Dynamic World is a 10m near-real-time (NRT) Land Use/Land Cover (LULC) dataset that includes class probabilities and label information for nine classes. Dynamic World predictions are available for the Sentinel-2 L1C collection from 2015-06-27 to present. The revisit frequency of Sentinel-2 is between 2-5 days depending on latitude. Dynamic World predictions are generated for Sentinel-2 L1C images with CLOUDY_PIXEL_PERCENTAGE <= 35%. Predictions are masked to remove clouds and cloud shadows using a combination of S2 Cloud Probability, Cloud Displacement Index, and Directional Distance Transform. Images in the Dynamic World collection have names matching the individual Sentinel-2 L1C asset names from which they were derived, e.g: ee.Image('COPERNICUS/S2/20160711T084022_20160711T084751_T35PKT') has a matching Dynamic World image named: ee.Image('GOOGLE/DYNAMICWORLD/V1/20160711T084022_20160711T084751_T35PKT'). All probability bands except the "label" band collectively sum to 1. To learn more about the Dynamic World dataset and see examples for generating composites, calculating regional statistics, and working with the time series, see the Introduction to Dynamic World tutorial series. Given Dynamic World class estimations are derived from single images using a spatial context from a small moving window, top-1 "probabilities" for predicted land covers that are in-part defined by cover over time, like crops, can be comparatively low in the absence of obvious distinguishing features. High-return surfaces in arid climates, sand, sunglint, etc may also exhibit this phenomenon. To select only pixels that confidently belong to a Dynamic World class, it is recommended to mask Dynamic World outputs by thresholding the estimated "probability" of the top-1 prediction.

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.

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.

Google Earth Engine used to compute the NDVI statistics added to Globe-LFMC. The input of the program is a point shapefile (“samplePlotsShapefile”, extensions .cpg, .dbf, .prj, .shp, .shx) representing the location of each Globe-LFMC site. This shapefile is available as additional data in figshare (see Code Availability). To run this GEE code the shapefile needs to be uploaded into the GEE Assets and, then, imported into the Code Editor with the name “plots” (without quotation marks).Google Earth Engine codeChange Notice - GEE_script_for_GlobeLFMC_ndvi_stats_v2.jsThe following acknowledgements have been added at the beginning of the code: “Portions of the following code are modifications based on work created and shared by Google in Earth Engine Data Catalog and Earth Engine Guides under the Apache 2.0 License. https://www.apache.org/licenses/LICENSE-2.0”Change Notice - samplePlotsShapefile_v2The shapefile describing the database sites has been corrected and updated with the correct coordinates.

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.

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.

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.

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 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.

Climate reanalysis and climate projection datasets offer the potential for researchers, students and instructors to access physically informed, global scale, temporally and spatially continuous climate data from the latter half of the 20th century to present, and explore different potential future climates. While these data are of significant use to research and teaching within biological, environmental and social sciences, potential users often face barriers to processing and accessing the data that cannot be overcome without specialist knowledge, facilities or assistance. Consequently, climate reanalysis and projection data are currently substantially under-utilised within research and education communities. To address this issue, we present two simple “point-and-click” graphical user interfaces: the Google Earth Engine Climate Tool (GEEClimT), providing access to climate reanalysis data products; and Google Earth Engine CMIP6 Explorer (GEECE), allowing processing and extraction of CMIP6 projection data, including the ability to create custom model ensembles. Together GEEClimT and GEECE provide easy access to over 387 terabytes of data that can be output in commonly used spreadsheet (CSV) or raster (GeoTIFF) formats to aid subsequent offline analysis. Data included in the two tools include: 20 atmospheric, terrestrial and oceanic reanalysis data products; a new dataset of annual resolution climate variables (comparable to WorldClim) calculated from ERA5-Land data for 1950-2022; and CMIP6 climate projection output for 34 model simulations for historical, SSP2-4.5 and SSP5-8.5 scenarios. New data products can also be easily added to the tools as they become available within the Google Earth Engine Data Catalog. Five case studies that use data from both tools are also provided. These show that GEEClimT and GEECE are easily expandable tools that remove multiple barriers to entry that will open use of climate reanalysis and projection data to a new and wider range of users.

ERA5-Land to zestaw danych z ponownym przeanalizowaniem, który zapewnia spójny obraz ewolucji zmiennych dotyczących lądu w ciągu kilku dekad w ulepszonej rozdzielczości w porównaniu z ERA5. Dane ERA5-Land zostały wygenerowane przez odtworzenie komponentu lądowego w ramach ponownej analizy klimatu ECMWF ERA5. Reanalysis łączy dane modelu z obserwacjami z całego świata…

The Earth Engine version of the Fire Information for Resource Management System (FIRMS) dataset contains the LANCE fire detection product in rasterized form. The near real-time (NRT) active fire locations are processed by LANCE using the standard MODIS MOD14/MYD14 Fire and Thermal Anomalies product. Each active fire location represents the centroid of a 1km pixel that is flagged by the algorithm as containing one or more fires within the pixel. The data are rasterized as follows: for each FIRMS active fire point, a 1km bounding box (BB) is defined; pixels in the MODIS sinusoidal projection that intersect the FIRMS BB are identified; if multiple FIRMS BBs intersect the same pixel, the one with higher confidence is retained; in case of a tie, the brighter one is retained. The data in the near-real-time dataset are not considered to be of science quality. Additional information can be found here. NOTE: VIIRS FIRMS datasets from NOAA20 and SUOMI are also available: NASA/LANCE/NOAA20_VIIRS/C2 NASA/LANCE/SNPP_VIIRS/C2

This collection contains precomputed results from running the Continuous Change Detection and Classification (CCDC) algorithm on 20 years of Landsat surface reflectance data. CCDC is a break-point finding algorithm that uses harmonic fitting with a dynamic RMSE threshold to detect breakpoints in time-series data. The dataset was created from the Landsat 5, 7, and 8 Collection-1, Tier-1, surface reflectance time series, using all daytime images between 1999-01-01 and 2019-12-31. Each image was preprocessed to mask pixels identified as cloud, shadow, or snow (according to the 'pixel_qa' band), saturated pixels, and pixels with an atmospheric opacity > 300 (as identified by the 'sr_atmos_opacity' and 'sr_aerosol' bands). Pixels repeated in north/south scene overlap were deduplicated. The results were output in 2-degree tiles for all landmasses between -60° and +85° latitude. The images are suitable to simply mosaic() into one global image. The CCDC algorithm was run with the default algorithm parameters except for the dateFormat: tmaskBands: ['green', 'swir'] minObservations: 6 chiSquareProbability: 0.99 minNumOfYearsScaler: 1.33 dateFormat: 1 (fractional year) lambda: 20 maxIterations: 25000 Each pixel in the output is encoded using variable length arrays. The outer length of each array (axis 0) corresponds to the number of breakpoints found at that location. The coefs bands contain 2-D arrays, where each inner array contains the scaling factors for the 8 terms in the linear harmonic model, in the order: [offset, t, cos(ωt), sin(ωt), cos(2ωt), sin(2ωt), cos(3ωt), sin(3ωt)], where ω = 2Π. The models are scale to produce refelectance units (0.0 - 1.0) for the optical bands and degrees (K) / 100.0 for the thermal band. Note that since the output bands are arrays and can only be downsampled using a SAMPLE pyramiding policy. At lower zoom levels, the results are usually no longer representative of the full-resolution data, and, for instance, tile boundaries can be seen due to the downsampled masks. It's therefore not recommended to use this dataset at resolutions less than 240m/pixel. There are no current plans to add post-2019 assets to this dataset.

An accurate global impervious surface map at a resolution of 30-m for 2015 by combining Landsat-8 OLI optical images, Sentinel-1 SAR images and VIIRS NTL images based on the Google Earth Engine (GEE) platform.

The Normalized Difference Vegetation Index is generated from the Near-IR and Red bands of each scene as (NIR - Red) / (NIR + Red), and ranges in value from -1.0 to 1.0. This product is generated from the MODIS/006/MCD43A4 surface reflectance composites.

بعد ‎2022-01-25، تمّ تغيير نطاق DN (القيمة) في مَشاهد Sentinel-2 التي تحتوي على القيمة ‎04.00 أو أعلى في PROCESSING_BASELINE بمقدار 1000. تعمل مجموعة HARMONIZED على نقل البيانات في المشاهد الأحدث لتصبح في النطاق نفسه المُستخدَم في المشاهد الأقدم. ‫Sentinel-2 هي مهمة تصوير متعددة الأطوال الموجية وذات نطاق واسع وعالية الدقة، وهي تدعم دراسات "كوبرنيكوس لرصد الأرض"، بما في ذلك رصد الغطاء النباتي والتربة والمياه، بالإضافة إلى رصد الممرات المائية الداخلية والمناطق الساحلية. تحتوي بيانات Sentinel-2 على 13 نطاقًا طيفيًا من النوع UINT16 تمثّل انعاكس سطح الأرض مقسومًا على 10,000. يُرجى الاطّلاع على دليل مستخدم Sentinel-2 للتعرّف على التفاصيل. ‫QA60 هو نطاق قناع بتات كان يحتوي على مضلّعات قناع السحب الممسوحة ضوئيًا حتى شباط (فبراير) 2022، عندما توقّف إنتاج هذه المضلّعات. اعتبارًا من شباط (فبراير) 2024، يتم إنشاء نطاقات QA60 المتوافقة مع الإصدارات القديمة من نطاقات تصنيف السحابة MSK_CLASSI. لمزيد من التفاصيل، اطّلِع على الشرح الكامل لكيفية احتساب أقنعة السحب. قد يحتوي كل منتج من منتجات Sentinel-2 (أرشيف بتنسيق ZIP) على عدة حبيبات. تصبح كل حبيبة مادة عرض منفصلة في Earth Engine. تتّبع أرقام تعريف مواد العرض ذات الدقة العالية لمادة عرض Sentinel-2 التنسيق التالي: COPERNICUS/S2/20151128T002653_20151128T102149_T56MNN. في هذه الحالة، يمثّل الجزء الرقمي الأول تاريخ الاستشعار ووقته، ويمثّل الجزء الرقمي الثاني تاريخ إنشاء المنتج ووقته، وتمثل السلسلة النهائية المكونة من 6 أحرف معرّفًا فريدًا للحبيبة ويشير إلى مرجع شبكة UTM (راجِع MGRS). يمكن العثور على بيانات المستوى 2 التي أنشأها "وكالة الفضاء الأوروبية" في المجموعة COPERNICUS/S2_SR. للحصول على مجموعات بيانات للمساعدة في رصد السحب و/أو ظلال السحب، يُرجى الاطّلاع على COPERNICUS/S2_CLOUD_PROBABILITY وGOOGLE/CLOUD_SCORE_PLUS/V1/S2_HARMONIZED. لمزيد من التفاصيل حول الدقة الشعاعية لآلة Sentinel-2، اطّلِع على هذه الصفحة.

This archive contains native resolution and super resolution (SR) Landsat imagery, derivative lake shorelines, and previously-published lake shorelines derived airborne remote sensing, used here for comparison. Landsat images are from 1985 (Landsat 5) and 2017 (Landsat 8) and are cropped to study areas used in the corresponding paper and converted to 8-bit format. SR images were created using the model of Lezine et al (2021a, 2021b), which outputs imagery at 10x-finer resolution, and they have the same extent and bit depth as the native resolution scenes included. Reference shoreline datasets are from Kyzivat et al. (2019a and 2019b) for the year 2017 and Walter Anthony et al. (2021a, 2021b) for Fairbanks, AK, USA in 1985. All derived and comparison shoreline datasets are cropped to the same extent, filtered to a common minimum lake size (40 m2 for 2017; 13 m2 for 1985), and smoothed via 10 m morphological closing. The SR-derived lakes were determined to have F-1 scores of 0.75 (2017 data) and 0.60 (1985 data) as compared to reference lakes for lakes larger than 500 m2, and accuracy is worse for smaller lakes. More details are in the forthcoming accompanying publication.

All raster images are in cloud-optimized geotiff (COG) format (.tif) with file naming shown in Table 1. Vector shoreline datasets are in ESRI shapefile format (.shp, .dbf, etc.), and file names use the abbreviations LR for low resolution, SR for high resolution, and GT for “ground truth” comparison airborne-derived datasets.

Landsat-5 and Landsat-8 images courtesy of the U.S. Geological Survey

For an interactive map demo of these datasets via Google Earth Engine Apps, visit: https://ekyzivat.users.earthengine.app/view/super-resolution-demo

Table 1: File naming scheme based on region, with some regions requiring two-scene mosaics.

Region

Landsat ID

Mosaic name

Yukon Flats Basin

LC08_L2SP_068014_20170708_20200903_02_T1

LC08_20170708_yflats_cog.tif

“

LC08_L2SP_068013_20170708_20201015_02_T1

“

Old Crow Flats

LC08_L2SP_067012_20170903_20200903_02_T1

-

Mackenzie River Delta

LC08_L2SP_064011_20170728_20200903_02_T1

LC08_20170728_inuvik_cog.tif

“

LC08_L2SP_064012_20170728_20200903_02_T1

“

Canadian Shield Margin

LC08_L2SP_050015_20170811_20200903_02_T1

LC08_20170811_cshield-margin_cog.tif

“

LC08_L2SP_048016_20170829_20200903_02_T1

“

Canadian Shield near Baker Creek

LC08_L2SP_046016_20170831_20200903_02_T1

-

Canadian Shield near Daring Lake

LC08_L2SP_045015_20170723_20201015_02_T1

-

Peace-Athabasca Delta

LC08_L2SP_043019_20170810_20200903_02_T1

-

Prairie Potholes North 1

LC08_L2SP_041021_20170812_20200903_02_T1

LC08_20170812_potholes-north1_cog.tif

“

LC08_L2SP_041022_20170812_20200903_02_T1

“

Prairie Potholes North 2

LC08_L2SP_038023_20170823_20200903_02_T1

-

Prairie Potholes South

LC08_L2SP_031027_20170907_20200903_02_T1

-

Fairbanks

LT05_L2SP_070014_19850831_20200918_02_T1

-

References:

Kyzivat, E. D., Smith, L. C., Pitcher, L. H., Fayne, J. V., Cooley, S. W., Cooper, M. G., Topp, S. N., Langhorst, T., Harlan, M. E., Horvat, C., Gleason, C. J., & Pavelsky, T. M. (2019b). A high-resolution airborne color-infrared camera water mask for the NASA ABoVE campaign. Remote Sensing, 11(18), 2163. https://doi.org/10.3390/rs11182163

Kyzivat, E.D., L.C. Smith, L.H. Pitcher, J.V. Fayne, S.W. Cooley, M.G. Cooper, S. Topp, T. Langhorst, M.E. Harlan, C.J. Gleason, and T.M. Pavelsky. 2019a. ABoVE: AirSWOT Water Masks from Color-Infrared Imagery over Alaska and Canada, 2017. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1707

Ekaterina M. D. Lezine, Kyzivat, E. D., & Smith, L. C. (2021a). Super-resolution surface water mapping on the Canadian shield using planet CubeSat images and a generative adversarial network. Canadian Journal of Remote Sensing, 47(2), 261–275. https://doi.org/10.1080/07038992.2021.1924646

Ekaterina M. D. Lezine, Kyzivat, E. D., & Smith, L. C. (2021b). Super-resolution surface water mapping on the canadian shield using planet CubeSat images and a generative adversarial network. Canadian Journal of Remote Sensing, 47(2), 261–275. https://doi.org/10.1080/07038992.2021.1924646

Walter Anthony, K.., Lindgren, P., Hanke, P., Engram, M., Anthony, P., Daanen, R. P., Bondurant, A., Liljedahl, A. K., Lenz, J., Grosse, G., Jones, B. M., Brosius, L., James, S. R., Minsley, B. J., Pastick, N. J., Munk, J., Chanton, J. P., Miller, C. E., & Meyer, F. J. (2021a). Decadal-scale hotspot methane ebullition within lakes following abrupt permafrost thaw. Environ. Res. Lett, 16, 35010. https://doi.org/10.1088/1748-9326/abc848

Walter Anthony, K., and P. Lindgren. 2021b. ABoVE: Historical Lake Shorelines and Areas near Fairbanks, Alaska, 1949-2009. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1859

Here's a collection of resources related to the TEAM application (https://jamesmcoll.users.earthengine.app/view/team) Raw Code: https://code.earthengine.google.com/f55a05fbf6e2468e01744d87ca178461

The API is powered by Google Earth Engine, and currently only supports pan-sharpened Landsat 8 imagery.