Depuis 2009, l'équipe d'observation de la Terre de la Direction générale des sciences et de la technologie (DGST) d'Agriculture et Agroalimentaire Canada (AAC) a commencé à générer des cartes numériques annuelles des types de cultures. En se concentrant sur les provinces des Prairies en 2009 et 2010, une méthodologie basée sur un arbre de décision a été appliquée à l'aide d'images satellite optiques (Landsat-5, AWiFS, DMC) et radar (Radarsat-2). Depuis la saison de croissance 2011, cette activité a été étendue à d'autres provinces pour soutenir un inventaire national des cultures.
À ce jour, cette approche permet de fournir de manière cohérente un inventaire des cultures qui répond à la précision cible globale d'au moins 85 % à une résolution spatiale finale de 30 m (56 m en 2009 et 2010).
Bracelets
Taille des pixels 30 mètres
Bandes de fréquences
Nom
Min
Max
Taille des pixels
Description
landcover
1
255
mètres
Classification de la couverture terrestre spécifique à la culture principale.
Tableau des classes de couverture du sol
Valeur
Couleur
Description
10
#000000
Cloud
20
#3333ff
Eau
30
#996666
Terres exposées et arides
34
#cc6699
Urbain et développé
35
#e1e1e1
Greenhouses
50
#ffff00
Shrubland
80
#993399
Marécage
85
#501b50
Peatland
110
#cccc00
Prairie
120
#cc6600
Agriculture (non différenciée)
122
#ffcc33
Pâturages et fourrages
130
#7899f6
Trop humide pour être semé
131
#ff9900
Fallow
132
#660000
Céréales
133
#dae31d
Orge
134
#d6cc00
Autres céréales
135
#d2db25
Millet
136
#d1d52b
Avoine
137
#cace32
Rye
138
#c3c63a
Épeautre
139
#b9bc44
Triticale
140
#a7b34d
Blé
141
#b9c64e
Switchgrass
142
#999900
Sorgho
143
#e9e2b1
Quinoa
145
#92a55b
Blé d'hiver
146
#809769
Blé de printemps
147
#ffff99
Maïs
148
#98887c
Tabac
149
#799b93
Ginseng
150
#5ea263
Graines oléagineuses
151
#52ae77
Borage
152
#41bf7a
Camelina
153
#d6ff70
Colza et navette
154
#8c8cff
Graines de lin
155
#d6cc00
Moutarde
156
#ff7f00
Safflower
157
#315491
Tournesol
158
#cc9933
Soja
160
#896e43
Clignote
161
#996633
Autres impulsions
162
#8f6c3d
Petits pois
163
#b6a472
De pois chiches
167
#82654a
Haricots
168
#a39069
Fababeans
174
#b85900
Lentilles
175
#b74b15
Légumes
176
#ff8a8a
Tomates
177
#ffcccc
Des pommes de terre
178
#6f55ca
Betteraves sucrières
179
#ffccff
Autres légumes
180
#dc5424
Fruits
181
#d05a30
Baies
182
#d20000
Myrtille
183
#cc0000
Canneberge
185
#dc3200
Autre Berry
188
#ff6666
Vergers
189
#c5453b
Autres fruits
190
#7442bd
Vignobles
191
#ffcccc
Le houblon
192
#b5fb05
Gazon
193
#ccff05
Herbes
194
#07f98c
Garderie
195
#00ffcc
Sarrasin
196
#cc33cc
Alpiste
197
#8e7672
Chanvre
198
#b1954f
Vetch
199
#749a66
Autres cultures
200
#009900
Forêt (non différenciée)
210
#006600
Conifères
220
#00cc00
Broadleaf
230
#cc9900
Bois mélangé
Propriétés des images
Propriétés de l'image
Nom
Type
Description
landcover_class_names
STRING_LIST
Tableau des noms de classification de la couverture terrestre des terres cultivées.
landcover_class_palette
STRING_LIST
Tableau de chaînes de code hexadécimales de couleurs utilisées pour la palette de classification.
landcover_class_values
INT_LIST
Valeur de la classification de la couverture terrestre.
Depuis 2009, l'équipe d'observation de la Terre de la Direction des sciences et de la technologie (DST) d'Agriculture et Agroalimentaire Canada (AAC) a commencé à générer des cartes numériques annuelles des types de cultures. En se concentrant sur les provinces des Prairies en 2009 et 2010, une méthodologie basée sur l'arbre de décision (DT) a été appliquée à l'aide de données optiques (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)"]]
