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.For more information, visit: http://open.canada.ca/data/en/dataset/58ca7629-4f6d-465a-88eb-ad7fd3a847e3

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. For more information, visit: http://open.canada.ca/data/en/dataset/6dc5170d-4167-47e4-b80a-93ed2b47f023

The Evaporative Stress Index (ESI) describes temporal anomalies in evapotranspiration (ET), highlighting areas with anomalously high or low rates of water use across the land surface. Here, ET is retrieved via energy balance using remotely sensed land-surface temperature (LST) time-change signals. LST is a fast- response variable, providing proxy information regarding rapidly evolving surface soil moisture and crop stress conditions at relatively high spatial resolution. The ESI also demonstrates capability for capturing early signals of "flash drought", brought on by extended periods of hot, dry and windy conditions leading to rapid soil moisture depletion. ESI values quantify standardized anomalies (σvalues) in the ratio of clear-sky actual-to-potential ET (fPET), derived using thermal infrared (TIR) satellite imagery from geostationary platforms. To capture a range in timescales, fPET composites are developed for 1, 2 and 3 month moving windows, advancing at 7-day intervals. Standardized anomalies are then computed with respect to normal conditions (mean and standard deviation) for each compositing interval assessed over a period of record from 2000-2015. For more information, visit: http://open.canada.ca/data/en/dataset/679f676a-330a-456f-9928-a4fafc95f9f8

This data shows spatial density of Corn cultivation in Canada. Regions with higher calculated spatial densities represent agricultural regions of Canada in which Corn is 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 Corn based on analysis of the 2009 to 2015 AAFC annual crop inventory data. For more information, visit: http://open.canada.ca/data/en/dataset/92f73de5-5f46-4a8c-bc5f-c3872f268ecb

The Grain Elevators in Canada dataset maps the list of grain elevators in Canada as provided by the Canadian Grain Commission (CGC). The elevators have been located as much as possible to an actual location rather than generalizing to the station name centroid. Additionally car spot information from CN, CP and the grain companies has been added where this has been published. This dataset attempts to provide a temporal and geographical extent of the grain elevators in Canada. For more information, visit: http://open.canada.ca/data/en/dataset/05870f11-a52a-4bf4-bc15-910fd0b8a1a3

This data shows spatial density of Soybean cultivation in Canada. Regions with higher calculated spatial densities represent agricultural regions of Canada in which Soybeans 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 Soybeans based on analysis of the 2009 to 2015 AAFC annual crop inventory data. For more information, visit: http://open.canada.ca/data/en/dataset/7c444a60-3b82-48d9-a197-efdff0154aaf

This data series represents the volumetric soil moisture (percent saturated soil) for the surface layer, expressed as a difference from the long term average. The average is calculated from all available years for each location and each time period, based on the length of the satellite data record. Values higher than zero represent areas that are wetter than the long term average, and areas lower than zero represent areas drier than the long term average. The data is created daily and is averaged for the ISO standard week and month. The data is produced from passive microwave satellite data collected by the Soil Moisture and Ocean Salinity (SMOS) satellite and converted to soil moisture using version 6.20 of the SMOS soil moisture processor. The data are produced by the European Space Agency and obtained under a Category 1 proposal for Level 2 soil moisture data. The data are gridded to a resolution of 0.25 degrees. Data quality flags have been applied to remove areas where rainfall is present during the acquisition, where snow cover is detected and when Radio Frequency Interference (RFI) is above an acceptable threshold. For more information, visit: http://open.canada.ca/data/en/dataset/81af24f4-76cb-4f33-8685-b3a6a576358d

The "South Tobacco Creek Watershed - 10 cm Contours" dataset is a linear representation of the LiDAR DEM data set to the closest 0.1 meters. For more information, visit: http://open.canada.ca/data/en/dataset/734078a9-9aa1-44a1-9e74-dc9387a9ecfe

This data shows spatial density of Barley cultivation in Canada. Regions with higher calculated spatial densities represent agricultural regions of Canada in which Barley is 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 Barley based on analysis of the 2009 to 2015 AAFC annual crop inventory data.For more information, visit: http://open.canada.ca/data/en/dataset/08ba1d1c-4985-46d4-be2d-495005689db2

The 2010 Plant Hardiness Zones of Canada outlines the different zones in Canada where various types of trees, shrubs and flowers will most likely survive. It is based on the average climatic conditions of each area. The first such map for North America, released by the United States Department of Agriculture in 1960, was based only on minimum winter temperatures. In 1967, Agriculture Canada scientists created a plant hardiness map using Canadian plant survival data and a wider range of climatic variables, including minimum winter temperatures, length of the frost-free period, summer rainfall, maximum temperatures, snow cover, January rainfall and maximum wind speed. Natural Resources Canada's Canadian Forest Service scientists have now updated the plant hardiness zones using the same variables and more recent climate data (1961-90). They have used modern climate mapping techniques and incorporated the effect of elevation. The new map indicates that there have been changes in the hardiness zones that are generally consistent with what is known about climate change. These changes are most pronounced in western Canada. The new hardiness map is divided into nine major zones: the harshest is 0 and the mildest is 8. Subzones (e.g., 4a or 4b, 5a or 5b) are also noted in the map legend. These subzones are most familiar to Canadian gardeners. Some significant local factors, such as micro-topography, amount of shelter and subtle local variations in snow cover, are too small to be captured on the map. Year-to-year variations in weather and gardening techniques can also have a significant impact on plant survival in any particular location. For more information see: http://open.canada.ca/data/en/dataset/50f9f293-f288-4de6-98ad-f69cf85d21ea