Icefields captured at 1:1,000,000 scale from Digital Chart of the World data for the Yukon and surrounding area.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)

This catalog contains annual 30 m spatial resolution snow dynamics metrics for each snow-year from 2018-2019 to 2023-2024 for all of Canada. We gather all Landsat and Sentinel-2 images collected over Canada and identify the status of each pixel observation on the image collection date: snow (and ice), non-snow (i.e., land, water), unclear (i.e., clouds, shadows). We built an algorithm to calculate snow cover metrics for each pixel during each winter: start date of the first (and biggest) snow period [startF, startB], end date of the last (and biggest) snow period [endL, endB], number of days with snow cover in total (or in the biggest snow period) [lengthT, lengthB], number of snow periods (i.e., separated times with multiple confirmed snow observations) [periods], and a status classification (e.g., continuous snow, snow free) [status]. We do not obtain a clear observation every day because of satellite orbit frequencies and clouds. This means that timing-based metrics are identified by the middle date between two clear observations, with uncertainty quantified as half the length of the gap (i.e., ± days) [startF_u, startB_u, endL_u, endB_u, lengthT_u, lengthB_u]. **This third party metadata element follows the Spatio Temporal Asset Catalog (STAC) specification.**

This catalog contains annual 30 m spatial resolution snow dynamics metrics for each snow-year from 2018-2019 to 2023-2024 for all of Canada. We gather all Landsat and Sentinel-2 images collected over Canada and identify the status of each pixel observation on the image collection date: snow (and ice), non-snow (i.e., land, water), unclear (i.e., clouds, shadows). We built an algorithm to calculate snow cover metrics for each pixel during each winter: start date of the first (and biggest) snow period [startF, startB], end date of the last (and biggest) snow period [endL, endB], number of days with snow cover in total (or in the biggest snow period) [lengthT, lengthB], number of snow periods (i.e., separated times with multiple confirmed snow observations) [periods], and a status classification (e.g., continuous snow, snow free) [status]. We do not obtain a clear observation every day because of satellite orbit frequencies and clouds. This means that timing-based metrics are identified by the middle date between two clear observations, with uncertainty quantified as half the length of the gap (i.e., ± days) [startF_u, startB_u, endL_u, endB_u, lengthT_u, lengthB_u]. **This third party metadata element follows the Spatio Temporal Asset Catalog (STAC) specification.**

This catalog contains annual 30 m spatial resolution snow dynamics metrics for each snow-year from 2018-2019 to 2023-2024 for all of Canada. We gather all Landsat and Sentinel-2 images collected over Canada and identify the status of each pixel observation on the image collection date: snow (and ice), non-snow (i.e., land, water), unclear (i.e., clouds, shadows). We built an algorithm to calculate snow cover metrics for each pixel during each winter: start date of the first (and biggest) snow period [startF, startB], end date of the last (and biggest) snow period [endL, endB], number of days with snow cover in total (or in the biggest snow period) [lengthT, lengthB], number of snow periods (i.e., separated times with multiple confirmed snow observations) [periods], and a status classification (e.g., continuous snow, snow free) [status]. We do not obtain a clear observation every day because of satellite orbit frequencies and clouds. This means that timing-based metrics are identified by the middle date between two clear observations, with uncertainty quantified as half the length of the gap (i.e., ± days) [startF_u, startB_u, endL_u, endB_u, lengthT_u, lengthB_u]. **This third party metadata element follows the Spatio Temporal Asset Catalog (STAC) specification.**

Snow and ice are important hydrological resources. Their minimum spatial extent here referred to as annual minimum snow/ice (MSI) cover, plays a very important role as an indicator of long-term changes and baseline capacity for surface water storage. The MSI probability is derived from sequence of seventeen 10-day clear-sky composites corresponding to April, 1 to September, 20 warm period for each year since 2000. Data from Moderate Resolution Imaging Spectroradiometer (MODIS) on Terra satellite for the period since 2000 have been processed with the special technology developed at the Canada Centre for Remote Sensing (CCRS) as described in Trishchenko, 2016; Trishchenko et al., 2016; 2009, 2006, Trishchenko and Ungureanu, 2021, Khlopenkov and Trishchenko, 2008, Luo et al., 2008. The presence of snow or ice is determined for each pixel of the image based on snow/ice scene identification procedure and the probability if computed for the entire warm season as a ratio of number of snow/ice flags to the total number of pixels available (less or equal to 17). The minimum snow and ice extent can be derived from the probability map by applying a certain threshold. New data version V5.0 replaces previous version V4.0 for all data available since 2000. All MSI files were reprocessed for all MODIS input data based on collection 6.1. The output format has not changed since previous version. It is described in Trishchenko (2024). The impact of input data change is small and can be detected only for time interval 2000-2015. Data starting 2016 has been already derived using MODIS collection 6.1 input.The differences between the MSI data based on MODIS Collection 5 (i.e. MSI V4) versus MODIS Collection 6.1 (i.e. MSI V5), on average, are quite small. The region-wide relative difference in the MSI extent varies from -3.97% to +1.75%. The mean value is -0.14%, the median value is 0.18% and standard deviation is 1.83%. As such, we do not expect any sizeable impact of the version change on our previous conclusions regarding trends and climate variations, except for refining the relative values of statistical parameters within the range of a few percents. References:TRISHCHENKO, A.P., 2024: Probability maps of the annual minimum snow and ice (MSI) presence over April,1 to September, 20 period since 2000 derived from MODIS 250m imagery over Canada and neighbouring regions. Data format description. CCRS, NRCan. 4pp.

Hydro Features is composed of the network of Canadian surface waters. Hydro Features entities are: Watercourse, Water Linear Flow, Hydro Obstacle (falls, rapids\...), Waterbody (lake, watercourse\...), Permanent Snow and Ice, Water Well, and Spring. CanVec is a digital cartographic reference product of Natural Resources Canada (NRCan). It originates from the best available data sources covering Canadian territory, offers quality topographical information in vector format, and complies with international geomatics standards. CanVec is a multi-source product coming mainly from the National Topographic Data Base (NTDB), the Mapping the North process conducted by the Canada Center for Mapping and Earth Observation (CCMEO), the Atlas of Canada data, the GeoBase initiative, and the data update using satellite imagery coverage (e.g. Landsat 7, Spot, Radarsat, etc.).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@gov.yk. ca](mailto:geomatics.help@yukon.ca)

This catalog contains annual 30 m spatial resolution snow dynamics metrics for each snow-year from 2018-2019 to 2023-2024 for all of Canada. We gather all Landsat and Sentinel-2 images collected over Canada and identify the status of each pixel observation on the image collection date: snow (and ice), non-snow (i.e., land, water), unclear (i.e., clouds, shadows). We built an algorithm to calculate snow cover metrics for each pixel during each winter: start date of the first (and biggest) snow period [startF, startB], end date of the last (and biggest) snow period [endL, endB], number of days with snow cover in total (or in the biggest snow period) [lengthT, lengthB], number of snow periods (i.e., separated times with multiple confirmed snow observations) [periods], and a status classification (e.g., continuous snow, snow free) [status]. We do not obtain a clear observation every day because of satellite orbit frequencies and clouds. This means that timing-based metrics are identified by the middle date between two clear observations, with uncertainty quantified as half the length of the gap (i.e., ± days) [startF_u, startB_u, endL_u, endB_u, lengthT_u, lengthB_u].

This catalog contains annual 30 m spatial resolution snow dynamics metrics for each snow-year from 2018-2019 to 2023-2024 for all of Canada. We gather all Landsat and Sentinel-2 images collected over Canada and identify the status of each pixel observation on the image collection date: snow (and ice), non-snow (i.e., land, water), unclear (i.e., clouds, shadows). We built an algorithm to calculate snow cover metrics for each pixel during each winter: start date of the first (and biggest) snow period [startF, startB], end date of the last (and biggest) snow period [endL, endB], number of days with snow cover in total (or in the biggest snow period) [lengthT, lengthB], number of snow periods (i.e., separated times with multiple confirmed snow observations) [periods], and a status classification (e.g., continuous snow, snow free) [status]. We do not obtain a clear observation every day because of satellite orbit frequencies and clouds. This means that timing-based metrics are identified by the middle date between two clear observations, with uncertainty quantified as half the length of the gap (i.e., ± days) [startF_u, startB_u, endL_u, endB_u, lengthT_u, lengthB_u]. **This third party metadata element follows the Spatio Temporal Asset Catalog (STAC) specification.**

This catalog contains annual 30 m spatial resolution snow dynamics metrics for each snow-year from 2018-2019 to 2023-2024 for all of Canada. We gather all Landsat and Sentinel-2 images collected over Canada and identify the status of each pixel observation on the image collection date: snow (and ice), non-snow (i.e., land, water), unclear (i.e., clouds, shadows). We built an algorithm to calculate snow cover metrics for each pixel during each winter: start date of the first (and biggest) snow period [startF, startB], end date of the last (and biggest) snow period [endL, endB], number of days with snow cover in total (or in the biggest snow period) [lengthT, lengthB], number of snow periods (i.e., separated times with multiple confirmed snow observations) [periods], and a status classification (e.g., continuous snow, snow free) [status]. We do not obtain a clear observation every day because of satellite orbit frequencies and clouds. This means that timing-based metrics are identified by the middle date between two clear observations, with uncertainty quantified as half the length of the gap (i.e., ± days) [startF_u, startB_u, endL_u, endB_u, lengthT_u, lengthB_u]. **This third party metadata element follows the Spatio Temporal Asset Catalog (STAC) specification.**

Hydro Features is composed of the network of Canadian surface waters. Hydro Features entities are: Watercourse, Water Linear Flow, Hydro Obstacle (falls, rapids\...), Waterbody (lake, watercourse\...), Permanent Snow and Ice, Water Well, and Spring. CanVec is a digital cartographic reference product of Natural Resources Canada (NRCan). It originates from the best available data sources covering Canadian territory, offers quality topographical information in vector format, and complies with international geomatics standards. CanVec is a multi-source product coming mainly from the National Topographic Data Base (NTDB), the Mapping the North process conducted by the Canada Center for Mapping and Earth Observation (CCMEO), the Atlas of Canada data, the GeoBase initiative, and the data update using satellite imagery coverage (e.g. Landsat 7, Spot, Radarsat, etc.).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)