Each pixel value corresponds to the actual number (count) of valid Best-quality Max-NDVI values used to calculate the mean weekly values for that pixel. Since 2020, the maximum number of possible observations used to create the Mean Best-Quality Max-NDVI for the 2000-2014 period is n=20. However, because data quality varies both temporally and geographically (e.g. cloud cover and snow cover in spring; cloud near large water bodies all year), the actual number (count) of observations used to create baselines can vary significantly for any given week and year.

The MODIS surface albedo dataset was produced by the Canada Center for Remote Sensing (CCRS), Natural Resources Canada. The dataset represents the solar shortwave broadband surface albedo and it is at a 10-day interval covering the entire Canadian landmass as well as northern USA, Alaska, and the Greenland. The dataset was derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the TERRA satellite which provides a global coverage every 1-2 days in 36 spectral bands ranging from visible to infrared and to thermal wavelengths between 405 and 14,385 nm, and was available since 2000. For the estimation of surface albedo, the first seven spectral bands of B1 to B7 ranging from 459 nm to 2155 nm were used. B1 and B2 have a 250 meter resolution and B3 to B7 have a 500 meter resolution. A downscaling method using a regression and normalization scheme was employed to downscale the bands B3 to B7 to 250 meter resolution while preserving radiometric properties of the original data. To obtain clear-sky observations from MODIS, composite images for a 10 day period were generated by using a series of advanced algorithms (Luo et al., 2008). The 10-day composites of B1-B7 reflectance were then used to retrieve spatially continuous spectral albedo by using a combined land/snow BRDF (Bi-directional Reflectance Distribution Function) model. In that method, the modified RossThick-LiSparse BRDF model (Maignan et al., 2004) for land and Kokhanovsky and Zege’s model (2004) for snow are linearly combined for mixed surface conditions. They are weighted by snow fraction (0.0 ~ 1.0). The seven spectral albedo were then converted into the shortwave broadband surface albedo using the empirical MODIS polynomial conversion equation of Liang et al. (1999). The data product is in LCC (Lambert Conformal Conic) projection with a 250m pixel resolution. There are 36 albedo images per year. A dataset representing the pixel state (e.g. cloud/shadow, snow/ice, water, land, et al.) was also generated for each 10-day corresponding to the surface albedo product. References: Kokhanovsky, A. A. and Zege, E. P., 2004, Scattering Optics of Snow, Applied Optics, 43, 1589-1602, doi:10.1364/AO.43.001589, 20. Liang, S., Strahler, A.H., Walthall, C., 1999. Retrieval of land surface albedo from satellite observations: a simulation study. J. Appl. Meteorol. 38, 712–725. Luo, Y., Trishchenko, A.P., Khlopenkov, K.V., 2008. Developing clear-sky, cloud and cloud shadow mask for producing clear-sky composites at 250-meter spatial resolution for the seven MODIS land bands over Canada and North America. Remote Sens. Environ. 112, 4167–4185. Maignan, F., F.M. Bréon and R. Lacaze, 2004, Bidirectional reflectance of Earth targets : evaluation of analytical models using a large set of spaceborne measurements with emphasis with the hot spot, Remote Sens. Environ., 90, 210-220.

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.

The 1 cm resolution digital surface model (DSM) was created from unmanned aerial vehicle (UAV) imagery acquired from a single day survey, July 28th 2016, in Cambridge Bay, Nunavut. Five control points taken from a Global Differential Positioning System were positioned in the corners and the center of the vegetation survey. The DSM covering 525m2 was produced by Canada Centre for Remote Sensing /Canada Centre for Mapping and Earth Observation. The UAV survey was completed in collaboration with the Canadian High Arctic Research Station (CHARS) for northern vegetation monitoring research. For more information, refer to our current Arctic vegetation research: Fraser et al; "UAV photogrammetry for mapping vegetation in the low-Arctic" Arctic Science, 2016, 2(3): 79-102. http://www.nrcresearchpress.com/doi/abs/10.1139/AS-2016-0008

Note: To visualize the data in the viewer, zoom into the area of interest. The National Air Photo Library (NAPL) of Natural Resources Canada archives over 6 million aerial photographs covering all of Canada, some of which date back to the 1920s. This collection includes Time Series of aerial orthophoto mosaics over a selection of major cities or targeted areas that allow the observation of various changes that occur over time in those selected regions. These mosaics are disseminated through the Data Cube Platform implemented by NRCan using geospatial big data management technologies. These technologies enable the rapid and efficient visualization of high-resolution geospatial data and allow for the rapid generation of dynamically derived products. The data is available as Cloud Optimized GeoTIFF (COG) files for direct access and as Web Map Services (WMS) or Web Coverage Services (WCS) with a temporal dimension for consumption in Web or GIS applications. The NAPL mosaics are made from the best spatial resolution available for each time period, which means that the orthophotos composing a NAPL Time Series are not necessarily coregistered. For this dataset, the spatial resolutions vary from 150 cm to 200 cm. The NAPL indexes and stores federal aerial photography for Canada, and maintains a comprehensive historical archive and public reference centre. The Earth Observation Data Management System (EODMS) online application allows clients to search and retrieve metadata for over 3 million out of 6 million air photos. The EODMS online application enables public and government users to search and order raw Government of Canada Earth Observation images and archived products managed by NRCan such as aerial photos and satellite imagery. To access air photos, you can visit the EODMS web site: https://eodms-sgdot.nrcan-rncan.gc.ca/index-en.html

Note: To visualize the data in the viewer, zoom into the area of interest. The National Air Photo Library (NAPL) of Natural Resources Canada archives over 6 million aerial photographs covering all of Canada, some of which date back to the 1920s. This collection includes Time Series of aerial orthophoto mosaics over a selection of major cities or targeted areas that allow the observation of various changes that occur over time in those selected regions. These mosaics are disseminated through the Data Cube Platform implemented by NRCan using geospatial big data management technologies. These technologies enable the rapid and efficient visualization of high-resolution geospatial data and allow for the rapid generation of dynamically derived products. The data is available as Cloud Optimized GeoTIFF (COG) files for direct access and as Web Map Services (WMS) or Web Coverage Services (WCS) with a temporal dimension for consumption in Web or GIS applications. The NAPL mosaics are made from the best spatial resolution available for each time period, which means that the orthophotos composing a NAPL Time Series are not necessarily coregistered. For this dataset, the spatial resolutions are: 100 cm for the year 1947 and 50 cm for the year 1977. The NAPL indexes and stores federal aerial photography for Canada, and maintains a comprehensive historical archive and public reference centre. The Earth Observation Data Management System (EODMS) online application allows clients to search and retrieve metadata for over 3 million out of 6 million air photos. The EODMS online application enables public and government users to search and order raw Government of Canada Earth Observation images and archived products managed by NRCan such as aerial photos and satellite imagery. To access air photos, you can visit the EODMS web site: https://eodms-sgdot.nrcan-rncan.gc.ca/index-en.html

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.asp Publication 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.

The Canadian long term satellite data record (LTDR) derived from 1-km resolution Advanced Very High Resolution Radiometer (AVHRR) data was produced by the Canada Center for Remote Sensing (CCRS). Processing included: geolocation, calibration, and compositing using Earth Observation Data Manager (Latifovic et al. 2005), cloud screening (Khlopenkov and Trishchenko, 2006), BRDF correction (Latifovic et. al., 2003), atmosphere and other corrections as described in Cihlar et. al. (2004). For temporal analysis of vegetation cross-sensor correction of Latifovic et al. (2012) is advised. Data collected by the AVHRR instrument on board the National Oceanic and Atmospheric Administration (NOAA) 9,11,14,16,17,18 and 19 satellites were used to generate Canada-wide 1-km 10-day AVHRR composites. Data are available starting in 1985. It is important to note that there are three types of AVHRR sensors: (i) AVHRR-1 flown onboard TIROS-N, NOAA-6, NOAA-8, and NOAA-10; (ii) AVHRR-2 flown onboard NOAA-7, NOAA-9, NOAA-11, NOAA-12, and NOAA-14; and (iii) AVHRR-3 currently operational onboard NOAA-15, NOAA-16, NOAA-17, NOAA-18 and NOAA-19. The AVHRR-1 has four channels, AVHRR-2 has five channels and the AVHRR-3 has six channels, although only five channels of AVHRR-3 can be operational at any one time. As such, channels 3A (1.6 m) and 3B (3.7 m) work interchangeably. The processing procedure was designed to minimize artefacts in AVHRR composite images. There are thirty six 10-day image composites per year. The following three processing levels are provided: P1) top of atmosphere reflectance and brightness temperature, P2) reflectance at surface and surface temperature and P3) reflectance at surface normalized to a common viewing geometry (BRDF normalization). The processing level P1 and P2 are provided for all 36 composites while level P3 is provided for 21 composites from April – October.

Röð uppréttra loftmynda úr loftmyndasafni Náttúrufræðistofnunar sem unnar voru á árunum 2013 til 2018 hjá Jarðvísindastofnun HÍ, sem partur af tveimur verkefnum: 1 - Mælingar á jöklabreytingum úr sögulegum loftmyndum. Þetta verkefni var unnið af Joaquín M.C. Belart í M.Sc. og Ph.D. hjá Jarðvísindastofnun. Útvaldar loftmyndir frá 1945 til 1994 voru skannaðar hjá Landmælingum Íslands sérstaklega fyrir þetta verkefni. Vinnsla þessara loftmynda fór fram með því að nota "Ground Control Points" (GCP) sem teknir voru úr lidarmælingum á íslenskum jöklum. Úrvinnsla gagna úr Drangajökli fór fram með ERDAS hugbúnaðinum. Nánari upplýsingar um vinnsluna er að finna í Magnússon o.fl., 2016 (https://tc.copernicus.org/articles/10/159/2016/tc-10-159-2016.html). Úrvinnsla gagna frá öðrum jöklum var unnin með MicMac hugbúnaðinum, einnig með GCP teknir af lidar. Nánari upplýsingar um vinnsluna eru fáanlegar í Belart o.fl., 2019 (https://www.cambridge.org/core/journals/journal-of-glaciology/article/geodetic-mass-balance-of-eyjafjallajokull-ice-cap -for-19452014-processing-guidelines-and-relation-to-climate/9B715A9E0413A6345C2B151B1173E71D) og Belart o.fl., 2020 (https://www.frontiersin.org/articles/10.31630/feart/full.316390/feart. 2 - Mælingar á hraunmagni Heklugosanna á 20. öld. Þetta verkefni var unnið af Gro B.M. Pedersen sem hluti af verkefni þar sem unnið var að umhverfiskortlagningu og vöktun Íslands með fjarkönnun "Environmental Mapping and Monitoring of Iceland by Remote Sensing" (EMMIRS, fjármagnað af Rannís) á árunum 2015-2018. Loftmyndirnar af Heklu frá 1945 til 1992 voru skannaðar af Landmælingum Íslands. Vinnsla þessara mynda var gerð með ERDAS hugbúnaðinum og nánari upplýsingar um vinnsluna er hægt að nálgast í Pedersen o.fl., 2018 (https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2017GL076887) --------------------------------------------------------------------------------------------------------------- A series of orthomosaics using the archives of aerial photographs from Náttúrufræðistofnun (Loftmyndasafn) created between 2013 and 2018 at the Institute of Earth Sciences, as part of two projects: 1 - Measurements of glacier changes from historical aerial photographs. This project was conducted by Joaquín M.C. Belart during his M.Sc. and his Ph.D. at the Institute of Earth Sciences. A selection of aerial photographs from 1945 to 1994 were scanned at Náttúrufræðistofnun specifically for this project. The processing of these aerial photographs was done using Ground Control Points (GCPs) extracted from lidar surveys of Icelandic glaciers. The processing of the data from Drangajökull ice cap was done using the ERDAS software. Further details on the processing are available in Magnússon et al., 2016 (https://tc.copernicus.org/articles/10/159/2016/tc-10-159-2016.html). The processing of the data from other glaciers was done using the MicMac software, also with GCPs extracted from lidar. Further details of the processing are available in Belart et al., 2019 (https://www.cambridge.org/core/journals/journal-of-glaciology/article/geodetic-mass-balance-of-eyjafjallajokull-ice-cap-for-19452014-processing-guidelines-and-relation-to-climate/9B715A9E0413A6345C2B151B1173E71D) and Belart et al., 2020 (https://www.frontiersin.org/articles/10.3389/feart.2020.00163/full) 2 - Measurements of the lava volumes of the Hekla eruptions in the 20th century. This project was conducted by Gro B.M. Pedersen as part of the Environmental Mapping and Monitoring of Iceland by Remote Sensing (EMMIRS, financed by Rannís) project between 2015-2018. The aerial photographs of Hekla from 1945 to 1992 were scanned by Náttúrufræðistofnun. The processing of these photographs was done using the ERDAS software, and further details of the processing are available in Pedersen et al., 2018 (https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2017GL076887) References: Belart J.M.C., Magnússon E., Berthier E., Pálsson, F., Aðalgeirsdóttir, G., & Jóhannesson, T. (2019). The geodetic mass balance of Eyjafjallajökull ice cap for 1945–2014: Processing guidelines and relation to climate. Journal of Glaciology, 65(251), 395-409. doi:10.1017/jog.2019.16 Belart J.M.C., Magnússon E., Berthier E., Gunnlaugsson Á.Þ., Pálsson F., Aðalgeirsdóttir G., Jóhannesson T, Thorsteinsson T and Björnsson H (2020) Mass Balance of 14 Icelandic Glaciers, 1945–2017: Spatial Variations and Links With Climate. Front. Earth Sci. 8:163. doi: 10.3389/feart.2020.00163 Magnússon, E., Belart, J.M.C., Pálsson, F., Ágústsson, H., and Crochet, P.: Geodetic mass balance record with rigorous uncertainty estimates deduced from aerial photographs and lidar data – Case study from Drangajökull ice cap, NW Iceland, The Cryosphere, 10, 159–177, https://doi.org/10.5194/tc-10-159-2016, 2016. Pedersen, G. B. M., Belart, J. M. C., Magnússon, E., Vilmundardóttir, O. K., Kizel, F., Sigurmundsson, F. S., et al. (2018). Hekla volcano, Iceland, in the 20th century: Lava volumes, production rates, and effusion rates. Geophysical Research Letters, 45, 1805–1813. https://doi.org/10.1002/2017GL076887