Footprintsfor all imagery in the Government of Yukon Medium Resolution Satellite ImageryService.Distributed from [GeoYukon](https://yukon.ca/geoyukon) by the [Government of Yukon](https://yukon.ca/maps) . Discover more digital map data and interactive maps from Yukon's digital map data collection.Formore information: [geomatics.help@yukon.ca](mailto:geomatics.help@yukon.ca).

These products are derived from RGB (red/green/blue) images, a satellite processing technique that uses a combination of satellite sensor bands (also called channels) and applies a red/green/blue (RGB) filter to each of them. The result is a false-color image, i.e. an image that does not correspond to what the human eye would see, but offers high contrast between different cloud types and surface features. The on-board sensor of a weather satellite obtains two basic types of information: visible light data (reflected light) reflecting off clouds and different surface types, also known as "reflectance", and infrared data (emitted radiation) which are short-wave and long-wave radiation emitted by clouds and surface features. RGBs are specially designed to combine this type of satellite data, resulting in an information-rich final product.Other products are based on the enhancement of channel data for a single wavelength, also aimed at highlighting meteorological features of the observed surface or clouds, but in a simpler way since only a single wavelength is involved. This older approach is still useful today, as its simplicity makes image interpretation easier in some cases.

RADARSAT-1, in operation from 1995 to 2013, is Canada's first earth observation satellite. Developed and operated by the Canadian Space Agency (CSA), it has provided essential information to government, scientists and commercial users.Ultimately, the RADARSAT-1 mission generated the largest synthetic-aperture radar (SAR) data archive in the world. In April 2019, 36,000 images were made accessible through the Earth Observation Data Management System (eodms-sgdot.nrcan-rncan.gc.ca).A heatmap of processed images was produced by the CSA and helps visualize the density of images available by mapped sector during the RADARSAT-1 mission.

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.

Footprints for all imagery in the G overnment of Yukon High Resolution Satellite I magery S ervice.Distributed from [GeoYukon](https://yukon.ca/geoyukon) by the [Government of Yukon](https://yukon.ca/maps) . Discover more digital map data and interactive maps from Yukon's digital map data collection.For more information: [geomatics.help@yukon.ca](mailto:geomatics.help@yukon.ca)

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 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) 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.The initial methodology was developed in partnership with AAFC Research Branch, and supported in part by the Canadian Space Agency. The long-term objective of this endeavour is to expand from the Prairies and produce an annual crop inventory of the entire agricultural extent of Canada.

The RADARSAT Constellation is the evolution of the RADARSAT Program with the objective of ensuring data continuity, improved operational use of Synthetic Aperture Radar (SAR) and improved system reliability. The three-satellite configuration provides daily revisits of Canada's vast territory and maritime approaches, as well as daily access to 90% of the world's surface.RCM is tasked solely by the Government of Canada, to acquire data, first and foremost in support of Government of Canada services and needs. RCM data and services contributes to ensuring the safety and security of Canadians; monitoring and protecting the environment; monitoring of climate change; managing Canada’s natural resources; and stimulating innovation, research and economic development. In addition to these core user areas, there are expected to be a wide range of ad hoc uses of RADARSAT Constellation data in many different applications within the public and private sectors, both in Canada and internationally. The current data set reflects the acquisition plans that are designed to meet the RCM SAR imaging demands of the Government of Canada. These are being made available publicly in advance of the acquisitions. To meet the data needs of the Government of Canada, acquisitions may be changed without notice. After their acquisition and processing, the RCM image products listed in the current data set, will be delivered to the Earth Observation Data Management System - EODMS (https://www.eodms-sgdot.nrcan-rncan.gc.ca/index-en.html) portal of Natural Resources Canada. Users can register to the EODMS portal as public users to retrieve the RCM image products. For those requiring a greater access to RCM imagery consisting of product types or spatial resolutions not available to public users: you may apply to upgrade your public account to an ‘RCM external vetted entity’ EODMS user type account. For more information on this process, please contact the Canadian Space Agency using the information available at the following link : https://www.asc-csa.gc.ca/eng/satellites/radarsat/access-to-data/how-to-become-a-user.aspPublication frequency :I. Future acquisition plans are published every two weeks for a two-week window that starts two weeks from the publication date. As an example, acquisition plan published on April 1st covers acquisitions from April 14 to 27. The next plan is published on April 14th and covers from April 28 to May 11.II. Past acquisitions plans are published monthly and covers a period of one month from the first to the last day As an example, acquisition plan published on April 1st covers acquisition made between the March 1 and March 31. The next plan covers the month of April.

This dataset accompanies the open access article "Improving satellite chlorophyll-a retrieval in the turbid waters of the Bay of Fundy, Canada" published in Estuaries and Coasts (https://doi.org/10.1007/s12237-024-01334-x). A full methods description is provided in the article. Briefly, we processed daily satellite data from the MODerate-resolution Imaging Spectroradiometer (MODIS) on the Aqua satellite from 2003 to 2021 at 300 m resolution to understand and quantify spatial and temporal trends in chlorophyll-a concentration (chl-a, a measure of phytoplankton biomass), and suspended particulate matter concentration (SPM) in the Bay of Fundy surface waters. This dataset provides the median yearly and seasonal climatology of chl-a (mg m-3 ) and SPM (g m-3) from 2003 to 2021 as geotiff layers. Here winter is defined as January to March, spring as April to June, summer as July to September, and fall as October to December. Chl-a was calculated with the OCX-SPMCor algorithm and SPM was calculated with the Nechad et al 2010 algorithm.Cite this data as: Wilson, K., Hilborn, A., Clay, S., Devred, E. Data ofSatellite derived surface chlorophyll-a and suspended particulate matter in the Bay of Fundy from 2003 to 2021. Published February 2024. Ocean Ecosystem Science Division, Fisheries and Oceans Canada, Dartmouth, N.S. https://open.canada.ca/data/en/dataset/272f5cf1-52bb-416b-b92a-8bc9384fc24d

Landsat-derived forest age for Canada 2022Satellite-based forest age map for 2022 across Canada's forested ecozones at a 30-m spatial resolution. Remotely sensed data from Landsat (disturbances, surface reflectance composites, forest structure) and MODIS (Gross Primary Production) are utilized to determine age. Age can be determined where disturbance can be identified directly (disturbance approach) or inferred using spectral information (recovery approach) or using inverted allometric equations to model age where there is no evidence of disturbance (allometric approach). The disturbance approach is based upon satellite data and mapped changes and is the most accurate. The recovery approach also avails upon satellite data plus logic regarding forest succession, with an accuracy that is greater than pure modeling. Given the lack of widespread recent disturbance over Canada's forests, the allometric approach is required over the greatest area (86.6%). Using information regarding realized heights and growth and yield modeling, ages are estimated where none are otherwise possible. Trees of all ages are mapped, with trees >150 years old combined in an - old tree - category. This product was developed within the framework of Canada’s National Terrestrial Ecosystem Monitoring System (NTEMS).See Maltman et al. (2023) for an overview of the methods, data, image processing, as well as information on agreement assessment using Canada's National Inventory (NFI). Maltman, J.C., Hermosilla, T., Wulder, M.A., Coops, N.C., White, J.C., 2023. Estimating and mapping forest age across Canada's forested ecosystems. Remote Sensing of Environment 290, 113529. ( Maltman et al. 2023).

Yukon mediumresolution satellite imagery is distributed from the Government of Yukonimagery repository. This is a dynamic service containing satellite imagery forlocations in the Yukon, Canada.This data is inYukon Albers equal area projection. It can be viewed and queried in theGeoYukon application: [https://mapservices.gov.yk.ca/GeoYukon](https://mapservices.gov.yk.ca/GeoYukon).For more informationcontact [geomatics.help@yukon.ca](mailto:geomatics.help@yukon.ca).