This product is a 1km resolution composite over the North American domain, which, for areas with radar coverage, can distinguish the occurrence, type and intensity of precipitation. This product uses two 1km radar composites as input: a North American composite cleaned using dual polarization technology, another particle classification radar composite (precipitation) and surface temperature from the High Resolution Deterministic Prediction System (HRDPS). The SPTP product is produced every 6 minutes.

The probability (likelihood) of ice freeze days, the number of days in the forecast period with a minimum temperature below the frost temperature, -5°C for herbaceous crops over the non-growing season (ifd_herb_nogrow_prob). Week 1 and week 2 forecasted probability is available daily from November 1 to March 31. Week 3 and week 4 forecasted probability is available weekly (Thursday) from November 1 to March 31. Over-wintering crops are biennial and perennial field crops such as herbaceous plants (strawberry, alfalfa, timothy, and many other forage crops) and woody fruit trees (apple, pear, peach, cherry, plum, apricot, chestnut, pecan, grape, etc.). These crops normally grow and develop in the growing season and become dormant in the non-growing season. However, extreme weather and climate events such as cold waves in the growing season and ice freezing events during the winter are a major constraint for their success of production and survival in Canada. The winter survival of these plants depends largely on agrometeorological conditions from late autumn to early spring, especially ice-freezing damage during the winter season. The optimum temperature for such crops is 25°C. Agriculture and Agri-Food Canada (AAFC) and Environment and Climate Change Canada (ECCC) have together developed a suite of extreme agrometeorological indices based on four main categories of weather factors: temperature, precipitation, heat, and wind. The extreme weather indices are intended as short-term prediction tools and generated using ECCC’s medium range forecasts to create a weekly index product on a daily basis.

Matrix of slopes in degrees produced from mosaics of images of digital terrain models from different years according to sectors (2008 to 2020)**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

This dataset provides treed area dynamics across Canada's 650 Mha forested ecosystems from 1984 to 2022, derived from Landsat-based annual land cover layers at a 30-m spatial resolution. It is developed within the framework of Canada’s National Terrestrial Ecosystem Monitoring System (NTEMS). This dataset identifies areas that remained treed, transitioned to treed (newly treed), or transitioned to other cover that is not treed vegetation (was-treed). The data enable national and regional assessments of long-term changes in treed area, capturing trends in treed area, post-disturbance recovery, and shifts in forest extent. When using this data, please cite as: Hermosilla, T., Wulder, M.A., White, J.C., Bater, C.W., Baral, S.K., Leach, J.A., 2025. Expansion of treed area over Canada’s forested ecosystems: spatial and temporal trends. Forestry: An International Journal of Forest Research 98(5) 786-799. https://doi.org/10.1093/forestry/cpaf015. (Hermosilla et al. 2025)

Map of burned area in Canada's forested ecosystems for the 2023 fire session at 30-m spatial resolution mapped from time-series data from Sentinel-2A and -2B, and Landsat-8 and -9 using the Tracking Intra- and Inter-year Change (TIIC) algorithm (Pelletier et al. 2024). It is developed within the framework of Canada’s National Terrestrial Ecosystem Monitoring System (NTEMS). Fires are grouped into two classes based on detection period: summer fires and fall fires. Summer burned pixels were detected between May 30 and September 17, and fall burned pixels were detected between September 17 and October 25. For summer fires, burned pixels were identified by TIIC as changed and typed as fire. For the fall period, TIIC only detected changes within a 4-km buffer of the NRCan fire perimeters (https://cwfis.cfs.nrcan.gc.ca/datamart). This approach was used to limit commission errors that can occur due to known limitations of mapping with optical data in the fall due to phenology, snow cover, or low sun angles. For the 2023 fire season, the TIIC algorithm detected 12.74 Mha of burned area in Canada's forested ecozones, representing 1.8% of the total forest-dominated ecozone area. Of the 12.74 Mha, 11.57 Mha (90.9%) was burned by summer fires and 1.16 Mha (9.1%) by fall fires (Pelletier et al, 2024). When using this data, please cite as: Pelletier, F., Cardille, J.A., Wulder, M.A., White, J.C., Hermosilla, T., 2024. Revisiting the 2023 wildfire season in Canada. Science of Remote Sensing. 10, 100145. (Pelletier et al. 2024).

Landsat-derived forest age for Canada 2022 Satellite-based forest age map for 2022 across Canada's forested ecozones at a 30-m spatial resolution. It is developed within the framework of Canada’s National Terrestrial Ecosystem Monitoring System (NTEMS). 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. 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).

Canada's National Forest Inventory (NFI) sampling program is designed to support reporting on forests at the national scale. On the other hand, continuous maps of forest attributes are required to support strategic analyses of regional policy and management issues. We have therefore produced maps covering 4.03 × 106 km2 of inventoried forest area for the 2001 base year using standardised observations from the NFI photo plots (PP) as reference data. We used the k nearest neighbours (kNN) method with 26 geospatial data layers including MODIS spectral data and climatic and topographic variables to produce maps of 127 forest attributes at a 250 × 250 m resolution. The stand-level attributes include land cover, structure, and tree species relative abundance. In this article, we report only on total live aboveground tree biomass, with all other attributes covered in the supplementary data (http://nrcresearchpress.com/doi/suppl/10.1139/cjfr-2013-0401). In general, deviations in predicted pixel-level values from those in a PP validation set are greater in mountainous regions and in areas with either low biomass or sparse PP sampling. Predicted pixel-level values are overestimated at small observed values and underestimated at large ones. Accuracy measures are improved through the spatial aggregation of pixels to 1 km2 and beyond. Overall, these new products provide unique baseline information for strategic-level analyses of forests (https://nfi.nfis.org) Collection: - **[Canada's National Forest Inventory (NFI) 2006](https://open.canada.ca/data/en/dataset/e2fadaeb-3106-4111-9d1c-f9791d83fbf4)**

Orthophotography of the territory of the MRC of Drummond in Center-du-Québec**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

The number of days in the forecast period with a minimum temperature below the frost temperature. It is -15°C for herbaceous crops over the dormant period (ifd_wood_nogrow). Week 1 and week 2 forecasted index is available daily from November 1 to March 31. Week 3 and week 4 forecasted index is available weekly (Thursday) from November 1 to March 31. Over-wintering crops are biennial and perennial field crops such as herbaceous plants (strawberry, alfalfa, timothy, and many other forage crops) and woody fruit trees (apple, pear, peach, cherry, plum, apricot, chestnut, pecan, grape, etc.). These crops normally grow and develop in the growing season and become dormant in the non-growing season. However, extreme weather and climate events such as cold waves in the growing season and ice freezing events during the winter are a major constraint for their success of production and survival in Canada. The winter survival of these plants depends largely on agrometeorological conditions from late autumn to early spring, especially ice-freezing damage during the winter season. Agriculture and Agri-Food Canada (AAFC) and Environment and Climate Change Canada (ECCC) have together developed a suite of extreme agrometeorological indices based on four main categories of weather factors: temperature, precipitation, heat, and wind. The extreme weather indices are intended as short-term prediction tools and generated using ECCC’s medium range forecasts to create a weekly index product on a daily and weekly basis.

Canada's National Forest Inventory (NFI) sampling program is designed to support reporting on forests at the national scale. On the other hand, continuous maps of forest attributes are required to support strategic analyses of regional policy and management issues. We have therefore produced maps covering 4.03 × 106 km2 of inventoried forest area for the 2001 base year using standardised observations from the NFI photo plots (PP) as reference data. We used the k nearest neighbours (kNN) method with 26 geospatial data layers including MODIS spectral data and climatic and topographic variables to produce maps of 127 forest attributes at a 250 × 250 m resolution. The stand-level attributes include land cover, structure, and tree species relative abundance. In this article, we report only on total live aboveground tree biomass, with all other attributes covered in the supplementary data (http://nrcresearchpress.com/doi/suppl/10.1139/cjfr-2013-0401). In general, deviations in predicted pixel-level values from those in a PP validation set are greater in mountainous regions and in areas with either low biomass or sparse PP sampling. Predicted pixel-level values are overestimated at small observed values and underestimated at large ones. Accuracy measures are improved through the spatial aggregation of pixels to 1 km2 and beyond. Overall, these new products provide unique baseline information for strategic-level analyses of forests (https://nfi.nfis.org) Collection: - **[Canada's National Forest Inventory (NFI) 2006](https://open.canada.ca/data/en/dataset/e2fadaeb-3106-4111-9d1c-f9791d83fbf4)**