In 2024, around 20 percent of people surveyed in Brazil said they agreed with the sentence the Earth is flat. Approximately eight percent of respondents stated that they did not know what the shape of planet Earth was.

This statistic depicts the market size of the global Earth observation satellite data and services market in 2017 and 2023, broken down by technology. In 2017, the optical Earth observation satellite data and services market was sized at 4.7 billion U.S. dollars worldwide.

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

NEAR MAG RDR volume sets contain a single data set, from one instrument and one mission phase (defined in the phase table in /AAREADME.TXT).

The atmosphere is a layer of gas that surrounds the earth, and its current composition is one of the primary factors that allows for the existence of life on our planet. The atmosphere protects the earth's surface from solar radiation, it traps heat that maintains livable conditions in the absence of sunlight, and its current pressure allows for the existence of liquid water on the planet's surface. Over 99 percent of the earth's atmosphere is comprised of nitrogen (78 percent) and oxygen (21 percent), while argon makes up most of the rest at over 0.9 percent. The

This data set contains the NEAR magnetometer (MAG) data for the EARTH phase. The data set begins on 1998-01-23T00:00:00.000 and ends 1998-01-26T23:59:59.999 . The data are raw telemetry data, provided in engineering units, that have been reformatted into FITS file format (NASA Office of Science and Technology (NOST), 100-1.0). In addition to the raw magnetometer data, a calibration file and algorithm are available. This data set is archived as a set of CDROM images as a part of the NEAR EDR volume set.

The dataset collection in focus comprises an assortment of tables, each carrying a distinct set of data. These tables are meticulously sourced from the website of Lantmäteriet (The Swedish Mapping, Cadastral and Land Registration Authority) in Sweden. The dataset provides a wide range of valuable data, including but not limited to, information about companies, geospatial data, meteorological data, statistical data, and earth observation & environmental data. The tables present the data in an organized manner, with the information arranged systematically in columns and rows. This makes it convenient to analyze and draw insights from the dataset. Overall, it's a comprehensive dataset collection that offers a diverse and substantial range of information.

NASA's Freeze/Thaw Earth System Data Record (FT-ESDR) Web Interface is a NASA MEaSUREs (Making Earth System Data Records for Use in Research Environments) funded effort to provide a consistent long-term global data record of land surface freeze/thaw (FT) state dynamics for all vegetated regions where low temperatures are a major constraint to ecosystem processes. The FT measurement is derived from temporal change classification of global satellite microwave remote sensing time series, including passive microwave radiometry from the Special Sensor Microwave Imager (SSM/I) and Advanced Microwave Scanning Radiometer for EOS (AMSR-E), and radar scatterometry from SeaWinds-on-QuikSCAT. The ecological significance and basis of the FT measurement from satellite microwave remote sensing is summarized in the literature (e.g., see "Relevant Publications" section below). The FT-ESDR is designed to:

 1) distinguish FT heterogeneity in accordance with mesoscale climate and landscape topographic features;

 2) establish biophysical linkages between FT processes and vegetation productivity, respiration and associated land-atmosphere carbon exchange;

 3) distinguish FT dynamics in accordance with episodic weather events, annual anomalies, periodic climate cycles and long-term climate change trajectories." 

 [Summary provided by the University of Montana.]

Abstract

Compilation of Earth Surface temperatures historical. Source: https://www.kaggle.com/berkeleyearth/climate-change-earth-surface-temperature-data

Documentation

Data compiled by the Berkeley Earth project, which is affiliated with Lawrence Berkeley National Laboratory. The Berkeley Earth Surface Temperature Study combines 1.6 billion temperature reports from 16 pre-existing archives. It is nicely packaged and allows for slicing into interesting subsets (for example by country). They publish the source data and the code for the transformations they applied. They also use methods that allow weather observations from shorter time series to be included, meaning fewer observations need to be thrown away.

In this dataset, we have include several files:

Global Land and Ocean-and-Land Temperatures (GlobalTemperatures.csv):

• Date: starts in 1750 for average land temperature and 1850 for max and min land temperatures and global ocean and land temperatures

%3C!-- --%3E

• LandAverageTemperature: global average land temperature in celsius

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• LandAverageTemperatureUncertainty: the 95% confidence interval around the average

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• LandMaxTemperature: global average maximum land temperature in celsius

%3C!-- --%3E

• LandMaxTemperatureUncertainty: the 95% confidence interval around the maximum land temperature

%3C!-- --%3E

• LandMinTemperature: global average minimum land temperature in celsius

%3C!-- --%3E

• LandMinTemperatureUncertainty: the 95% confidence interval around the minimum land temperature

%3C!-- --%3E

• LandAndOceanAverageTemperature: global average land and ocean temperature in celsius

%3C!-- --%3E

• LandAndOceanAverageTemperatureUncertainty: the 95% confidence interval around the global average land and ocean temperature

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**Other files include: **

• Global Average Land Temperature by Country (GlobalLandTemperaturesByCountry.csv)

%3C!-- --%3E

• Global Average Land Temperature by State (GlobalLandTemperaturesByState.csv)

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• Global Land Temperatures By Major City (GlobalLandTemperaturesByMajorCity.csv)

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• Global Land Temperatures By City (GlobalLandTemperaturesByCity.csv)

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The raw data comes from the Berkeley Earth data page.

Thermospheric density and crosswind data products derived from GOCE data. Latest baseline _0200. The GOCE+ Air Density and Wind Retrieval using GOCE Data project produced a dataset of thermospheric density and crosswind data products which were derived from ion thruster activation data from GOCE telemetry. The data was combined with the mission's accelerometer and star camera data products. The products provide data continuty and extend the accelerometer-derived thermosphere density data sets from the CHAMP and GRACE missions. The resulting density and wind observations are made available in the form of time series and grids. These data can be applied in investigations of solar-terrestrial physics, as well as for the improvement and validation of models used in space operations. Funded by ESA through the Support To Science Element (STSE) of ESA's Earth Observation Envelope Programme, supporting the science applications of ESA's Living Planet programme, the project was a partnership between TU Delft, CNES and Hypersonic Technology Göttingen. Dataset history Date Change Reason 18/04/2019 - Time series data v2.0, covering the whole mission - Updated data set user manual - New satellite geometry and aerodynamic model - New vertical wind field - New data for the deorbit phase, (GPS+ACC and GPS-only versions) Updated satellite models and additional data 14/07/2016 - Time series data v1.5, covering the whole mission - Updated data set user manual Removal of noisy data 31/07/2014 - Time series data v1.4, covering the whole mission - Gridded data, now including error estimates - Updated data set user manual; Updated validation report; Updated ATBD Full GOCE dataset available 28/09/2013 Version 1.3 density/winds timeseries and gridded data released. User manual updated to v1.3 Bug fix and other changes 04/09/2013 Version 1.2 density/winds timeseries and gridded data released, with user manual First public data release of thermospheric density/winds data

A community-based data facility to support, sustain, and advance the geosciences by providing data services for observational solid earth data from the Ocean, Earth, and Polar Sciences. IEDA systems enable these data to be discovered and reused by a diverse community now and in the future. Data services include data access, data analysis, data compliance, data publication, DOI search, and web services. Desktop apps GeoMapApp and Virtual Ocean are available to explore, visualize and analyze your own data within the context of hundreds of other earth science data from around the world. IEDA is a partnership between EarthChem and the Marine Geoscience Data System (MGDS). EarthChem and MGDS systems include the geochemical databases PetDB and SedDB, the geochemistry data network EarthChem, the Ridge2000 and MARGINS Data Portals, the Academic Seismic Portal field data collection, the Antarctic and Southern Ocean Data System, the Global Multi Resolution Topography synthesis, and the System for Earth Sample Registration SESAR.

This layer presents detectable thermal activity from MODIS satellites for the last 7 days. MODIS Global Fires is a product of NASA’s Earth Observing System Data and Information System (EOSDIS), part of NASA's Earth Science Data. EOSDIS integrates remote sensing and GIS technologies to deliver global MODIS hotspot/fire locations to natural resource managers and other stakeholders around the World.

Area Covered: World

What can I do with this layer?
The MODIS thermal activity layer can be used to visualize and assess wildfires worldwide. However, it should be noted that this dataset contains many “false positives” (e.g., oil/natural gas wells or volcanoes) since the satellite will detect any large thermal signal.


It takes approximately 2 – 4 hours after satellite overpass for MODIS Rapid Response to process the data, and for the Fire Information for Resource Management System (FIRMS) to update the website. Occasionally, hardware errors can result in processing delays beyond the 2-4 hour range. Additional information on the MODIS system status can be found at MODIS Rapid Response.

Attribute Information

• Latitude and Longitude: The center point location of the 1km (approx.) pixel flagged as containing one or more fires/hotspots (fire size is not 1km, but variable). Stored by Point Geometry. See What does a hotspot/fire detection mean on the ground?
• Brightness: The brightness temperature measured (in Kelvin) using the MODIS channels 21/22 and channel 31.
• Scan and Track: The actual spatial resolution of the scanned pixel. Although the algorithm works at 1km resolution, the MODIS pixels get bigger toward the edge of the scan. See What does scan and track mean?
• Date and Time: Acquisition date of the hotspot/active fire pixel and time of satellite overpass in UTC (client presentation in local time). Stored by Acquisition Date.
  
• Acquisition Date: Derived Date/Time field combining Date and Time attributes.
• Satellite: Whether the detection was picked up by the Terra or Aqua satellite.
• Confidence: The detection confidence is a quality flag of the individual hotspot/active fire pixel.
• Version: Version refers to the processing collection and source of data. The number before the decimal refers to the collection (e.g. MODIS Collection 6). The number after the decimal indicates the source of Level 1B data; data processed in near-real time by MODIS Rapid Response will have the source code “CollectionNumber.0”. Data sourced from MODAPS (with a 2-month lag) and processed by FIRMS using the standard MOD14/MYD14 Thermal Anomalies algorithm will have a source code “CollectionNumber.x”. For example, data with the version listed as 5.0 is collection 5, processed by MRR, data with the version listed as 5.1 is collection 5 data processed by FIRMS using Level 1B data from MODAPS.
• Bright.T31: Channel 31 brightness temperature (in Kelvins) of the hotspot/active fire pixel.
• FRP: Fire Radiative Power. Depicts the pixel-integrated fire radiative power in MW (MegaWatts). FRP provides information on the measured radiant heat output of detected fires. The amount of radiant heat energy liberated per unit time (the Fire Radiative Power) is thought to be related to the rate at which fuel is being consumed (Wooster et. al. (2005)).
• DayNight: The standard processing algorithm uses the solar zenith angle (SZA) to threshold the day/night value; if the SZA exceeds 85 degrees it is assigned a night value. SZA values less than 85 degrees are assigned a day time value. For the NRT algorithm the day/night flag is assigned by ascending (day) vs descending (night) observation. It is expected that the NRT assignment of the day/night flag will be amended to be consistent with the standard processing.
• Hours Old: Derived field that provides age of record in hours between Acquisition date/time and latest update date/time. 0 = less than 1 hour ago, 1 = less than 2 hours ago, 2 = less than 3 hours ago, and so on.

This map is provided for informational purposes and is not monitored 24/7 for accuracy and

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.

EarthCARE data products encompass essential supporting auxiliary (AUX) and orbit data critical for accurate sensor data processing and analysis. Orbit data consists of on-board satellite data and orbital information predicted or determined by the Flight Operations Segment (FOS). For EarthCARE, this includes Reconstructed Orbit and Attitude Files, which provide detailed satellite positioning and orientation information. The integration of AUX and orbit data into EarthCARE's data processing workflow ensures the production of high-quality, scientifically valuable datasets for atmospheric research, climate modelling, and environmental monitoring.

Myanmar Communication: Setellite Earth Station: International data was reported at 1.000 Unit in 2016. This stayed constant from the previous number of 1.000 Unit for 2015. Myanmar Communication: Setellite Earth Station: International data is updated yearly, averaging 1.000 Unit from Mar 1991 (Median) to 2016, with 22 observations. The data reached an all-time high of 2.000 Unit in 2011 and a record low of 1.000 Unit in 2016. Myanmar Communication: Setellite Earth Station: International data remains active status in CEIC and is reported by Central Statistical Organization. The data is categorized under Global Database’s Myanmar – Table MM.TB001: Communication Statistics.

Australia has been receiving Earth Observations from Space (EOS) for over 50 years. Meteorological imagery dates from 1960 and Earth observation imagery from 1979. Australia has developed world-class scientific, environmental and emergency management EOS applications.

However, in the top fifty economies of the world, Australia is one of only three nations which does not have a space program. The satellites on which Australia depends are supplied by other countries which is a potential problem due to Australia having limited control over data continuity and data access.

The National Remote Sensing Technical Reference Group (NRSTRG) was established by Geoscience Australia as an advisory panel in 2004. It represents a cross-section of the remote sensing community and is made up of representatives from government, universities and private companies. Through the NRSTRG these parties provide Geoscience Australia with advice on technical and policy matters related to remote sensing.

In February 2009 the NRSTRG met for a day specifically to discuss Australia's reliance on EOS, with a view to informing the development of space policy. This report is the outcome of that meeting. Australia has some 92 programs dependent on EOS data. These programs are concerned with environmental issues, natural resource management, water, agriculture, meteorology, forestry, emergency management, border security, mapping and planning. Approximately half these programs have a high dependency on EOS data. While these programs are quite diverse there is considerable overlap in the technology and data.

Of Australia's EOS dependent programs 71 (77%) are valued between $100,000 and $10 million and 82 (89%) of all these programs have a medium or high dependency on EOS data demonstrating Australia's dependency on space based imaging.

Earth observation dependencies within currently active Federal and state government programs are calculated to be worth just over $949 million, calculated by weighting the level of dependency on EOS for each program. This includes two programs greater than $100 million in scale and one program greater than a billion dollars in scale.

This document is intended as a summary of Australia's current space and Earth observation dependencies, compiled by the NRSTRG, to be presented to the Federal Government's Space Policy Unit, a section of the Department of Innovation, Industry, Science and Research, as an aid to space policy formation.

About this dataset

Context

The dataset tabulates the Earth population by gender and age. The dataset can be utilized to understand the gender distribution and demographics of Earth.

Content

The dataset constitues the following two datasets across these two themes

• Earth, TX Population Breakdown by Gender
• Earth, TX Population Breakdown by Gender and Age

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Statistics illustrates consumption, production, prices, and trade of Rare Earth Metals in Asia from 2007 to 2024.

Earth Radiation Budget (ERB) MATRIX data were computed at NASA from raw ERB observations. Thirty-seven different ERB parameters are included, with either a daily, 6-day cyclic, or monthly ... temporal resolution.

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