The Sentinel-2 mission is a land monitoring constellation of two satellites that provide high resolution optical imagery and provide continuity for the current SPOT and Landsat missions. The mission provides a global coverage of the Earth's land surface every 5 days, making the data of great use in on-going studies. L1C data are available from June 2015 globally. L1C data provide Top of the atmosphere (TOA) reflectance.

The Sentinel - 1 radar imaging mission is composed of a constellation of two polar-orbiting satellites providing continous all-weather, day and night imagery for Land and Maritime Monitoring. C-band synthentic aperture radar imaging has the advantage of operating at wavelenghts that are not obstructed by clouds or lack of illumination and therefore can acquire data during day or night under all weather conditions. With 6 days repeat cycle on the entire world and daily acquistions of sea ice zones and Europe's major shipping routes, Sentinel-1 ensures reliable data availability to support emergency services and applications requiring time series observations. Sentinel-1 continues the retired ERS and ENVISAT missions. Level 1 GRD products are available since October 2014.

Landsat8 products stored in the catalog provided by SINERGISE SENTINEL Hub. The Landsat program is the longest running enterprise for acquisition of satellite imagery of Earth, running from 1972.The most recent, Landsat 8, was launched on February 11, 2013. The images are a unique resource for global change research and applications in agriculture, cartography, geology, forestry, regional planning, surveillance and education. Landsat 8 data has eight spectral bands with spatial resolutions ranging from 15 to 60 meters; the temporal resolution is 16 days.

Landsat 8-9 Level 2 collection includes both Landsat 8 and the most recently launched Landsat 9 satellites (provided by NASA/USGS), both carrying the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS) instruments, with 9 optical and 2 thermal bands. These two sensors provide seasonal coverage of the global landmass. Landsat 8-9 Level 2 data from the most recently released collection 2, provides atmospherically corrected Surface Reflectance and Surface Brightness Temperature products generated from Collection 2 Level-1 scenes that have been processed to Tier 1 or Tier 2. Collection 2 level 2 data are available since February 2013 for Landsat 8 and since January 2022 for Landsat 9 and new data are continously added usually within 24 hours after Level 1 scenes are available.

Harmonized Landsat Sentinel is a NASA initiative to produce a Virtual Constellation of surface reflectance (SR) data from the Operational Land Imager (OLI) and Multi-Spectral Instrument (MSI) aboard the Landsat 8-9 and Sentinel-2 remote sensing satellites, respectively. The combined measurement enables global observations of the land every 2–3 days. Input products are Landsat 8-9 Collection 2 Level 1 top-of-atmosphere reflectance and Sentinel-2 L1C top-of-atmosphere reflectance, which NASA radiometrically harmonizes to the maximum extent, resamples to common 30-meter resolution, and grids using the Sentinel-2 Military Grid Reference System (MGRS) UTM grid. Because of this, the products are different from Landsat 8-9 Collection 2 Level 2 surface reflectance and Sentinel-2 L2A surface reflectance.

Sentinel-2 satelliittikuvamosaiikit perustuvat Sentinel-2A/2B satelliittien MultiSpectral Instrument (MSI) kuvausinstrumenttien ottamiin kuviin. Mosaiikit tuotetaan Sentinel-2 Global Mosaic palvelulla (https://s2gm.sentinel-hub.com/) joka on osa Euroopan Unionin Copernicus ohjelmaa. Mosaiikin kuvapikselin arvo on maanpinnan heijastussuhteen estimaatti, kerrottuna kertoimella 10000. Kaikkien kanavien pikselikoko on 10 metriä, eli 20 metrin kanavat on uudelleennäytteistetty 10 metrin pikselikokoon. Tarkemmat tiedot MSI-instrumentin kanavista löytyvät osoitteesta https://sentinel.esa.int/web/sentinel/user-guides/sentinel-2-msi/resolutions/radiometric. Metatietomosaiikkia, eli mosaiikki joka kertoisi pikseleiden kuvaushetket, ei tuoteta tällä hetkellä. RGB-mosaiikit ovat vääräväriyhdistelmiä, kuvan kanavat ovat Kanava 8, lähi-infrapuna (832.8 nm) Kanava 4, punainen (664.6 nm) Kanava 3, vihreä (559.8 nm) (suluissa kanavan keskikohdan aallonpituus) Nämä mosaiikit on skaalattu 8-bittisiksi lukualueelle 0-255. Jaettavat vuosimosaiikit: pta_sjp_s2gm_b080403_20170701_20170930_8bit.tif pta_sjp_s2gm_b080403_20180701_20180930_8bit.tif pta_sjp_s2gm_b080403_20190701_20190930_8bit.tif pta_sjp_s2gm_b080403_20200701_20200930_8bit.tif pta_sjp_s2gm_b080403_20210701_20210930_8bit.tif Lähtöaineiston käyttöoikeudet: https://s2gm.sentinel-hub.com/privacy Lisätietoja kanavittaisista mosaiikeista: https://ckan.ymparisto.fi/dataset/sentinel-2-satellite-image-mosaics-s2gm-sentinel-2-satelliittikuvamosaiikki-s2gm -- Sentinel-2 satellite image (S2GM) 2018-2021 false color mosaics Sentinel-2 satellite image mosaics are produced from data provided by MultiSpectral Instruments (MSI) onboard Sentinel-2A and -2B satellites. These mosaics have been produced using Sentinel-2 Global Mosaic-service (https://s2gm.sentinel-hub.com/) of Copernicus program of European Union. The value of pixel is the estimate of ground reflectance, multiplied by coefficient 10000. Mosaics contain following bands with central wavelength of bands; The pixel size of all bands is 10 meters, in other words the 20 meter bands have been resampled to 10 meter pixel. The more detailed information about the bands can be found from https://sentinel.esa.int/web/sentinel/user-guides/sentinel-2-msi/resolutions/radiometric. Currently, there are no metadata image describing the acquisition date of selected pixel. RGB-mosaics are in false color combination, in other words band combination is 1: Band 8, Near infra-red (832.8 nm) 2: Band 4, Red (664.6 nm) 3: Band 3, Green (559.8 nm) (central wavelength of bands) These mosaics are scaled to 8-bit numbers (0-255). Available yearly mosaics: pta_sjp_s2gm_b080403_20170701_20170930_8bit.tif pta_sjp_s2gm_b080403_20180701_20180930_8bit.tif pta_sjp_s2gm_b080403_20190701_20190930_8bit.tif pta_sjp_s2gm_b080403_20200701_20200930_8bit.tif pta_sjp_s2gm_b080403_20210701_20210930_8bit.tif Terms of use (original data): https://s2gm.sentinel-hub.com/privacy More information https://ckan.ymparisto.fi/dataset/sentinel-2-satellite-image-mosaics-s2gm-sentinel-2-satelliittikuvamosaiikki-s2gm

Sentinel-2 satellite image mosaics are produced from data provided by MultiSpectral Instruments (MSI) onboard Sentinel-2A and -2B satellites. These mosaics have been produced using previous version of Sentinel-2 Global Mosaic-service (https://s2gm.sentinel-hub.com/) of Copernicus program of European Union. The value of pixel is the estimate of ground reflectance, multiplied by coefficient 10000. Mosaics contain following bands with central wavelength of bands:

• B02: Blue (492.4 nm)
• B03: Green (559.8 nm)
• B04: Red (664.6 nm)
• B05: Red-edge (704.1 nm)
• B06: Red-edge (740.5 nm)
• B07: Red-edge (782.8 nm)
• B08: Near infra-red (832.8 nm)
• B11: Middle Infrared (1613.7 nm)
• B12: Middle infrared (2202.4 nm)

The pixel size of all bands is 10 meters, in other words the 20 meter bands (B05, B06, B07, B11, B12) have been resampled to 10 meter pixel. The more detailed information about the bands can be found from https://sentinel.esa.int/web/sentinel/user-guides/sentinel-2-msi/resolutions/radiometric. Currently, there are no metadata image describing the acquisition date of selected pixel.

Processing

Steps of mosaicking process (https://usermanual.readthedocs.io/en/latest/) at Sentinel-2 Global Mosaic service are:

• Selection of Sentinel-2 images of chosen area of interest and time period.
• Atmospheric correction of images using Sen2cor-algorithm.
• Selection of the least different pixel of time period from a set of good pixels.
• Production of output mosaic with chosen bands, pixel size and coordinate system.

Tilewise mosaics are downloaded to local computer, where post-processing phase consists of merging of tiles to form bandwise mosaics, filling of gaps using neighboring values and coordinate transform to Finnish coordinate system TM35Fin (EPSG 3067).

Download

Mosaics are available as bandwise and RGB-mosaics. RGB-mosaics are in false color combination, in other words band combination is

• 1: Band 8, Near infra-red
• 2: Band 4, Red
• 3: Band 3, Green

These mosaics are scaled to 8-bit numbers (0-255). Bandwise mosaics contain individual bands in 16-bit numbers and resampled to 10 meter pixel.

Following RGB quarter mosaics are available: * https://pta.data.lit.fmi.fi/sen2/s2m_s2gm_mos/pta_sjp_s2gm_b080403_20170701_20170930_8bit.tif * https://pta.data.lit.fmi.fi/sen2/s2m_s2gm_mos/pta_sjp_s2gm_b080403_20180701_20180930_8bit.tif * https://pta.data.lit.fmi.fi/sen2/s2m_s2gm_mos/pta_sjp_s2gm_b080403_20190401_20190630_8bit.tif * https://pta.data.lit.fmi.fi/sen2/s2m_s2gm_mos/pta_sjp_s2gm_b080403_20190701_20190930_8bit.tif * https://pta.data.lit.fmi.fi/sen2/s2m_s2gm_mos/pta_sjp_s2gm_b080403_20200401_20200630_8bit.tif * https://pta.data.lit.fmi.fi/sen2/s2m_s2gm_mos/pta_sjp_s2gm_b080403_20200701_20200930_8bit.tif * https://pta.data.lit.fmi.fi/sen2/s2m_s2gm_mos/pta_sjp_s2gm_b080403_20210401_20210630_8bit.tif * https://pta.data.lit.fmi.fi/sen2/s2m_s2gm_mos/pta_sjp_s2gm_b080403_20210701_20210930_8bit.tif

Following RGB monthly mosaics are available: * https://pta.data.lit.fmi.fi/sen2/s2m_s2gm_mos/pta_sjp_s2gm_b080403_20200401_20200430_8bit.tif * https://pta.data.lit.fmi.fi/sen2/s2m_s2gm_mos/pta_sjp_s2gm_b080403_20200501_20200531_8bit.tif * https://pta.data.lit.fmi.fi/sen2/s2m_s2gm_mos/pta_sjp_s2gm_b080403_20200601_20200630_8bit.tif * https://pta.data.lit.fmi.fi/sen2/s2m_s2gm_mos/pta_sjp_s2gm_b080403_20200701_20200731_8bit.tif * https://pta.data.lit.fmi.fi/sen2/s2m_s2gm_mos/pta_sjp_s2gm_b080403_20200801_20200831_8bit.tif * https://pta.data.lit.fmi.fi/sen2/s2m_s2gm_mos/pta_sjp_s2gm_b080403_20200901_20200930_8bit.tif

Bandwise mosaics are available as: * https://pta.data.lit.fmi.fi/sen2/s2m_s2gm_mos/pta_sjp_s2gm_band_time.tif

where

• band: b02, b03, b04, b05, b06, b07, b08, b11 or b12
• time: 20170701_20170930, 20180701_20180930, 20190401_20190630, 20190701_20190930, 20200401_20200630, 20200701_20200930, 20210401_20210630, 20210701_20210731 or 20210701_20210930

This Syke’s dataset can be used according to open data license (CC BY 4.0)

Sentinel-2 satelliittikuvamosaiikki 2017-2021 (S2GM)

Sentinel-2 satelliittikuvamosaiikit perustuvat Sentinel-2A/2B satelliittien MultiSpectral Instrument (MSI) kuvausinstrumenttien ottamiin kuviin. Mosaiikit on tuotettu Sentinel-2 Global Mosaic palvelun ensimmäisellä versiolla (https://s2gm.sentinel-hub.com/) joka on osa Euroopan Unionin Copernicus ohjelmaa. Mosaiikin kuvapikselin arvo on maanpinnan heijastussuhteen estimaatti, kerrottuna kertoimella 10000. Mosaiikeissa on seuraavat kanavat (suluissa kanavan keskikohdan aallonpituus):

• B02: Blue (492.4 nm)
• B03: Green (559.8 nm)
• B04: Red (664.6 nm)
• B05: Red-edge (704.1 nm)
• B06: Red-edge (740.5 nm)
• B07: Red-edge (782.8 nm)
• B08: Near infra-red (832.8 nm)
• B11: Middle Infrared (1613.7 nm)
• B12: Middle infrared (2202.4 nm)

Kaikkien kanavien pikselikoko on 10 metriä, eli 20 metrin kanavat (B05, B06, B07, B11, B12) on uudelleennäytteistetty 10 metrin pikselikokoon. Tarkemmat tiedot MSI-instrumentin kanavista löytyvät osoitteesta https://sentinel.esa.int/web/sentinel/user-guides/sentinel-2-msi/resolutions/radiometric. Metatietomosaiikkia, eli mosaiikki joka kertoisi pikseleiden kuvaushetket, ei ole tuotettu.

Prosessointi

Sentinel-2 Global Mosaic palvelun osat (https://usermanual.readthedocs.io/en/latest/) ovat:

• Valitun alueen ja ajanjakson Sentinel-2 kuvien valinta.
• Kuvien ilmakehäkorjaus Sen2cor-algoritmilla.
• Mosaiikin pikselin arvoksi valitaan ajanjakson kuvien pikseleitä se joka poikkeaa vähiten muista.
• Tiilittäisen mosaiikin tuottaminen valituilla kanavilla, pikselikoolla ja koordinaattijärjestelmällä.

Tiilittäiset mosaiikit ladataan paikalliselle tietokoneelle jolla tehdään jälkiprosessointivaihe, eli tiilet yhdistetään kanavittaisiksi mosaiikeiksi, aukot täytetään naapuruston avulla ja tehdään koordinaattimuunnos suomalaiseen koordinaatistoon (TM35Fin, EPSG 3067).

Tarkempia tietoja Sentinel-2 Global Mosaic-palvelusta löytyy osoitteesta https://s2gm.sentinel-hub.com/documentation.

Saatavuus

Mosaiikit ovat saatavilla sekä kanavittaisina että RGB-mosaiikkeina. RGB-mosaiikit ovat vääräväriyhdistelmiä, eli kuvan kanavat ovat

• Kanava 8, lähi-infrapuna
• Kanava 4, punainen
• Kanava 3, vihreä

Nämä mosaiikit on skaalattu 8-bittisiksi lukualueelle 0-255. Kanavittaiset mosaiikit käsittävät yksittäiset kanavat 16-bittisillä numeroilla 10 metrin pikselikoolla.

Ladattavissa olevat neljännesvuosittaiset RGB-mosaiikit: * https://pta.data.lit.fmi.fi/sen2/s2m_s2gm_mos/pta_sjp_s2gm_b080403_20170701_20170930_8bit.tif * https://pta.data.lit.fmi.fi/sen2/s2m_s2gm_mos/pta_sjp_s2gm_b080403_20180701_20180930_8bit.tif * https://pta.data.lit.fmi.fi/sen2/s2m_s2gm_mos/pta_sjp_s2gm_b080403_20190401_20190630_8bit.tif * https://pta.data.lit.fmi.fi/sen2/s2m_s2gm_mos/pta_sjp_s2gm_b080403_20190701_20190930_8bit.tif * https://pta.data.lit.fmi.fi/sen2/s2m_s2gm_mos/pta_sjp_s2gm_b080403_20200401_20200630_8bit.tif * https://pta.data.lit.fmi.fi/sen2/s2m_s2gm_mos/pta_sjp_s2gm_b080403_20200701_20200930_8bit.tif * https://pta.data.lit.fmi.fi/sen2/s2m_s2gm_mos/pta_sjp_s2gm_b080403_20210401_20210630_8bit.tif * https://pta.data.lit.fmi.fi/sen2/s2m_s2gm_mos/pta_sjp_s2gm_b080403_20210701_20210930_8bit.tif

Ladattavissa olevat kuukautis-RGB-mosaiikit: * https://pta.data.lit.fmi.fi/sen2/s2m_s2gm_mos/pta_sjp_s2gm_b080403_20200401_20200430_8bit.tif * https://pta.data.lit.fmi.fi/sen2/s2m_s2gm_mos/pta_sjp_s2gm_b080403_20200501_20200531_8bit.tif * https://pta.data.lit.fmi.fi/sen2/s2m_s2gm_mos/pta_sjp_s2gm_b080403_20200601_20200630_8bit.tif * https://pta.data.lit.fmi.fi/sen2/s2m_s2gm_mos/pta_sjp_s2gm_b080403_20200701_20200731_8bit.tif * https://pta.data.lit.fmi.fi/sen2/s2m_s2gm_mos/pta_sjp_s2gm_b080403_20200801_20200831_8bit.tif * https://pta.data.lit.fmi.fi/sen2/s2m_s2gm_mos/pta_sjp_s2gm_b080403_20200901_20200930_8bit.tif

Kanavittaiset mosaiikit: * https://pta.data.lit.fmi.fi/sen2/s2m_s2gm_mos/pta_sjp_s2gm_band_time.tif

jossa * band: b02, b03, b04, b05, b06, b07, b08, b11 tai b12 * time: 20170701_20170930, 20180701_20180930, 20190401_20190630, 20190701_20190930, 20200401_20200630, 20200701_20200930, 20210401_20210630, 20210701_20210731 tai 20210701_20210930

Aineisto kuuluu SYKEn avoimiin aineistoihin (CC BY 4.0).

Sentinel-2 satellite image mosaics are produced from data provided by MultiSpectral Instruments (MSI) onboard Sentinel-2A and -2B satellites. These mosaics have been produced using Sentinel-2 Global Mosaic-service (https://s2gm.sentinel-hub.com/) of Copernicus program of European Union. The value of pixel is the estimate of ground reflectance, multiplied by coefficient 10000.

The data comes from the Copernicus Sentinel-5P satellite and uses data from the Copernicus Sentinel-5P satellite and shows the averaged methane concentrations across the globe — using weekly averaged maps. The methane map shown here is measured by the Tropomi instrument on the Sentinel 5 Precursor satellite. The Copernicus Sentinel-5P CH4 measurements were first filtered according to the recommendation in the Product Readme file (only data with a qa_value > 0.50 was used). Then the measurements are mapped on a fixed latitude-longitude grid of 4096 x 8192 pixels. The grid is turned into an EPSG:4326 geotiff file using the appropriate color scale, which is again turned into an EPSG:3857 tile map. Data gaps are visible based on the product quality filtering and the fact that over the sea only measurements for sun-glint situations are being provided.

This dataset is true colour cloud-free composite satellite imagery optimised for mapping shallow marine habitats in northern Australia, based on 10-meter resolution Sentinel 2 data collected from 2015 to 2024. It contains composite imagery for 333 Sentinel 2 tiles of northern Australia and the Great Barrier Reef. This dataset offers improved visual clarity of shallow water features as compared to existing satellite imagery, allowing deeper marine features to be observed. These composites were specifically designed to address challenges such as sun glint, clouds and turbidity that typically hinder marine environment analyses. No tides were considered in the selection of the imagery and so this imagery corresponds to an 'All tide' image, approximating mean sea level.

This dataset is an updated version (Version 2), published in July 2024, which succeeds the initial draft version (Version 1, published in March 2024). The current version spans imagery from 2015–2024, an extension of the earlier timeframe that covered 2018–2022. This longer temporal range allowed the imagery to be cleaner with lower image noise allowing deeper marine features to be visible. The deprecated draft version was removed from online download to save on storage space and is now only available on request.

While the final imagery corresponds to true colour based primarily Sentinel 2 bands B2 (blue), B3 (green), and B4 (red), the near infrared (B8) band was used as part of sun glint correction and automated selection of low noise imagery.

Contrast enhancement was applied to the imagery to compress the original 12 bit per channel Sentinel 2 imagery into the final 8-bit per channel GeoTiffs. Black and white point correction was used to enhance the contrast as much as possible without too much clipping of the darkest and lightest marine features. Gamma correction of 2 (red), 2 (green) and 2.3 (blue) was applied allow a wider dynamic range to be represented in the 8-bit data, helping to ensure that little precision was lost in representing darker marine features. As a result, the image brightness is not linearly scaled. Further details of the corrections applied is available from https://github.com/eatlas/AU_NESP-MaC-3-17_AIMS_S2-comp/blob/main/src/processors/s2processor.py.


Methods:

The satellite image composites were created by combining multiple Sentinel 2 images using the Google Earth Engine. The core algorithm was:
1. For each Sentinel 2 tile filter the "COPERNICUS/S2_HARMONIZED" image collection by
- tile ID
- maximum cloud cover 20%
- date between '2015-06-27' and '2024-05-31'
- asset_size > 100000000 (remove small fragments of tiles)
Note: A maximum cloud cover of 20% was used to improve the processing times. In most cases this filtering does not have an effect on the final composite as images with higher cloud coverage mostly result in higher noise levels and are not used in the final composite.
2. Split images by "SENSING_ORBIT_NUMBER" (see "Using SENSING_ORBIT_NUMBER for a more balanced composite" for more information).
3. For each SENSING_ORBIT_NUMBER collection filter out all noise-adding images:
3.1 Calculate image noise level for each image in the collection (see "Image noise level calculation for more information") and sort collection by noise level.
3.2 Remove all images with a very high noise index (>15).
3.3 Calculate a baseline noise level using a minimum number of images (min_images_in_collection=30). This minimum number of images is needed to ensure a smoth composite where cloud "holes" in one image are covered by other images.
3.4 Iterate over remaining images (images not used in base noise level calculation) and check if adding image to the composite adds to or reduces the noise. If it reduces the noise add it to the composite. If it increases the noise stop iterating over images.
4. Combine SENSING_ORBIT_NUMBER collections into one image collection.
5. Remove sun-glint (true colour only) and apply atmospheric correction on each image (see "Sun-glint removal and atmospheric correction" for more information).
6. Duplicate image collection to first create a composite image without cloud masking and using the 30th percentile of the images in the collection (i.e. for each pixel the 30th percentile value of all images is used).
7. Apply cloud masking to all images in the original image collection (see "Cloud Masking" for more information) and create a composite by using the 30th percentile of the images in the collection (i.e. for each pixel the 30th percentile value of all images is used).
8. Combine the two composite images (no cloud mask composite and cloud mask composite). This solves the problem of some coral cays and islands being misinterpreted as clouds and therefore creating holes in the composite image. These holes are "plugged" with the underlying composite without cloud masking. (Lawrey et al. 2022)
9. The final composite was exported as cloud optimized 8 bit GeoTIFF

Note: The following tiles were generated with no "maximum cloud cover" as they did not have enough images to create a composite with the standard settings: 46LGM, 46LGN, 46LHM, 50KKD, 50KPG, 53LMH, 53LMJ, 53LNH, 53LPH, 53LPJ, 54LVP, 57JVH, 59JKJ.

Compositing Process:

The dataset was created using a multi-step compositing process. A percentile-based image compositing technique was employed, with the 15th percentile chosen as the optimal value for most regions. This percentile was identified as the most effective in minimizing noise and enhancing key features such as coral reefs, islands, and other shallow water habitats. The 15th percentile was chosen as a trade off between the desire to select darker pixels that typically correspond to clearer water, and very dark values (often occurring at the 10th percentile) corresponding to cloud shadows.

The cloud masking predictor would often misinterpret very pale areas, such as cays and beaches as clouds. To overcome this limitation a dual-image compositing method was used. A primary composite was generated with cloud masks applied, and a secondary, composite with no cloud masking was layered beneath to fill in potential gaps (or “holes”) caused by the cloud masking mistakes

Image noise level calculation:

The noise level for each image in this dataset is calculated to ensure high-quality composites by minimizing the inclusion of noisy images. This process begins by creating a water mask using the Normalized Difference Water Index (NDWI) derived from the NIR and Green bands. High reflectance areas in the NIR and SWIR bands, indicative of sun-glint, are identified and masked by the water mask to focus on water areas affected by sun-glint. The proportion of high sun-glint pixels within these water areas is calculated and amplified to compute a noise index. If no water pixels are detected, a high noise index value is assigned.

In any set of satellite images, some will be taken under favourable conditions (low wind, low sun-glint, and minimal cloud cover), while others will be affected by high sun-glint or cloud. Combining multiple images into a composite reduces noise by averaging out these fluctuations.

When all images have the same noise level, increasing the number of images in the composite reduces the overall noise. However, in practice, there is a mix of high and low noise images. The optimal composite is created by including as many low-noise images as possible while excluding high-noise ones. The challenge lies in the determining the acceptable noise threshold for a given scene as some areas are more cloudy and sun glint affected than others.

To address this, we rank the available Sentinel 2 images for each scene by their noise index, from lowest to highest. The goal is to determine the ideal number of images (N) to include in the composite to minimize overall noise. For each N, we use the lowest noise images and estimate the final composite noise based on the noise index. This is repeated for all values of N up to a maximum of 200 images, and we select the N that results in the lowest noise.

This approach has some limitations. It estimates noise based on sun glint and residual clouds (after cloud masking) using NIR bands, without accounting for image turbidity. The final composite noise is not directly measured as this would be computationally expensive. It is instead estimated by dividing the average noise of the selected images by the square root of the number of images. We found this method tends to underestimate the ideal image count, so we adjusted the noise estimates, scaling them by the inverse of their ranking, to favor larger sets of images. The algorithm is not fully optimized, and further refinement is needed to improve accuracy.

Full details of the algorithm can be found in https://github.com/eatlas/AU_NESP-MaC-3-17_AIMS_S2-comp/blob/main/src/utilities/noise_predictor.py

Sun glint removal and atmospheric correction:

Sun glint was removed from the images using the infrared B8 band to estimate the reflection off the water from the sun glint. B8 penetrates water less than 0.5 m and so in water areas it only detects reflections off the surface of the water. The sun glint detected by B8 correlates very highly with the sun glint experienced by the visible channels (B2, B3 and B4) and so the sun glint in these channels can be removed by subtracting B8 from these channels.

Eric Lawrey developed this algorithm by fine tuning the value of the scaling between the B8 channel and each individual visible channel (B2, B3 and B4) so that the maximum level of sun glint would be removed. This work was based on a representative set of images, trying to determine a set of values that represent a good compromise across different water surface

Maxar Technologies offers multi-spectral, high resolution, commercial satellite imagery acquired by currently four on-orbit and three retired constellation of satellites namely WorldView, GeoEye, QuickBird and IKONOS. Sentinel Hub offers a possibility to purchase, order and access Worldview and Geoeye satellite imagery ordered through European Space Imaging. The data available for purchase through Sentinel Hub are GeoEye-1 (GE01), WorldView-2 (WV02), WorldView-3 (WV03) and WorldView-4 (WV04). WorldView-4 data available for purchase is the imagery archive acquired from November 2016 to January 2019 while the satellite was operational. The rest of the constellation offers both, imagery archive available since 2009 and a tasking capability to acquire images systematically over an area of interest. The archived images are available sporadically over an area of interest since images are in general not acquired systematically. Sentinel Hub provides images in Top of the atmosphere (TOA) reflectance.

Please cite this when using the dataset and code.

The offshore infrastructure is rapidly spreading in the Scottish waters to satisfy the increasing energy demand. The up-to-date knowledge of their distribution and size is critical for the development and management of marine ecosystems. With the development of remote sensing techniques, satellite data have been widely used in offshore infrastructure detection on the vast ocean. However, the automatic and accurate identification on remote sensing data is still challenging that every kind of data have limitations. Here we combine the Sentinel-1 SAR data and Sentinel-2 Multi-Spectral Instrument (MSI) imagery to propose an automatic method for the location detection and size evaluation of offshore infrastructure in Scottish waters. Specifically, three strategies (transformed median composite, 2D-SSA filtering and threshold segmentation) were designed to first extract the contour range on Sentinel-1 data. Then morphological operations were applied on Sentinel-2 true color image to obtain the precise location and size of each offshore infrastructure.

All the Sentinel-1 and Sentinel-2 data are downloaded from https://www.sentinel-hub.com/explore/eobrowser/

The file "loc_S1" is used for the contour range detection in Sentinel-1;

The file "size_of_rig" is for the specific location detection and size evaluation of each rig and semi-permanent;

The file "diameter_wind_turbine" is for the diameter length estimate for wind turbines.

The Copernicus Sentinel-2 mission provides optical imagery for a wide range of applications including land, water and atmospheric monitoring. Beginning in 2015, the mission is based on a constellation of identical satellites working in tandem to cover Earth’s land and coastal waters every five days. Each satellite carries a multispectral sensor that generates optical images in the visible, near-infrared and shortwave-infrared part of the electromagnetic spectrum at spatial resolutions of 10, 20, and 60-meters.This imagery layer provides the full archive of Sentinel-2 Level-2A imagery. It is time enabled and includes a number of predefined processing templates for visualization and analysis. Key Properties Geographic Coverage: Global Landmasses - More...Temporal Coverage: 2015 – PresentSpatial Resolution: 10, 20, and 60-meter (see Multispectral Bands table for more information)Revisit Time*: ~5-daysProduct Level: Level-2A Surface ReflectanceSource Data Coordinate System: Universal Transverse Mercator (UTM) WGS84Service Coordinate System: Web Mercator Auxiliary Sphere WGS84 (EPSG:3857)Analysis: Optimized for analysisMultispectal Bands: BandDescriptionWavelength (µm)Spatial Resolution (m)1B1_Aerosols0.433 - 0.453602B2_Blue0.458 - 0.523103B3_Green0.543 - 0.578104B4_Red0.650 - 0.680105B5_RedEdge0.698 - 0.713206B6_RedEdge0.733 - 0.748207B7_RedEdge0.773 - 0.793208B8_NearInfraRed0.785 - 0.900109B8A_NarrowNIR0.855 - 0.8752010B9_WaterVapour0.935 - 0.9556011B11_ShortWaveInfraRed1.565 - 1.6552012B12_ShortWaveInfraRed2.100 - 2.2802013B13_AOTMapNA1014B14_WVPMapNA2015B15_SCLNA20 Usage Information and Best PracticesProcessing TemplatesThis layer includes a number of preconfigured processing templates (raster function templates) to provide on-the-fly data rendering and calculations for visualization and analysis. Each processing template includes labels and descriptions to characterize the intended usage. This may include for visualization, for analysis, or for both visualization and analysis. VisualizationThe default rendering on this layer is Natural Color for Visualization (bands 4,3,2).There are a number of on-the-fly renderings/processing templates designed specifically for data visualization.By default, the most recent and most cloud free scenes from the Landsat archive are prioritized and dynamically fused into a single mosaicked image layer. To discover and isolate specific images for visualization in Map Viewer, try using the Image Collection Explorer. AnalysisIn order to leverage the optimization for analysis, the capability must be enabled by your ArcGIS organization administrator. More information on enabling this feature can be found in the ‘Regional data hosting’ section of this help doc.Optimized for analysis means this layer does not have size constraints for analysis and it is recommended for multisource analysis with other layers optimized for analysis. See this group for a complete list of imagery layers optimized for analysis.Using the "None" processing template option as input to analysis provides all bands with raw pixel values and is recommended for many use cases. Otherwise, only processing templates that include a "for analysis" designation should be used as input to analysis.The appropriate scale factors are dynamically applied to the imagery in this layer, providing scientific floating point Surface Reflectance pixel values.Prior to running analysis, users should always provide some form of data selection with either a layer filter (e.g. for a specific date range, cloud cover percent, mission, etc.) or by selecting specific images. To discover and isolate specific images for analysis in Map Viewer, try using the Image Collection Explorer. GeneralIf you are new to Sentinel-2 imagery, the Sentinel-2 Explorer provides a good introductory user experience for working with this imagery layer. For more information, see this Quick Start Guide or this Detailed Tutorial. Data SourceSentinel-2 imagery is credited to the European Space Agency (ESA) and the European Commission. The imagery in this layer is sourced from the Microsoft Planetary Computer Open Data Catalog.

The Copernicus DEM is a Digital Surface Model (DSM) which represents the bare-Earth surface and all above ground natural and built features. It is based on WorldDEM™ DSM that is derived from TanDEM-X and is infilled on a local basis with the following DEMs: ASTER, SRTM90, SRTM30, SRTM30plus, GMTED2010, TerraSAR-X Radargrammetric DEM, ALOS World 3D-30m. Copernicus Programme provides Copernicus DEM in 3 different instances: COP-DEM EEA-10, COP-DEM GLO-30 and COP-DEM GLO-90 where "COP-DEM GLO-90" tiles and most of the "COP-DEM GLO-30 " tiles are available worldwide with free license. Sentinel Hub provides two instances named COPERNICUS_90 which uses "COP-DEM GLO-90" and COPERNICUS_30 which uses "COP-DEM GLO-30 Public" and "COP-DEM GLO-90" in areas where "COP-DEM GLO-30 Public" tiles are not yet released to the public by Copernicus Programme. Copernicus DEM provides elevation data and can also be used for the orthorectification of satellite imagery (e.g Sentinel 1).

The Sentinel 2 data (2019.01.25) for Northern South China Sea was downloaded from Sentinel open access hub of https://scihub.copernicus.eu/dhus/#/home.

Surface reflectance is the fraction of incoming solar radiation that is reflected from Earth's surface. Variations in satellite measured radiance due to atmospheric properties have been corrected for, so images acquired over the same area at different times are comparable and can be used readily to detect changes on Earth’s surface. DE Africa provides Sentinel 2 Level-2A surface reflectance data from European Commission's Copernicus Programme. Sentinel-2 is an Earth observation mission that systematically acquires optical imagery at up to 10 m spatial resolution. The mission is based on a constellation of two identical satellites in the same orbit, 180° apart for optimal coverage and data delivery. Together, they cover all Earth's land surfaces, large islands, inland and coastal waters every 3-5 days. Each of the Sentinel-2 satellites carries a wide swath high-resolution multispectral imager with 13 spectral bands. This product has a temporal coverage of 2017 to current and is updated as new images are acquired. Images in different spectral bands are provided at spatial resolutions of 10, 20 or 60 m. The surface reflectance values are scaled to be between 0 and 10,000. Sentinel-2 Level-2A data are provided by the European Space Agency (ESA). Data prior to 2017 are processed from Level-1C to Level-2A with ESA's Sen2Cor software by Sinergise. All images are converted to Cloud Optimised GeoTIFF format by Element 84, Inc. For more information on the Sentinel-2 Level-2A surface reflectance product, see https://earth.esa.int/web/sentinel/technical-guides/sentinel-2-msi/level-2a/algorithm This product is accessible through OGC Web Service (https://ows.digitalearth.africa/), for analysis in DE Africa Sandbox JupyterLab (https://github.com/digitalearthafrica/deafrica-sandbox-notebooks/wiki) and for direct download from AWS S3 (https://data.digitalearth.africa/).

The Cloud to Street - Microsoft Flood Dataset (C2S-MS Floods) is a dataset of near-coincident Sentinel-1 and Sentinel-2 data paired with water labels from 18 global flood events. These labels are derived products of MODIS sensor on board NASA's Aqua and Terra satellites produced as a part of the study, "Satellite imaging reveals increased proportion of population exposed to floods," Nature (2021), doi: 10.1038/s41586-021-03695-w. In this collection, we keep the water label which represents the maximum observed flood extent during the time period of the event and the cloud/cloud shadow label for Sentinel-2. For a detailed description of the methods used to generate these labels, please refer to the original paper.

The full SENTINEL-2 mission comprises twin polar-orbiting satellites in the same orbit, phased at 180° to each other. The mission carries optical sensors and monitors variability in land surface …Show full descriptionThe full SENTINEL-2 mission comprises twin polar-orbiting satellites in the same orbit, phased at 180° to each other. The mission carries optical sensors and monitors variability in land surface conditions.

The SENTINEL-1 mission comprises a constellation of two polar-orbiting satellites, operating day and night performing C-band synthetic aperture radar imaging, enabling them to acquire imagery …Show full descriptionThe SENTINEL-1 mission comprises a constellation of two polar-orbiting satellites, operating day and night performing C-band synthetic aperture radar imaging, enabling them to acquire imagery regardless of the weather.

This dataset shows the locations of Emperor Penguin colonies in East Antarctica in 2022, derived from the analysis of Sentinel 2 satellite imagery captured on various dates between April to December 2022. Sentinel 2 Level 2A imagery (10m resolution) was accessed via the European Space Agency’s Sentinel Playground (https://www.sentinel-hub.com/explore/sentinel-playground). The method involved searching areas where colonies had previously been reported, on imagery with generally less than 40% cloud cover (for methods see Fretwell and Trathan, 2021, Remote Sens. Ecol. Cons. 7, 139-153). Areas where faecal staining on ice was clearly visible were used as an indicator of Emperor Penguin colony presence. Colony locations identified by this method were collated into a point dataset, consisting of the colony name, location (latitude and longitude), the date of the satellite imagery from which the point location was derived, and a description of the location including any notable landmarks in the vicinity, e.g. named features, rock outcrops, grounded icebergs etc. The locations of 26 colonies from Umebosi Rock (~43° E) to Yule Bay (165.5° E) were determined through the use of satellite images. These colonies are ‘known’ colonies identified in previous studies and are named according to their location. It is important to note that these colonies tend to occur in the same general area every year, but their exact location may shift somewhat between years in response to variable sea ice conditions. The purpose of this dataset is therefore to identify exact colony locations at the specified date (the date of the satellite image from which the colony location was derived) and given the mobile nature of these colonies it is imperative that dates be included with any future colony location data. This dataset may be added to over time as colony locations vary from year to year. This dataset was developed from analysis of Sentinel 2 satellite imagery (https://sentinel.esa.int/web/sentinel/missions/sentinel-2)

A vector dataset of Field Boundaries, automatically delineated from Sentinel-2 satellite imagery from May-June 2022.

Automatic field delineation refers to the process of automatically tracing the boundaries of agricultural parcels from satellite or aerial imagery. We consider an agricultural parcel as a spatially homogeneous land unit used for agricultural purposes, where a single crop is grown. The result of the FD is a set of closed vector polygons marking the extent of each agricultural parcel. Such polygons are the input to a multitude of applications, ranging from the management of agricultural resources, such as the Area Monitoring for the Common Agricultural Policy, to precision farming, to the estimation of damages to crop yield due to natural (e.g. drought, floods), and human-made disasters (e.g. war). Automatic estimation of parcels with high fidelity in a timely manner allows therefore to characterize the changes of agricultural landscapes due to anthropogenic activities, agricultural practices, and climate change consequences.

Additional links

• Automatic Field Delineation Blog post
• Dataset description on Fiboa