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 …

GeoMAD is the Digital Earth Africa (DE Africa) surface reflectance geomedian and triple Median Absolute Deviation data service. It is a cloud-free composite of satellite data compiled over specific timeframes. This service is ideal for longer-term time series analysis, cloudless imagery and statistical accuracy.

GeoMAD has two main components: Geomedian and Median Absolute Deviations (MADs)

The geomedian component combines measurements collected over the specified timeframe to produce one representative, multispectral measurement for every pixel unit of the African continent. The end result is a comprehensive dataset that can be used to generate true-colour images for visual inspection of anthropogenic or natural landmarks. The full spectral dataset can be used to develop more complex algorithms.

For each pixel, invalid data is discarded, and remaining observations are mathematically summarised using the geomedian statistic. Flyover coverage provided by collecting data over a period of time also helps scope intermittently cloudy areas.

Variations between the geomedian and the individual measurements are captured by the three Median Absolute Deviation (MAD) layers. These are higher-order statistical measurements calculating variation relative to the geomedian. The MAD layers can be used on their own or together with geomedian to gain insights about the land surface and understand change over time.Key PropertiesGeographic Coverage: Continental Africa - approximately 37° North to 35° SouthTemporal Coverage: 2017 – 2022*Spatial Resolution: 10 x 10 meterUpdate Frequency: Annual from 2017 - 2022Product Type: Surface Reflectance (SR)Product Level: Analysis Ready (ARD)Number of Bands: 14 BandsParent Dataset: Sentinel-2 Level-2A Surface ReflectanceSource Data Coordinate System: WGS 84 / NSIDC EASE-Grid 2.0 Global (EPSG:6933)Service Coordinate System: WGS 84 / NSIDC EASE-Grid 2.0 Global (EPSG:6933)*Time is enabled on this service using UTC – Coordinated Universal Time. To assure you are seeing the correct year for each annual slice of data, the time zone must be set specifically to UTC in the Map Viewer settings each time this layer is opened in a new map. More information on this setting can be found here: Set the map time zone.ApplicationsGeoMAD is the Digital Earth Africa (DE Africa) surface reflectance geomedian and triple Median Absolute Deviation data service. It is a cloud-free composite of satellite data compiled over specific timeframes. This service is ideal for:Longer-term time series analysisCloud-free imageryStatistical accuracyAvailable BandsBand IDDescriptionValue rangeData typeNo data valueB02Geomedian B02 (Blue)1 - 10000uint160B03Geomedian B03 (Green)1 - 10000uint160B04Geomedian B04 (Red)1 - 10000uint160B05Geomedian B05 (Red edge 1)1 - 10000uint160B06Geomedian B06 (Red edge 2)1 - 10000uint160B07Geomedian B07 (Red edge 3)1 - 10000uint160B08Geomedian B08 (Near infrared (NIR) 1)1 - 10000uint160B8AGeomedian B8A (NIR 2)1 - 10000uint160B11Geomedian B11 (Short-wave infrared (SWIR) 1)1 - 10000uint160B12Geomedian B12 (SWIR 2)1 - 10000uint160SMADSpectral Median Absolute Deviation0 - 1float32NaNEMADEuclidean Median Absolute Deviation0 - 31623float32NaNBCMADBray-Curtis Median Absolute Deviation0 - 1float32NaNCOUNTNumber of clear observations1 - 65535uint160Bands can be subdivided as follows:

Geomedian — 10 bands: The geomedian is calculated using the spectral bands of data collected during the specified time period. Surface reflectance values have been scaled between 1 and 10000 to allow for more efficient data storage as unsigned 16-bit integers (uint16). Note parent datasets often contain more bands, some of which are not used in GeoMAD. The geomedian band IDs correspond to bands in the parent Sentinel-2 Level-2A data. For example, the Annual GeoMAD band B02 contains the annual geomedian of the Sentinel-2 B02 band. Median Absolute Deviations (MADs) — 3 bands: Deviations from the geomedian are quantified through median absolute deviation calculations. The GeoMAD service utilises three MADs, each stored in a separate band: Euclidean MAD (EMAD), spectral MAD (SMAD), and Bray-Curtis MAD (BCMAD). Each MAD is calculated using the same ten bands as in the geomedian. SMAD and BCMAD are normalised ratios, therefore they are unitless and their values always fall between 0 and 1. EMAD is a function of surface reflectance but is neither a ratio nor normalised, therefore its valid value range depends on the number of bands used in the geomedian calculation.Count — 1 band: The number of clear satellite measurements of a pixel for that calendar year. This is around 60 annually, but doubles at areas of overlap between scenes. “Count” is not incorporated in either the geomedian or MADs calculations. It is intended for metadata analysis and data validation.ProcessingAll clear observations for the given time period are collated from the parent dataset. Cloudy pixels are identified and excluded. The geomedian and MADs calculations are then performed by the hdstats package. Annual GeoMAD datasets for the period use hdstats version 0.2.More details on this dataset can be found here.

Harmonized Landsat Sentinel is a NASA initiative to produce a Virtual Constellation of surface reflectance (SR) data from the Operational Land Manager (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 L1 and S2-L1C top-of-atmosphere reflectance. Landsat data is available from April 2013 and Sentinel-2 data from November 2015.

Optical satellite data (Sentinel-2) which, among other things, show the reflectances of the land surface for the four channels Red, Green, Blue and near Infrared in a spatial resolution of 10 metres. Switzerland is mapped by four orbits of the satellite overflight. The overlapping orbits result in a complete image of Switzerland approximately every three to five days, but the usefulness of the data is heavily dependent on meteorological conditions, as the imaging sensor cannot see through clouds. In addition to the already applied localisation of the data, a co-registration of the data optimised for Switzerland is applied for a sub-pixel positional accuracy and the data is delivered in a uniform projection. Also included are optimised quality layers with masks for clouds (and cloud shadows) and topographic shadows. Contains modified Copernicus Sentinel data.

Dopo il 25/01/2022, l'intervallo DN (valore) delle scene Sentinel-2 con PROCESSING_BASELINE "04.00" o superiore viene spostato di 1000. La raccolta ARMONIZZATA sposta i dati nelle scene più recenti in modo che si trovino nello stesso intervallo delle scene precedenti. Sentinel-2 è una missione di imaging multispettrale ad alta risoluzione e ad ampia fascia che supporta gli studi di monitoraggio del territorio di Copernicus, tra cui…

The Harmonized Landsat Sentinel-2 (HLS) project provides consistent surface reflectance (SR) and top of atmosphere (TOA) brightness data from a virtual constellation of satellite sensors. The Operational Land Imager (OLI) is housed aboard the joint NASA/USGS Landsat 8 and Landsat 9 satellites, while the Multi-Spectral Instrument (MSI) is mounted aboard Europe’s Copernicus Sentinel-2A, Sentinel-2B, and Sentinel-2C satellites. The combined measurement enables global observations of the land every 2–3 days at 30-meter (m) spatial resolution. The HLS project uses a set of algorithms to obtain seamless products from OLI and MSI that include atmospheric correction, cloud and cloud-shadow masking, spatial co-registration and common gridding, illumination and view angle normalization, and spectral bandpass adjustment.The HLSL30 product provides 30-m Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) and is derived from Landsat 8/9 OLI data products. The HLSS30 and HLSL30 products are gridded to the same resolution and Military Grid Reference System (MGRS) tiling system and thus are “stackable” for time series analysis.The HLSL30 product is provided in Cloud Optimized GeoTIFF (COG) format, and each band is distributed as a separate file. There are 11 bands included in the HLSL30 product along with one quality assessment (QA) band and four angle bands. See the User Guide for a more detailed description of the individual bands provided in the HLSL30 product.Known Issues

• Unrealistically high aerosol and low surface reflectance over bright areas: The atmospheric correction over bright targets occasionally retrieves unrealistically high aerosol and thus makes the surface reflectance too low. High aerosol retrievals, both false high aerosol and realistically high aerosol, are masked when quality bits 6 and 7 are both set to 1 (see Table 9 in the User Guide); the corresponding spectral data should be discarded from analysis.
• Issues over high latitudes: For scenes greater than or equal to 80 degrees north, multiple overpasses can be gridded into a single MGRS tile resulting in an L30 granule with data sensed at two different times. In this same area, it is also possible that Landsat overpasses that should be gridded into a single MGRS tile are actually written as separate data files. Finally, for scenes with a latitude greater than or equal to 65 degrees north, ascending Landsat scenes may have a slightly higher error in the BRDF correction because the algorithm is calibrated using descending scenes.
• Fmask omission errors: There are known issues regarding the Fmask band of this data product that impacts HLSL30 data prior to April of 2022. The HLS Fmask data band may have omission errors in water detection for cases where water detection using spectral data alone is difficult, and omission and commission errors in cloud shadow detection for areas with great topographic relief. This issue does not impact other bands in the dataset.
• Inconsistent snow surface reflectance between Landsat and Sentinel-2: The HLS snow surface reflectance can be highly inconsistent between Landsat and Sentinel-2. When assessed on same-day acquisitions from Landsat and Sentinel-2, Landsat reflectance is generally higher than Sentinel-2 reflectance in the visible bands.
• Unrealistically high snow surface reflectance in the visible bands: By design, the Land Surface Reflectance Code (LaSRC) atmospheric correction does not attempt aerosol retrieval over snow; instead, a default aerosol optical thickness (AOT) is used to drive the snow surface reflectance. If the snow detection fails, the full LaSRC is used in both AOT retrieval and surface reflectance derivation over snow, which produces surface reflectance values as high as 1.6 in the visible bands. This is a common problem for spring images at high latitudes.
• Unrealistically low surface reflectance surrounding snow/ice: Related to the above, the AOT retrieval over snow/ice is generally too high. When this artificially high AOT is used to derive the surface reflectance of the neighboring non-snow pixels, very low surface reflectance will result. These pixels will appear very dark in the visible bands. If the surface reflectance value of a pixel is below -0.2, a NO_DATA value of -9999 is used.
• Unrealistically low reflectance surrounding clouds: Like for snow, the HLS atmospheric correction does not attempt aerosol retrieval over clouds and a default AOT is used instead. But if the cloud detection fails, an artificially high AOT will be retrieved over clouds. If the high AOT is used to derive the surface reflectance of the neighboring cloud-free pixels, very low surface reflectance values will result. If the surface reflectance value of a pixel is below -0.2, a NO_DATA value of -9999 is used.
• Unusually low reflectance around other bright land targets: While the HLS atmospheric correction retrieves AOT over non-cloud, non-snow bright pixels, the retrieved AOT over bright targets can be unrealistically high in some cases, similar to cloud or snow. If this unrealistically high AOT is used to derive the surface reflectance of the neighboring pixels, very low surface reflectance values can result as shown in Figure 2. If the surface reflectance value of a pixel is below -0.2, a NO_DATA value of -9999 is used. These types of bright targets are mostly man-made, such as buildings, parking lots, and roads.
• Dark plumes over water: The HLS atmospheric correction does not attempt aerosol retrieval over water. For water pixels, the AOT retrieved from the nearest land pixels is used to derive the surface reflectance, but if the retrieval is incorrect, e.g. from a cloud pixel, this high AOT will create dark stripes over water, as shown in Figure 3. This happens more often over large water bodies, such as lakes and bays, than over narrow rivers.
• Landsat WRS-2 Path/Row boundary in L30 reflectance: HLS performs atmospheric correction on Landsat Level 1 images in the original Worldwide Reference System 2 (WRS2) path/row before the derived surface reflectance is reprojected into Military Grid Reference System (MGRS) tiles. If a WRS-2 Landsat image is very cloudy, the AOT from a few remaining clear pixels might be used for the atmospheric correction of the entire image. The AOT that is used can be quite different from the value for the adjacent row in the same path, which results in an artificial abrupt change from one row to the next, as shown in Figure 4. This occurrence is very rare.
• Landsat WRS2 path/row boundary in cloud masks: The cloud mask algorithm Fmask creates mask labels by applying thresholds to the histograms of some metrics for each path/row independently. If two adjacent rows in the same path have distinct distributions within the metrics, abrupt changes in masking patterns can appear across the row boundary, as shown in Figure 5. This occurrence is very rare.
• Fmask configuration was deficient for 2-3 months in 2021: The HLS installation of Fmask failed to include auxiliary digital elevation model (DEM) and European Space Agency (ESA) Global Surface Water Occurrence data for a 2-3 month run in 2021. This impacted the masking results over water and in mountainous regions.
• The reflectance “scale_factor” and “offset” for some L30 and S30 bands were not set: The HLS reflectance scaling factor is 0.0001 and offset is 0. However, this information was not set in the Cloud Optimized GeoTIFF (COG) files of some bands for a small number of granules. The lack of this information creates a problem for automatic conversion of the reflectance data, requiring explicit scaling in applications. The problem has been corrected, but the affected granules have not been reprocessed.
• Incomplete map projection information: For a time, HLS imagery was produced with an incomplete coordinate reference system (CRS). The metadata contains the Universal Transverse Mercator (UTM) zone and coordinates necessary to geolocate pixels within the image but might not be in a standard form, especially for granules produced early in the HLS mission. As a result, an error will occur in certain image processing packages due to the incomplete CRS. The simplest solution is to update to the latest version of Geospatial Data Abstraction Library (GDAL) and/or rasterio, which use the available information without error.
• False northing of 10^7 for the L30 angle data: The L30 and S30 products do not use a false northing for the UTM projection, and the angle data are supposed to follow the same convention. However, the L30 angle data incorrectly uses a false northing of 10^7. There is no problem with the angle data itself, but the false northing needs to be set to 0 for it to be aligned with the reflectance.
• L30 from Landsat L1GT scenes: Landsat L1GT scenes were not intended for HLS due to their poor geolocation. However, some scenes made it through screening for a short period of HLS production. L1GT L30 scenes mainly consist of extensive cloud or snow that can be eliminated using the Fmask quality bits layer. Users can also identify an L1GT-originated L30 granule by examining the HLS cmr.xml metadata file.
• The UTC dates in the L30/S30 filenames may not be the local dates: UTC dates are used by ESA and the U.S. Geological Survey (USGS) in naming their Level 1 images, and HLS processing retains this information to name the L30 and S30 products. Landsat and Sentinel-2 overpass eastern Australia and New Zealand around 10AM local solar time, but this area is in either UTC+10:00 or +11:00 zone; therefore, the UTC

This dataset contains Level-3 Dynamic OPERA surface water extent product version 1. The data are validated surface water extent observations beginning April 2023. Known issues and caveats on usage are described under Documentation. The input dataset for generating each product is the Harmonized Landsat-8 and Sentinel-2A/B/C (HLS) product version 2.0. HLS products provide surface reflectance (SR) data from the Operational Land Imager (OLI) aboard the Landsat 8 satellite and the MultiSpectral Instrument (MSI) aboard the Sentinel-2A/B/C satellite. The surface water extent products are distributed over projected map coordinates using the Universal Transverse Mercator (UTM) projection. Each UTM tile covers an area of 109.8 km × 109.8 km. This area is divided into 3,660 rows and 3,660 columns at 30-m pixel spacing. Each product is distributed as a set of 10 GeoTIFF (Geographic Tagged Image File Format) files including water classification, associated confidence, land cover classification, terrain shadow layer, cloud/cloud-shadow classification, Digital elevation model (DEM), and Diagnostic layer.The digital elevation model (DEM) provided as a layer of the DSWx-HLS product (band 10) was generated using the Copernicus DEM 30-m and Copernicus DEM 90-m models provided by the European Space Agency. The Copernicus DEM 30-m and Copernicus DEM 90-m were produced using Copernicus WorldDEM-30 © DLR e.V. 2010-2014 and © Airbus Defence and Space GmbH 2014-2018 provided under COPERNICUS by the European Union and ESA; all rights reserved. The organizations in charge of the OPERA project, the Copernicus programme, and Airbus Defence and Space GmbH by law or by delegation do not assume any legal responsibility or liability, whether express or implied, arising from the use of this DEM.The OPERA DSWx-HLS product contains modified Copernicus Sentinel data (2023-2025).To access the calibration/validation database for OPERA Dynamic Surface Water Extent Products, please contact podaac@podaac.jpl.nasa.gov

The HLSL30 V1.5 data product was decommissioned on January 4, 2022. Users are encouraged to use the improved HLSL30 V2 data product.

The Harmonized Landsat Sentinel-2 (HLS) project provides consistent surface reflectance (SR) and top of atmosphere (TOA) brightness data from the Operational Land Imager (OLI) aboard the joint NASA/USGS Landsat 8 satellite and the Multi-Spectral Instrument (MSI) aboard Europe’s Copernicus Sentinel-2A and Sentinel-2B satellites. The combined measurement enables global observations of the land every 2–3 days at 30-meter (m) spatial resolution. The HLS project uses a set of algorithms to obtain seamless products from OLI and MSI that include atmospheric correction, cloud and cloud-shadow masking, spatial co-registration and common gridding, illumination and view angle normalization, and spectral bandpass adjustment.

The HLSL30 product provides 30-m Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) and is derived from Landsat 8 OLI data products. The HLSS30 and HLSL30 products are gridded to the same resolution and Military Grid Reference System (MGRS) tiling system and thus are “stackable” for time series analysis.

The HLSL30 product is provided in Cloud Optimized GeoTIFF (COG) format, and each band is distributed as a separate file. There are 10 bands included in the HLSL30 product along with one quality assessment (QA) band and four angle bands. For a more detailed description of the individual bands provided in the HLSL30 product, please see the User Guide.

Provisional HLS V1.5 data have not been validated for their science quality and should not be used in science research or applications.

Known Issues

• HLSL30.015 products are based on input Landsat 8 L1TP (precision terrain corrected) products, which require identification of ground control targets for precision geometric correction. Images where ground control is not available (e.g., very cloudy images) cannot be processed to L1TP and are not included in the HLSL30 dataset.

• Interruptions in data service occurred during a restaging of backlogged data between June 1 and June 15, 2021 for both HLSS30 and HLSL30 version 1.5 data products. During this time period increased errors in the processing workflow resulted in a significant number of data ingestion failures and thus, significant gaps in data availability. Given the pending release of the version 2.0, science quality HLS products, these missing data will not be filled for version 1.5. Users of the provisional version 1.5 products should be aware of the significant data gap in this two week window. The version 2.0 products will incorporate these data back into the archive. If you have any feedback or questions on the data please contact Customer Services or join our HLS conversion on the Earthdata Forum.

Evaluating Sentinel-2 Super-Resolution Algorithms for Automated Building Delineation

This dataset is associated with the Master's Thesis "Evaluating Sentinel-2 Super-Resolution Algorithms for Automated Building Delineation" and includes all relevant datasets that were created to facilitate experiments conducted. The thesis included the evaluation of SR algorithms on the downstream task of building delineation on the example of Austria. To achieve this, several datasets had to be accessed and created, which are featured in this repository. Further information regarding the process involved, code repositories, and the published thesis are accessible under the GitHub repository: https://github.com/Zerhigh/Evaluating_Sentinel-2_Super-Resolution_Algorithms_for_Automated_Building_Delineation

Structure & Processing Details

All image files are processed similarly:

• Remote sensing images are saved as geotiffs with provided spatial transformation parameters. When using these images, retain their spatial attributes.
• Images are processed and annotated with STAC metadata, with each folder containing its own collection.

The following datasets are available:

• main datasets:
  • hr_masks: 2.5m resolution cadastral masks with building footprints
  • hr_orthophoto: 2.5m resolution orthophotos of Austria
  • lr_s2: 10m resolution Sentinel-2 images of Austria (temporally and spatially aligned with the other data sources)
• image_samples: samples dataset representing the structure of this data repository
• building_delineation_inference: building delineation masks extracted from super-resolved or interpolated Sentinel-2 and orthophoto images
• metric_results: results from the conducted experiments on presented metrics
• stratification_tables: train/validation/test splits for different dataset configurations
• super_resolved: super-resolved Sentinel-2 images (from lr_s2) output from all used SR models
• tracasa_evaluation: dataset to achieve evaluation on a small subset for proprietary SR models
• thesis_figures: figures and plots featured in the written thesis

This dataset contains only the image data and results, code repositories are available on the linked GitHub repository.

This dataset contains preprocessed Sentinel-2 imagery and corresponding Dynamic World land-cover labels for six wetland areas in the Netherlands. It was created to support land-cover classification and segmentation tasks in ecologically dynamic floodplain environments. The data covers the period January 2017 to November 2024 and includes only scenes with less than 5% cloud cover.

Sentinel-2 imagery was retrieved using the Google Earth Engine (GEE) API from the COPERNICUS/S2 SR HARMONIZED collection, which provides Harmonized Level-2A data at 10 m spatial resolution. From the 26 available bands, 9 were selected based on their relevance for wetland delineation: RGB, Red Edge 1–3, Near-Infrared (NIR), and Shortwave Infrared (SWIR 1–2). The imagery was tiled into 256×256 pixel patches and filtered for quality (e.g., excluding patches with >10% black pixels).

Dynamic World land-cover labels (Brown et al., 2022) were used to generate pixel-wise semantic segmentation masks by selecting the most probable class (out of 9 land-cover types) for each pixel. The resulting masks are single-band images where pixel values 0–8 represent land-cover classes as follows:

0: Water 1: Trees 2: Grass 3: Flooded Vegetation 4: Crops 5: Shrub & Scrub 6: Built 7: Bare 8: Snow & Ice

The dataset includes the following splits:

Training set: Gelderse Poort, Oostvaardersplassen, Loosdrechtse Plassen, Land van Saeftinghe (1,701 images)

Validation set: Lauwersmeer (948 images)

Test set: Biesbosch (1,140 images)

This resource enables benchmarking of supervised and self-supervised learning methods for wetland classification in medium-resolution optical satellite data.

Reference:Brown, C.F., Brumby, S.P., Guzder-Williams, B., Birch, T., Hyde, S.B., Mazzariello, J., Czerwinski, W., Pasquarella, V.J., Haertel, R., Ilyushchenko, S., Schwehr, K., Weisse, M., Stolle, F., Hanson, C., Guinan, O., Moore, R., & Tait, A.M. (2022). Dynamic World, Near real-time global 10 m land use land cover mapping. Scientific Data, 9(1). https://doi.org/10.1038/s41597-022-01307-4

Countrywide vegetation height models (VHM) were generated for Armenia based on Copernicus Sentinel-2 imagery within the framework of the FORACCA project. “The Forest Restoration and Climate Change in Armenia” (FORACCA) project is funded by the Swiss Agency for Development and Cooperation (SDC). The project is implemented by the Swiss Federal Institute for Forest, Snow, and Landscape Research (WSL) in collaboration with Zoï Environment Network as well as the Forest Alliance of Armenia. A Convolutional Neural Network (CNN) model was trained in Switzerland to estimate the maximum vegetation height at the spatial resolution of the Sentinel-2 pixel of 10 m. Vegetation heights from the spatially higher-resolved VHM Lidar NFI were used as reference data for the CNN training. Then, the model was spatially transferred and VHMs for Armenia were generated annually based on available Sentinel-2 imagery from May – September of the respective year. Further details on the dataset and model to create the Sentinel-2 VHMs can be found in the paper Jiang et al. (2023, https://doi.org/10.1016/j.srs.2023.100099). Contains modified Copernicus Sentinel data.

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 collection, accessible via the United States Geological Survey (USGS) EarthExplorer platform, contains worldwide Landsat-8 and 9 Collection 2 data since the beginning of the two missions. Collection 2 is the result of reprocessing efforts on the archive and on fresh products with significant improvement with respect to Collection 1 in terms of data quality, obtained by means of advancements in data processing algorithm development. The primary characteristic is a relevant improvement in the absolute geolocation accuracy (now re-baselined to the European Space Agency Copernicus Sentinel-2 Global Reference Image, GRI) but includes also updated digital elevation modelling sources, improved Radiometric Calibration (even correction for the TIRS striping effect), enhanced Quality Assessment Bands, updated and consistent metadata files, and usage of Cloud Optimised Georeferenced (COG) Tagged Image File Format. Landsat-8 and 9 Level 1 products combine data from the two Landsat instruments, OLI and TIRS. The Level 1 products generated can be either L1TP or L1GT: L1TP - Level 1 Precision Terrain (Corrected) (L1T) products: Radiometrically calibrated and orthorectified using ground control points (GCPs) and digital elevation model (DEM) data to correct for relief displacement. The highest quality Level 1 products suitable for pixel-level time series analysis. GCPs used for L1TP correction are derived from the Global Land Survey 2000 (GLS2000) dataset. L1GT - Level 1 Systematic Terrain (Corrected) (L1GT) products: L1GT data products consist of L0 product data with systematic radiometric, geometric and terrain corrections applied and resampled for registration to a cartographic projection, referenced to the WGS84, G873, or current version. Three different classes of Level 1 products are available: Real Time (RT): Newly acquired Landsat-8 OLI/TIRS data are processed upon downlink but use an initial TIRS line-of-sight model parameters; the data are made available in less than 12 hours (4-6 hours typically). Once the data have been reprocessed with the refined TIRS parameters, the products are transitioned to either Tier 1 or Tier 2 and removed from the Real-Time tier (in 14-16 days). Landsat-8 only. Tier 1 (T1): Landsat scenes with the highest available data quality are placed into Tier 1 and are considered suitable for time-series analysis. Tier 1 includes Level 1 Precision and Terrain (L1TP) corrected data that have well-characterised radiometry and are inter-calibrated across the different Landsat instruments. The georegistration of Tier 1 scenes is consistent and within prescribed image-to-image tolerances of ≦ 12-metre radial root mean square error (RMSE). Tier 2 (T2): Landsat scenes not meeting Tier 1 criteria during processing are assigned to Tier 2. Tier 2 scenes adhere to the same radiometric standard as Tier 1 scenes, but do not meet the Tier 1 geometry specification due to less accurate orbital information (specific to older Landsat sensors), significant cloud cover, insufficient ground control, or other factors. This includes Systematic Terrain (L1GT) and Systematic (L1GS) processed data. Landsat-8 and 9 Level 2 products are generated from L1GT and L1TP Level 1 products that meet the <76 degrees Solar Zenith Angle constraint and include the required auxiliary data inputs to generate a scientifically viable product. The data are available a couple of days after the Level 1 T1/T2. The Level 2 products generated can be L2SP or L2SR: L2SP - Level 2 Science Products: include Surface Reflectance (SR), Surface Temperature (ST), ST intermediate bands, an angle coefficients file, and Quality Assessment (QA) Bands. L2SR - Level 2 Surface Reflectance: include Surface Reflectance (SR), an angle coefficients file, and Quality Assessment (QA) Bands; it is generated if ST could not be generated. Landsat-8 and 9 Level 3 science products represent biophysical properties of Earth's surface and are generated either from Landsat U.S. Analysis Ready Data inputs (tile-based products) or from Landsat Level 2 scene-based inputs (scene-based products). The following Level 3 products are available: Dynamic Surface Water Extent products: Describes the existence and condition of surface water. Tile-based. Fractional Snow Covered Area products: Indicates the percentage of a pixel covered by snow. Tile-based. Burned Area products: Represents per pixel burn classification and burn probability. Tile-based. Provisional Actual Evapotranspiration products: The quantity of water that is removed from a surface due to the processes of evaporation and transpiration. Scene-based.

This dataset provides NDVI time series data in comma-delimited format from the phenocam location using five satellite products: 1) Proba-V L1c product 2) Landsat 7 SR product 3) Sentinel-2 Level-1C product 4) Sentinel 2 Level-2A data product 5) Suomi National Polar-Orbiting Partnership (S-NPP) NASA Visible Infrared Imaging Radiometer Suite (VIIRS) VNP13A1 data product The dataset also includes scripts to download these data from Google Earth Engine. The data are provided in support of the following publication: "Solar and sensor geometry, not vegetation response, drive satellite NDVI phenology in widespread ecosystems of the western United States". The data and scripts allow users to replicate, test, or further explore results. The comma-delimited csv files are named according to the satellite and product. The javascript Google Earth Engine code files (within the folder "Code") are also named by satellite/product, with the Proba and VIIRS time series code combined into a single file, and the other products as separate files. A graph of the data is included as the file 'SensorCompare4SB.jpg' and shows the NDVI time series from the products described above. The data in this graph can also be viewed in figure 10 of the associated journal article.

Countrywide vegetation height models (VHM) were generated for Switzerland based on Copernicus Sentinel-2 imagery and the digital terrain model (DTM) swissALTI3D from the Swiss Federal Office of Topography swisstopo. A Convolutional Neural Network (CNN) model was trained to estimate the maximum vegetation height at the spatial resolution of the Sentinel-2 pixel of 10 m. Vegetation heights from the spatially higher-resolved VHM Lidar NFI were used as reference data for the CNN training. Within the framework of the Swiss National Forest Inventory (NFI), the VHMs were modelled annually based on available Sentinel-2 imagery from May – September of the respective year. Further details on the creation of the VHM Sentinel NFI can be found in the paper Jiang et al. (2023, https://doi.org/10.1016/j.srs.2023.100099). Contains modified Copernicus Sentinel data.

Optische Satellitendaten (Sentinel-2) welche unter anderem die Reflektanzen der Landoberfläche (surface reflectance - SR) für die vier Kanäle Rot, Grün, Blau und nahes Infrarot in einer räumlichen Auflösung von zehn Metern darstellen. Die Schweiz wird von vier Bahnen des Satellitenüberfluges (Orbits) abgebildet. Die überlappenden Bahnen ergeben ca. alle drei bis fünf Tage eine vollständige Aufnahme der Schweiz, allerdings ist die Brauchbarkeit der Daten stark von den meteorologischen Bedingungen abhängig, da der Aufnahmesensor nicht durch Wolken hindurchsehen kann. Zusätzlich zu der bereits angewendeten Verortung der Daten wird eine auf die Schweiz optimierte Ko-Registrierung der Daten für eine sub-Pixel Lagegenauigkeit angewendet und die Daten werden in einer einheitlichen Projektion geliefert. Ebenso mitgeliefert werden optimierte Qualitätslayer mit Masken für Wolken(-schatten) und topographischen Schattenwurf. Enthält geänderte Copernicus Sentinel-Daten.

This dataset contains the European Coverage of Landsat 8 Collection 2 data, both Level 1 and Level 2, since the beginning of the mission. Landsat 8 Collection 2 is the result of reprocessing effort on the archive and on fresh products with significant improvement with respect to Collection 1 on data quality, obtained by means of advancements in data processing, algorithm development. The primary characteristic is a relevant improvement in the absolute geolocation accuracy (now re-baselined to the European Space Agency Copernicus Sentinel-2 Global Reference Image, GRI) but includes also updated digital elevation modelling sources, improved Radiometric Calibration (even correction for the TIRS striping effect), enhanced Quality Assessment Bands, updated and consistent metadata files, usage of Cloud Optimized Georeferenced (COG) Tagged Image File Format. Landsat 8 level 1 products combine data from the 2 Landsat instruments, OLI and TIRS. The level 1 products generated can be either L1TP or L1GT: • L1TP - Level 1 Precision Terrain (Corrected) (L1T) products: Radiometrically calibrated and orthorectified using ground control points (GCPs) and digital elevation model (DEM) data to correct for relief displacement. The highest quality Level-1 products suitable for pixel-level time series analysis. GCPs used for L1TP correction are derived from the Global Land Survey 2000 (GLS2000) data set. • L1GT - Level 1 Systematic Terrain (Corrected) (L1GT) products: L1GT data products consist of L0 product data with systematic radiometric, geometric and terrain corrections applied and resampled for registration to a cartographic projection, referenced to the WGS84, G873, or current version. The dissemination server contains three different classes of Level1 products • Real Time (RT): Newly acquired Landsat 8 OLI/TIRS data are processed upon downlink but use an initial TIRS line-of-sight model parameters; the data is made available in less than 12 hours (4-6 hours typically). Once the data have been reprocessed with the refined TIRS parameters, the products are transitioned to either Tier 1 or Tier 2 and removed from the Real-Time tier (in 14-16 days). • Tier 1 (T1): Landsat scenes with the highest available data quality are placed into Tier 1 and are considered suitable for time-series analysis. Tier 1 includes Level-1 Precision and Terrain (L1TP) corrected data that have well-characterized radiometry and are inter-calibrated across the different Landsat instruments. The georegistration of Tier 1 scenes is consistent and within prescribed image-to-image tolerances of ≦ 12-meter radial root mean square error (RMSE). • Tier 2 (T2): Landsat scenes not meeting Tier 1 criteria during processing are assigned to Tier 2. Tier 2 scenes adhere to the same radiometric standard as Tier 1 scenes, but do not meet the Tier 1 geometry specification due to less accurate orbital information (specific to older Landsat sensors), significant cloud cover, insufficient ground control, or other factors. This includes Systematic Terrain (L1GT) and Systematic (L1GS) processed data. Landsat 8 level 2 products are generated from L1GT and L1TP Level 1 products that meet the <76 degrees Solar Zenith Angle constraint and include the required auxiliary data inputs to generate a scientifically viable product. The data are available a couple of days after the Level1 T1/T2. The level 2 products generated can be L2SP or L2SR: • L2SP - Level 2 Science Products (L2SP) products: include Surface Reflectance (SR), Surface Temperature (ST), ST intermediate bands, an angle coefficients file, and Quality Assessment (QA) Bands. • L2SR - Level 2 Surface Reflectance (L2SR) products: include Surface Reflectance (SR), an angle coefficients file, and Quality Assessment (QA) Bands; it is generated if ST could not be generated Two different categories of Level 1 products are offered: LC with Optical, Thermal and Quality Map images, LO with Optical and Quality Map images (Thermal not available). For the Level 2 data, only LC combined products are generated

This Collection provides Sentinel-3 SRAL Level-2 Ocean Altimetry products, which contain data on ocean radar altimetry measurements.

Datos ópticos satelitales (Sentinel-2) que muestran, entre otras cosas, la reflectancia superficial (SR) de los cuatro canales rojo, verde, azul e infrarrojo cercano en una resolución espacial de diez metros. Suiza está representada por cuatro órbitas de sobrevuelo de satélites. Las órbitas superpuestas dan como resultado una imagen completa de Suiza aproximadamente cada tres o cinco días, pero la usabilidad de los datos depende en gran medida de las condiciones meteorológicas, ya que el sensor de imagen no puede ver a través de las nubes. Además del posicionamiento ya aplicado de los datos, se aplica un registro conjunto de los datos optimizados para Suiza para la precisión de la posición del subpíxel y los datos se entregan en una proyección uniforme. También se incluyen capas de calidad optimizada con máscaras para nubes (sombra) y sombras topográficas. El tratamiento de datos se encuentra todavía en la fase de la Comisión (construcción y validación) hasta el primer trimestre de 2025. Por lo tanto, los datos pueden estar sujetos a cambios durante este período. Contiene datos modificados de Copernicus Sentinel.

The Ocean and Land Colour Instrument (OLCI) on board European Earth Observation satellite mission, SENTINEL-3, is a push-broom imaging spectrometer that measures solar radiation reflected by the Earth at a ground spatial resolution of 300m, over all surfaces, in 21 spectral bands. OLCI is based on the imaging design of ENVISAT's Medium Resolution Imaging Spectrometer (MERIS). It has a 1270km wide swath.

For more information about the instrument and the mission, visit "Sentinel Online" at https://sentinel.esa.int/web/sentinel/home

The OLCI Level-1B product, S3A_OL_1_EFR, is Level 1 full resolution (i.e. at native instrument spatial resolution - ~300m) Top of the Atmosphere Reflectance product. This is composed of an information package map, called a manifest, 22 measurement data files, and seven annotation data files. The 21 measurement data files (one for each band) Top Of Atmosphere (TOA) radiances, calibrated to geophysical units (W.m-2. sr-1 µm-1), georeferenced onto the Earth's surface, spatially resampled onto an evenly spaced grid. Seven annotation files information on illumination and observation geometry, environment data (meteorological data) and quality and classification flags. Both measurement data files and annotation data files are written in netCDF 4 format. The manifest file is in XML format and contains metadata associated with the instrument and the processing. The S3A_OL_1_EFR is generated in Earth Observation (EO) processing mode and all parameters in this product are provided for each re-gridded pixel on the product image and for each removed pixel.

The OL_1_EFR product package is described below:

Element name Description Manifest.safe SENTINEL-SAFE product manifest Oa##_radiance.nc Radiance for OLCI acquisition bands 01 to 21 Removed_pixels.nc Removed pixels information needed for Level-1C generation Time_coordinates.nc Time stamp annotations Geo_coordinates.nc High resolution georeferencing data Quality_flags.nc Classification and quality flags Tie_geo_coordinates.nc Low resolution georeferencing data Tie_geometries.nc Sun and view angles Tie_meteo.nc ECMWF meteorology data Instrument_data.nc Instrument data

note: Oa## represents all the OLCI channels (Oa1 to Oa21).

For more information about the product, read the SENTINEL-3 OLCI User Guide at https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-olci.