En 2009, el equipo de Observación de la Tierra de la Subdivisión de Ciencia y Tecnología (STB) de Agriculture and Agri-Food Canada (AAFC) comenzó el proceso de generación de mapas digitales anuales de tipos de cultivos. En 2009 y 2010, se aplicó una metodología basada en árboles de decisión (DT) con imágenes satelitales ópticas (Landsat-5, AWiFS, DMC) y de radar (Radarsat-2) en las provincias de las praderas. A partir de la temporada de cultivo del 2011, esta actividad se extendió a otras provincias para respaldar un inventario nacional de cultivos.
Hasta la fecha, este enfoque puede proporcionar de forma constante un inventario de cultivos que cumple con el objetivo general de precisión de al menos el 85% con una resolución espacial final de 30 m (56 m en 2009 y 2010).
Bandas
Tamaño de píxel 30 metros
Bandas
Nombre
Mín.
Máx.
Tamaño de los píxeles
Descripción
landcover
1
255
metros
Clasificación principal de la cobertura terrestre específica del cultivo.
Tabla de clases de cobertura del suelo
Valor
Color
Descripción
10
#000000
Cloud
20
#3333ff
Agua
30
#996666
Tierra expuesta y estéril
34
#cc6699
Urbano y desarrollado
35
#e1e1e1
Greenhouses
50
#ffff00
Shrubland
80
#993399
Pantano
85
#501b50
Peatland
110
#cccc00
Pradera
120
#cc6600
Agricultura (sin diferenciar)
122
#ffcc33
Pastos y forrajes
130
#7899f6
Demasiado húmedo para sembrar
131
#ff9900
Barbecho
132
#660000
Cereales
133
#dae31d
Cebada
134
#d6cc00
Otros granos
135
#d2db25
Mijo
136
#d1d52b
Avena
137
#cace32
Centeno
138
#c3c63a
Espelta
139
#b9bc44
Triticale
140
#a7b34d
Trigo
141
#b9c64e
Switchgrass
142
#999900
Sorgo
143
#e9e2b1
Quinoa
145
#92a55b
Trigo de invierno
146
#809769
Trigo de primavera
147
#ffff99
Maíz
148
#98887c
Tabaco
149
#799b93
Ginseng
150
#5ea263
Semillas oleaginosas
151
#52ae77
Borage
152
#41bf7a
Camelina
153
#d6ff70
Colza y canola
154
#8c8cff
Linaza
155
#d6cc00
Mostaza
156
#ff7f00
Safflower
157
#315491
Un girasol
158
#cc9933
Soja
160
#896e43
Pulsos
161
#996633
Otros pulsos
162
#8f6c3d
Guisantes
163
#b6a472
Garbanzos
167
#82654a
Frijoles
168
#a39069
Fababeans
174
#b85900
Lentejas
175
#b74b15
Unos vegetales
176
#ff8a8a
Tomates
177
#ffcccc
Papas
178
#6f55ca
Remolacha azucarera
179
#ffccff
Otros vegetales
180
#dc5424
Frutas
181
#d05a30
Bayas
182
#d20000
Arándano
183
#cc0000
Cranberry
185
#dc3200
Otras bayas
188
#ff6666
Huertos
189
#c5453b
Otras frutas
190
#7442bd
Viñedos
191
#ffcccc
Lúpulo
192
#b5fb05
Césped
193
#ccff05
Hierbas
194
#07f98c
Guardería
195
#00ffcc
Alforfón
196
#cc33cc
Alpiste
197
#8e7672
Cáñamo
198
#b1954f
Arveja
199
#749a66
Otros cultivos
200
#009900
Bosque (sin diferenciar)
210
#006600
Coníferas
220
#00cc00
Broadleaf
230
#cc9900
Mixedwood
Propiedades de imágenes
Propiedades de la imagen
Nombre
Tipo
Descripción
landcover_class_names
STRING_LIST
Es un array de nombres de clasificación de la cobertura de la tierra de las tierras de cultivo.
landcover_class_palette
STRING_LIST
Es un array de cadenas de color en código hexadecimal que se usa para la paleta de clasificación.
landcover_class_values
INT_LIST
Valor de la clasificación de la cobertura del suelo.
En 2009, el equipo de Observación de la Tierra de la Subdivisión de Ciencia y Tecnología (STB) de Agriculture and Agri-Food Canada (AAFC) comenzó el proceso de generación de mapas digitales anuales de tipos de cultivos. En 2009 y 2010, se aplicó una metodología basada en árboles de decisión (DT) que se centró en las provincias de las praderas y utilizó datos ópticos (Landsat-5, …
[null,null,[],[[["\u003cp\u003eThe Annual Crop Inventory (ACI) dataset provides annual crop type maps for Canada, starting from 2009 and updated yearly.\u003c/p\u003e\n"],["\u003cp\u003eThe dataset utilizes a Decision Tree methodology and satellite imagery (optical and radar) to classify cropland with an accuracy target of at least 85%.\u003c/p\u003e\n"],["\u003cp\u003eIt offers a 30-meter resolution and includes a comprehensive land cover classification with values representing various crop types, land uses, and infrastructure.\u003c/p\u003e\n"],["\u003cp\u003eThe ACI dataset is provided by Agriculture and Agri-Food Canada and is openly accessible under the OGL-Canada-2.0 license.\u003c/p\u003e\n"],["\u003cp\u003eUsers can access and analyze the dataset using Google Earth Engine for research, education, and non-profit purposes.\u003c/p\u003e\n"]]],["Agriculture and Agri-Food Canada (AAFC) initiated annual crop type mapping in 2009, utilizing optical and radar satellite imagery. The dataset, available from 2009 to 2023, employs a Decision Tree methodology to classify land cover, including specific crops. With a 30-meter pixel size and annual cadence, this inventory achieves at least 85% overall accuracy. The classification includes 255 different crops or land types, with a landcover band representing them.\n"],null,["# Canada AAFC Annual Crop Inventory\n\nDataset Availability\n: 2009-01-01T00:00:00Z--2023-01-01T00:00:00Z\n\nDataset Provider\n:\n\n\n [Agriculture and Agri-Food Canada](https://open.canada.ca/data/en/dataset/ba2645d5-4458-414d-b196-6303ac06c1c9)\n\nCadence\n: 1 Year\n\nTags\n:\n [agriculture](/earth-engine/datasets/tags/agriculture) [canada](/earth-engine/datasets/tags/canada) [crop](/earth-engine/datasets/tags/crop) [landcover](/earth-engine/datasets/tags/landcover) \naafc \n\n#### Description\n\nStarting in 2009, the Earth Observation Team of the Science and Technology\nBranch (STB) at Agriculture and Agri-Food Canada (AAFC) began the process\nof generating annual crop type digital maps. Focusing on the Prairie\nProvinces in 2009 and 2010, a Decision Tree (DT) based methodology was\napplied using optical (Landsat-5, AWiFS, DMC) and radar (Radarsat-2) based\nsatellite images. Beginning with the 2011 growing season, this activity has\nbeen extended to other provinces in support of a national crop inventory.\nTo date this approach can consistently deliver a crop inventory that meets\nthe overall target accuracy of at least 85% at a final spatial resolution of\n30m (56m in 2009 and 2010).\n\n### Bands\n\n\n**Pixel Size**\n\n30 meters\n\n**Bands**\n\n| Name | Min | Max | Pixel Size | Description |\n|-------------|-----|-----|------------|-----------------------------------------------|\n| `landcover` | 1 | 255 | meters | Main crop-specific land cover classification. |\n\n**landcover Class Table**\n\n| Value | Color | Description |\n|-------|---------|--------------------------------|\n| 10 | #000000 | Cloud |\n| 20 | #3333ff | Water |\n| 30 | #996666 | Exposed Land and Barren |\n| 34 | #cc6699 | Urban and Developed |\n| 35 | #e1e1e1 | Greenhouses |\n| 50 | #ffff00 | Shrubland |\n| 80 | #993399 | Wetland |\n| 85 | #501b50 | Peatland |\n| 110 | #cccc00 | Grassland |\n| 120 | #cc6600 | Agriculture (undifferentiated) |\n| 122 | #ffcc33 | Pasture and Forages |\n| 130 | #7899f6 | Too Wet to be Seeded |\n| 131 | #ff9900 | Fallow |\n| 132 | #660000 | Cereals |\n| 133 | #dae31d | Barley |\n| 134 | #d6cc00 | Other Grains |\n| 135 | #d2db25 | Millet |\n| 136 | #d1d52b | Oats |\n| 137 | #cace32 | Rye |\n| 138 | #c3c63a | Spelt |\n| 139 | #b9bc44 | Triticale |\n| 140 | #a7b34d | Wheat |\n| 141 | #b9c64e | Switchgrass |\n| 142 | #999900 | Sorghum |\n| 143 | #e9e2b1 | Quinoa |\n| 145 | #92a55b | Winter Wheat |\n| 146 | #809769 | Spring Wheat |\n| 147 | #ffff99 | Corn |\n| 148 | #98887c | Tobacco |\n| 149 | #799b93 | Ginseng |\n| 150 | #5ea263 | Oilseeds |\n| 151 | #52ae77 | Borage |\n| 152 | #41bf7a | Camelina |\n| 153 | #d6ff70 | Canola and Rapeseed |\n| 154 | #8c8cff | Flaxseed |\n| 155 | #d6cc00 | Mustard |\n| 156 | #ff7f00 | Safflower |\n| 157 | #315491 | Sunflower |\n| 158 | #cc9933 | Soybeans |\n| 160 | #896e43 | Pulses |\n| 161 | #996633 | Other Pulses |\n| 162 | #8f6c3d | Peas |\n| 163 | #b6a472 | Chickpeas |\n| 167 | #82654a | Beans |\n| 168 | #a39069 | Fababeans |\n| 174 | #b85900 | Lentils |\n| 175 | #b74b15 | Vegetables |\n| 176 | #ff8a8a | Tomatoes |\n| 177 | #ffcccc | Potatoes |\n| 178 | #6f55ca | Sugarbeets |\n| 179 | #ffccff | Other Vegetables |\n| 180 | #dc5424 | Fruits |\n| 181 | #d05a30 | Berries |\n| 182 | #d20000 | Blueberry |\n| 183 | #cc0000 | Cranberry |\n| 185 | #dc3200 | Other Berry |\n| 188 | #ff6666 | Orchards |\n| 189 | #c5453b | Other Fruits |\n| 190 | #7442bd | Vineyards |\n| 191 | #ffcccc | Hops |\n| 192 | #b5fb05 | Sod |\n| 193 | #ccff05 | Herbs |\n| 194 | #07f98c | Nursery |\n| 195 | #00ffcc | Buckwheat |\n| 196 | #cc33cc | Canaryseed |\n| 197 | #8e7672 | Hemp |\n| 198 | #b1954f | Vetch |\n| 199 | #749a66 | Other Crops |\n| 200 | #009900 | Forest (undifferentiated) |\n| 210 | #006600 | Coniferous |\n| 220 | #00cc00 | Broadleaf |\n| 230 | #cc9900 | Mixedwood |\n\n### Image Properties\n\n**Image Properties**\n\n| Name | Type | Description |\n|-------------------------|-------------|----------------------------------------------------------------------|\n| landcover_class_names | STRING_LIST | Array of cropland landcover classification names. |\n| landcover_class_palette | STRING_LIST | Array of hex code color strings used for the classification palette. |\n| landcover_class_values | INT_LIST | Value of the land cover classification. |\n\n### Terms of Use\n\n**Terms of Use**\n\n[OGL-Canada-2.0](https://spdx.org/licenses/OGL-Canada-2.0.html)\n\n### Citations\n\nCitations:\n\n- Agriculture and Agri-Food Canada Annual Crop Inventory. {YEAR}\n\n### Explore with Earth Engine\n\n| **Important:** Earth Engine is a platform for petabyte-scale scientific analysis and visualization of geospatial datasets, both for public benefit and for business and government users. Earth Engine is free to use for research, education, and nonprofit use. To get started, please [register for Earth Engine access.](https://console.cloud.google.com/earth-engine)\n\n### Code Editor (JavaScript)\n\n```javascript\nvar dataset = ee.ImageCollection('AAFC/ACI');\nvar crop2016 = dataset\n .filter(ee.Filter.date('2016-01-01', '2016-12-31'))\n .first();\nMap.setCenter(-103.8881, 53.0372, 10);\nMap.addLayer(crop2016, {}, '2016 Canada AAFC Annual Crop Inventory');\n```\n[Open in Code Editor](https://code.earthengine.google.com/?scriptPath=Examples:Datasets/AAFC/AAFC_ACI) \n[Canada AAFC Annual Crop Inventory](/earth-engine/datasets/catalog/AAFC_ACI) \nStarting 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, ... \nAAFC/ACI, agriculture,canada,crop,landcover \n2009-01-01T00:00:00Z/2023-01-01T00:00:00Z \n36.83 -135.17 62.25 -51.24 \nGoogle Earth Engine \nhttps://developers.google.com/earth-engine/datasets\n\n- [](https://doi.org/https://open.canada.ca/data/en/dataset/ba2645d5-4458-414d-b196-6303ac06c1c9)\n- [](https://doi.org/https://developers.google.com/earth-engine/datasets/catalog/AAFC_ACI)"]]
