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 …

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.

This dataset contains atmospherically corrected surface reflectance and land surface temperature derived from the data produced by the Landsat 7 ETM+ sensor. These images contain 4 visible and near-infrared (VNIR) bands and 2 short-wave infrared (SWIR) bands processed to orthorectified surface reflectance, and one thermal infrared (TIR) band processed to orthorectified surface temperature. They also contain intermediate bands used in calculation of the ST products, as well as QA bands. Landsat 7 SR products are created with the Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) algorithm (version 3.4.0). All Collection 2 ST products are created with a single-channel algorithm jointly created by the Rochester Institute of Technology (RIT) and National Aeronautics and Space Administration (NASA) Jet Propulsion Laboratory (JPL). Strips of collected data are packaged into overlapping "scenes" covering approximately 170km x 183km using a standardized reference grid. Some assets have only SR data, in which case ST bands are present but empty. For assets with both ST and SR bands, 'PROCESSING_LEVEL' is set to 'L2SP'. For assets with only SR bands, 'PROCESSING_LEVEL' is set to 'L2SR'. Additional documentation and usage examples. Landsat Collection 2 files are publicly available in a Google Cloud Storage bucket on requester-pays basis. The files are indexed in a regularly updated BigQuery table for ease of analysis: earth-engine-public-data.geo_index.landsat_c2_index. Data provider notes: Data products must contain both optical and thermal data to be successfully processed to surface temperature, as ASTER NDVI is required to temporally adjust the ASTER GED product to the target Landsat scene. Therefore, night time acquisitions cannot be processed to surface temperature. A known error exists in the surface temperature retrievals relative to clouds and possibly cloud shadows. The characterization of these issues has been documented by Cook et al., (2014). ASTER GED contains areas of missing mean emissivity data required for successful ST product generation. If there is missing ASTER GED information, there will be missing ST data in those areas. The ASTER GED dataset is created from all clear-sky pixels of ASTER scenes acquired from 2000 through 2008. While this dataset has a global spatial extent, there are areas missing mean emissivity information due to persistent cloud contamination in the ASTER measurements. The USGS further screens unphysical values (emissivity < 0.6) in ASTER GED to remove any emissivity underestimation due to undetected clouds. For any given pixel with no ASTER GED input or unphysical emissivity value, the resulting Landsat ST products have missing pixels. The missing Landsat ST pixels will be consistent through time (1982-present) given the static nature of ASTER GED mean climatology data. For more information refer to landsat-collection-2-surface-temperature-data-gaps-due-missing Note that Landsat 7's orbit has been drifting to an earlier acquisition time since 2017.

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.

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.

The Lithology dataset provides classes of the general types of parent material of soil on the surface. It is not derived from any DEM. The Conservation Science Partners (CSP) Ecologically Relevant Geomorphology (ERGo) Datasets, Landforms and Physiography contain detailed, multi-scale data on landforms and physiographic (aka land facet) patterns. Although there are many potential uses of these data, the original purpose for these data was to develop an ecologically relevant classification and map of landforms and physiographic classes that are suitable for climate adaptation planning. Because there is large uncertainty associated with future climate conditions and even more uncertainty around ecological responses, providing information about what is unlikely to change offers a strong foundation for managers to build robust climate adaptation plans. The quantification of these features of the landscape is sensitive to the resolution, so we provide the highest resolution possible given the extent and characteristics of a given index.

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.

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 …

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.

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.

Data Management • Create and edit fusion tables • Upload imagery, vector, and tabular data using Fusion Tables and KMLs • Share data with other Google Earth Engine (GEE) users as well as download imagery after manipulation in GEE.

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).

This product is part of the TreeMap data suite. It provides detailed spatial information on forest characteristics including number of live and dead trees, biomass, and carbon across the entire forested extent of the continental United States in 2016. TreeMap v2016 contains one image, a 22-band 30 x 30m resolution …

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.

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)

Data Acquisition • Acquiring data stored on Google’s servers for use in Google Earth Engine.

Charts, Histograms, and Time Series • Create a histogram graph from band values of an image collection • Create a time series graph from band values of an image collection