The Global Inland Water dataset shows inland surface water bodies, including fresh and saline lakes, rivers, and reservoirs.
From the GLS 2000 epoch, 3,650,723 km2 of inland water were identified, around three quarters of which were in North America and Asia. Boreal forests and tundra hold the largest portion of inland water, about 40% of the global total. The data exhibits strong linear correlation with both the MODIS dataset as well as 30-m resolution datasets over the United States and Canada. Residual errors were due primarily to the seasonality of water cover, snow and ice, and residual clouds.
The dataset contains one or more image for each available Landsat WRS2 path/row.
Documentation:
Global Inland Water 数据集显示内陆地表水体，包括淡水和咸水湖泊、河流和水库。
从 GLS 2000 纪元开始，确定了 3,650,723 平方公里的内陆水域，其中约四分之三位于北美和亚洲。北方森林和苔原拥有最大的内陆水域，约占全球总量的 40%。这些数据与 MODIS 数据集以及美国和加拿大的 30 米分辨率数据集都表现出很强的线性相关性。残留误差主要是由于水覆盖、冰雪和残留云的季节性。
对于每个可用的 Landsat WRS2 路径/行，数据集包含一个或多个图像。
文档：
用户指导
算法理论基础文件 (ATBD)
Dataset Availability
2000-01-01T00:00:00 - 2000-12-31T00:00:00
Dataset Provider
NASA LP DAAC at the USGS EROS Center
Collection Snippet
ee.ImageCollection("GLCF/GLS_WATER")
Resolution
30 meters
Bands Table
| Name | Description |
| water | Water classification |
Class Table: water
| Value | Color | Color Value | Description |
| 1 | #FAFAFA | Land | |
| 2 | #00C5FF | Water | |
| 4 | #DF73FF | Snow/ice | |
| 200 | #828282 | Cloud shadow | |
| 201 | #CCCCCC | Cloud |
影像属性：
| Name | Type | Description |
| path | Double | Path |
| pathrow | String | Path and row |
| row | Double | Row |
| water_class_names | Double | Water class names |
| water_class_palette | Double | Water class palette |
| water_class_values | Water class values |
代码：
var dataset = ee.ImageCollection('GLCF/GLS_WATER'); var water = dataset.select('water'); var waterVis = { min: 1.0, max: 4.0, palette: ['FAFAFA', '00C5FF', 'DF73FF', '828282', 'CCCCCC'], }; Map.setCenter(-79.3094, 44.5693, 8); Map.addLayer(water, waterVis, 'Water');
数据引用：
Data Citation: Global Inland Water, {Year, ...}, Global Land Cover Facility, www.landcover.org.
Paper/Methods Citation: Feng, Min, Joseph O. Sexton, Saurabh Channan, and John R. Townshend. 2015. A Global, High-Resolution (30-M) Inland Water Body Dataset for 2000: First Results of a Topographic-Spectral Classification Algorithm. International Journal of Digital Earth. doi:10.1080/17538947.2015.1026420.
