{"references": ["Gong, P. et al. Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data. International Journal of Remote Sensing 34, 2607-2654, doi:10.1080/01431161.2012.748992 (2013).", "Olofsson, P. et al. A global land-cover validation data set, part I: fundamental design principles. International Journal of Remote Sensing 33, 5768-5788, doi:10.1080/01431161.2012.674230 (2012).", "Tateishi, R. et al. Production of global land cover data \u2013 GLCNMO. International Journal of Digital Earth 4, 22-49, doi:10.1080/17538941003777521 (2011).", "Tateishi, R. et al. Production of global land cover data-GLCNMO2008. J. Geogr. Geol. 6, 99-123 (2014).", "Stehman, S. V., Olofsson, P., Woodcock, C. E., Herold, M. & Friedl, M. A. A global land-cover validation data set, II: augmenting a stratified sampling design to estimate accuracy by region and land-cover class. International Journal of Remote Sensing 33, 6975-6993, doi:10.1080/01431161.2012.695092 (2012).", "Schneider, A., Friedl, M. A. & Potere, D. A new map of global urban extent from MODIS satellite data. Environmental Research Letters 4, 044003, doi:10.1088/1748-9326/4/4/044003 (2009).", "Friedl, M. A. et al. MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets. Remote Sensing of Environment 114, 168-182, doi:https://doi.org/10.1016/j.rse.2009.08.016 (2010).", "Xiong, J. et al. Automated cropland mapping of continental Africa using Google Earth Engine cloud computing. ISPRS Journal of Photogrammetry and Remote Sensing 126, 225-244, doi:https://doi.org/10.1016/j.isprsjprs.2017.01.019 (2017).", "Lehner, B. & D?ll, P. Development and validation of a global database of lakes, reservoirs and wetlands. Journal of Hydrology 296, 1-22, doi:https://doi.org/10.1016/j.jhydrol.2004.03.028 (2004).", "Tootchi, A., Jost, A. & Ducharne, A. Multi-source global wetland maps combining surface water imagery and groundwater constraints. Earth Syst. Sci. Data 11, 189-220, doi:10.5194/essd-11-189-2019 (2019)."]}

A dataset of global land cover validation samples in 2015. In order to guarantee the confidence and objective of the validation samples, several existing reference datasets such as GLCNMO2008 training dataset, VIIRS reference dataset, STEP reference dataset, Global cropland reference data and so on, high resolution imagery in the Google earth and time-series NDVI,NDSI values of each related point are integrated to derive the global validation datasets. The dataset is provided in .shp format.