Journal of Remote Sensing / 2021 / Article / Tab 5

Review Article

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

Table 5

Summary of the reported accuracies of the different 30 m impervious surface products.

StudyRegionAccuracy description

Zhang et al. [22]15 local regionsMSMT_IS30 had an overall accuracy of 95.1% and kappa coefficient of 0.898 as against 85.6% and 0.695 for NUACI, 89.6% and 0.780 for FROM_GLC, 90.3% and 0.794 for GHSL, 88.4% and 0.753 for GlobeLand30, and 88.0% and 0.745 for HBASE.
Kang et al. [39]IndonesiaThe user’s accuracies of MSMT_IS30, FROM_GLC, and GlobeLand30 were 73.61%, 52.41%, and 75.68%, respectively, and the corresponding producer’s accuracies were 99.07%, 81.31%, and 78.50%, respectively.
Sun et al. [106]ChinaThe consistency between GlobeLand30, GHSL, and NUACI and a reference dataset gave values of of 0.57, 0.47, and 0.36, respectively.
Gao et al. [45]European UnionUsing the LUCAS (Land Use/Cover Area frame Survey) reference data, the producer’s accuracies of MSMT_IS30, FROM_GLC, and GlobeLand30 were , , and , respectively; the corresponding user’s accuracies were , , and , respectively.
Validation dataset link:
Gong et al. [21]GlobalBased on 500 validation points, GAIA had an overall accuracy of 89%, kappa coefficient of 0.78, producer’s accuracy of 78%, and user’s accuracy of 99% in 2015.
Chen et al. [14]
Chen et al. [101]
GlobalIndependent accuracy assessments of the artificial surface class in GlobeLand30 showed that the user’s accuracy of this product is higher than 80%.
Xing et al. [49]BeijingThe GlobeLand30-2010 impervious layer was validated as having an overall accuracy of 91.37%, producer’s accuracy of 92.76%, and user’s accuracy of 98.19% using 66,058 validation points in Beijing.
Liu et al. [19]GlobalThe global overall accuracy, producer’s accuracy, and user’s accuracy for NUACI are 0.81–0.84, 0.50–0.60, and 0.49–0.61, respectively. The values of the kappa coefficient for NUACI and GlobeLand30 are 0.43–0.50 and 0.25-0.49 at the global level, respectively.
Validation dataset link:
Yang et al. [50]MalaysiaThe overall accuracies of GHSL and GlobeLand30 were 89.21% and 80.54%, respectively, the corresponding producer’s accuracies were 87% and 73%, and the user’s accuracies were 86% and 77%.
Marconcini et al. [51]Global (450 test sites)GHSL and GlobeLand30 had an overall accuracy of 70.64% and 67.86%, respectively, the kappa coefficients were 0.4603 and 0.4051, the producer’s accuracies were 45.98% and 40.03%, and the user’s accuracies were 84.26% and 83.55%, respectively.
Liu et al. [107]Global (45 cities)GHSL had an overall accuracy of 84.18%, -measure of 0.71, user’s accuracy of 84.06%, and producer’s accuracy of 68.77% for 45 cities across the globe. In addition, GHSL had an overall accuracy of 78.92% and 85.86%, a user’s accuracy of 85.46% and 81.51%, and a producer’s accuracy of 82.77% and 56.78% for urban and rural areas, respectively.
Pesaresi et al. [52]EuropeGHSL had an overall accuracy of 96.28% and a kappa coefficient of 0.3233 based on LUCAS reference data.
Leyk et al. [53]26 counties in the USGHSL had an -measure of 0.650, -mean of 0.767, and kappa coefficient of 0.562 for the built-up layer in 2015.
Wang et al. [48]AsiaFor the Asian HBASE classification model, according to the result of the cross-validation, the user’s accuracy, producer’s accuracy, overall accuracy, and kappa coefficient were 91.5%, 90.3%, 97.9%, and 0.90, respectively.