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. L2A data are available from November 2016 over Europe region and globally since January 2017. L2A data provide Bottom of the atmosphere (BOA) reflectance.

Sentinel-2 L2A 120m mosaic is a derived product, which contains best pixel values for 10-daily periods, modelled by removing the cloudy pixels and then performing interpolation among remaining values. As clouds can be missed and as there are some parts of the world which have lengthy cloudy periods, clouds might be remaining in some parts. The actual modelling script is available here.

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 ongoing studies. This dataset is the same as the Sentinel-2 dataset, except the JP2K files were converted into Cloud-Optimized GeoTIFFs (COGs). Additionally, SpatioTemporal Asset Catalog metadata has were in a JSON file alongside the data, and a STAC API called Earth-search is freely available to search the archive. This dataset contains all of the scenes in the original Sentinel-2 Public Dataset and will grow as that does. L2A data are available from April 2017 over wider Europe region and globally since December 2018.

After 2022-01-25, Sentinel-2 scenes with PROCESSING_BASELINE '04.00' or above have their DN (value) range shifted by 1000. The HARMONIZED collection shifts data in newer scenes to be in the same range as in older scenes. Sentinel-2 is a wide-swath, high-resolution, multi-spectral imaging mission supporting Copernicus Land Monitoring studies, including the …

xS2Wakes: A dataset for xAI of Wakes in S-2 (L2A).

Summary

The dataset is derived from Sentinel-2 Level-2A (L2A) satellite images and focuses on the marine domain over Danish fjords. It provides a comprehensive collection of ship wakes and background clutter (referred to as "no_wake_crop") for remote sensing applications. The dataset has undergone post-processing through the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm with a clip limit value of 0.12 and a tile size of 16x16. The dataset comprises four spectral bands: B2, B3, B4, and B8.

Importance and Relevance to Remote Sensing Community

Multifaceted Applications of Wake Detection

Ship wake detection serves as a cornerstone in a multitude of domains that are critical to both human and environmental well-being:

Navigational Safety: Understanding ship wakes can provide insights into water currents and traffic patterns. This is vital for ensuring the safe passage of marine vessels, particularly in narrow straits and busy ports.

Environmental Monitoring: The study of ship wakes can reveal the influence of vessels on aquatic ecosystems. For instance, excessive wake turbulence can lead to coastal erosion and can disrupt marine habitats.

Maritime Surveillance: Wake detection plays a crucial role in maintaining maritime security. Tracking the wakes of vessels can help in identifying illegal activities such as smuggling or unauthorized fishing.

Specifications

• Data Source: Sentinel-2 L2A
• Region of Interest: Danish fjords
• Classes: Wake, No-Wake
• Number of Samples:
  • Wake: 123
  • No-Wake: 150
• Spectral Bands: B2 (Blue), B3 (Green), B4 (Red), B8 (NIR)
• Post-Processing: CLAHE (Clip Limit = 0.12, Tile Size = 16x16)
• Average Wake Chip Size: 390x351 pixels
• Average No-Wake Chip Size: 380x390 pixels

Wake Detection and Analysis

Traditional Methods and Their Limitations

Traditionally, the process of ship wake detection has largely been a manual endeavor or employed simplistic statistical algorithms. Analysts would sift through satellite or aerial images to identify ship wakes, a process that is both time-consuming and prone to human error. Even automated statistical methods often lack the robustness needed to differentiate between true wakes and false positives, such as aquatic plants or natural water disturbances.

Role of xAI (Explainable AI) in Wake Identification

The introduction of explainable AI (xAI) techniques brings another layer of sophistication to wake analysis. While traditional machine learning models may offer high performance, they often act as "black boxes," making it difficult to understand how they arrive at a certain conclusion. In a critical domain like navigational safety or maritime surveillance, the ability to interpret and understand model decisions is indispensable. xAI methods can make these machine learning models more transparent, providing insights into their decision-making processes, which in turn can aid in fine-tuning or fully trusting the models.

Spectral Bands Selected

The inclusion of four key spectral bands—B2, B3, B4, and B8—offers the scope for multi-spectral analysis. Different bands can capture varying features of water and wake textures, thereby offering a richer feature set for machine learning models. We use these spectral bands as referred to in [Liu, Yingfei, Jun Zhao, and Yan Qin. "A novel technique for ship wake detection from optical images." Remote Sensing of Environment 258 (2021): 112375.]

Understanding Optical vs. SAR Imaging Modalities

It is important to note the fundamental differences between wakes captured in Synthetic Aperture Radar (SAR) images and those in optical imagery. In SAR images, narrow-V wakes often arise due to Bragg scattering, a phenomenon that does not exist at optical wavelengths. In optical images, bright lines close to turbulent wakes are actually foams generated by the interaction between the surface horizontal flow of turbulent wakes and the surrounding background waves (Ermakov et al., 2014; Milgram et al., 1993; Peltzer et al., 1992). This can make the detection of wakes in optical images more challenging as there are usually no bright lines near turbulent wakes, and Kelvin arms may also show dark contrast. Methods that solely rely on searching for a trough and peak pair, taking the trough as the turbulent wake, would miss many actual wakes and could also result in the identification of false wakes.

Contrast Enhancement

The application of the CLAHE (Contrast Limited Adaptive Histogram Equalization) algorithm to this dataset allows for enhanced local contrast, enabling subtle features to become more pronounced. This significantly aids machine learning algorithms in feature extraction, thereby improving their ability to distinguish between complex patterns.

Environment and Clutter Assessment

In addition to wakes, the dataset contains samples labeled as "No-Wake," which include environmental clutter and clouds. These samples are crucial for training robust models that can differentiate wakes from similar-looking natural phenomena.

This collection contains Sentinel-2 Level 2A surface reflectances, which are computed for the country of Germany using the time-series based MAJA processor. During the Level 2A processing, the data are corrected for atmospheric effects and clouds and their shadows are detected. The MAJA L2A product is available online for the last 12 months. Further data are kept in the archive and are available upon request. Please see https://logiciels.cnes.fr/en/content/maja for additional information on the MAJA product. The MAJA product offers an alternative to the official ESA L2A product and has been processed with consideration of the characteristics of the Sentinel-2 mission (fast collection of time series, constant sensor perspective, and global coverage). Assumptions about the temporal constancy of the ground cover are taken into account for a robust detection of clouds and a more flexible determination of aerosol properties. As a result, an improved determination of the reflectance of sunlight at the earth's surface (pixel values of the multispectral image) is derived. Further Sentinel-2 Level 2A data computed using MAJA are available on the following website: https://theia.cnes.fr

SpatioTemporal Asset Catalog (STAC) Item - S2B_MSIL2A_20250627T143749_N0511_R039_T31XEL_20250627T164327 in sentinel-2-l2a

Satellite imagery has several applications, including land use and land cover classification, change detection, object detection, etc. Satellite based remote sensing sensors often encounter cloud coverage due to which clear imagery of earth is not collected. The clouded regions should be excluded, or cloud removal algorithms must be applied, before the imagery can be used for analysis. Most of these preprocessing steps require a cloud mask. In case of single-scene imagery, though tedious, it is relatively easy to manually create a cloud mask. However, for a larger number of images, an automated approach for identifying clouds is necessary. This model can be used to automatically generate a cloud mask from Sentinel-2 imagery.Using the modelFollow the guide to use the model. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.Fine-tuning the modelThis model can be fine-tuned using the Train Deep Learning Model tool. Follow the guide to fine-tune this model.InputSentinel-2 L2A imagery in the form of a raster, mosaic dataset or image service.OutputClassified raster containing three classes: Low density, Medium density and High density.Applicable geographiesThis model is expected to work well in Europe and the United States. This model works well for land based areas. Large water bodies such as ocean, seas and lakes should be avoided.Model architectureThis model uses the UNet model architecture implemented in ArcGIS API for Python.Accuracy metricsThis model has an overall accuracy of 94 percent with L2A imagery. The table below summarizes the precision, recall and F1-score of the model on the validation dataset. The comparatively low precision, recall and F1 score for Low density clouds might cause false detection of such clouds in certain urban areas. Also, for certain seasonal clouds some extremely bright pixels might be missed out.ClassPrecisionRecallF1 scoreHigh density0.9600.9750.968Medium density0.9050.8970.901Low density0.7740.5710.657Sample resultsHere are a few results from the model.

This layer displays a global map of land use/land cover (LULC) derived from ESA Sentinel-2 imagery at 10m resolution. Each year is generated with Impact Observatory’s deep learning AI land classification model, trained using billions of human-labeled image pixels from the National Geographic Society. The global maps are produced by applying this model to the Sentinel-2 Level-2A image collection on Microsoft’s Planetary Computer, processing over 400,000 Earth observations per year.The algorithm generates LULC predictions for nine classes, described in detail below. The year 2017 has a land cover class assigned for every pixel, but its class is based upon fewer images than the other years. The years 2018-2023 are based upon a more complete set of imagery. For this reason, the year 2017 may have less accurate land cover class assignments than the years 2018-2023.Variable mapped: Land use/land cover in 2017, 2018, 2019, 2020, 2021, 2022, 2023Source Data Coordinate System: Universal Transverse Mercator (UTM) WGS84Service Coordinate System: Web Mercator Auxiliary Sphere WGS84 (EPSG:3857)Extent: GlobalSource imagery: Sentinel-2 L2ACell Size: 10-metersType: ThematicAttribution: Esri, Impact ObservatoryWhat can you do with this layer?Global land use/land cover maps provide information on conservation planning, food security, and hydrologic modeling, among other things. This dataset can be used to visualize land use/land cover anywhere on Earth. This layer can also be used in analyses that require land use/land cover input. For example, the Zonal toolset allows a user to understand the composition of a specified area by reporting the total estimates for each of the classes. NOTE: Land use focus does not provide the spatial detail of a land cover map. As such, for the built area classification, yards, parks, and groves will appear as built area rather than trees or rangeland classes.Class definitionsValueNameDescription1WaterAreas where water was predominantly present throughout the year; may not cover areas with sporadic or ephemeral water; contains little to no sparse vegetation, no rock outcrop nor built up features like docks; examples: rivers, ponds, lakes, oceans, flooded salt plains.2TreesAny significant clustering of tall (~15 feet or higher) dense vegetation, typically with a closed or dense canopy; examples: wooded vegetation, clusters of dense tall vegetation within savannas, plantations, swamp or mangroves (dense/tall vegetation with ephemeral water or canopy too thick to detect water underneath).4Flooded vegetationAreas of any type of vegetation with obvious intermixing of water throughout a majority of the year; seasonally flooded area that is a mix of grass/shrub/trees/bare ground; examples: flooded mangroves, emergent vegetation, rice paddies and other heavily irrigated and inundated agriculture.5CropsHuman planted/plotted cereals, grasses, and crops not at tree height; examples: corn, wheat, soy, fallow plots of structured land.7Built AreaHuman made structures; major road and rail networks; large homogenous impervious surfaces including parking structures, office buildings and residential housing; examples: houses, dense villages / towns / cities, paved roads, asphalt.8Bare groundAreas of rock or soil with very sparse to no vegetation for the entire year; large areas of sand and deserts with no to little vegetation; examples: exposed rock or soil, desert and sand dunes, dry salt flats/pans, dried lake beds, mines.9Snow/IceLarge homogenous areas of permanent snow or ice, typically only in mountain areas or highest latitudes; examples: glaciers, permanent snowpack, snow fields.10CloudsNo land cover information due to persistent cloud cover.11RangelandOpen areas covered in homogenous grasses with little to no taller vegetation; wild cereals and grasses with no obvious human plotting (i.e., not a plotted field); examples: natural meadows and fields with sparse to no tree cover, open savanna with few to no trees, parks/golf courses/lawns, pastures. Mix of small clusters of plants or single plants dispersed on a landscape that shows exposed soil or rock; scrub-filled clearings within dense forests that are clearly not taller than trees; examples: moderate to sparse cover of bushes, shrubs and tufts of grass, savannas with very sparse grasses, trees or other plants.Classification ProcessThese maps include Version 003 of the global Sentinel-2 land use/land cover data product. It is produced by a deep learning model trained using over five billion hand-labeled Sentinel-2 pixels, sampled from over 20,000 sites distributed across all major biomes of the world.The underlying deep learning model uses 6-bands of Sentinel-2 L2A surface reflectance data: visible blue, green, red, near infrared, and two shortwave infrared bands. To create the final map, the model is run on multiple dates of imagery throughout the year, and the outputs are composited into a final representative map for each year.The input Sentinel-2 L2A data was accessed via Microsoft’s Planetary Computer and scaled using Microsoft Azure Batch.CitationKarra, Kontgis, et al. “Global land use/land cover with Sentinel-2 and deep learning.” IGARSS 2021-2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021.AcknowledgementsTraining data for this project makes use of the National Geographic Society Dynamic World training dataset, produced for the Dynamic World Project by National Geographic Society in partnership with Google and the World Resources Institute.

SpatioTemporal Asset Catalog (STAC) Item - S2B_MSIL2A_20250627T143749_N0511_R039_T35XNM_20250627T164327 in sentinel-2-l2a

SpatioTemporal Asset Catalog (STAC) Item - S2B_MSIL2A_20250627T143749_N0511_R039_T31XEM_20250627T164327 in sentinel-2-l2a

SpatioTemporal Asset Catalog (STAC) Item - S2B_MSIL2A_20250601T105619_N0511_R094_T33WWV_20250601T132448 in sentinel-2-l2a

The increasing demand for high spatial resolution in remote sensing imagery has led to the necessity of super-resolution (SR) algorithms that convert low-resolution (LR) images into high-resolution (HR) ones. To address this need, we introduce SEN2NAIP, a large remote sensing dataset designed to support conventional and reference-based SR model training. SEN2NAIP is structured into two components to provide a broad spectrum of research and application needs. The first component comprises a cross-sensor dataset of 2,851 pairs of LR images from Sentinel-2 L2A and HR images from the National Agriculture Imagery Program (NAIP). Leveraging this dataset, we developed a degradation model capable of converting NAIP images to match the characteristics of Sentinel-2 imagery (S2like). Subsequently, this degradation model was utilized to create the second component, a synthetic dataset comprising 17,657 NAIP and S2like image pairs. With the SEN2NAIP dataset, we aim to provide a valuable resource that facilitates the exploration of new techniques for enhancing the spatial resolution of Sentinel-2 satellite imagery.

This dataset contains pixels sampled from Sentinel-2 images. 198 scenes on the period going from early 2016 to the end of 2020 from 128 different MGRS tiles were used.

For each acquisition, the data was obtained at 2 processing levels: 1C
(from PEPS, CNES' mirror of Sentinel data) and 2A (from Theia's
catalogue), the latter having been produced by the MAJA processor.

For each acquisition, 100,000 pixels where sampled. Only non-saturated
pixels were selected, regardless of their cloud or shadow status. Pixel
positions were selected on the 20m resolution grid. For each 20m pixel
position, the following information was recorded:
- whether the pixel was detected as a cloud or a shadow (without
distinction of these 2 states),
- the reflectance in the 20m bands for levels 1C and 2A,
- the reflectance of the 4 corresponding pixels of each of the 10m
resolution bands for levels 1C and 2A,
- the reflectance at the 20m pixel position of the 60m resolution bands
after bicubic resampling for level 1C.
- the solar and viewing angles for each pixel.

For each sampled scene, a CSV file with the name TILE_DATE_samples.csv (for example T05KRA_20171124_samples.csv) is provided.

Each row in the file corresponds to a pixel. The columns provide the following variables:
- An integer used as unique identifier of the pixel in the file.
- The tile name in TIJXYZ format.
- The date in YYYMMDD format.
- The coverage: the percentage of the tile covered by the relative orbit of the acquisition.
- The x and y integer coordinates of the 20m. resolution pixel in the array.
- The reflectances of the 4 10 m resolution pixels corresponding to the 20 m. resolution pixels. The columns are named using the format Level_Band_i with Level being L1C or L2A, and i in {1, 2, 3, 4}. For the band, we keep the ESA (L1C) and Theia (L2A) respective nomenclatures, so we have L1C_B02_1 but L2A_B2_1.
- The reflectances of the 20 m resolution bands with columns named Level_Band and the same band name conventions, so we have L1C_B05 and L2A_B5.
- The reflectances of the L1C 60m resolution bands resampled to the 20m grid. The 3 columns are named L1C_B01, L1C_B09, L1C_B10.
- The reflectances of all the bands (10m and 20m resolution bands for L1C and L2A and the 60 m resolution bands for L1C) resampled to a 60 m resolution grid. The columns are named for instance L1C_B02_60 or L2A_B7_60.
- The solar Zenith and Azimuth angles: sun_zen, sun_az:
- The sensor Zenith and Azimuth angles split into even and odd detectors (inc_even_zen, inc_odd_zen, inc_even_az, inc_odd_az). The angles values are not recorded (empty value) for the detector to which the pixel does not belong to.
- A binary value (CLM) for the cloud and cloud shadow mask (0 if the pixel is clear, 1 if it is a cloud or a cloud shadow). This information is retrieved from the L2A masks.

The list of column names is the following:
- tile
- date
- coverage
- x
- y
- L1C_B02_0
- L1C_B02_1
- L1C_B02_2
- L1C_B02_3
- L1C_B03_0
- L1C_B03_1
- L1C_B03_2
- L1C_B03_3
- L1C_B04_0
- L1C_B04_1
- L1C_B04_2
- L1C_B04_3
- L1C_B08_0
- L1C_B08_1
- L1C_B08_2
- L1C_B08_3
- L1C_B05
- L1C_B06
- L1C_B07
- L1C_B8A
- L1C_B11
- L1C_B12
- L1C_B01
- L1C_B09
- L1C_B10
- L2A_B2_0
- L2A_B2_1
- L2A_B2_2
- L2A_B2_3
- L2A_B3_0
- L2A_B3_1
- L2A_B3_2
- L2A_B3_3
- L2A_B4_0
- L2A_B4_1
- L2A_B4_2
- L2A_B4_3
- L2A_B8_0
- L2A_B8_1
- L2A_B8_2
- L2A_B8_3
- L2A_B5
- L2A_B6
- L2A_B7
- L2A_B8A
- L2A_B11
- L2A_B12
- sun_zen
- sun_az
- inc_even_zen
- inc_odd_zen
- inc_even_az
- inc_odd_az
- L1C_B02_60
- L1C_B03_60
- L1C_B04_60
- L1C_B08_60
- L1C_B05_60
- L1C_B06_60
- L1C_B07_60
- L1C_B8A_60
- L1C_B11_60
- L1C_B12_60
- L2A_B2_60
- L2A_B3_60
- L2A_B4_60
- L2A_B8_60
- L2A_B5_60
- L2A_B6_60
- L2A_B7_60
- L2A_B8A_60
- L2A_B11_60
- L2A_B12_60
- CLM

The list of available CSV files is the following:

- T05KRA_20171124_samples.csv
- T05KRA_20180329_samples.csv
- T05KRA_20180523_samples.csv
- T05KRA_20190408_samples.csv
- T05KRA_20191129_samples.csv
- T05KRA_20200402_samples.csv
- T05KRA_20200601_samples.csv
- T05KRA_20200805_samples.csv
- T05KRA_20201203_samples.csv
- T06KTF_20160324_samples.csv
- T06KTF_20171010_samples.csv
- T06KTF_20180627_samples.csv
- T06KTF_20181224_samples.csv
- T06KTF_20200616_samples.csv
- T06KTF_20200924_samples.csv
- T11SPC_20180918_samples.csv
- T11SPC_20180928_samples.csv
- T11SPC_20181013_samples.csv
- T11SPC_20181112_samples.csv
- T14SPF_20170321_samples.csv
- T14SQE_20170308_samples.csv
- T14SQE_20190402_samples.csv
- T14SQF_20180405_samples.csv
- T14SQF_20190420_samples.csv
- T18TUR_20180509_samples.csv
- T18TVS_20191026_samples.csv
- T18TXS_20171018_samples.csv
- T18UVU_20190703_samples.csv
- T18UVU_20190827_samples.csv
- T18UWU_20190327_samples.csv
- T18UXU_20200210_samples.csv
- T18UXV_20191013_samples.csv
- T18UYV_20191217_samples.csv
- T19LHH_20200925_samples.csv
- T19LHJ_20190723_samples.csv
- T19LHJ_20200218_samples.csv
- T19TCL_20170811_samples.csv
- T19TCL_20201103_samples.csv
- T20LKP_20171001_samples.csv
- T20LKP_20191125_samples.csv
- T20LLQ_20180901_samples.csv
- T20NNP_20190807_samples.csv
- T20PPC_20191103_samples.csv
- T21NYG_20191002_samples.csv
- T21NZG_20180714_samples.csv
- T22KHA_20191027_samples.csv
- T22MGB_20201216_samples.csv
- T22MHB_20200803_samples.csv
- T22NCH_20181009_samples.csv
- T23KKR_20160808_samples.csv
- T23MKS_20200830_samples.csv
- T23MLS_20191219_samples.csv
- T23MLT_20190319_samples.csv
- T23MLT_20191114_samples.csv
- T23MLT_20200313_samples.csv
- T24MWU_20190916_samples.csv
- T24MWU_20201214_samples.csv
- T24MYT_20160918_samples.csv
- T24MYT_20200425_samples.csv
- T25LBL_20161214_samples.csv
- T25LBL_20180801_samples.csv
- T25LBL_20181209_samples.csv
- T25MBM_20171214_samples.csv
- T25MBN_20200113_samples.csv
- T25MBP_20180607_samples.csv
- T28PCB_20181129_samples.csv
- T28PDU_20181101_samples.csv
- T28PEV_20170405_samples.csv
- T28PGA_20170909_samples.csv
- T28PHC_20170323_samples.csv
- T28QCD_20160513_samples.csv
- T28QFD_20171002_samples.csv
- T28QFD_20180729_samples.csv
- T29SNC_20180927_samples.csv
- T29SPR_20201222_samples.csv
- T30PUT_20200522_samples.csv
- T30QZE_20181213_samples.csv
- T30QZE_20190924_samples.csv
- T30TTM_20181128_samples.csv
- T30TYM_20200126_samples.csv
- T31PGS_20191023_samples.csv
- T31QBU_20170107_samples.csv
- T31QCU_20170308_samples.csv
- T31QEU_20180330_samples.csv
- T31SDV_20171126_samples.csv
- T31SFA_20180907_samples.csv
- T31TDL_20190903_samples.csv
- T31UFS_20170814_samples.csv
- T31UGT_20171229_samples.csv
- T32PLS_20200919_samples.csv
- T32ULD_20161224_samples.csv
- T32ULE_20181124_samples.csv
- T32UMD_20190224_samples.csv
- T33KWP_20190729_samples.csv
- T33TUJ_20201007_samples.csv
- T36SXB_20191127_samples.csv
- T36SYB_20180317_samples.csv
- T36SYB_20200902_samples.csv
- T36SYB_20200907_samples.csv
- T36SYC_20180307_samples.csv
- T36SYC_20180327_samples.csv
- T36SYD_20170804_samples.csv
- T36SYD_20190111_samples.csv
- T37SBT_20190126_samples.csv
- T37SBU_20161122_samples.csv
- T37SBU_20201022_samples.csv
- T38KMB_20170817_samples.csv
- T38KNU_20200228_samples.csv
- T38KPE_20191026_samples.csv
- T38TNL_20180328_samples.csv
- T39KTU_20201126_samples.csv
- T40KCB_20160805_samples.csv
- T40KCB_20170323_samples.csv
- T40KCB_20170820_samples.csv
- T40KCB_20171123_samples.csv
- T40KCB_20200317_samples.csv
- T40KCB_20200829_samples.csv
- T42FVL_20180319_samples.csv
- T42FVL_20180729_samples.csv
- T42FVL_20201208_samples.csv
- T42FWL_20170405_samples.csv
- T42FWL_20200812_samples.csv
- T43QHA_20170315_samples.csv
- T43QHA_20180213_samples.csv
- T43QHV_20181110_samples.csv
- T43TCG_20200925_samples.csv
- T43TEG_20181227_samples.csv
- T43TEG_20201017_samples.csv
- T43TFH_20190516_samples.csv
- T44QLG_20181016_samples.csv
- T44TKM_20180408_samples.csv
- T44TLM_20181214_samples.csv
- T44TLN_20171224_samples.csv
- T44TLN_20190123_samples.csv
- T44TLN_20190128_samples.csv
- T45QWE_20200305_samples.csv
- T45QXG_20170316_samples.csv
- T45QXG_20190510_samples.csv
- T45QYF_20190721_samples.csv
- T45RXH_20190110_samples.csv
- T45RYH_20181002_samples.csv
- T45RYH_20201105_samples.csv
- T45RYJ_20190316_samples.csv
- T45RYK_20190105_samples.csv
- T45RYK_20190301_samples.csv
- T46QBM_20190117_samples.csv
- T46QBM_20190201_samples.csv
- T46QCL_20161213_samples.csv
- T46QCL_20191014_samples.csv
- T46RBN_20181118_samples.csv
- T46RCN_20181118_samples.csv
- T46RCN_20200127_samples.csv
- T46RCQ_20200117_samples.csv
- T47QRB_20200221_samples.csv
- T47QRB_20201117_samples.csv
- T47QRC_20170820_samples.csv
- T47QRC_20180318_samples.csv
- T47QRC_20200302_samples.csv
- T47QRC_20201217_samples.csv
- T48PVQ_20170814_samples.csv
- T48PVU_20170521_samples.csv
- T48PWA_20181222_samples.csv
- T48PWB_20180928_samples.csv
- T48PWR_20200525_samples.csv
- T48QTE_20201227_samples.csv
- T49MFM_20181031_samples.csv

- T49MFN_20200109_samples.csv

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.

SpatioTemporal Asset Catalog (STAC) Item - S2B_MSIL2A_20250627T143749_N0511_R039_T31XEK_20250627T164327 in sentinel-2-l2a

1 Description

SEN2VENµS is an open dataset for the super-resolution of Sentinel-2 images by leveraging simultaneous acquisitions with the VENµS satellite. The dataset is composed of 10m and 20m cloud-free surface reflectance patches from Sentinel-2, with their reference spatially-registered surface reflectance patches at 5 meters resolution acquired on the same day by the VENµS satellite. This dataset covers 29 locations with a total of 132 955 patches of 256x256 pixels at 5 meters resolution, and can be used for the training of super-resolution algorithms to bring spatial resolution of 8 of the Sentinel-2 bands down to 5 meters.

Changelog with respect to version 1.0.0 (https://zenodo.org/records/6514159)

All patches are now stored in indivual geoTiFF files with proper geo-referencing, regrouped in zip files per site and per category,

The dataset now includes 20 meter resolution SWIR bands B11 and B12 from Sentinel-2 (L2A from Theia). Note that there is no HR reference for those bands, since the VENµS sensor has no SWIR band.

2 Files organization

The dataset is composed of separate sub-datasets embedded in separate zip files, one for each site, as described in table 1. Note that there might be slight variations in number of patches and number of pairs with respect to version 1.0.0, due do incorrect count of samples in previous version (an empty tensor was still accounted for).

Table 1: Number of patches and pairs for each site, along with VENµS viewing zenith angle

Site Number of patches Number of pairs VENµS Zenith Angle

FR-LQ1 4888 18 1.795402

NARYN 3813 24 5.010906

FGMANAUS 129 4 7.232127

MAD-AMBO 1442 18 14.788115

ARM 15859 39 15.160683

BAMBENW2 9018 34 17.766533

ES-IC3XG 8822 34 18.807686

ANJI 2312 14 19.310494

ATTO 2258 9 22.048651

ESGISB-3 6057 19 23.683871

ESGISB-1 2891 12 24.561609

FR-BIL 7105 30 24.802892

K34-AMAZ 1384 20 24.982675

ESGISB-2 3067 13 26.209776

ALSACE 2653 16 26.877071

LERIDA-1 2281 5 28.524780

ESTUAMAR 911 12 28.871947

SUDOUE-5 2176 20 29.170244

KUDALIAR 7269 20 29.180855

SUDOUE-6 2435 14 29.192055

SUDOUE-4 935 7 29.516127

SUDOUE-3 5363 14 29.998115

SO1 12018 36 30.255978

SUDOUE-2 9700 27 31.295256

ES-LTERA 1701 19 31.971764

FR-LAM 7299 22 32.054056

SO2 738 22 32.218481

BENGA 5857 28 32.587334

JAM2018 2564 18 33.718953

Each site zip file contains a subfolder with the site name. This subfolder contains secondary zip files for each date, following this naming convention as the pair id: {site_name}_{acquisition_date}_{mgrs_tile}. For each date, 5 zip files are available, as shown in table 2.Each zip file contain subfolder {bands}/{resolution}/ in which one GeoTiFF file per patch is stored, with the following naming convention: {site_name}_{idx}_{acquisition_date}_{mgr_tile}_{bands}_{resolution}.tif. Pixel values are encoded as 16 bits signed integers and should be converted back to floating point surface reflectance by dividing each and every value by 10 000 upon reading.

Table 2: Naming convention for zip files associated to each date.

File Content

{id}_05m_b2b3b4b8.zip 5m patches ((256\times256) pix.) for S2 B2, B3, B4 and B8 (from VENµS)

{id}_10m_b2b3b4b8.zip 10m patches ((128\times128) pix.) for S2 B2, B3, B4 and B8 (from Sentinel-2)

{id}_05m_b5b6b7b8a.zip 5m patches ((256\times256) pix.) for S2 B5, B6, B7 and B8A (from VENµS)

{id}_20m_b5b6b7b8a.zip 20m patches ((64\times64) pix.) for S2 B5, B6, B7 and B8A (from Sentinel-2)

{id}_20m_b11b12.zip 20m patches ((64\times64) pix.) for S2 B11 and B12 (from Sentinel-2)

Each file comes with a master index.csv CSV (Comma Separated Values) file, with one row for each pair sampled in the given site. Columns are named after the {bands}_{resolution} pattern, and contains the full path to the corresponding GeoTiFF wihin the corresponding zip file:

{site}_{acquisition_date}_{mgrs_tile}_{bands}_{resolution}.zip/{bands}/{resolution}/{site}_{idx}_{acquisition_date}_{mgrs_tile}_{bands}_{resolution}.tif

3 Licencing

3.1 Sentinel-2 patches

3.1.1 Copyright

Value-added data processed by CNES for the Theia data centre www.theia-land.fr using Copernicus products. The processing uses algorithms developed by Theia's Scientific Expertise Centres. Note: Copernicus Sentinel-2 Level 1C data is subject to this license: https://theia.cnes.fr/atdistrib/documents/TC_Sentinel_Data_31072014.pdf

3.1.2 Licence

Files *_b2b3b4b8_10m.tif, *_b5b6b7b8a_20m.tif and *_b11b12_20m.tif are distributed under the the original licence of the Sentinel-2 Theia L2A products, which is the Etalab Open Licence Version 2.0 2.

3.2 VENµS patches

3.2.1 Copyright

Value-added data processed by CNES for the Theia data centre www.theia-land.fr using VENµS satellite imagery from CNES and Israeli Space Agency. The processing uses algorithms developed by Theia's Scientific Expertise Centres.

3.2.2 Licence

Files *_b2b3b4b8_05m.tif and *_b5b6b7b8a_05m.tif are distributed under the original licence of the VENµS products, which is Creative Commons BY-NC 4.0 3.

3.3 Remaining files

All remaining files are distributed under the Creative Commons BY 4.0 4 licence.

4 Note to users

Note that even if the VenµS2 dataset is sorted by sites and by pairs, we strongly encourage users to apply the full set of machine learning best practices when using it : random keeping separate pairs (or even sites) for testing purpose, and randomization of patches accross sites and pairs in the training and validation sets.

5 Citing

Please cite the following data paper (preprint, submitted to MDPI Data) and zenodo link when publishing work derived from this dataset:

Michel, J.; Vinasco-Salinas, J.; Inglada, J.; Hagolle, O. SEN2VENµS, a Dataset for the Training of Sentinel-2 Super-Resolution Algorithms. Data 2022, 7, 96. https://doi.org/10.3390/data7070096

10.5281/zenodo.14603764

Footnotes:

1

https://pytorch.org/

2

https://theia.cnes.fr/atdistrib/documents/Licence-Theia-CNES-Sentinel-ETALAB-v2.0-en.pdf

3

https://creativecommons.org/licenses/by-nc/4.0/

4

https://creativecommons.org/licenses/by/4.0/

SpatioTemporal Asset Catalog (STAC) Item - S2B_MSIL2A_20250601T105619_N0511_R094_T32WPD_20250601T132448 in sentinel-2-l2a

This is a dataset for mapping muddy waters based on Sentinel-2 (L2A products) satellite imagery. The image data are saved as GeoTIFF files and metadata files are provided in json format. There are 19 images in total, based on 16 distinct European Areas of Interest (AOIs), covering a total of 9 countries such as:

• Greece
• Italy
• France
• Spain
• Belgium
• UK
• Sweden
• Finland and
• Serbia

From the Sentinel-2 L2A products were extracted 10 spectral bands and then resampled to a 10m spatial resolution. All spectral bands used can be found in the Metadata/Source files. The annotated images comprise 3 classes, "Non-muddy", "Muddy" and "Ambiguous". More details about the annotation methodology can be found on the accepted abstract (file: Accepted_Abstract_03_15_2024.pdf) or the published paper, that you can find here: 10.1109/IGARSS53475.2024.10642051.

SpatioTemporal Asset Catalog (STAC) Item - 20NLK_2024-04-01_2024-08-01 in sentinel2-temporal-mosaics