AndreaBozzo/ceres-open-data-index huggingface dataset:
Skysat ESA archive

The SkySat ESA archive collection consists of SkySat products requested by ESA supported projects over their areas of interest around the world and that ESA collected over the years. The dataset regularly grows as ESA collects new SkySat products.  Two different product types are offered, Ground Sampling Distance at nadir up to 65 cm for PAN and up to 0.8m for MS  EO-SIP Product Type Product description Content SSC_DEF_SC Basic and Ortho scene  * Level 1B 4-bands Analytic /DN Basic scene  * Level 1B 4-bands Panchromatic /DN Basic scene  * Level 1A 1-band Panchromatic DN Pre Sup resolution Basic scene  * Level 3B 3-bands Visual Ortho Scene  * Level 3B 4-bands Pansharpened Multispectral Ortho Scene  * Level 3B 4-bands Analytic/DN/SR Ortho Scene  * Level 3B 1-band Panchromatic /DN Ortho Scene SSC_DEF_CO Ortho Collect  * Visual 3-band Pansharpened Image  * Multispectral 4-band Pansharpened Image  * Multispectral 4-band Analytic/DN/SR Image (B, G, R,N)  * 1-band Panchromatic Image  The Basic Scene product is uncalibrated, not radiometrically corrected for atmosphere or for any geometric distortions inherent in the imaging process •Analytic - unorthorectified, radiometrically corrected, multispectral BGRN •Analytic DN - unorthorectified, multispectral BGRN •Panchromatic - unorthorectified, radiometrically corrected, panchromatic (PAN) •Panchromatic DN - unorthorectified, panchromatic (PAN) •L1A Panchromatic DN - unorthorectified, pre-super resolution, panchromatic (PAN)   The Ortho Scene product is sensor and geometrically corrected, and is projected to a cartographic map projection.  •Visual - orthorectified, pansharpened, and color-corrected (using a color curve) 3-band RGB Imagery •Pansharpened Multispectral - orthorectified, pansharpened 4-band BGRN Imagery •Analytic SR - orthorectified, multispectral BGRN. Atmospherically corrected Surface Reflectance product. •Analytic - orthorectified, multispectral BGRN. Radiometric corrections applied to correct for any sensor artifacts and transformation to top-of-atmosphere radiance •Analytic DN - orthorectified, multispectral BGRN, uncalibrated digital number imagery product Radiometric corrections applied to correct for any sensor artifacts •Panchromatic - orthorectified, radiometrically correct, panchromatic (PAN) •Panchromatic DN - orthorectified, panchromatic (PAN), uncalibrated digital number imagery product   The Ortho Collect product is created by composing SkySat Ortho Scenes along an imaging strip. The product may contain artifacts resulting from the composing process, particular offsets in areas of stitched source scenes.  Spatial coverage: Check the spatial coverage of the collection on a _$$map$$ https://tpm-ds.eo.esa.int/smcat/SkySat/ available on the Third Party Missions Dissemination Service.
Envisat ASAR WS Medium Resolution L1 [ASA_WSM_1P]

This strip-line product has been generated from Level 0 data collected when the instrument was in Wide Swath Mode. The product includes slant range to ground range corrections and it covers a continuous area along the imaging swath.  It is intended to perform applications-oriented analysis on large scale phenomena over a wide region and for multi-temporal imaging.  This is the standard product for ASAR Wide Swath Mode.  The ASAR WS L0 full mission data archive has been bulk processed to Level 1 (ASA_WSM_1P) in Envisat format with the IPF-ASAR processor version 6.03.  Spatial Resolution: 150 m slant range x 150 m azimuth.
GeoEye-1 ESA archive

The GeoEye-1 archive collection consists of GeoEye-1 products requested by ESA supported projects over their areas of interest around the world. The dataset regularly grows as ESA collects new products over the years. Panchromatic (up to 40cm resolution) and 4-Bands (up to 1.65m resolution) products are available; the 4-Bands includes various options such as Multispectral (separate channel for BLUE, GREEN, RED, NIR1), Pan-sharpened (BLUE, GREEN, RED, NIR1), Bundle (separate bands for PAN, BLUE, GREEN, RED, NIR1), Natural Color (pan-sharpened BLUE, GREEN, RED), Colored Infrared (pan-sharpened GREEN, RED, NIR1) The processing levels are:  • STANDARD (2A): normalized for topographic relief • VIEW READY STANDARD (OR2A): ready for orthorectification • VIEW READY STEREO: collected in-track for stereo viewing and manipulation • MAP-READY (ORTHO) 1:12.000 Orthorectified: additional processing unnecessary Spatial coverage: Check the spatial coverage of the collection on a _$$map$$ https://tpm-ds.eo.esa.int/smcat/GeoEye-1/ available on the Third Party Missions Dissemination Service.
Image 2007 European coverage

The Image 2007 collection is composed by products acquired by Disaster Monitoring Constellation 1st generation (DMC-1) satellites over European countries (plus Turkey) in 2007. The data provided in this collection are 32m multispectral images captured by the DMC SLIM-6 imager sensor, with two processing levels: • L1R Band registered product derived from the L0R product • L1T Orthorectified product derived from the L1R product using manually collected GCPs from Landsat ETM+ data and SRTM DEM V31 data Data disseminated come from the following satellites belonging to DMC-1 constellation: • UK-DMC-1 • Bejing-1 • NigeriaSat-1 Spatial coverage: Check the spatial coverage of the collection on a _$$map$$ https://tpm-ds.eo.esa.int/smcat/Image2007/ available on the Third Party Missions Dissemination Service.
ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 2 aerosol products from ATSR-2 (ADV algorithm), Version 2.31

The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics.   This dataset comprises  Level 2 aerosol products from the ATSR-2 instrument on the ERS-2 satellite, derived using the ADV algorithm, version 2.31.   Data are available for the period 1995-2002.For further details about these data products please see the linked documentation.
KOMPSAT-2 ESA archive

Kompsat-2 ESA archive collection is composed by bundle (Panchromatic and Multispectral separated images) products from the Multi-Spectral Camera (MSC) onboard KOMPSAT-2 acquired from 2007 to 2014: 1m resolution for PAN, 4m resolution for MS. Spectral Bands:  • Pan: 500 - 900 nm (locate, identify and measure surface features and objects primarily by their physical appearance) • MS1 (blue): 450 - 520 nm (mapping shallow water, differentiating soil from vegetation) • MS2 (green): 520 - 600 nm (differentiating vegetation by health) • MS3 (red): 630 - 690 nm (differentiating vegetation by species) • MS4 (near-infrared): 760 - 900 nm (mapping vegetation, mapping vegetation vigor/health, Differentiating vegetation by species) Spatial coverage: Check the spatial coverage of the collection on a _$$map$$ https://tpm-ds.eo.esa.int/smcat/Kompsat2/ available on the Third Party Missions Dissemination Service.
IRS-1C/1D European coverage

IRS-1C/1D dataset is composed of products generated by the Indian Remote Sensing (IRS) Satellites 1C/1D PAN sensor. The products, acquired from 1996 to 2004 over Europe, are radiometrically and ortho corrected level 1 black and white images at 5 metre resolution and cover an area of up to 70 x 70 km.  Sensor: PAN Type: Panchromatic Resolution (m): 5 Coverage (km x km): 70 x 70  System or radiometrically corrected Ortho corrected (DN) Acquisition in Neustrelitz: 1996 - 2004 5 70 x 70 X X  Spatial coverage: Check the spatial coverage of the collection on a _$$map$$ https://tpm-ds.eo.esa.int/smcat/IRS1/ available on the Third Party Missions Dissemination Service.
RapidEye ESA archive

The RapidEye ESA archive is a subset of the RapidEye Full archive that ESA collected over the years. The dataset regularly grows as ESA collects new RapidEye products. Spatial coverage: Check the spatial coverage of the collection on a _$$map$$ https://tpm-ds.eo.esa.int/socat/RapidEye available on the Third Party Missions Dissemination Service.
Envisat ASAR AP Precision L1 [ASA_APP_1P]

This ASAR Alternating Polarisation Mode Precision product is generated from Level 0 data collected when the instrument is in Alternating Polarisation Mode (7 possible swaths). The product contains two CO-registered images corresponding to one of the three polarisation combination submodes (HH and VV, HH and HV, VV and VH). This is a stand-alone multi-look, ground range, narrow swath digital image generated using the SPECAN algorithm and the most up to date auxiliary information available at the time of processing. Engineering corrections and relative calibration (antenna elevation gain, range spreading loss) are applied to compensate for well-understood sources of system variability. Generation of this product uses a technique to allow half the looks of an image to be acquired in horizontal polarisation and the other half in vertical polarisation and processed to 30-m resolution (with the exception of IS1). Absolute calibration parameters are available depending on external calibration activities and are provided in the product annotations. Spatial Resolution: 30 m ground range x 30 m azimuth
Normalised Difference Vegetation Index Statistics 1999-2019 (raster 1 km), global, 10-daily - version 3

The Normalised Difference Vegetation Index (NDVI) is a widely used, dimensionless index that is indicative for vegetation density and is defined as NDVI=(NIR-Red)/(NIR+Red) where NIR corresponds to the reflectance in the near infrared bands, and Red to the reflectance in the red bands. The time series of 10-daily NDVI 1km version 3 observations is used to calculate Long Term Statistics (LTS), over the 20-year period 1999-2019, and Short Term Statistics (STS), over the 5-year period (2015-2019), for each of the 36 10-daily periods (dekads) of the year. The calculated statistics include the minimum, median, maximum, mean, standard deviation and the number of observations in the covered time series period. These statistics can be used as a reference for actual NDVI observations, which allows monitoring of anomalous vegetation conditions.
Lake Water Quality 2016-2018 (raster 1 km), global, 10-daily - version 1

Monitoring water quality in lakes and reservoirs is key in maintaining safe water for drinking, bathing, fishing and agriculture and aquaculture activities. Long-term trends and short-term changes are indicators of environmental health and changes in the water catchment area. Directives such as the EU's Water Framework Directive or the US EPA Clean Water Act request information about the ecological status of all lakes larger than 50 ha. Satellite monitoring helps to systematically cover a large number of lakes and reservoirs, reducing needs for monitoring infrastructure (e.g. vessels) and efforts. The Lake Water Quality products provide a semi-continuous observation record for a large number of medium and large-sized lakes, according to the Global Lakes and Wetlands Database (GLWD) or otherwise of specific environmental monitoring interest. They consist of three water quality parameters: (i) the turbidity of a lake, that describes water clarity or whether sunlight can penetrate deeper parts of the lake. Turbidity often varies seasonally, both with the discharge of rivers and growth of phytoplankton; (ii) the trophic state index that is an indicator of the productivity of a lake in terms of phytoplankton, and indirectly, over longer time scales, reflects the eutrophication status of a water body and (iii)  the lake surface reflectances that describe the apparent colour of the water body and are intended for scientific users interested in further development of algorithms. The visual reflectance bands can also be combined into true-colour images.
TropForest Associated DOI

The objective of the ESA TropForest project was to create a harmonised geo-database of ready-to-use satellite imagery to support 2010 global forest assessment performed by the Joint Research Centre (JRC) of the European Commission and by the Food and Agriculture Organization (FAO). Assessments for year 2010 were essential for building realistic deforestation benchmark rates at global to regional levels. To reach this objective, the project aimed to create a harmonised ortho-rectified/pre-processed imagery geo-database based on satellite data acquisitions (ALOS AVNIR-2, GEOSAT-1 SLIM6, KOMPSAT-2 MSC) performed during year 2009 and 2010, for the Tropical Latin America (excluding Mexico) and for the Tropical South and Southeast Asia (excluding China), resulting in 1971 sites located at 1 deg x 1 deg geographical lat/long intersections. The project finally delivered 1866 sites (94.7% of target) due to cloud coverages too high for missing sites
METOP GOME-2 - Cloud Optical Thickness (COT) - Global

The Global Ozone Monitoring Experiment-2 (GOME-2) instrument continues the long-term monitoring of atmospheric trace gas constituents started with GOME / ERS-2 and SCIAMACHY / Envisat. Currently, there are three GOME-2 instruments operating on board EUMETSAT's Meteorological Operational satellites MetOp-A, -B and -C, launched in October 2006, September 2012, and November 2018, respectively. GOME-2 can measure a range of atmospheric trace constituents, with the emphasis on global ozone distributions. Furthermore, cloud properties and intensities of ultraviolet radiation are retrieved. These data are crucial for monitoring the atmospheric composition and the detection of pollutants. DLR generates operational GOME-2 / MetOp level 2 products in the framework of EUMETSAT's Satellite Application Facility on Atmospheric Chemistry Monitoring (AC-SAF). GOME-2 near-real-time products are available already two hours after sensing.		OCRA (Optical Cloud Recognition Algorithm) and ROCINN (Retrieval of Cloud Information using Neural Networks) are used for retrieving the following geophysical cloud properties from GOME and GOME-2 data: cloud fraction (cloud cover), cloud-top pressure (cloud-top height), and cloud optical thickness (cloud-top albedo). OCRA is an optical sensor cloud detection algorithm that uses the PMD devices on GOME / GOME-2 to deliver cloud fractions for GOME / GOME-2 scenes. ROCINN takes the OCRA cloud fraction as input and uses a neural network training scheme to invert GOME / GOME-2 reflectivities in and around the O2-A band. VLIDORT [Spurr (2006)] templates of reflectances based on full polarization scattering of light are used to train the neural network. ROCINN retrieves cloud-top pressure and cloud-top albedo. The cloud optical thickness is computed using libRadtran [Mayer and Kylling (2005)] radiative transfer simulations taking as input the cloud-top albedo retrieved with ROCINN. For more details please refer to relevant peer-review papers listed on the GOME and GOME-2 documentation pages: https://atmos.eoc.dlr.de/app/docs/
ESA Fire Climate Change Initiative (Fire_cci): Small Fire Dataset (SFD) Burned Area pixel product for Sub-Saharan Africa, version 2.0

The ESA Fire Disturbance Climate Change Initiative (Fire_cci) project has produced maps of global burned area developed from satellite observations. The Small Fire Dataset (SFD) pixel products have been obtained by combining spectral information from Sentinel-2 MSI data and thermal information from VIIRS VNP14IMGML active fire products.This dataset is part of v2.0 of the Small Fire Dataset (also known as FireCCISFD11), which covers Sub-Saharan Africa for the year 2019.   Data is available here at pixel resolution (0.00017966259 degrees, corresponding to approximately 20m at the Equator).   Gridded data products are also available in a separate dataset.
METOP GOME-2 - Nitrogen Dioxide (NO2) - Global

The Global Ozone Monitoring Experiment-2 (GOME-2) instrument continues the long-term monitoring of atmospheric trace gas constituents started with GOME / ERS-2 and SCIAMACHY / Envisat. Currently, there are three GOME-2 instruments operating on board EUMETSAT's Meteorological Operational satellites MetOp-A, -B, and -C, launched in October 2006, September 2012, and November 2018, respectively. GOME-2 can measure a range of atmospheric trace constituents, with the emphasis on global ozone distributions. Furthermore, cloud properties and intensities of ultraviolet radiation are retrieved. These data are crucial for monitoring the atmospheric composition and the detection of pollutants. DLR generates operational GOME-2 / MetOp level 2 products in the framework of EUMETSAT's Satellite Application Facility on Atmospheric Chemistry Monitoring (AC-SAF). GOME-2 near-real-time products are available already two hours after sensing.			The operational NO2 total column products are generated using the algorithm GDP (GOME Data Processor) version 4.x integrated into the UPAS (Universal Processor for UV / VIS Atmospheric Spectrometers) processor for generating level 2 trace gas and cloud products.			The total NO2 column is retrieved from GOME solar back-scattered measurements in the visible wavelength region (425-450 nm), using the Differential Optical Absorption Spectroscopy (DOAS) method.			For more details please refer to relevant peer-review papers listed on the GOME and GOME-2 documentation pages: https://atmos.eoc.dlr.de/app/docs/
METOP GOME-2 - Formaldehyde (HCHO) - Global

The Global Ozone Monitoring Experiment-2 (GOME-2) instrument continues the long-term monitoring of atmospheric trace gas constituents started with GOME / ERS-2 and SCIAMACHY / Envisat. Currently, there are three GOME-2 instruments operating on board EUMETSAT's Meteorological Operational satellites MetOp-A, -B, and -C, launched in October 2006, September 2012, and November 2018, respectively. GOME-2 can measure a range of atmospheric trace constituents, with the emphasis on global ozone distributions. Furthermore, cloud properties and intensities of ultraviolet radiation are retrieved. These data are crucial for monitoring the atmospheric composition and the detection of pollutants. DLR generates operational GOME-2 / MetOp level 2 products in the framework of EUMETSAT's Satellite Application Facility on Atmospheric Chemistry Monitoring (AC-SAF). GOME-2 near-real-time products are available already two hours after sensing.			The operational HCHO total column products are generated using the algorithm GDP (GOME Data Processor) version 4.x integrated into the UPAS (Universal Processor for UV / VIS Atmospheric Spectrometers) processor for generating level 2 trace gas and cloud products.			For more details please refer to relevant peer-review papers listed on the GOME and GOME-2 documentation pages: https://atmos.eoc.dlr.de/app/docs/
MERIS - Vegetation Index (NDVI) - Europe, Daily

The "AVHRR compatible Normalized Difference Vegetation Index derived from MERIS data (MERIS_AVHRR_NDVI)" was developed in a co-operative effort of DLR (German Remote Sensing Data Centre, DFD) and Brockmann Consult GmbH (BC) in the frame of the MAPP project (MERIS Application and Regional Products Projects). For the generation of regional specific value added MERIS level-3 products, MERIS full-resolution (FR) data are processed on a regular (daily) basis using ESA standard level-1b and level-2 data as input. The regular reception of MERIS-FR data is realized at DFD ground station in Neustrelitz.The Medium Resolution Imaging MERIS on Board ESA's ENVISAT provides spectral high resolution image data in the visible-near infrared spectral region (412-900 nm) at a spatial resolution of 300 m. For more details on ENVISAT and MERIS see http://envisat.esa.int The Advanced Very High Resolution Radiometer (AVHRR) compatible vegetation index (MERIS_AVHRR_NDVI) derived from data of the MEdium Resolution Imaging Spectrometer (MERIS) is regarded as a continuity index with 300 meter resolution for the well-known Normalized Difference Vegetation Index (NDVI) derived from AVHRR (given in 1km spatial resolution). The NDVI is an important factor describing the biological status of canopies. This product is thus used by scientists for deriving plant and canopy parameters. Consultants use time series of the NDVI for advising farmers with best practice.For more details the reader is referred tohttp://wdc.dlr.de/sensors/meris/ and http://wdc.dlr.de/sensors/meris/documents/Mapp_ATBD_final_i3r0dez2001.pdfThis product provides daily maps.
MERIS - Water Parameters - Baltic Sea, Seasonal

The Medium Resolution Imaging Spectrometer (MERIS) on Board ESA’s ENVISAT provides spectral high resolution image data in the visible-near infrared spectral region (412-900 nm) at a spatial resolution of 300 m. For more details on ENVISAT and MERIS see http://envisat.esa.int/Spectral high resolution measurements allow to assess different water constituents in optically complex case-2 waters (IOCCG, 2000). The main groups of constituents are Chlorophyll, corresponding to living phytoplankton, suspended minerals or sediments and dissolved organic matter. They are characterised by their specific inherent optical properties, in particular scattering and absorption spectra.The Baltic Sea Water Constituents product was developed in a co-operative effort of DLR (Remote Sensing Technology Institute IMF, German Remote Sensing Data Centre DFD), Brockmann Consult (BC) and Baltic Sea Research Institute (IOW) in the frame of the MAPP project (MERIS Application and Regional Products Projects). The data are processed on a regular (daily) basis using ESA standard Level-1 and -2 data as input and producing regional specific value added Level-3 products. The regular data reception is realised at DFD ground station in Neustrelitz. For more details the reader is referred tohttp://wdc.dlr.de/sensors/meris/ and http://wdc.dlr.de/sensors/meris/documents/Mapp_ATBD_final_i3r0dez2001.pdfThis product provides seasonal maps.