P. Gong et al. land-cover classification system as well as the International Geosphere-Biosphere Programme (IGBP) system. Using the four classification algorithms, we obtained the initial set of global land-cover maps. The SVM produced the highest overall classification accuracy (OCA) of 64.9% assessed with our test samples, with RF (59.8%), J4.8 (57.9%), and MLC (53.9%) ranked from the second to the fourth. We also estimated the OCAs using a subset of our test samples (8629) each of which represented a homogeneous area greater than 500 m × 500 m. Using this subset, we found the OCA for the SVM to be 71.5%. As a consistent source for estimating the coverage of global land-cover types in the world, estimation from the test samples shows that only 6.90% of the world is planted for agricultural production. The total area of cropland is 11.51% if unplanted croplands are included. The forests, grasslands, and shrublands cover 28.35%, 13.37%, and 11.49% of the world, respectively. The impervious surface covers only 0.66% of the world. Inland waterbodies, barren lands, and snow and ice cover 3.56%, 16.51%, and 12.81% of the world, respectively.

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Surface water is the most dynamic land‐cover type. Transitions between water and nonwater types (such as vegetation and ice) can happen momentarily. More frequent mapping is necessary to study the changing patterns of water. However, monitoring of long‐term global water changes at high spatial resolution and in high temporal frequency is challenging. Here we report the generation of a daily global water map data set at 500‐m resolution from 2001 to 2016 based on the daily reflectance time series from Moderate Resolution Imaging Spectroradiometer. Each single‐date image is classified into three types: water, ice/snow, and land. Following temporal consistency check and spatial‐temporal interpolation for missing data, we conducted a series of validation of the water data set. The producer's accuracy and user's accuracy are 94.61% and 93.57%, respectively, when checked with classification results derived from 30‐m resolution Landsat images. Both the producer's accuracy and user's accuracy reached better than 90% when compared with manually interpreted large‐sized sample units (≥1,000 m × 1,000 m) collected in a previous global land cover mapping project. Generally, the global inland water area reaches its maximum (~3.80 × 106 km2) in September and minimum (~1.50 × 106 km2) in February during an annual cycle. Short‐duration water bodies, sea level rise effects, different types of rice field use can be detected from the daily water maps. The size distribution of global water bodies is also discussed from the perspective of the number of water bodies and the corresponding water area. In addition, the daily water maps can precisely reflect water freezing and help correct water areas with inconsistent cloud flags in the MOD09GA quality assessment layer.