Google Earth Engine ——数据全解析专辑（Canada AAFC Annual Crop Inventory）
Google Earth Engine ——数据全解析专辑（Canada AAFC Annual Crop Inventory）
Starting in 2009, the Earth Observation Team of the Science and Technology Branch (STB) at Agriculture and Agri-Food Canada (AAFC) began the process of generating annual crop type digital maps. Focusing on the Prairie Provinces in 2009 and 2010, a Decision Tree (DT) based methodology was applied using optical (Landsat-5, AWiFS, DMC) and radar (Radarsat-2) based satellite images. Beginning with the 2011 growing season, this activity has been extended to other provinces in support of a national crop inventory. To date this approach can consistently deliver a crop inventory that meets the overall target accuracy of at least 85% at a final spatial resolution of 30m (56m in 2009 and 2010).
从 2009 年开始，加拿大农业和农业食品部 (AAFC) 科技部 (STB) 的地球观测团队开始了生成一年生作物类型数字地图的过程。 2009 年和 2010 年以草原省份为重点，使用基于光学（Landsat-5、AWiFS、DMC）和雷达（Radarsat-2）的卫星图像应用了基于决策树 (DT) 的方法。从 2011 年的生长季节开始，这项活动已扩展到其他省份，以支持国家作物清单。迄今为止，这种方法可以始终如一地提供在最终空间分辨率为 30m（2009 年和 2010 年为 56m）时满足至少 85% 总体目标精度的作物清单。Resolution
30 meters
Bands Table
Name | Description | Min | Max |
|---|---|---|---|
landcover | Main crop-specific land cover classification. | 1 | 255 |
Class Table: landcover
Value | Color | Color Value | Description |
|---|---|---|---|
10 | #000000 | Cloud | |
20 | #3333ff | Water | |
30 | #996666 | Exposed Land and Barren | |
34 | #cc6699 | Urban and Developed | |
35 | #e1e1e1 | Greenhouses | |
50 | #ffff00 | Shrubland | |
80 | #993399 | Wetland | |
85 | #501b50 | Peatland | |
110 | #cccc00 | Grassland | |
120 | #cc6600 | Agriculture (undifferentiated) | |
122 | #ffcc33 | Pasture and Forages | |
130 | #7899f6 | Too Wet to be Seeded | |
131 | #ff9900 | Fallow | |
132 | #660000 | Cereals | |
133 | #dae31d | Barley | |
134 | #d6cc00 | Other Grains | |
135 | #d2db25 | Millet | |
136 | #d1d52b | Oats | |
137 | #cace32 | Rye | |
138 | #c3c63a | Spelt | |
139 | #b9bc44 | Triticale | |
140 | #a7b34d | Wheat | |
141 | #b9c64e | Switchgrass | |
142 | #999900 | Quinoa | |
142 | #999900 | Sorghum | |
145 | #92a55b | Winter Wheat | |
146 | #809769 | Spring Wheat | |
147 | #ffff99 | Corn | |
148 | #98887c | Tobacco | |
149 | #799b93 | Ginseng | |
150 | #5ea263 | Oilseeds | |
151 | #52ae77 | Borage | |
152 | #41bf7a | Camelina | |
153 | #d6ff70 | Canola and Rapeseed | |
154 | #8c8cff | Flaxseed | |
155 | #d6cc00 | Mustard | |
156 | #ff7f00 | Safflower | |
157 | #315491 | Sunflower | |
158 | #cc9933 | Soybeans | |
160 | #896e43 | Pulses | |
161 | #996633 | Other Pulses | |
162 | #8f6c3d | Peas | |
163 | #b6a472 | Chickpeas | |
167 | #82654a | Beans | |
168 | #a39069 | Fababeans | |
174 | #b85900 | Lentils | |
175 | #b74b15 | Vegetables | |
176 | #ff8a8a | Tomatoes | |
177 | #ffcccc | Potatoes | |
178 | #6f55ca | Sugarbeets | |
179 | #ffccff | Other Vegetables | |
180 | #dc5424 | Fruits | |
181 | #d05a30 | Berries | |
182 | #d20000 | Blueberry | |
183 | #cc0000 | Cranberry | |
185 | #dc3200 | Other Berry | |
188 | #ff6666 | Orchards | |
189 | #c5453b | Other Fruits | |
190 | #7442bd | Vineyards | |
191 | #ffcccc | Hops | |
192 | #b5fb05 | Sod | |
193 | #ccff05 | Herbs | |
194 | #07f98c | Nursery | |
195 | #00ffcc | Buckwheat | |
196 | #cc33cc | Canaryseed | |
197 | #8e7672 | Hemp | |
198 | #b1954f | Vetch | |
199 | #749a66 | Other Crops | |
200 | #009900 | Forest (undifferentiated) | |
210 | #006600 | Coniferous | |
220 | #00cc00 | Broadleaf | |
230 | #cc9900 | Mixedwood |
影像属性：
Name | Type | Description |
|---|---|---|
landcover_class_names | List of Strings | Array of cropland landcover classification names. |
landcover_class_palette | List of Strings | Array of hex code color strings used for the classification palette. |
landcover_class_values | List of Ints | Value of the land cover classification. |
引用：
Dataset Availability
2009-01-01T00:00:00 - 2019-01-01T00:00:00
Dataset Provider
Agriculture and Agri-Food Canada
Collection Snippet
ee.ImageCollection("AAFC/ACI")
Agriculture and Agri-Food Canada Annual Crop Inventory. {YEAR}
数据代码：
var dataset = ee.ImageCollection('AAFC/ACI');
var crop2016 = dataset
.filter(ee.Filter.date('2016-01-01', '2016-12-31'))
.first();
Map.setCenter(-103.8881, 53.0371, 10);
Map.addLayer(crop2016);
有需要研究加拿大农业生态的同学可以拿走！