Journal of Remote Sensing / 2021 / Article / Tab 9

Review Article

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

Table 9

Summary of the reported accuracies of the five 30 m global cropland products.

StudyRegionAccuracy description

Zhang et al. [16]GlobalIn GLC_FCS30, GlobeLand30, and FROM_GLC, cropland had the producer’s accuracies of 88.0%, 88.2%, and 47.7% and user’s accuracies of 83.9%, 88.7%, and 74.7%, respectively.
Validation dataset link: 10.5281/zenodo.3551994
Kang et al. [39]IndonesiaIn GLC_FCS30, GlobeLand30, and FROM_GLC, cropland had the producer’s accuracies of 85.28%, 32.73%, and 62.04% and user’s accuracies of 48.82%, 75.52%, and 66.34%, respectively.
Zhong et al. [63]ChinaIn FROM_GLC, cropland had an overall accuracy of 79.61%, a kappa coefficient of 0.58, and a misclassification error of 19.70%.
Xu et al. [35]AfricaThe FROM_GLC cropland layer had a user’s accuracy of 44.55% and a producer’s accuracy of 69.71% using 6819 validation points.
Perez Hoyos et al. [66]Africa, America, and AsiaGlobeLand30 generally provided adequate results to monitor cropland areas, and the overall accuracy was around 80% for the three continents studied.
Lu et al. [64]ChinaThe results of this study indicated that GlobeLand30 performed better than the other four datasets including FROM_GLC30, regardless of the cropland area and the spatial location. Specifically, the overall accuracy and kappa coefficient of GlobeLand30 were 79.61% and 0.58, respectively, whereas the overall accuracy and kappa coefficient of FROM_GLC30 were 76.23% and 0.52, respectively.
Chen et al. [67]Shaanxi Province, ChinaIn GlobeLand30 and FROM_GC, cropland had overall accuracies of 80.61% and 77.67% and kappa coefficients of 0.5554 and 0.5032, respectively.
Yu et al. [115]ChinaGlobeLand30 shows that there is little loss of the cropland area but increasing fragmentation of cropland in China. Specifically, the results show that 703 out of 2420 countries experienced both cropland loss and increased fragmentation.
Jacobson et al. [68]East AfricaGlobeLand30 was found to have an overall accuracy of 86.98% and a kappa coefficient of 0.692 using the Google Earth grid dataset.
Laso Bayas et al. [69]TanzaniaThe overall weighted accuracies of GlobeLand30 and FROM_GC were and , respectively. FROM_GC was shown to underestimate the cropland in this area.
Oliphant et al. [27]GlobalGFSAD30 had an overall accuracy of 91.7%, a producer’s accuracy of 83.4%, and a user’s accuracy of 78.3% using 19,171 validation points. For the different regions (North America, South America, Southeast Asia, Africa, Mongolia, New Zealand, China, Europe, the Middle East, Russia, South Asia, and Australia), the overall accuracy of GFSAD30 varied from 84.5% to 98.3%.
Validation dataset link: 10.5067/MEaSUREs/GFSAD/GFSAD30VAL.001
Teluguntla et al. [73]Australia and ChinaThe overall accuracies of GFSAD30 exceeded 94% in Australia and China; the cropland omission errors were 1.2% for Australia and 20% for China.
Validation dataset link: 10.5067/MEaSUREs/GFSAD/GFSAD30VAL.001
Phalke et al. [74]Europe, the Middle East, Russia, and Central AsiaGFSAD30 had an overall accuracy of 90.8% and a weighted accuracy of 93.8% across all zones based on 3000 validation samples. A comparison of GFSAD30 and GlobeLand30 suggested an overall similarity of 88.8% and a kappa coefficient of 0.7.
Training and validation dataset link: https://croplands.org
Xiong et al. [75]
Xiong et al. [76]
AfricaFor the African continent as a whole, GFSAD30 had a weighted overall accuracy of 94.5%, a producer’s accuracy of 85.9% (an omission error of 14.1%), and a user’s accuracy of 68.5% (a commission error of 31.5%). The overall accuracy varied from 90.8% to 96.8% for the different agroecological zones.
The similarity between GFSAD30 and GlobeLand30 and FROM_GC was also tested. The results indicated that GFSAD30 matched GlobeLand30 for 77.3% of the cropland samples and matched FROM_GC for 68.8% of the cropland samples.
Validation dataset link: 10.5067/MEaSUREs/GFSAD/GFSAD30AFCE.001
Oliphant et al. [77]Southeast and Northeast AsiaGFSAD30 had an overall accuracy of 88.1%, a producer’s accuracy of 81.6% (an omission error of 18.4%), and a user’s accuracy of 76.7% (a commission error of 23.3%). The accuracy varied from 83.2% to 96.4% for the different agroecological zones.
Validation dataset link: https://croplands.org/app/ data/search
Yadav et al. [72]GlobalAn accuracy assessment of the GFSAD30 cropland map was performed across 15 regions using a reference dataset containing 19,171 samples. The overall accuracy of this cropland extent map for the world was 91.7%.
Validation dataset link: 10.5067/MEaSUREs/GFSAD/GFSAD30VAL.001
Samasse et al. [70]Five West African countriesThe average user’s accuracies for GFSAD30 and GlobeLand30 in the five countries were 73.1% and 79.5%, respectively. The omission and commission errors were about 25% and 7%, respectively, for GFSAD30 and about 29% and 5%, respectively, for GlobeLand30.
Yu et al. [29]GlobalAn analysis of the agreement between FROM_GC and FAOSTAT data was conducted, and the results indicated that FROM_GC had a significant linear relationship with the FAOSTAT data, giving an of 0.97.