The Global Land Surface Water Dataset in 30m Resolution in 2010 (GlobeLand30-WTR2010 for short) was developed based on data mining methodology by integrating and analyzing the 9907 scenes of the USA Landsat TM5, ETM+ data and 2640 scenes of the China environment disaster mitigation satellite (HJ-1) data in 2010（±1）. The total area of the land surface water is 3,675,400 km2, which is 2.73% of the global land surface area. More than 40% of land surface water is located in North America. The global data were organized into 853 tiles, according to the 5° (latitude) x 6° (longitude) within the region from 60°S to 60 N, and 5° (latitude) x 12° (longitude) within the region from 60° N to 80°N (the Antarctic continent is not included). The data tiles are combined into 5 compressed data groups (Asia, Europe, North America, South America, and Africa, and Oceanic Countries), Four different data files are comprised in each of these data groups. They are: (1) land surface water data (raster data with GeoTIFF format); (2) coordinate information data (TIFF WORD format); (3) areas of selected remote sensing data (.shp format); and (4) a metadata file (XML format). In addition, the 853 data file list, including the file names, corresponding geographic coordinates and zoning codes, are listed at the file. The dataset is one of the layers of the Global Land Cover Dataset in 30m Resolution in 2010 (GlobeLand30_2010), which were donated to the United Nations by China in September 2014. Data citation: CHEN Jun et al. : Global Land Surface Water Dataset in 30m Resolution (2010) ( GlobeLand30-WTR2010 ) ,Global Change Research Data Publishing & Repository,DOI:10.3974/geodb.2014.02.01.V1, http://www.geodoi.ac.cn/WebEn/doi.aspx?DOI=10.3974/geodb.2014.02.01.V1 Available at: http://www.geodoi.ac.cn/WebEn/doi.aspx?Id=159

The Global Artificial Land Surface in 30 meters resolution (GlobeLand30-ATS2010 for short) was developed based on the data mining methodology by integrating and analyzing the 9907 scenes of the USA Landsat TM5, ETM+ data and 2640 scenes of the China environment disaster mitigation satellite (HJ-1) data in 2010 （±1）. Since the Artificial Land Surface is mostly a mosaic of, for example, buildings, trees, roads, small-water bodies, and grasslands that are frequently combined, it makes data mining for identifying the artificial land surface more difficult. The Pixel-Object-Knowledge (POK）methodology was applied in this study and data development. The 30m dataset shows where and how many residents there are in cities and villages, as well as industrial lands, airports, and roads worldwide. Data citation: CHEN Jun et al. : 2014.Global Artificial Land Surface Dataset in 30m Resolution (2010) ( GlobeLand30_ATS2010 ) ,Global Change Research Data Publishing & repository, DOI:10.3974/geodb.2014.02.02.V1 Available at: http://www.geodoi.ac.cn/WebEn/doi.aspx?Id=163

The first 30 m resolution global land cover data set with 10 classes and for the year 2000 and 2010. Global Land Cover (GLC) information is fundamental for environmental change studies, land resource management, sustainable development, and many other societal benefits. Although GLC data exists at spatial resolutions of 300 m and 1000 m, a 30 m resolution mapping approach is now a feasible option for the next generation of GLC products. Since most significant human impacts on the land system can be captured at this scale, a number of researchers are focusing on such products. This paper reports the operational approach used in such a project, which aims to deliver reliable data products. Over 10,000 Landsat-like satellite images are required to cover the entire Earth at 30 m resolution. To derive a GLC map from such a large volume of data necessitates the development of effective, efficient, economic and operational approaches. Automated approaches usually provide higher efficiency and thus more economic solutions, yet existing automated classification has been deemed ineffective because of the low classification accuracy achievable (typically below 65%) at global scale at 30 m resolution. As a result, an approach based on the integration of pixel- and object-based methods with knowledge (POK-based) has been developed. Data citation: CHEN Jun et al.: 2015.Global land cover mapping at 30 m resolution: A POK-based operational approach. ISPRS Journal of Photogrammetry and Remote Sensing Volume 103, May 2015, Pages 7–27 http://dx.doi.org/10.1016/j.isprsjprs.2014.09.002 Available at: http://www.geodoi.ac.cn/WebEn/doi.aspx?Id=163