water; pixels that are detected as months in a year are defined as temporary water.
Dataset link: https://global-surface-water.appspot.com/download
Multiple classification criteria with globally uniform thresholds are used together with the complete archive of Landsat-5, Landsat-7, and Landsat-8 imagery acquired between 1984 and 2015 to extract inland water bodies based on the GEE.Pekel et al. [25]
GLADWater1999–2018 (annual, monthly)The definition of water of this study refers to the inland water that of a 30 m pixel. In addition, pixels with a range (the difference between the maximum frequency and the minimum frequency of the occurrence of water in 3 consecutive years) ≤ 33% and a mean (the average frequency of the occurrence of water in 3 consecutive years) ≥ 90% are labeled as permanent water, while pixels with a are labeled as temporary water.
Dataset link: https://glad.umd.edu/dataset/global-surface-water-dynamics
A Landsat sensor-based ensemble classification tree method is applied to the complete 1999–2018 Landsat-5, Landsat-7, and Landsat-8 archive; for 1999–2018, the water bodies are then classified based on the GEE. For each sensor, the global classification tree models are developed using a training set of fully classified scenes.Pickens et al. [26]