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 Google Satellite Embedding dataset is a global, analysis-ready collection of learned geospatial embeddings. Each 10-meter pixel in this dataset is a 64-dimensional representation, or "embedding vector," that encodes temporal trajectories of surface conditions at and around that pixel as measured by various Earth observation instruments and datasets, over a …

Meet Earth EngineGoogle Earth Engine combines a multi-petabyte catalog of satellite imagery and geospatial datasets with planetary-scale analysis capabilities and makes it available for scientists, researchers, and developers to detect changes, map trends, and quantify differences on the Earth's surface.SATELLITE IMAGERY+YOUR ALGORITHMS+REAL WORLD APPLICATIONSLEARN MOREGLOBAL-SCALE INSIGHTExplore our interactive timelapse viewer to travel back in time and see how the world has changed over the past twenty-nine years. Timelapse is one example of how Earth Engine can help gain insight into petabyte-scale datasets.EXPLORE TIMELAPSEREADY-TO-USE DATASETSThe public data archive includes more than thirty years of historical imagery and scientific datasets, updated and expanded daily. It contains over twenty petabytes of geospatial data instantly available for analysis.EXPLORE DATASETSSIMPLE, YET POWERFUL APIThe Earth Engine API is available in Python and JavaScript, making it easy to harness the power of Google’s cloud for your own geospatial analysis.EXPLORE THE APIGoogle Earth Engine has made it possible for the first time in history to rapidly and accurately process vast amounts of satellite imagery, identifying where and when tree cover change has occurred at high resolution. Global Forest Watch would not exist without it. For those who care about the future of the planet Google Earth Engine is a great blessing!-Dr. Andrew Steer, President and CEO of the World Resources Institute.CONVENIENT TOOLSUse our web-based code editor for fast, interactive algorithm development with instant access to petabytes of data.LEARN ABOUT THE CODE EDITORSCIENTIFIC AND HUMANITARIAN IMPACTScientists and non-profits use Earth Engine for remote sensing research, predicting disease outbreaks, natural resource management, and more.SEE CASE STUDIESREADY TO BE PART OF THE SOLUTION?SIGN UP NOWTERMS OF SERVICE PRIVACY ABOUT GOOGLE

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.

The National Agriculture Imagery Program (NAIP) acquires aerial imagery during the agricultural growing seasons in the continental U.S. NAIP projects are contracted each year based upon available funding and the imagery acquisition cycle. Beginning in 2003, NAIP was acquired on a 5-year cycle. 2008 was a transition year, and a …

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.

Training Classifiers, Supervised Classification and Error Assessment • How to add raster and vector data from the catalog in Google Earth Engine; • Train a classifier; • Perform the error assessment; • Download the results.

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.

This example is about how to use Google Earth Engine API on Jupyter Notebooks. We show the example of how to get Landsat Net Primary Production (NPP) CONUS DataSet from Google Earth Engine Data Catalog.

The two related FAO datasets on Drained Organic Soils provide estimates of: DROSA-A: area of Organic Soils (in hectares) drained for agricultural activities (cropland and grazed grassland) DROSE-A: carbon (C) and nitrous oxide (N2O) estimates (in gigagrams) from the agricultural drainage of organic soils under these land uses. Annual data are available at 0.0083333 X 0.0083333 resolution (~1 km at the equator), with global coverage for the period 1992 - 2018. FAOSTAT estimates follow the Intergovernmental Panel on Climate Change Guidelines (IPCC) and use histosols as proxy for the presence of organic soils and annual land cover maps as time- dependent component. Additionally, soils characteristics, land use, and climate information are applied in the analysis. The carbon emissions can be converted to CO2, multiplying pixel values by the ratio of the molecular weight of carbon dioxide (CO2) to that of C (44/12). Organic soils develop in wet soil ecosystems. They include tropical and boreal peatlands, high-latitude bogs, ferns, and mires. Organic soils cover globally a mere 3 percent of the terrestrial land area but represent up to 30 percent of the total soil carbon, thus playing an important role in maintaining the earth's carbon balance. Agriculture is a major cause of drainage of organic soils around the world. Drainage exposes to aerobic conditions the organic matter of organic soils that oxidizes releasing large amounts of harmful greenhouse gases (GHG) to the atmosphere. DROSA-A and DROSE-A are the basis for country and regional statistics on drained organic soils disseminated in three FAOSTAT datasets (Cultivation of Organic Soils; Cropland; and Grassland).

The Shuttle Radar Topography Mission (SRTM) digital elevation dataset was originally produced to provide consistent, high-quality elevation data at near global scope. This version of the SRTM digital elevation data has been processed to fill data voids, and to facilitate its ease of 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 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.

The Shuttle Radar Topography Mission (SRTM, see Farr et al. 2007) digital elevation data is an international research effort that obtained digital elevation models on a near-global scale. This SRTM V3 product (SRTM Plus) is provided by NASA JPL at a resolution of 1 arc-second (approximately 30m). This dataset has undergone a void-filling process using open-source data (ASTER GDEM2, GMTED2010, and NED), as opposed to other versions that contain voids or have been void-filled with commercial sources. For more information on the different versions see the SRTM Quick Guide. Documentation: User's Guide General Documentation Algorithm Theoretical Basis Document (ATBD)

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

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.

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.

L'outil Hakai Google Earth Engine Kelp (outil GEEK) a été développé dans le cadre d'une collaboration entre l'Institut Hakai, l'Université de Victoria et le ministère des Pêches et des Océans pour tirer parti des capacités de cloud computing pour analyser l'imagerie satellite Landsat (30 m) afin d'extraire l'étendue de la canopée et du varech. La méthodologie originale est décrite dans Nijland et al. 2019*.

Remarque : Ce jeu de données est conçu comme une « lecture seule », car nous continuons à améliorer les résultats. Il vise à démontrer l'utilité de l'archive Landsat pour cartographier le varech. Ces données sont visibles sur la carte Web GEEK disponible ici.

Ce package de données contient deux jeux de données :

Etendue annuelle maximale estivale du varech formant la canopée (1984 - 2019) en tant que rasters. Etendue maximale décennale du varech formant la canopée (1984 - 1990, 1991 - 2000, 2001 - 2010, 2011 - 2020)

Ce jeu de données a été généré à la suite de modifications apportées aux méthodologies GEEK originales. Les paramètres utilisés pour générer les rasters étaient des scènes d'images avec :

Plage de mois Imagescene = 1er mai - 30 septembre Clouds maximum dans la scène = 80% Marée maximale = 3,2 m (+0,5 MWL des marées de la côte centrale selon les méthodes KIM-1) Marée minimale = 0 m Tampon de rivage appliqué au masque de terrain = 1 pixel (30 m) NDVI* minimum (pour qu'un pixel individuel soit classé comme varech) = -0,05 Nombre minimum de fois qu'un pixel de varech individuel doit être détecté en tant que varech au cours d'une seule année = 30 % de toutes les détections d'une année donnée K moyenne minimale (moyenne du NDVI pour tous les pixels à un emplacement donné détecté comme varech) = -0,05 * NDVI = indice de végétation de différence normalisée.

Ces paramètres ont été choisis sur la base d'une évaluation de la précision à l'aide d'une étendue de varech dérivée d'images WorldView-2 (2 m) de juillet 2014 et août 2014. Ces données ont été rééchantillonnées à 30 m. Bien que de nombreuses itérations exécutées pour l'outil aient donné des résultats très similaires, des paramètres ont été sélectionnés qui ont maximisé la précision du varech pour la comparaison de 2014.

Les résultats de l'évaluation de la précision ont été les suivants : Erreur de commission de 50 % Erreur d'omission de 25 %

En termes simples, les méthodes actuelles conduisent à un niveau élevé de « faux positifs », mais elles capturent avec précision l'étendue du varech par rapport au jeu de données de validation. Cette erreur peut être attribuée à la sensibilité de l'utilisation d'un seul NDVI pour détecter le varech. Nous observons des variations des seuils NDVI à la fois au sein d'une seule scène et entre les scènes.

L'objectif du jeu de données de séries chronologiques est censé prendre en compte une partie de cette erreur, car les pixels détectés seulement un par décennie sont supprimés.

Ce jeu de données fait partie du programme de cartographie de l'habitat de Hakai. L'objectif principal du programme de cartographie de l'habitat de Hakai est de générer des inventaires spatiaux des habitats côtiers, d'étudier comment ces habitats évoluent au fil du temps et les moteurs de ce changement.

*Nijland, W., Reshitnyk, L. et Rubidge, E. (2019). Télédétection par satellite de varech formant une canopée sur un littoral complexe : une nouvelle procédure utilisant les archives d'images Landsat. Télédétection de l'environnement, 220, 41-50. doi:10.1016/j.rse.2018.10.032

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.

The SEN12TP dataset (Sentinel-1 and -2 imagery, timely paired) contains 2319 scenes of Sentinel-1 radar and Sentinel-2 optical imagery together with elevation and land cover information of 1236 distinct ROIs taken between 28 March 2017 and 31 December 2020. Each scene has a size of 20km x 20km at 10m pixel spacing. The time difference between optical and radar images is at most 12h, but for almost all scenes it is around 6h since the orbits of Sentinel-1 and -2 are shifted like that. Next to the \(\sigma^\circ\) radar backscatter also the radiometric terrain corrected \(\gamma^\circ\) radar backscatter is calculated and included. \(\gamma^\circ\) values are calculated using the volumetric model presented by Vollrath et. al 2020.

The uncompressed dataset has a size of 222 GB and is split spatially into a train (~90%) and a test set (~10%). For easier download the train set is split into four separate zip archives.

Please cite the following paper when using the dataset, in which the design and creation is detailed:
T. Roßberg and M. Schmitt. A globally applicable method for NDVI estimation from Sentinel-1 SAR backscatter using a deep neural network and the SEN12TP dataset. PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science, 2023. https://doi.org/10.1007/s41064-023-00238-y.

The file sen12tp-metadata.json includes metadata of the selected scenes. It includes for each scene the geometry, an ID for the ROI and the scene, the climate and land cover information used when sampling the central point, the timestamps (in ms) when the Sentinel-1 and -2 image was taken, the month of the year, and the EPSG code of the local UTM Grid (e.g. EPSG:32643 - WGS 84 / UTM zone 43N).

Naming scheme: The images are contained in directories called {roi_id}_{scene_id}, as for some unique regions image pairs of multiple dates are included. In each directory are six files for the different modalities with the naming {scene_id}_{modality}.tif. Multiple modalities are included: radar backscatter and multispectral optical images, the elevation as DSM (digital surface model) and different land cover maps.

The following bands are included in the tif files, for an further explanation see the documentation on GEE. All bands are resampled to 10m resolution and reprojected to the coordinate reference system of the Sentinel-2 image.

Checking the file integrity
After downloading and decompression the file integrity can be checked using the provided file of md5 checksum.
Under Linux: md5sum --check --quiet md5sums.txt

References:

Vollrath, Andreas, Adugna Mullissa, Johannes Reiche (2020). "Angular-Based Radiometric Slope Correction for Sentinel-1 on Google Earth Engine". In: Remote Sensing 12.1, Art no. 1867. https://doi.org/10.3390/rs12111867.