Journal of Remote Sensing / 2021 / Article / Tab 3

Review Article

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

Table 3

Summary of the reported accuracies for the three 30 m GLC products.

StudyRegionAccuracy description

Zhang et al. [16]GlobalGLC_FCS30-2015 achieved the best performance of 82.5%, compared with 59.1% for FROM_GLC-2015 and 75.9% for GlobeLand30-2010.
Validation dataset link: https://zenodo.org/record/3551995#.XdttCugzZPY
Kang et al. [39]IndonesiaGLC_FCS30-2015 has the highest overall accuracy (65.59%), followed by GlobeLand30-2010 (61.65%) and FROM_GLC-2015 (57.71%).
Gao et al. [45]European UnionThe overall accuracy coefficient of the GlobeLand30-2010 product is the highest at . This is followed by GLC_FCS30-2015 () and FROM_GLC-2015 ().
Validation dataset link: https://ec.europa.eu/eurostat/web/lucas/data/lucas-grid
Chen et al. [14]GlobalAn overall classification accuracy of 80.3% was achieved by GlobeLand30.
Tsendbazar et al. [87]AfricaThe overall accuracy of GlobeLand30-2010 is 57.1% over the African continent.
Sun et al. [46]Central AsiaGlobeLand30-2010 data have an overall accuracy of 46% and a kappa coefficient of 0.283.
Wang et al. [84]ChinaThe overall accuracy of GlobeLand30-2010 for China is 84.2%.
Yang et al. [34]ChinaThe overall accuracy of GlobeLand30-2010 is 82.39%.
See et al. [88]KenyaThe GlobeLand30 gave an overall accuracy ranging from 53% to 61%.
Fonte et al. [89]NepalThe overall accuracy is 61% and 54% in Tanzania and Kathmandu, Nepal, respectively.
Brovelli et al. [90]ItalyThe overall accuracy is higher than 80% according to a validation covering eight regions across Italy.
Balogun et al. [91]MalaysiaOverall accuracies of 63.45% and 65.70%, respectively, were obtained from the error matrix using sample counts and the new unbiased area estimator with GlobeLand30.
Manakos et al. [92]Thessaly, GreeceGlobeLand30-2010 achieved an overall accuracy of 84% and a weighted overall accuracy of 86% using 539 validation samples.
Zhang et al. [93]SiberiaGlobeLand30-2000 and GlobeLand30-2010 achieved overall accuracies of 85.8% and 86.9% and kappa coefficients of 0.79 and 0.81 over Siberia.
Jokar Arsanjani et al. [94]IranGlobeLand30-2010 achieved an overall accuracy of 77.95% based on 738,900 validation samples from Iran.
Jokar Arsanjani et al. [95]GermanyOverall, good correspondence was confirmed between the GlobeLand30 and the other datasets, ranging from 74% for OSM to 92% for CORINE.
Gong et al. [12]GlobalFROM_GLC achieved an overall accuracy of 64.9% for the complete set of validation samples and 71.5% for the homogeneous validation samples.
Validation dataset link: http://data.ess.tsinghua.edu.cn/data
Yu et al. [96]GlobalThe overall accuracy of FROM_GLC is 63.69%, and the kappa coefficient is 0.543.
Zhao et al. [97]GlobalThe overall accuracy of the improved version of FROM_GLC (FROM_GLC_agg) is 65.51%.
Validation dataset link: http://data.ess.tsinghua.edu.cn/data
Xu et al. [35]AfricaThe second version of the FROM_GLC African land-cover map has an overall accuracy of 74.40%.
Dong et al. [98]Beijing, ChinaThe overall accuracy for FROM_GLC based on comparable vector datasets, Google Earth images, and field survey data is 71.08%, 79.63%, and 80.36%, respectively.
Guo et al. [99]PakistanThe overall accuracy of FROM_GLC in summer (61.2%) is slightly higher than that in winter (59.0%).