Mapping global land cover in 2001 and 2010 with spatial-temporal consistency at 250 m resolution

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Assessing the robustness of Random Forests to map land cover with high resolution satellite image time series over large areas (2016, background): “Likewise, global land cover initiatives suffer from the lack of sensors combining high temporal and spatial resolutions (Wardlow and Egbert, 2008; Gong et al., 2013; Chen et al., 2015; Wang et al., 2015).”
GLC_FCS30: global land-cover product with fine classification system at 30 m using time-series Landsat imagery (2020, methodology): “5 six optical bands – blue, green, red, NIR, SWIR1 and SWIR2 – were used for land-cover classification because the coastal band is easily effected by the atmosphere conditions (Wang et al., 2016).”
Stable classification with limited sample: transferring a 30-m resolution sample set collected in 2015 to mapping 10-m resolution global land cover in 2017. (2019, background): “It had been extensively tested in our previous global land cover mapping efforts [4,7].”
A 30-m landsat-derived cropland extent product of Australia and China using random forest machine learning algorithm on Google Earth Engine cloud computing platform (2018, unknown): “Pixel-based approaches include: a) Random forest algorithm (Tatsumi et al., 2015; Wang et al., 2015; Gislason et al, 2006); (b) Support vector machines (Mountrakis et al., 2011; Shao and Lunetta, 2012); (c) decision tree algorithms (Ozdogan and Gutman, 2008; Waldner et al., 2016); (d) Tassel cap…”