In 2020, the Earth Observation Team of the Science and Technology Branch (STB) at Agriculture and Agri-Food Canada (AAFC) repeated the process of generating annual crop inventory digital maps using satellite imagery to for all of Canada, in support of a national crop inventory. A Decision Tree (DT) based methodology was applied using optical (Landsat-8, Sentinel-2) based satellite images, and having a final spatial resolution of 30m. In conjunction with satellite acquisitions, ground-truth information was provided by: provincial crop insurance companies in Alberta, Manitoba, & Quebec; point observations from the PEI Department of Environment, Water and Climate Change; the Ontario Ministry of Agriculture, Food and Rural Affairs; and data collection supported by our regional AAFC Research and Development Centres in St. John’s, Charlottetown, Fredericton, and Guelph. Due to COVID-19 travel restrictions, complete sampling coverages in NL, NS, NB and BC were not possible, as a result the general agriculture class (120) is found in these provinces in areas where there was no ground data collected.

In 2010 the Earth Observation Team of the Science and Technology Branch (STB) at Agriculture and Agri-Food Canada (AAFC) continued the process of generating annual crop inventory digital maps using satellite imagery. Focusing on the Prairie Provinces, a Decision Tree (DT) based methodology was applied using both optical (AWiFS, Landsat-5, DMC) and radar (RADARSAT-2) based satellite imagery, and having a final spatial resolution of 56m. Methods were also developed to enhance the optical classification with RADARSAT-2 imagery, addressing issues associated with cloud cover. In conjunction with satellite acquisitions, ground-truth information was provided by provincial crop insurance companies and point observations from our regional AAFC colleagues. The overall process for Crop Inventory Map includes: satellite data acquisition; field data acquisition for classification training and accuracy assessment; and, operational implementation of the classification methodology.

In 2018, the Earth Observation Team of the Science and Technology Branch (STB) at Agriculture and Agri-Food Canada (AAFC) repeated the process of generating annual crop inventory digital maps using satellite imagery to for all of Canada, in support of a national crop inventory. A Decision Tree (DT) based methodology was applied using optical (Landsat-8, Sentinel-2) and radar (RADARSAT-2) based satellite images, and having a final spatial resolution of 30m. In conjunction with satellite acquisitions, ground-truth information was provided by: provincial crop insurance companies in Alberta, Saskatchewan, Manitoba, & Quebec; point observations from the BC Ministry of Agriculture, & the Ontario Ministry of Agriculture, Food and Rural Affairs; and data collection supported by our regional AAFC Research and Development Centres in St. John’s, Kentville, Charlottetown, Fredericton, Guelph, and Summerland

In 2017, the Earth Observation Team of the Science and Technology Branch (STB) at Agriculture and Agri-Food Canada (AAFC) repeated the process of generating annual crop inventory digital maps using satellite imagery to for all of Canada, in support of a national crop inventory. A Decision Tree (DT) based methodology was applied using optical (Landsat-8, Sentinel-2, Gaofen-1) and radar (RADARSAT-2) based satellite images, and having a final spatial resolution of 30m. In conjunction with satellite acquisitions, ground-truth information was provided by: provincial crop insurance companies in Alberta, Saskatchewan, Manitoba, & Quebec; point observations from the BC Ministry of Agriculture, & the Ontario Ministry of Agriculture, Food and Rural Affairs; and data collection supported by our regional AAFC Research and Development Centres in St. John’s, Kentville, Charlottetown, Fredericton, Guelph, and Summerland

In 2023, the Earth Observation Team of the Science and Technology Branch (STB) at Agriculture and Agri-Food Canada (AAFC) repeated the process of generating annual crop inventory digital maps using satellite imagery for all Canadian provinces, in support of a national crop inventory. This year we again produced a map of the agricultural regions in the Yukon Territory. A Decision Tree (DT) based methodology was applied using optical (Landsat-8, Landsat-9, Sentinel-2), and radar (RCM) based satellite images, and having a final spatial resolution of 30m. In conjunction with satellite acquisitions, ground-truth information was provided by: provincial crop insurance companies in Alberta, Saskatchewan, Manitoba, & Quebec; point observations from the PEI Department of Environment, Water and Climate Change; and data collection supported by our regional AAFC Research and Development Centres in St. John's, Kentville, Fredericton, Guelph, Summerland and Whitehorse. New this season, Forest Fire Perimeter Estimate polygons from Natural Resources Canada’s Canadian Forest Service were used to show burned areas of landcover. (Class - 60).

In 2022, the Earth Observation Team of the Science and Technology Branch (STB) at Agriculture and Agri-Food Canada (AAFC) repeated the process of generating annual crop inventory digital maps using satellite imagery for all Canadian provinces, in support of a national crop inventory. New this year, a map of the agricultural regions in the Yukon Territory was also produced. A Decision Tree (DT) based methodology was applied using optical (Landsat-8, Landsat-9, Sentinel-2), and radar (RCM) based satellite images, and having a final spatial resolution of 30m. In conjunction with satellite acquisitions, ground-truth information was provided by: provincial crop insurance companies in Alberta, Saskatchewan, Manitoba, & Quebec; point observations from the PEI Department of Environment, Water and Climate Change; Ontario Ministry of Agriculture, Food and Rural Affairs; University of Guelph - Ridgetown campus; British Columbia Ministry of Agriculture; and data collection supported by our regional AAFC Research and Development Centres in St. John's, Kentville, Fredericton, Guelph, Summerland and Whitehorse.

In 2014, the Earth Observation Team of the Science and Technology Branch (STB) at Agriculture and Agri-Food Canada (AAFC) repeated the process of generating annual crop inventory digital maps using satellite imagery to for all of Canada, in support of a national crop inventory. A Decision Tree (DT) based methodology was applied using optical (Landsat-8) and radar (RADARSAT-2) based satellite images, and having a final spatial resolution of 30m. In conjunction with satellite acquisitions, ground-truth information was provided by provincial crop insurance companies and point observations from the BC Ministry of Agriculture and our regional AAFC colleagues.

In 2022, the Earth Observation Team of the Science and Technology Branch (STB) at Agriculture and Agri-Food Canada (AAFC) repeated the process of generating annual crop inventory digital maps using satellite imagery for all Canadian provinces, in support of a national crop inventory. New this year, a map of the agricultural regions in the Yukon Territory was also produced. A Decision Tree (DT) based methodology was applied using optical (Landsat-8, Landsat-9, Sentinel-2), and radar (RCM) based satellite images, and having a final spatial resolution of 30m. In conjunction with satellite acquisitions, ground-truth information was provided by: provincial crop insurance companies in Alberta, Saskatchewan, Manitoba, & Quebec; point observations from the PEI Department of Environment, Water and Climate Change; Ontario Ministry of Agriculture, Food and Rural Affairs; University of Guelph - Ridgetown campus; British Columbia Ministry of Agriculture; and data collection supported by our regional AAFC Research and Development Centres in St. John's, Kentville, Fredericton, Guelph, Summerland and Whitehorse.

In 2017, the Earth Observation Team of the Science and Technology Branch (STB) at Agriculture and Agri-Food Canada (AAFC) repeated the process of generating annual crop inventory digital maps using satellite imagery to for all of Canada, in support of a national crop inventory. A Decision Tree (DT) based methodology was applied using optical (Landsat-8, Sentinel-2, Gaofen-1) and radar (RADARSAT-2) based satellite images, and having a final spatial resolution of 30m. In conjunction with satellite acquisitions, ground-truth information was provided by: provincial crop insurance companies in Alberta, Saskatchewan, Manitoba, and Quebec; point observations from the BC Ministry of Agriculture, and the Ontario Ministry of Agriculture, Food and Rural Affairs; and data collection supported by our regional AAFC Research and Development Centres in St. John's, Kentville, Charlottetown, Fredericton, Guelph, and Summerland. For more information, visit: https://open.canada.ca/data/en/dataset/cb3d7dec-ecc6-498b-ac17-949e03f29549

In 2017, the Earth Observation Team of the Science and Technology Branch (STB) at Agriculture and Agri-Food Canada (AAFC) repeated the process of generating annual crop inventory digital maps using satellite imagery to for all of Canada, in support of a national crop inventory. A Decision Tree (DT) based methodology was applied using optical (Landsat-8, Sentinel-2, Gaofen-1) and radar (RADARSAT-2) based satellite images, and having a final spatial resolution of 30m. In conjunction with satellite acquisitions, ground-truth information was provided by: provincial crop insurance companies in Alberta, Saskatchewan, Manitoba, & Quebec; point observations from the BC Ministry of Agriculture, & the Ontario Ministry of Agriculture, Food and Rural Affairs; and data collection supported by our regional AAFC Research and Development Centres in St. John’s, Kentville, Charlottetown, Fredericton, Guelph, and Summerland

In 2023, the Earth Observation Team of the Science and Technology Branch (STB) at Agriculture and Agri-Food Canada (AAFC) repeated the process of generating annual crop inventory digital maps using satellite imagery for all Canadian provinces, in support of a national crop inventory. This year we again produced a map of the agricultural regions in the Yukon Territory. A Decision Tree (DT) based methodology was applied using optical (Landsat-8, Landsat-9, Sentinel-2), and radar (RCM) based satellite images, and having a final spatial resolution of 30m. In conjunction with satellite acquisitions, ground-truth information was provided by: provincial crop insurance companies in Alberta, Saskatchewan, Manitoba, & Quebec; point observations from the PEI Department of Environment, Water and Climate Change; and data collection supported by our regional AAFC Research and Development Centres in St. John's, Kentville, Fredericton, Guelph, Summerland and Whitehorse. New this season, Forest Fire Perimeter Estimate polygons from Natural Resources Canada’s Canadian Forest Service were used to show burned areas of landcover. (Class - 60).

2009'dan itibaren, Agriculture and Agri-Food Canada (AAFC) Bilim ve Teknoloji Bölümü (STB) Yer Gözlem Ekibi, yıllık mahsul türü dijital haritaları oluşturma sürecine başladı. 2009 ve 2010'da Prairie Eyaletleri'ne odaklanarak, optik (Landsat-5, …

Od 2009 r. zespół ds. obserwacji Ziemi z Wydziału Nauki i Technologii (STB) w Agriculture and Agri-Food Canada (AAFC) rozpoczął proces generowania rocznych map cyfrowych typów upraw. Skupiając się na prowincjach prerii w latach 2009 i 2010, zastosowano metodę opartą na drzewie decyzyjnym (DT) z wykorzystaniem zdjęć satelitarnych z teleskopu optycznego (Landsat-5, AWiFS, DMC) i radaru (Radarsat-2). Od sezonu wegetacyjnego w 2011 r. ta aktywność została rozszerzona na inne prowincje w ramach krajowego spisu upraw. Do tej pory to podejście pozwalało na systematyczne dostarczanie zasobów reklamowych z uwzględnieniem przycięcia, które spełniają ogólny cel dokładności na poziomie co najmniej 85% przy końcowej rozdzielczości przestrzennej 30 m (56 m w 2009 i 2010 r.).

In 2011, the Earth Observation Team of the Science and Technology Branch (STB) at Agriculture and Agri-Food Canada (AAFC) expanded the process of generating annual crop inventory digital maps using satellite imagery to include British Columbia, Ontario, Quebec, and the Maritime provinces, in support of a national crop inventory. A Decision Tree (DT) based methodology was applied using optical (Landsat-5, DMC) and radar (RADARSAT-2) based satellite images, and having a final spatial resolution of 30m. In conjunction with satellite acquisitions, ground-truth information was provided by provincial crop insurance companies and point observations from our regional AAFC colleagues.

In 2019, the Earth Observation Team of the Science and Technology Branch (STB) at Agriculture and Agri-Food Canada (AAFC) repeated the process of generating annual crop inventory digital maps using satellite imagery to for all of Canada, in support of a national crop inventory. A Decision Tree (DT) based methodology was applied using optical (Landsat-8, Sentinel-2) and radar (RADARSAT-2) based satellite images, and having a final spatial resolution of 30m. In conjunction with satellite acquisitions, ground-truth information was provided by: provincial crop insurance companies in Alberta, Saskatchewan, Manitoba, & Quebec; point observations from the PEI Department of Environment, Water and Climate Change and data collection supported by our regional AAFC Research and Development Centres in St. John’s, Kentville, Charlottetown, Fredericton, and Guelph.

This data shows spatial density of annual crops cultivation in Canada. Regions with higher calculated spatial densities represent agricultural regions of Canada in which annual crops are more expected. Results are provided as rasters with numerical values for each pixel indicating the spatial density calculated for that location. Higher spatial density values represent higher likelihood to have annual crops based on analysis of the 2009 to 2021 AAFC annual crop inventory data.

Crop rotation is an agricultural production and land management practice beneficial to sustainable agriculture in Canada. Agriculture and Agri-Food Canada (AAFC) produces crop rotation data annually showing the crop rotations used within the agricultural extent of Canada for the last four complete growing seasons (based on available data). This data can be used by producers, land managers, and policy makers to assess current rotations to assist in future land management decisions.Crop rotation data is derirved from AAFC's publicly-available annual crop inventory data.

The Agriculture and Agri-Food Canada (AAFC) Annual Crop Inventory (ACI) is produced at a national scale, covering Canada’s entire agricultural extent. It has been generated operationally since 2011 for the entire country (2009 for the Prairie Provinces). This product is spatially continuous and maps the most probable crop type for every field in Canada, along with the land cover of non-agricultural lands (e.g. wetlands, forest, urban, etc.). It allows AAFC to study and model crop rotation patterns at the field level. To develop the Quantitative Crop Rotation Characteristics of Canada, historical ACI data representing the time series of crops at agricultural field level with annual intervals were applied. They provided the time series of categories of crops for statistical analyses. The results of this work included spatial data sets with several calculated attributes representing crop rotation statistical characteristics. Specifically, a crop sequence turbulence index was shown to be an efficient quantitative measure of mapping the spatial distribution of the sustainability of crop rotation in regions where dominantly annual crops were the active crop rotation. In addition, based on characteristics of observed crops in their time series sequence and the quantitative sequence dynamics represented by the turbulence index, major spatial clusters of crop rotation styles were calculated. To analyze the turbulence index, crop rotation cluster class representing the general style of crop rotation must also be considered. The crop rotation quantitative attributes calculated in this project which are at the field level, can be converted into useful information directing us towards the health and sustainability of crop rotations.

This data shows spatial density of forage crops in Canada. Regions with higher calculated spatial densities represent agricultural regions of Canada in which forage crops are more expected. Results are provided as rasters with numerical values for each pixel indicating the spatial density calculated for that location. Higher spatial density values represent higher likelihood to have forage crops based on analysis of the 2009 to 2021 AAFC annual crop inventory data.

This data shows spatial density of Cereals cultivation in Canada. Regions with higher calculated spatial densities represent agricultural regions of Canada in which Cereals are more expected. Results are provided as rasters with numerical values for each pixel indicating the spatial density calculated for that location. Higher spatial density values represent higher likelihood to have Cereals based on analysis of the 2009 to 2021 AAFC annual crop inventory data. Cereals consist of the following specific crop types from the AAFC annual crop inventory; Cereals, Barley (including Spring and Winter), Greenfeed / Mixed Cereals, Millet, Oats, Rye (including Spring and Winter), Spelt, Triticale (including Spring and Winter), Wheat (including Spring and Winter), and Other Cereal