哥白尼全球土地服务（CGLS）被指定为土地服务的一个组成部分，以运营一个多用途的服务组件，提供一系列关于全球范围内土地表面的状况和演变的生物地球物理产品。
100米分辨率的动态土地覆盖图（CGLS-LC100）是CGLS组合中的一个新产品，提供100米空间分辨率的全球土地覆盖图。CGLS土地覆盖产品提供了一个主要的土地覆盖方案。除了这些离散的类别，该产品还包括所有基本土地覆盖类别的连续实地层，为土地覆盖类型的植被/地面覆盖提供比例估计。这种连续的分类方案可以比标准分类方案更好地描述异质土地覆盖的区域，因此，可以根据应用用途进行调整（例如，森林监测、作物监测、生物多样性和保护、监测非洲的环境和安全、气候建模等）。
这些一致的土地覆盖图（v3.0.1）是为2015-2019年整个地球提供的，来源于PROBA-V 100米时间序列、高质量的土地覆盖训练点数据库和一些辅助数据集，在过去的几年中，一级精度达到80%。计划从2020年开始，通过使用哨兵时间序列提供年度更新。
也见。
Dataset Availability
2015-01-01T00:00:00 - 2019-12-31T23:59:59
Dataset Provider
Collection Snippet
ee.ImageCollection("COPERNICUS/Landcover/100m/Proba-V-C3/Global")
Resolution
100 meters
Bands Table
| Name | Description | Min | Max | Units |
|---|---|---|---|---|
| discrete_classification | Land cover classification | 0 | 200 | |
| discrete_classification-proba | Quality indicator (classification probability) of the discrete classification | 0 | 100 | % |
| forest_type | Forest type for all pixels with tree percentage vegetation cover bigger than 1 % | 0 | 5 | |
| bare-coverfraction | Percent vegetation cover for bare-sparse-vegetation land cover class | 0 | 100 | % |
| crops-coverfraction | Percent vegetation cover for cropland land cover class | 0 | 100 | % |
| grass-coverfraction | Percent vegetation cover for herbaceous vegetation land cover class | 0 | 100 | % |
| moss-coverfraction | Percent vegetation cover for moss and lichen land cover class | 0 | 100 | % |
| shrub-coverfraction | Percent vegetation cover for shrubland land cover class | 0 | 100 | % |
| tree-coverfraction | Percent vegetation cover for forest land cover class | 0 | 100 | % |
| snow-coverfraction | Percent ground cover for snow and ice land cover class | 0 | 100 | % |
| urban-coverfraction | Percent ground cover for built-up land cover class | 0 | 100 | % |
| water-permanent-coverfraction | Percent ground cover for permanent water land cover class | 0 | 100 | % |
| water-seasonal-coverfraction | Percent ground cover for seasonal water land cover class | 0 | 100 | % |
| data-density-indicator | Data density indicator for algorithm input data | 0 | 100 | |
| change-confidence | This layer is only provided for years after the BaseYear 2015. * 0 - No change. No change in discrete class between year and previous year detected. * 1 - Potential change. BFASTmon detected break in second half of NRT year - potential change. * 2 - Medium confidence. Imprint of urban, permanent water, snow or wetland OR change detected by BFAST but HMM model didn’t confirm this break in higher resolution OR change detected by BFASTmon in the first half of NRT year. * 3 - High confidence. BFAST detected a change and HMM confirmed this change in higher resolution. | 0 | 3 |
Class Table: discrete_classification
| Value | Color | Color Value | Description |
|---|---|---|---|
| 0 | #282828 | Unknown. No or not enough satellite data available. | |
| 20 | #FFBB22 | Shrubs. Woody perennial plants with persistent and woody stems and without any defined main stem being less than 5 m tall. The shrub foliage can be either evergreen or deciduous. | |
| 30 | #FFFF4C | Herbaceous vegetation. Plants without persistent stem or shoots above ground and lacking definite firm structure. Tree and shrub cover is less than 10 %. | |
| 40 | #F096FF | Cultivated and managed vegetation / agriculture. Lands covered with temporary crops followed by harvest and a bare soil period (e.g., single and multiple cropping systems). Note that perennial woody crops will be classified as the appropriate forest or shrub land cover type. | |
| 50 | #FA0000 | Urban / built up. Land covered by buildings and other man-made structures. | |
| 60 | #B4B4B4 | Bare / sparse vegetation. Lands with exposed soil, sand, or rocks and never has more than 10 % vegetated cover during any time of the year. | |
| 70 | #F0F0F0 | Snow and ice. Lands under snow or ice cover throughout the year. | |
| 80 | #0032C8 | Permanent water bodies. Lakes, reservoirs, and rivers. Can be either fresh or salt-water bodies. | |
| 90 | #0096A0 | Herbaceous wetland. Lands with a permanent mixture of water and herbaceous or woody vegetation. The vegetation can be present in either salt, brackish, or fresh water. | |
| 100 | #FAE6A0 | Moss and lichen. | |
| 111 | #58481F | Closed forest, evergreen needle leaf. Tree canopy >70 %, almost all needle leaf trees remain green all year. Canopy is never without green foliage. | |
| 112 | #009900 | Closed forest, evergreen broad leaf. Tree canopy >70 %, almost all broadleaf trees remain green year round. Canopy is never without green foliage. | |
| 113 | #70663E | Closed forest, deciduous needle leaf. Tree canopy >70 %, consists of seasonal needle leaf tree communities with an annual cycle of leaf-on and leaf-off periods. | |
| 114 | #00CC00 | Closed forest, deciduous broad leaf. Tree canopy >70 %, consists of seasonal broadleaf tree communities with an annual cycle of leaf-on and leaf-off periods. | |
| 115 | #4E751F | Closed forest, mixed. | |
| 116 | #007800 | Closed forest, not matching any of the other definitions. | |
| 121 | #666000 | Open forest, evergreen needle leaf. Top layer- trees 15-70 % and second layer- mixed of shrubs and grassland, almost all needle leaf trees remain green all year. Canopy is never without green foliage. | |
| 122 | #8DB400 | Open forest, evergreen broad leaf. Top layer- trees 15-70 % and second layer- mixed of shrubs and grassland, almost all broadleaf trees remain green year round. Canopy is never without green foliage. | |
| 123 | #8D7400 | Open forest, deciduous needle leaf. Top layer- trees 15-70 % and second layer- mixed of shrubs and grassland, consists of seasonal needle leaf tree communities with an annual cycle of leaf-on and leaf-off periods. | |
| 124 | #A0DC00 | Open forest, deciduous broad leaf. Top layer- trees 15-70 % and second layer- mixed of shrubs and grassland, consists of seasonal broadleaf tree communities with an annual cycle of leaf-on and leaf-off periods. | |
| 125 | #929900 | Open forest, mixed. | |
| 126 | #648C00 | Open forest, not matching any of the other definitions. | |
| 200 | #000080 | Oceans, seas. Can be either fresh or salt-water bodies. |
Class Table: forest_type
| Value | Color | Color Value | Description |
|---|---|---|---|
| 0 | #282828 | Unknown | |
| 1 | #666000 | Evergreen needle leaf | |
| 2 | #009900 | Evergreen broad leaf | |
| 3 | #70663E | Deciduous needle leaf | |
| 4 | #A0DC00 | Deciduous broad leaf | |
| 5 | #929900 | Mix of forest types |
image 属性：
| Name | Type | Description |
|---|---|---|
| discrete_classification_class_names | List of Strings | Land cover class names |
| discrete_classification_class_palette | List of Strings | Land cover class palette |
| discrete_classification_class_values | List of Ints | Value of the land cover classification. |
| forest_type_class_names | List of Strings | forest cover class names |
| forest_type_class_palette | List of Strings | forest cover class palette |
| forest_type_class_values | List of Ints | forest cover class values |
数据引用：
Buchhorn, M. ; Lesiv, M. ; Tsendbazar, N. - E. ; Herold, M. ; Bertels, L. ; Smets, B. Copernicus Global Land Cover Layers—Collection 2. Remote Sensing 2020, 12Volume 108, 1044. doi:10.3390/rs12061044
代码：
var dataset = ee.Image("COPERNICUS/Landcover/100m/Proba-V-C3/Global/2019")
.select('discrete_classification');
Map.setCenter(-88.6, 26.4, 1);
Map.addLayer(dataset, {}, "Land Cover");
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