In the "Weekly Best-Quality Maximum-NDVI anomalies" dataset series, each pixel value corresponds to the difference (anomaly) between the mean n-year "Best-Quality" Max-NDVI of the week specified (e.g. Week 18, 2000-2014) and the "Best-Quality" Max-NDVI of the same week in a specific year (e.g. Week 18, 2014). Max-NDVI anomalies < 0 indicate where weekly Max-NDVI is lower than normal. Anomalies > 0 indicate where weekly Max-NDVI is higher than normal. Anomalies close to 0 indicate where weekly Max-NDVI is similar to normal. For more information, visit: http://open.canada.ca/data/en/dataset/ea6b4be2-9826-47f3-a387-33ddf02592f4

The 2000 Land Use (LU) map covers all areas of Canada south of 60oN at a spatial resolution of 30 metres. The LU classes follow the protocol of the Intergovernmental Panel on Climate Change (IPCC) and consist of: Forest, Water, Cropland, Grassland, Settlement and Otherland. The 2000 Land Use (LU) map was developed in response to a need for explicit, high-accuracy, high-resolution land use data to meet AAFC's commitments in international reporting.For more information, visit: http://open.canada.ca/data/en/dataset/b5f413d9-9acc-4ad7-b9a7-38486ed5fee7

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

The 'Circa 1995 Landcover of the Prairies' dataset is a geospatial raster data layer portraying the rudimentary land cover types of all grain-growing areas of Manitoba, Saskatchewan, Alberta and northeastern British Columbia at a 30-metre resolution for the 1995 timeframe. It is the collection of all the classified imagery (1993 to 1995) of the Western Grain Transition Payment Program (WGTPP) assembled into a single seamless raster data layer. For more information, visit: http://open.canada.ca/data/en/dataset/e9dee957-e04d-46fb-b7e4-701739736173

The Canadian Drought Monitor (CDM) is a composite product developed from a wide assortment of information such as the Normalized Difference Vegetation Index (NDVI), streamflow values, Palmer Drought Index, and drought indicators used by the agriculture, forest and water management sectors. Drought prone regions are analyzed based on precipitation, temperature, drought model index maps, and climate data and are interpreted by federal, provincial and academic scientists. Once a consensus is reached, a monthly map showing drought designations for Canada is digitized. Agriculture and Agri-Food Canada's National Agroclimate Information Service (NAIS) updates this dataset on a monthly basis, usually by the 10th of every month to correspond to the end of the previous month, and subsequent Canadian input into the larger North American Drought Monitor (NA-DM). The drought areas are classified as follows: - D0 (Abnormally Dry) - represents an event that occurs once every 3-5 years - D1 (Moderate Drought) - represents an event that occurs every 5-10 years - D2 (Severe Drought) - represents an event that occurs every 10-20 years - D3 (Extreme Drought) - represents an event that occurs every 20-25 years - D4 (Exceptional Drought) - represents an event that occurs every 50 years. For more information visit: https://open.canada.ca/data/en/dataset/292646cd-619f-4200-afb1-8b2c52f984a2

The "AAFC Annual Unit Runoff in Canada - 2013" report aims to illustrate runoff trends across the country by calculating annual unit runoff for a variety of probabilities of exceedence commonly used by decision makers. Annual unit runoff is a measure of runoff volume per square kilometre. It includes a point data set for the hydrologic stations that were analyzed and seven sets of linework to show the adjusted isolines for 10%, 25%, 50%, 70%, 75%, 80%, and 90% probability of exceedence. It is an update and expansion of the work completed in the 1994 report "Annual Unit Runoff on the Canadian Prairies". For more information, visit: http://open.canada.ca/data/en/dataset/a905bafc-74b5-4ec5-b5f9-94b2e19815d0

The "Soils of Canada, Derived" national scale thematic datasets display the distribution and areal extent of soil attributes such as drainage, texture of parent material, kind of material, and classification of soils in terms of provincial Detailed Soil Surveys (DDS) polygons, Soil Landscape Polygons (SLCs), Soil Order and Great Group. The relief and associated slopes of the Canadian landscape are depicted on the local surface form thematic dataset. The purpose of the "Soils of Canada, Derived" series is to facilitate the cartographic display and basic queries of the Soil Landscapes of Canada at a national scale. For more detailed or sophisticated analysis, users should investigate the full "Soil Landscapes of Canada" product. For more information, visit: http://open.canada.ca/data/en/dataset/8f496e3f-1e54-4dbb-a501-a91eccf616b8

The Agriculture and Agri-Food Canada's LiDAR Projects dataset was created from existing spatial data. It contains the footprints (outlines) of all the LiDAR data that is openly distributed by Agriculture and Agri-Food Canada. LiDAR (Light Detection And Ranging) is a method of acquiring survey points using optical remote sensing technology. The dataset indicates basic information about the location, source and properties of the data. For more information, visit: http://open.canada.ca/data/en/dataset/a760f9e0-7013-4187-9261-2e69b01edd9a

The Agri-Environmental Indicator (AEI) dataset series provides information that was created using indicators that assess the environmental impact of agricultural activities. These agri-environmental indicators integrate information on soils, climate and land surface features with statistics on land use and crop and livestock management practices. The datasets provide valuable, location-specific information on the overall environmental risks and conditions in agriculture across Canada and how these change over time. This dataset series collects AEI data that is related to geographic features and can be represented on a map. Other types of AEI data are not included. The datasets can be organized into the following major groups: farm land management, soil health, water quality, air quality, and food and beverage industry (not included). Farm land management datasets: soil cover, wildlife habitat, and farm land management (not included). Soil health datasets: soil erosion, soil organic matter, trace elements, and soil salinity. Water quality datasets: nitrogen, phosphorus, coliforms, and pesticides. Air quality datasets: greenhouse gases, ammonia, particulate matter. For more information, visit: http://open.canada.ca/data/en/dataset/e996d9be-6a3b-4059-9afc-17dc68385f05

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