The source of the layers in this mxd are derived products originating from the Agri-Environmental (AEI) dataset series. The original source data was re- formatted to enable time display on the layers, with individual soil landscape polygons being dissolved out to allow web optimization. For Layer Names with a year in the title, the source points to the Time Series Datasets, however they have a definition query applied to only display the data corresponding t o a particular year. The datasets in the series should be used in web applications only.

The "Prairie Agricultural Landscapes (PAL)" datasets identify areas of the agricultural portions of the Canadian Prairies with similar land and water resources, land use and farming practices. They are represented by vector polygons. Based on selected attributes from the Soil Landscapes of Canada (SLC) and the 1996 Census of Agriculture, the Prairies were classified into 13 (thirteen) classes of Land Practices Group and five (5) Major Land Practices Groups. Typical attributes used to define the Land Practice Groups include: land in pasture, land in summerfallow, crop mixture, farm size and the level of chemical and fertilizer inputs. The five (5) Major Groups were devised to help better understand the relationships between the groups. For more information, visit: https://open.canada.ca/data/en/dataset/0b2303be-ef05-49a8-8082-44a3eabcfa57

This data series represents the volumetric soil moisture (percent saturated soil) for the surface layer (<5 cm). The data is created daily and is averaged for the ISO standard week and month. The datais produced from passive microwave satellite data collected by the Soil Moisture and Ocean Salinity (SMOS) satellite and converted to soil moisture using version 5.51 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/7dd83e31-c843-421d-9764-e0569b40ee33

The Plant Hardiness Zones map 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

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

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 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

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

In 2015, 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. For more information, visit: http://open.canada.ca/data/en/dataset/3688e7d9-7520-42bd-a3eb-8854b685fef3

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