Download Data In Google Earth
Elenor Pickle
Thermal satellite sensors can provide surface temperature and emissivity information. The Earth Engine data catalog includes both land and sea surface temperature products derived from several spacecraft sensors, including MODIS, ASTER, and AVHRR, in addition to raw Landsat thermal data.
You can use atmospheric data to help correct image data from other sensors, or you can study it in its own right. The Earth Engine catalog includes atmospheric datasets such as ozone data from NASA's TOMS and OMI instruments and the MODIS Monthly Gridded Atmospheric Product.
Weather datasets describe forecasted and measured conditions over short periods of time, including precipitation, temperature, humidity, and wind, and other variables. Earth Engine includes forecast data from NOAA's Global Forecast System (GFS) and the NCEP Climate Forecast System (CFSv2), as well as sensor data from sources like the Tropical Rainfall Measuring Mission (TRMM).
Landsat, a joint program of the USGS and NASA, has been observing the Earth continuously from 1972 through the present day. Today the Landsat satellites image the entire Earth's surface at a 30-meter resolution about once every two weeks, including multispectral and thermal data.
The Copernicus Program is an ambitious initiative headed by the European Commission in partnership with the European Space Agency (ESA). The Sentinels include all-weather radar images from Sentinel-1A and -1B, high-resolution optical images from Sentinel 2A and 2B, as well as ocean and land data suitable for environmental and climate monitoring from Sentinel 3.
High-resolution imagery captures the finer details of landscapes and urban environments. The US National Agriculture Imagery Program (NAIP) offers aerial image data of the US at one-meter resolution, including nearly complete coverage every several years since 2003.
Land cover maps describe the physical landscape in terms of land cover classes such as forest, grassland, and water. Earth Engine includes a wide variety of land cover datasets, from near real-time Dynamic World to global products such as ESA World Cover.
Cropland data is key to understanding global water consumption and agricultural production. Earth Engine includes a number of cropland data products such as the USDA NASS Cropland Data Layers, as well as layers from the Global Food Security-Support Analysis Data (GFSAD) including cropland extent, crop dominance, and watering sources.
The Earthdata Login provides a single mechanism for user registration and profile management for all EOSDIS system components (DAACs, Tools, Services). Your Earthdata login also helps the EOSDIS program better understand the usage of EOSDIS services to improve user experience through customization of tools and improvement of services. EOSDIS data are openly available to all and free of charge except where governed by international agreements.
Natural Earth is a public domain map dataset available at 1:10m, 1:50m, and 1:110 million scales. Featuring tightly integrated vector and raster data, with Natural Earth you can make a variety of visually pleasing, well-crafted maps with cartography or GIS software.
Natural Earth solves a problem: finding suitable data for making small-scale maps. In a time when the web is awash in geospatial data, cartographers are forced to waste time sifting through confusing tangles of poorly attributed data to make clean, legible maps. Because your time is valuable, Natural Earth data comes ready-to-use.
The carefully generalized linework maintains consistent, recognizable geographic shapes at 1:10m, 1:50m, and 1:110m scales. Natural Earth was built from the ground up so you will find that all data layers align precisely with one another. For example, where rivers and country borders are one and the same, the lines are coincident.
Natural Earth, however, is more than just a collection of pretty lines. The data attributes are equally important for mapmaking. Most data contain embedded feature names, which are ranked by relative importance. Other attributes facilitate faster map production, such as width attributes assigned to river segments for creating tapers.
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.
See our impact on the Earth from a new perspective through 37 years of satellite imagery in Timelapse in Google Earth. Timelapse is one example of how Earth Engine can help gain insight into petabyte-scale datasets.
The public data archive includes more than thirty years of historical imagery and scientific datasets, updated and expanded daily. It contains over eighty petabytes of geospatial data instantly available for analysis.
Berkeley Earth provides high-resolution land and ocean time series data and gridded temperature data. Our peer-reviewed methodology incorporates more temperature observations than other available products, and often has better coverage. Global datasets begin in 1850, with some land-only areas reported back to 1750. The newest generation of our products are augmented by machine learning techniques to improve the spatial resolution. This allows Berkeley Earth to provide the most comprehensive, high-resolution instrumental temperature data product available.
Below we have made our data accessible at the global, national/regional, and local levels. Source and intermediate data is available as well. For technical questions or inquiries about the data, please contact da...@berkeleyearth.org.
Datasets are also provided in a gridded NetCDF format. Two types of grids are provided, a grid based on dividing the Earth into 15984 equal-area grid cells and a latitude-longitude grid. The equal area grid is the primary data format used in most of our analyses and provides generally smaller files; however, that format may be less convenient for many users.
Berkeley Earth is preparing an updated high-resolution temperature data set. Some materials related to this update are being released as a beta version to allow for additional feedback before final publication. Results are preliminary and subject to change without notice.
The new high-resolution Berkeley Earth data set has been used to construct new country, regional and local summaries. This beta version is being produced to allow for additional feedback before final publication. Results are preliminary and subject to change without notice.
From the current (lower-resolution) version of the Berkeley Earth data set, we provide rough city-level estimates at 110 km resolution with monthly average low, high and mean temperature For improved data see the next section.
The new high-resolution dataset, augmented with machine learning technology, does a much better job reconstructing climate change histories at the level of individual urban areas. The target resolution is 25 km. Available periods vary, with most cities starting from roughly 1850-1900. A few cities start as early as 1750.
During the Berkeley Earth averaging process we compare each station to other stations in its local neighborhood, which allows us to identify discontinuities and other heterogeneities in the time series from individual weather stations. The averaging process is then designed to automatically compensate for various biases that appear to be present. After the average field is constructed, it is possible to create a set of estimated bias corrections that suggest what the weather station might have reported had apparent biasing events not occurred. This breakpoint-adjusted data set provides a collection of adjusted, homogeneous station data that is recommended for users who want to avoid heterogeneities in station temperature data.
Source data consists of the raw temperature reports that form the foundation of our averaging system. Source observations are provided as originally reported and will contain many quality control and redundancy issues. Intermediate data is constructed from the source data by merging redundant records, identifying a variety of quality control problems, and creating monthly averages from daily reports when necessary. The definitive repository for Source and Intermediate data is located in the SVN, which is built nightly.
This includes all time series from the originating datasets. Due to duplication with the same data being reported by multiple agencies, on average there will be 3-4 time series reports with each site. Only limited quality control flagging has been performed at this stage.
LATEST BASELINES FOLDER: Contains all the interoperable products generated in a consistent way with the application of all significant data quality improvements, but not necessarily covering the entire mission.
VirES for Swarm, the virtual research service, is a highly interactive data manipulation and retrieval interface for Swarm constellation mission products. It includes tools for studying various geomagnetic models by comparing them to the Swarm satellite measurements at given space weather and ionospheric conditions.
Swarm Quality Control Reports are produced on a weekly basis and cover the quality and the data acquired by the three Swarm satellites and the products produced from this data. The quality control methods and diagnostics are continuously evolving and improving throughout the mission lifetime, in order to report also on the product evolutions and status of the instruments.
The concept of a digital twin of Earth envisages the convergence of Big Earth Data with physics-based models in an interactive computational framework that enables monitoring and prediction of environmental and social perturbations for use in sustainable governance. Although computational advances are rapidly progressing, digital twins of Earth have not yet been produced. In this Review, we summarize the methodological and cyberinfrastructure advances in Big Data that have advanced the progress towards a digital Earth twin. Data assimilation provides the framework for incorporation of high-resolution observations into Earth system models but lacks the decision-making interface and learning ability needed for the digital twin. Machine learning (and particularly deep learning) in Earth system science is now more capable of reaching the high dimensionality, complexity and nonlinearity of real-life Earth systems and is expanding the learning ability from Big Data. Progress in causal inference and reinforcement learning are, respectively, increasing the interpretability of Big Data and the ability of simulations to solve sequential decision-making problems. Social sensing data could provide inputs for multiagent deep reinforcement learning via feedback loops between agents and the environment, enabling large-scale applications in human system modelling. Future research must focus on finding the optimal way to integrate these individual methodologies to achieve digital twins.
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