Top of Atmosphere (TOA) reflectance data in bands from the USGS Landsat 5 and Landsat 8 satellites were accessed via Google Earth Engine. CANUE staff used Google Earth Engine functions to create cloud free annual composites, and mask water features, then export the resulting band data. NDVI indices were calculated as (band 4 - Band 3)/(Band 4 Band 3) for Landsat 5 data, and as (band 5 - band 4)/(band 5 Band 4) for Landsat 8 data. These composites are created from all the scenes in each annual period beginning from the first day of the year and continuing to the last day of the year. No data were available for 2012, due to decommissioning of Landsat 5 in 2011 prior to the start of Landsat 8 in 2013. No cross-calibration between the sensors was performed, please be aware there may be small bias differences between NDVI values calculated using Landsat 5 and Landsat 8. Final NDVI metrics were linked to all 6-digit DMTI Spatial single link postal code locations in Canada, and for surrounding areas within 100m, 250m, 500m, and 1km.

After 2022-01-25, Sentinel-2 scenes with PROCESSING_BASELINE '04.00' or above have their DN (value) range shifted by 1000. The HARMONIZED collection shifts data in newer scenes to be in the same range as in older scenes. Sentinel-2 is a wide-swath, high-resolution, multi-spectral imaging mission supporting Copernicus Land Monitoring studies, including the monitoring of vegetation, soil and water cover, as well as observation of inland waterways and coastal areas. The Sentinel-2 data contain 13 UINT16 spectral bands representing TOA reflectance scaled by 10000. See the Sentinel-2 User Handbook for details. QA60 is a bitmask band that contained rasterized cloud mask polygons until Feb 2022, when these polygons stopped being produced. Starting in February 2024, legacy-consistent QA60 bands are constructed from the MSK_CLASSI cloud classification bands. For more details, see the full explanation of how cloud masks are computed.. Each Sentinel-2 product (zip archive) may contain multiple granules. Each granule becomes a separate Earth Engine asset. EE asset ids for Sentinel-2 assets have the following format: COPERNICUS/S2/20151128T002653_20151128T102149_T56MNN. Here the first numeric part represents the sensing date and time, the second numeric part represents the product generation date and time, and the final 6-character string is a unique granule identifier indicating its UTM grid reference (see MGRS). The Level-2 data produced by ESA can be found in the collection COPERNICUS/S2_SR. For datasets to assist with cloud and/or cloud shadow detection, see COPERNICUS/S2_CLOUD_PROBABILITY and GOOGLE/CLOUD_SCORE_PLUS/V1/S2_HARMONIZED. For more details on Sentinel-2 radiometric resolution, see this page.

Clay content in % (kg / kg) at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution Based on machine learning predictions from global compilation of soil profiles and samples. Processing steps are described in detail here. Antarctica is not included. To access and …

CANUE staff developed annual estimates of maximum mean warm-season land surface temperature (LST) recorded by LandSat 8 at 30m resolution. To reduce the effect of missing data/cloud cover/shadows, the highest mean warm-season value reported over three years was retained - for example, the data for 2021 represent the maximum of the mean land surface temperature at a pixel location between April 1st and September 30th in 2019, 2020 and 2021. Land surface temperature was calculated in Google Earth Engine, using a public algorithm (see supplementary documentation). In general, annual mean LST may not reflect ambient air temperatures experienced by individuals at any given time, but does identify areas that are hotter during the day and therefore more likely to radiate excess heat at night - both factors that contribute to heat islands within urban areas.

Top of Atmosphere (TOA) reflectance data in bands from the USGS Landsat 5 and Landsat 8 satellites were accessed via Google Earth Engine. CANUE staff used Google Earth Engine functions to create cloud free annual composites, and mask water features, then export the resulting band data. NDVI indices were calculated as (band 4 - Band 3)/(Band 4 Band 3) for Landsat 5 data, and as (band 5 - band 4)/(band 5 Band 4) for Landsat 8 data. These composites are created from all the scenes in each annual period beginning from the first day of the year and continuing to the last day of the year. No data were available for 2012, due to decommissioning of Landsat 5 in 2011 prior to the start of Landsat 8 in 2013. No cross-calibration between the sensors was performed, please be aware there may be small bias differences between NDVI values calculated using Landsat 5 and Landsat 8. Final NDVI metrics were linked to all 6-digit DMTI Spatial single link postal code locations in Canada, and for surrounding areas within 100m, 250m, 500m, and 1km.

Top of Atmosphere (TOA) reflectance data in bands from the USGS Landsat 5 and Landsat 8 satellites were accessed via Google Earth Engine. CANUE staff used Google Earth Engine functions to create cloud free mean growing season composites, and mask water features, then export the resulting band data. Growing season is defined as May 1st through August 31st. NDVI indices were then calculated as (band 4 - Band 3)/(Band 4 Band 3) for Landsat 5 data, and as (band 5 - band 4)/(band 5 Band 4) for Landsat 8 data. No data were available for 2012, due to decommissioning of Landsat 5 in 2011 prior to the start of Landsat 8 in 2013. No cross-calibration between the sensors was performed, please be aware there may be small bias differences between NDVI values calculated using Landsat 5 and Landsat 8. Final NDVI metrics were linked to all 6-digit DMTI Spatial single link postal code locations in Canada, and for surrounding areas within 100m, 250m, 500m, and 1km.

Top of Atmosphere (TOA) reflectance data in bands from the USGS Landsat 5 and Landsat 8 satellites were accessed via Google Earth Engine. CANUE staff used Google Earth Engine functions to create cloud free annual composites, and mask water features, then export the resulting band data. NDVI indices were calculated as (band 4 - Band 3)/(Band 4 Band 3) for Landsat 5 data, and as (band 5 - band 4)/(band 5 Band 4) for Landsat 8 data. These composites are created from all the scenes in each annual period beginning from the first day of the year and continuing to the last day of the year. No data were available for 2012, due to decommissioning of Landsat 5 in 2011 prior to the start of Landsat 8 in 2013. No cross-calibration between the sensors was performed, please be aware there may be small bias differences between NDVI values calculated using Landsat 5 and Landsat 8. Final NDVI metrics were linked to all 6-digit DMTI Spatial single link postal code locations in Canada, and for surrounding areas within 100m, 250m, 500m, and 1km.

Nighttime satellite imagery were accessed via Google Earth Engine). Version 4 of the DMSP-OLS Nighttime Lights Time Series consists of cloud-free composites made using all the available archived DMSP-OLS smooth resolution data for calendar years. In cases where two satellites were collecting data - two composites were produced. The products are 30 arc second grids, spanning -180 to 180 degrees longitude and -65 to 75 degrees latitude. Several attributes are included - we used stable_lights which represents lights from cities, towns, and other sites with persistent lighting, including gas flares. Ephemeral events, such as fires have been discarded. The background noise was identified and replaced with values of zero.These data were provided to Google Earth Engine by teh National Centers for Environmental Information - National Oceanic and Atmospheric Administration of the United States (see Supporting Documentation).CANUE staff exported the annual data and extracted values of annual mean nighttime brightness for all postal codes in Canada for each year from 1992 to 2013 (DMTI Spatial, 2015).

Google Earth Engine combines a multi-petabyte catalog of satellite imagery and geospatial datasets with planetary-scale analysis capabilities. Scientists, researchers, and developers use Earth Engine to detect changes, map trends, and quantify differences on the Earth's surface. Earth Engine is now available for commercial use, and remains free for academic and research use.

The Sentinel-1 mission provides data from a dual-polarization C-band Synthetic Aperture Radar (SAR) instrument at 5.405GHz (C band). This collection includes the S1 Ground Range Detected (GRD) scenes, processed using the Sentinel-1 Toolbox to generate a calibrated, ortho-corrected product. The collection is updated daily. New assets are ingested within two days after they become available. This collection contains all of the GRD scenes. Each scene has one of 3 resolutions (10, 25 or 40 meters), 4 band combinations (corresponding to scene polarization) and 3 instrument modes. Use of the collection in a mosaic context will likely require filtering down to a homogeneous set of bands and parameters. See this article for details of collection use and preprocessing. Each scene contains either 1 or 2 out of 4 possible polarization bands, depending on the instrument's polarization settings. The possible combinations are single band VV, single band HH, dual band VV+VH, and dual band HH+HV: VV: single co-polarization, vertical transmit/vertical receive HH: single co-polarization, horizontal transmit/horizontal receive VV + VH: dual-band cross-polarization, vertical transmit/horizontal receive HH + HV: dual-band cross-polarization, horizontal transmit/vertical receive Each scene also includes an additional 'angle' band that contains the approximate incidence angle from ellipsoid in degrees at every point. This band is generated by interpolating the 'incidenceAngle' property of the 'geolocationGridPoint' gridded field provided with each asset. Each scene was pre-processed with Sentinel-1 Toolbox using the following steps: Thermal noise removal Radiometric calibration Terrain correction using SRTM 30 or ASTER DEM for areas greater than 60 degrees latitude, where SRTM is not available. The final terrain-corrected values are converted to decibels via log scaling (10*log10(x)). For more information about these pre-processing steps, please refer to the Sentinel-1 Pre-processing article. For further advice on working with Sentinel-1 imagery, see Guido Lemoine's tutorial on SAR basics and Mort Canty's tutorial on SAR change detection. This collection is computed on-the-fly. If you want to use the underlying collection with raw power values (which is updated faster), see COPERNICUS/S1_GRD_FLOAT.

ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past. This dataset includes all 50 variables as available on CDS. ERA5-Land data is available from 1950 to three months from real-time. Please consult the ERA5-Land "Known Issues" section. In particular, note that three components of the total evapotranspiration have values swapped as follows: variable "Evaporation from bare soil" (mars parameter code 228101 (evabs)) has the values corresponding to the "Evaporation from vegetation transpiration" (mars parameter 228103 (evavt)), variable "Evaporation from open water surfaces excluding oceans (mars parameter code 228102 (evaow)) has the values corresponding to the "Evaporation from bare soil" (mars parameter code 228101 (evabs)), variable "Evaporation from vegetation transpiration" (mars parameter code 228103 (evavt)) has the values corresponding to the "Evaporation from open water surfaces excluding oceans" (mars parameter code 228102 (evaow)). The asset is a daily aggregate of ECMWF ERA5 Land hourly assets which includes both flow and non-flow bands. Flow bands are formed by collecting the first hour's data of the following day which holds aggregated sum of previous day and while the non-flow bands are created by averaging all hourly data of the day. The flow bands are labeled with the "_sum" identifier, which approach is different from the daily data produced by Copernicus Climate Data Store, where flow bands are averaged too. Daily aggregates have been pre-calculated to facilitate many applications requiring easy and fast access to the data. Precipitation and other flow (accumulated) bands might occasionally have negative values, which doesn't make physical sense. At other times their values might be excessively high. This problem is due to how the GRIB format saves data: it simplifies or "packs" the data into smaller, less precise numbers, which can introduce errors. These errors get worse when the data varies a lot. Because of this, when we look at the data for a whole day to compute daily totals, sometimes the highest amount of rainfall recorded at one time can seem larger than the total rainfall measured for the entire day. To learn more, Please see: "Why are there sometimes small negative precipitation accumulations"

Top of Atmosphere (TOA) reflectance data in bands from the USGS Landsat 5 and Landsat 8 satellites were accessed via Google Earth Engine. CANUE staff used Google Earth Engine functions to create cloud free mean growing season composites, and mask water features, then export the resulting band data. Growing season is defined as May 1st through August 31st. NDVI indices were then calculated as (band 4 - Band 3)/(Band 4 Band 3) for Landsat 5 data, and as (band 5 - band 4)/(band 5 Band 4) for Landsat 8 data. No data were available for 2012, due to decommissioning of Landsat 5 in 2011 prior to the start of Landsat 8 in 2013. No cross-calibration between the sensors was performed, please be aware there may be small bias differences between NDVI values calculated using Landsat 5 and Landsat 8. Final NDVI metrics were linked to all 6-digit DMTI Spatial single link postal code locations in Canada, and for surrounding areas within 100m, 250m, 500m, and 1km.

Top of Atmosphere (TOA) reflectance data in bands from the USGS Landsat 5 and Landsat 8 satellites were accessed via Google Earth Engine. CANUE staff used Google Earth Engine functions to create cloud free annual composites, and mask water features, then export the resulting band data. NDVI indices were calculated as (band 4 - Band 3)/(Band 4 Band 3) for Landsat 5 data, and as (band 5 - band 4)/(band 5 Band 4) for Landsat 8 data. These composites are created from all the scenes in each annual period beginning from the first day of the year and continuing to the last day of the year. No data were available for 2012, due to decommissioning of Landsat 5 in 2011 prior to the start of Landsat 8 in 2013. No cross-calibration between the sensors was performed, please be aware there may be small bias differences between NDVI values calculated using Landsat 5 and Landsat 8. Final NDVI metrics were linked to all 6-digit DMTI Spatial single link postal code locations in Canada, and for surrounding areas within 100m, 250m, 500m, and 1km.

The MOD11A1 V6.1 product provides daily land surface temperature (LST) and emissivity values in a 1200 x 1200 kilometer grid. The temperature value is derived from the MOD11_L2 swath product. Above 30 degrees latitude, some pixels may have multiple observations where the criteria for clear-sky are met. When this occurs, the pixel value is the average of all qualifying observations. Provided along with both the day-time and night-time surface temperature bands and their quality indicator layers are MODIS bands 31 and 32 and six observation layers. Documentation: User's Guide Algorithm Theoretical Basis Document (ATBD) General Documentation

Annual maximum NDVI calculated by Google from Landsat 5 and Landsat 8 were accessed via Google Earth Engine. These composites are created from all the scenes in each annual period beginning from the first day of the year and continuing to the last day of the year. All the images from each year are included in the composite, with the greenest pixel as the composite value, where the greenest pixel is the maximum value of the Normalized Difference Vegetation Index (NDVI). No data were available for 2012, due to decommissioning of Landsat 5 in 2011 prior to the start of Landsat 8 in 2013. No cross-calibration between the sensors was performed, please be aware there may be small bias differences between NDVI values calculated using Landsat 5 and Landsat 8. Final NDVI metrics were linked to all 6-digit DMTI Spatial single link postal code locations in Canada, and for surrounding areas within 100m, 250m, 500m, and 1km.

CANUE staff developed the Green Roads data set by combining street network files from Open Street Map 9OSM) (downloaded Nov 29, 2020) and annual average normalized difference vegetation index (NDVI) data from LandSat 8 circa 2016 from Google Earth Engine. OSM roads categorized as primary, secondary, tertiary, tertiary link, residential, unclassified and unknown were extracted from OSM, combined into a single file and clipped to urban areas. Urban areas were defined as all dissemination blocks classified as small population centres (population 1,000 to 29,999), medium population centres (population 30,000 to 99,999) or large population centres (population 100,000 or greater) in the 2016 Census. The urban roads layer was used to extract all LandSat 8 pixels with NDVI data (30m resolution). All extracted pixels with an NDVI value of 0.3 or greater, indicating green vegetation, were converted into points. Finally, the total number or points and the average NDVI value was calculated within buffers of 250m, 500m, 750m and 1000m of DMTI single-link postal codes from 2016.

Monthly Aggregated NEX-GDDP Ensemble Climate Projections: Historical (1985–2005) and RCP 4.5 and RCP 8.5 (2006–2080) This dataset is a monthly-scale aggregation of the NEX-GDDP: NASA Earth Exchange Global Daily Downscaled Climate Projections processed using Google Earth Engine (Gorelick 2017). The native delivery on Google Earth Engine is at the daily timescale for each individual CMIP5 GCM model. This dataset was created to facilitate use of NEX-GDDP and reduce processing times for projects that seek an ensemble model with a coarser temporal resolution. The aggregated data have been made available in Google Earth Engine via 'users/cartoscience/GCM_NASA-NEX-GDDP/NEX-GDDP-PRODUCT-ID_Ensemble-Monthly_YEAR' (see code below on how to access), and all 171 GeoTIFFS have been uploaded to this dataverse entry. Relevant links: https://www.nasa.gov/nex https://www.nccs.nasa.gov/services/data-collections/land-based-products/nex-gddp https://esgf.nccs.nasa.gov/esgdoc/NEX-GDDP_Tech_Note_v0.pdf https://developers.google.com/earth-engine/datasets/catalog/NASA_NEX-GDDP https://journals.ametsoc.org/view/journals/bams/93/4/bams-d-11-00094.1.xml https://rd.springer.com/article/10.1007/s10584-011-0156-z#page-1 The dataset can be accessed within Google Earth Engine using the following code: var histYears = ee.List.sequence(1985,2005).getInfo() var rcpYears = ee.List.sequence(2006,2080).getInfo() var path1 = 'users/cartoscience/GCM_NASA-NEX-GDDP/NEX-GDDP-' var path2 = '_Ensemble-Monthly_' var product product = 'Hist' var hist = ee.ImageCollection( histYears.map(function(y) { return ee.Image(path1+product+path2+y) }) ) product = 'RCP45' var rcp45 = ee.ImageCollection( rcpYears.map(function(y) { return ee.Image(path1+product+path2+y) }) ) product = 'RCP85' var rcp85 = ee.ImageCollection( rcpYears.map(function(y) { return ee.Image(path1+product+path2+y) }) ) print( 'Hist (1985–2005)', hist, 'RCP45 (2006–2080)', rcp45, 'RCP85 (2006–2080)', rcp85 ) var first = hist.first() var tMax = first.select('tasmin_1') var tMin = first.select('tasmax_1') var tMean = first.select('tmean_1') var pSum = first.select('pr_1') Map.addLayer(tMax, {min: -10, max: 40}, 'Average min temperature Jan 1985 (Hist)', false) Map.addLayer(tMin, {min: 10, max: 40}, 'Average max temperature Jan 1985 (Hist)', false) Map.addLayer(tMean, {min: 10, max: 40}, 'Average temperature Jan 1985 (Hist)', false) Map.addLayer(pSum, {min: 10, max: 500}, 'Accumulated rainfall Jan 1985 (Hist)', true) https://code.earthengine.google.com/5bfd9741274679dded7a95d1b57ca51d Ensemble average based on the following models: ACCESS1-0,BNU-ESM,CCSM4,CESM1-BGC,CNRM-CM5, CSIRO-Mk3-6-0,CanESM2,GFDL-CM3,GFDL-ESM2G, GFDL-ESM2M,IPSL-CM5A-LR,IPSL-CM5A-MR,MIROC-ESM-CHEM, MIROC-ESM,MIROC5,MPI-ESM-LR,MPI-ESM-MR,MRI-CGCM3, NorESM1-M,bcc-csm1-1,inmcm4 Each annual GeoTIFF contains 48 bands (4 variables across 12 months)— Temperature: Monthly mean (tasmin, tasmax, tmean) Precipitation: Monthly sum (pr) Bands 1–48 correspond with: tasmin_1, tasmax_1, tmean_1, pr_1, tasmin_2, tasmax_2, tmean_2, pr_2, tasmin_3, tasmax_3, tmean_3, pr_3, tasmin_4, tasmax_4, tmean_4, pr_4, tasmin_5, tasmax_5, tmean_5, pr_5, tasmin_6, tasmax_6, tmean_6, pr_6, tasmin_7, tasmax_7, tmean_7, pr_7, tasmin_8, tasmax_8, tmean_8, pr_8, tasmin_9, tasmax_9, tmean_9, pr_9, tasmin_10, tasmax_10, tmean_10, pr_10, tasmin_11, tasmax_11, tmean_11, pr_11, tasmin_12, tasmax_12, tmean_12, pr_12 *Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D. and Moore, R., 2017. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, pp.18–27. Project information: SEAGUL: Southeast Asia Globalization, Urbanization, Land and Environment Changes http://seagul.info/ https://lcluc.umd.edu/projects/divergent-local-responses-globalization-urbanization-land-transition-and-environmental This project was made possible by the the NASA Land-Cover/Land-Use Change Program (Grant #: 80NSSC20K0740)

Annual maximum NDVI calculated by Google from Landsat 5 and Landsat 8 were accessed via Google Earth Engine. These composites are created from all the scenes in each annual period beginning from the first day of the year and continuing to the last day of the year. All the images from each year are included in the composite, with the greenest pixel as the composite value, where the greenest pixel is the maximum value of the Normalized Difference Vegetation Index (NDVI). No data were available for 2012, due to decommissioning of Landsat 5 in 2011 prior to the start of Landsat 8 in 2013. No cross-calibration between the sensors was performed, please be aware there may be small bias differences between NDVI values calculated using Landsat 5 and Landsat 8. Final NDVI metrics were linked to all 6-digit DMTI Spatial single link postal code locations in Canada, and for surrounding areas within 100m, 250m, 500m, and 1km.

The spreadsheet includes a tab for each figure and table in the publication titled "Mapping Actual Evapotranspiration using Landsat for the Conterminous United States: Google Earth Engine Implementation and Assessment of the SSEBop Model" by Senay et al. 2021. Each tab includes the graphic and the data used to create it.