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47 results for “Cloud mask”
Sentinel-2 Cloud Mask Catalogue
<p><strong>Overview</strong></p> <p>This dataset comprises cloud masks for 513 1022-by-1022 pixel subscenes, at 20m resolution, sampled random from the 2018 Level-1C Sentinel-2 archive. The design of this dataset follows from some observations about cloud masking: (i) performance over an entire product is highly correlated, thus subscenes provide more value per-pixel than full scenes, (ii) current cloud masking datasets often focus on specific regions, or hand-select the products used, which introduces a bias into the dataset that is not representative of the real-world data, (iii) cloud mask performance appears to be highly correlated to surface type and cloud structure, so testing should include analysis of failure modes in relation to these variables.</p> <p>The data was annotated semi-automatically, using the <a href="https://github.com/ESA-PhiLab/iris">IRIS toolkit</a>, which allows users to dynamically train a Random Forest (implemented using <a href="https://github.com/microsoft/LightGBM">LightGBM</a>), speeding up annotations by iteratively improving it's predictions, but preserving the annotator's ability to make final manual changes when needed. This hybrid approach allowed us to process many more masks than would have been possible manually, which we felt was vital in creating a large enough dataset to approximate the statistics of the whole Sentinel-2 archive.</p> <p>In addition to the pixel-wise, 3 class (CLEAR, CLOUD, CLOUD_SHADOW) segmentation masks, we also provide users with binary<br> classification "tags" for each subscene that can be used in testing to determine performance in specific circumstances. These include:</p> <ul> <li><strong>SURFACE TYPE</strong>: <em>11 categories</em></li> <li><strong>CLOUD TYPE</strong>: <em>7 categories</em></li> <li><strong>CLOUD HEIGHT</strong>: <em>low, high</em></li> <li><strong>CLOUD THICKNESS</strong>: <em>thin, thick</em></li> <li><strong>CLOUD EXTENT</strong>: <em>isolated, extended</em></li> </ul> <p> </p> <p>Wherever practical, cloud shadows were also annotated, however this was sometimes not possible due to high-relief terrain, or large ambiguities. In total, 424 were marked with shadows (if present), and 89 have shadows that were not annotatable due to very ambiguous shadow boundaries, or terrain that cast significant shadows. If users wish to train an algorithm specifically for cloud shadow masks, we advise them to remove those 89 images for which shadow was not possible, however, bear in mind that this will systematically reduce the difficulty of the shadow class compared to real-world use, as these contain the most difficult shadow examples.</p> <p>In addition to the 20m sampled subscenes and masks, we also provide users with shapefiles that define the boundary of the mask on the original Sentinel-2 scene. If users wish to retrieve the L1C bands at their original resolutions, they can use these to do so.</p> <p>Please see the README for further details on the dataset structure and more.</p> <p> </p> <p><strong>Contributions & Acknowledgements</strong></p> <p>The data were collected, annotated, checked, formatted and published by Alistair Francis and John Mrziglod.</p> <p>Support and advice was provided by Prof. Jan-Peter Muller and Dr. Panagiotis Sidiropoulos, for which we are grateful.</p> <p>We would like to extend our thanks to Dr. Pierre-Philippe Mathieu and the rest of the team at <em>ESA PhiLab</em>, who provided the environment in which this project was conceived, and continued to give technical support throughout.</p> <p>Finally, we thank the <em>ESA Network of Resources</em> for sponsoring this project by providing ICT resources.</p> <p> </p> <p> </p>
Snow-Cloud Validation Masks for Multispectral Satellite Data.
<p>Geotiffs of manually validated snow, cloud, & clear-sky snow free pixels for 13 Landsat 8 images. These acquisitions are of mid-latitude mountainous regions that contain both snow and cloud cover. Four spectral libraries of snow and cloud are also provided. These are the snow and cloud spectra extracted from both these 13 scenes and the 13 L8 SPARCS Cloud Validation Masks that contained both snow and cloud. 1&2.) Snow and cloud top-of-atmosphere reflectance for the eight Landsat 8 OLI 30 meter optical bands, aggregated from the 26 scenes. 3&4.) The top-of-atmosphere reflectance for the eight Landsat 8 OLI 30 meter optical bands of all snow misidentified as cloud and cloud misidentified as snow by CFMASK, the cloud mask that ships in the BQA file of Landsat 8 Collection 1.</p>
VENuS cloud mask training dataset
<p>Training dataset for cloud masking for VENμS satellite images. Samples are pixel-wise labelled scenes over the land of Israel, with a special focus on the southern, arid area.</p> <p> </p> <p>Contains three classes:</p> <p>* 0: Cloud-less pixels<br> * 1: Thick clouds<br> * 2: Thin clouds</p>
KappaSet: Sentinel-2 KappaZeta Cloud and Cloud Shadow Masks
<p><strong>General information</strong></p> <p>The dataset consists of 9251 labelled sub-tiles from 1038 Sentinel-2 (S2) Level-1C (L1C) products distributed over the globe. In terms of seasonal distribution, S2 products can be divided into the following groups:</p> <ul> <li> <p>Winter products: 29 austral and 142 boreal S2 products</p> </li> <li> <p>Sprint products: 45 austral and 257 boreal S2 products</p> </li> <li> <p>Summer products: 30 austral and 293 boreal S2 products</p> </li> <li> <p>Autumn products: 29 austral and 213 boreal S2 products</p> </li> </ul> <p>Each S2 product was oversampled at 10 m resolution for 512 x 512 pixels sub-tiles. From each S2 product, the most challenging ~5 sub-tiles per product were selected for labelling. Each selected L1C S2 product represents different clouds, such as cumulus, stratus, or cirrus, which are spread over various geographical locations around the world. The classification pixel-wise map consists of the following categories:</p> <ul> <li> <p>0 – UNDEFINED: pixels that the labeler is not sure which class they belong to;</p> </li> <li> <p>1 – CLEAR: pixels without clouds or cloud shadows;</p> </li> <li> <p>2 – CLOUD SHADOW: pixels with cloud shadows;</p> </li> <li> <p>3 – SEMI TRANSPARENT CLOUD: pixels with thin clouds through which the land is visible; include cirrus clouds that are on the high cloud level (5-15km).</p> </li> <li> <p>4 – CLOUD: pixels with cloud; include stratus and cumulus clouds that are on the low cloud level (from 0-0.2km to 2km).</p> </li> <li> <p>5 – MISSING: missing or invalid pixels.</p> </li> </ul> <p>The dataset was labelled using Computer Vision Annotation Tool (CVAT) and Segments.ai. With the possibility of integrating an active learning process in Segments.ai, the labelling was performed semi-automatically. The distribution of the dataset is presented in the Figure below. Color represents the season from which the product was chosen.</p> <p>The dataset limitations must be considered: the data mostly covers terrestrial regions (around 91%) and includes some water areas (around 9%); only around 7% of the dataset contains snow. Current sub-tiles do not have georeferencing. </p> <p><strong>Contributions and Acknowledgements</strong></p> <p>The data were annotated by Olga Wold, Mariana Rohtsalu, Nikita Murin, Joosep Truupõld and Fariha Harun. The data verification and Software Development were performed by Indrek Sünter, Heido Trofimov, Anton Kostiukhin, Marharyta Domnich, Mihkel Järveoja, Olga Wold and Tetiana Shtym. The methodology was developed by Kaupo Voormansik, Indrek Sünter, Marharyta Domnich and Tetiana Shtym.</p> <p>The data were collected, processed, and checked as a part of “KappaMask: AI-based Cloudmask Processor for Sentinel-2” project. We thank Segments.ai team for providing a wonderful annotation tool that was actively used to prepare the dataset. In the end, we thank European Space Agency (ESA) for supporting, advising, and funding the project.</p> <p>The project was funded by <em><strong>European Space Agency,</strong></em> Contract No. 4000132124/20/I-DT.</p>
Sentinel-2 KappaZeta Cloud and Cloud Shadow Masks
<p><strong>General information</strong></p> <p>The dataset consists of 4403 labelled subscenes from 155 Sentinel-2 (S2) Level-1C (L1C) products distributed over the Northern European terrestrial area. Each S2 product was oversampled at 10 m resolution for 512 x 512 pixels subscenes. 6 L1C S2 products were labelled fully. Among other 149 S2 products the most challenging ~10 subscenes per product were selected for labelling. In total the dataset represents 4403 labelled Sentinel-2 subscenes, where each sub-tile is 512 x 512 pixels at 10 m resolution. The dataset consists of around 30 S2 products per month from April to August and 3 S2 products per month for September and October. Each selected L1C S2 product represents different clouds, such as cumulus, stratus, or cirrus, which are spread over various geographical locations in Northern Europe.</p> <p>The classification pixel-wise map consists of the following categories:</p> <ul> <li>0 – MISSING: missing or invalid pixels;</li> <li>1 – CLEAR: pixels without clouds or cloud shadows;</li> <li>2 – CLOUD SHADOW: pixels with cloud shadows;</li> <li>3 – SEMI TRANSPARENT CLOUD: pixels with thin clouds through which the land is visible; include cirrus clouds that are on the high cloud level (5-15km).</li> <li>4 – CLOUD: pixels with cloud; include stratus and cumulus clouds that are on the low cloud level (from 0-0.2km to 2km).</li> <li>5 – UNDEFINED: pixels that the labeler is not sure which class they belong to.</li> </ul> <p>The dataset was labelled using Computer Vision Annotation Tool (<a href="https://github.com/openvinotoolkit/cvat">CVAT</a>) and <a href="https://segments.ai/">Segments.ai</a>. With the possibility of integrating active learning process in Segments.ai, the labelling was performed semi-automatically.</p> <p>The dataset limitations must be considered: the data is covering only terrestrial region and does not include water areas; the dataset is not presented in winter conditions; the dataset represent summer conditions, therefore September and October contain only test products used for validation. Current subscenes do not have georeferencing, however, we are working towards including them in next version.</p> <p>More details about the dataset structure can be found in README. </p> <p><strong>Contributions and Acknowledgements</strong></p> <p>The data were annotated by Fariha Harun and Olga Wold. The data verification and Software Development was performed by Indrek Sünter, Heido Trofimov, Anton Kostiukhin, Marharyta Domnich, Mihkel Järveoja, Olga Wold. Methodology was developed by Kaupo Voormansik, Indrek Sünter, Marharyta Domnich.<br> We would like to thank Segments.ai annotation tool for instant and an individual customer support. We are grateful to European Space Agency for reviews and suggestions. We would like to extend our thanks to Prof. Gholamreza Anbarjafari for the feedback and directions.<br> The project was funded by<strong><em> European Space Agency</em></strong>, Contract No. 4000132124/20/I-DT.</p>
Sentinel-2 reference cloud masks generated by an active learning method
<p> <strong>Reference classifications generated with Active Learning for Cloud Detection (ALCD)</strong></p> <p>This data set provides a reference cloud mask data set for 38 Sentinel-2 scenes. These reference masks have been created with the ALCD tool, developed by Louis Baetens, under the direction of Olivier Hagolle at CESBIO/CNES[1]. They were created to validate the cloud masks generated by the MAJA software [2].</p> <p>- The `Reference_dataset` directory contains 31 scenes selected in 2017 or 2018.<br> - The `Hollstein` directory contains 7 scenes that were used to validate the ALCD tool by comparison to manually generated reference images kindlyprovided by Hollstein et al[3]<br> One of these scenes is present in both directories. For the validation of MAJA, the "Hollstein" scenes were not used because of their acquisition at a time period when Sentinel-2 was not yet operational, with a degraded repetitivity of observations.</p> <p><strong># Description of the data structure</strong><br> The name of each scene directory is the name of the corresponding Sentinel-2 L1C product.<br> In the scene directory, three sub-directories can be found.<br> - `Classification`<br> - `Samples`<br> - `Statistics`</p> <p><strong># Description of the files</strong><br> - `Classification/classification_map.tif` --- the main product, which is the classified scene. 7 classes are available. Each one is represented with a different integer.<br> 0: no_data.<br> 1: not used.<br> 2: low clouds.<br> 3: high clouds.<br> 4: clouds shadows.<br> 5: land.<br> 6: water.<br> 7: snow.</p> <p>- `Classification/confidence_enhanced.tif` --- enhanced confidence map of the classification. The values are between 0 and 255 (coded on 1 bit).<br> The original confidence map is, for each pixel, the proportion of votes for the majority class as the classification map has been created via a Random Forest algorithm.<br> A median filter has been applied to this confidence map. Finally, the value was saved on 1 bit, leading to the value being between 0 and 255.</p> <p>- `Classification/contours.png` --- the contours of the classes from the classification map, overlayed on the scene. The color code depends on each class.<br> Green: low and high clouds. Yellow: cloud shadows. Blue: water. Purple: snow.</p> <p>- `Classification/used_parameters.json` --- the parameters that were used to classify the scene. It includes the tile code, the cloudy and clear dates, along with their product reference.</p> <p>- `Samples/` --- this directory contains all the shapefiles, one per class.</p> <p>- `Statistics/k_fold_summary.json` --- results of the 10-fold cross-validation on the scene.<br> 5 metrics are computed, in the order given in the "metrics_names". "all_metrics" is a list of the 10 folds, with the 5 metrics in the correct order for each fold.<br> "means" and "stds" are the means and standard deviations of the 10 folds.</p> <p><br> <strong># References</strong></p> <p>[1] Baetens, L.; Desjardins, C.; Hagolle, O. Validation of Copernicus Sentinel-2 Cloud Masks Obtained from MAJA, Sen2Cor, and FMask Processors Using Reference Cloud Masks Generated with a Supervised Active Learning Procedure. <em>Remote Sens.</em> <strong>2019</strong>, <em>11</em>, 433.</p> <p>[2] A multi-temporal method for cloud detection, applied to FORMOSAT-2, VENµS, LANDSAT and SENTINEL-2 images, O Hagolle, M Huc, D. Villa Pascual, G Dedieu, Remote Sensing of Environment 114 (8), 1747-1755, 2010</p> <p>[3] Hollstein, A.; Segl, K.; Guanter, L.; Brell, M.; Enesco, M. Ready-to-Use Methods for the Detection of Clouds, Cirrus, Snow, Shadow, Water and Clear Sky Pixels in Sentinel-2 MSI Images. Remote Sens. 2016, 8, 666</p>
Forcing, cloud feedbacks, cloud masking, and internal variability in the cloud radiative effect satellite record (Data)
<p>README file for ERA5-PRP datasets used in:</p> <p>Raghuraman et al., 2023, Journal of Climate,<br> "Forcing, cloud feedbacks, cloud masking, and internal variability in the cloud radiative effect satellite record"</p> <p>Point of Contact: Shiv Priyam Raghuraman, shivr@alumni.princeton.edu</p> <p>30 ERA5-PRP files:</p> <p>2 files: '2000_2020-allsky_small.nc' and '2000_2020-clearsky_small.nc'</p> <ul> <li>all input quantities varying</li> </ul> <p>2 files: 'clim-allsky-clim-var-all.nc' and 'clim-clearsky-clim-var-all.nc'</p> <ul> <li>all input quantities at climatology</li> </ul> <p>13 files: 'clim-var'</p> <ul> <li>Input quantity X at climatology, rest varying</li> <li>X = clouds, q, fal, skt, t, ghg, or o3. Fluxes computed in clear-sky and all-sky, apart from clouds which will only have all-sky.</li> </ul> <p>13 files: 'clim-except-var'</p> <ul> <li>Input quantity X varying, rest at climatology</li> <li>X = clouds, q, fal, skt, t, ghg, or o3. Fluxes computed in clear-sky and all-sky, apart from clouds which will only have all-sky.</li> </ul> <p>Other datasets:</p> <p>GFDL AM4 AMIP and Control<br> https://doi.org/10.5281/zenodo.4784726</p> <p>CMIP6 Control, Historical, RFMIP<br> Downloaded from ESGF</p>
AHI-CALIOP Collocated Data for Training and Validation of Cloud Masking Neural Networks
<p>Collocated data between AHI at 2km resolution (nadir) and CALIOP 1km cloud product v4.20 used for training and validating cloud identification neural networks. The main training and validation data from 2019 is stored in monthly directories, whilst the collocated dataset used to compare the NN, JMA and BoM cloud mask performances is the file "superdf.h5". All collocated data is stored as .h5 files and was built using the Python Pandas package. In this archive, the data has been stored as compressed directories for each month or as a single compressed file in the case of "superdf.h5" using tar with bzip2 compression or just bzip2 compression respectively.</p>
Cloud masks of GAN-CDM based models
<p>This is the result of GAN-CDM based models reported in Li, J., Wu, Z., Sheng, Q., Wang, B., Hu, Z., Zheng, S., Camps-Vall, G., Molinier, M., 2022. A hybrid generative adversarial network for weakly-supervised cloud detection in multispectral images. Remote Sens. Environ. 280, 113197. https://doi.org/10.1016/j.rse.2022.113197</p> <p>The file name means: dataset-binary mask under certain threshold-modelversion</p>
TOOCAN cloud mask
<p>This dataset comprises a high cloud mask derived from the TOOCAN dataset (Fiolleau et al., 2020) within the region spanning from 0N to 9.6N latitude and 140E to 149.6E longitude. The TOOCAN dataset offers calibrated infra-red brightness temperatures across the entire tropical band in the period 2012-2016.<br> The images have 0.04° spatial resolution and 30 minute temporal frequency.</p> <p>Fiolleau, T., R. Roca, S. Cloché, D. Bouniol, P. Raberanto, 2020: Homogenization of geostationary infrared imager channels for cold cloud studies using Megha-Tropiques/ScaRaB. IEEE Trans. Geosci. Remote Sens., vol 58, no. 9, pp. 6609-6622. doi: 10.1109/TGRS.2020.2978171</p>
Polar Radiant Energy in the Far InfraRed Experiment (PREFIRE) Satellite 2 Cloud Mask R01
Polar Radiant Energy in the Far InfraRed Experiment (PREFIRE) Satellite 2 Cloud Mask (PREFIRE_SAT2_2B-MSK) contains a binary clear/cloud indicator and probabilities derived from data collected by the PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE) aboard PREFIRE-SAT2. Dual CubeSats each carry a PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE), a push broom spectrometer with 63 channels measuring mid- and far-infrared (FIR) radiation from approximately 5 to 53 µm. Most polar emissions are in the FIR but have not been measured on a large scale. PREFIRE aims to fill knowledge gaps in the global energy budget by more accurately characterizing polar emissions. This information will then be assimilated into global circulation and other models to predict future conditions more accurately.PREFIRE_SAT2_2B-MSK is derived from mid-infrared and far-infrared measurements collected globally by TIRS-PREFIRE aboard PREFIRE-SAT2 using a Machine Learning technique. Specifically, inputs are PREFIRE Spectral Radiance (PREFIRE_SAT2_1B-RAD) from PREFIRE Satellite 2 (PREFIRE_SAT2_2B-RAD) and PREFIRE Auxiliary Meteorology Data created for PREFIRE Satellite 2 (PREFIRE_SAT2_AUX-MET). PREFIRE_SAT2_2B-MSK is used to identify cloudy and clear scenes in TIRS-PREFIRE data, which supports the production of other Level 2 PREFIRE products. These include variables such as top of atmosphere spectral flux, surface spectral emissivity, and atmospheric properties such as temperature and water vapor profiles. Science data retrieval started June 29, 2024 and is ongoing. Geographic coverage is global, with the greatest concentration of data in the polar regions. Within the orbital swath there are eight distinct tracks of data associated with the eight separate spatial scenes for each PREFIRE-TIRS. At the beginning of the mission, the approximate scene footprint sizes were 11.8 km x 34.8 km (cross-track x along-track), with gaps between each scene of approximately 24.2 km. The entire swath was ~264 km across. Note that the scene footprint and swath sizes quoted here are for the orbit altitude soon after launch. However, the footprint size will slowly become smaller as the orbit altitude decreases with time. This data has a temporal resolution of 0.707 seconds and is available in netCDF-4.The cloud identification data for the sister instrument aboard PREFIRE-SAT1 can be found in the PREFIRE_SAT1_2B-MSK collection.
ECOSTRESS Cloud Mask Daily L2 Global 70m V001
The ECO2CLD Version 1 data product was decommissioned on May 21, 2025. Users are encouraged to use the [ECO_L2_CLOUD](https://doi.org/10.5067/ECOSTRESS/ECO_L2_CLOUD.002) Version 2 and [ECO_L2G_CLOUD](https://doi.org/10.5067/ECOSTRESS/ECO_L2G_CLOUD.002) Version 2 data products.The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52 degrees N and 52 degrees S latitudes. The ECO2CLD Version 1 data product provides a cloud mask that can be used to determine cloud cover for the ECO1BRAD, ECO2LSTE, ECO3ETPTJPL, ECO4ESIPTJPL, and ECO4WUE data products. The ECOSTRESS Level 2 cloud product is derived using the five calibrated thermal bands in a multispectral cloud-conservative thresholding approach. The details of the algorithm are provided in the Algorithm Theoretical Basis Document (ATBD). The corresponding [ECO1BGEO](https://doi.org/10.5067/ECOSTRESS/ECO1BGEO.001) data product is required to georeference the ECO2CLD data product.The ECO2CLD Version 1 data product contains a single cloud mask layer. Information on how to interpret the bit fields in the cloud mask is provided in section 3.1 of the User Guide.Known Issues* Data acquisition gap: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented and science acquisitions resumed. To optimize the new acquisition approach TIR bands 2, 4, and 5 are being downloaded. The data products are as previously, except the bands not downloaded contain fill values (L1 radiance and L2 emissivity). This approach was implemented from May 15, 2019, through April 28, 2023.* Data acquisition gap: From February 8 to February 16, 2020, an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020, due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected.* Data acquisition: ECOSTRESS has now successfully returned to 5-band mode after being in 3-band mode since 2019. This feature was successfully enabled following a Data Processing Unit firmware update (version 4.1) to the payload on April 28, 2023. To better balance contiguous science data scene variables, 3-band collection is currently being interleaved with 5-band acquisitions over the orbital day/night periods.
Polar Radiant Energy in the Far InfraRed Experiment (PREFIRE) Satellite 1 Cloud Mask R01
Polar Radiant Energy in the Far InfraRed Experiment (PREFIRE) Satellite 1 Cloud Mask (PREFIRE_SAT1_2B-MSK) contains a binary clear/cloud indicator and probabilities derived from data collected by the PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE) aboard PREFIRE-SAT1. Dual CubeSats each carry a PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE), a push broom spectrometer with 63 channels measuring mid- and far-infrared (FIR) radiation from approximately 5 to 53 µm. Most polar emissions are in the FIR but have not been measured on a large scale. PREFIRE aims to fill knowledge gaps in the global energy budget by more accurately characterizing polar emissions. This information will then be assimilated into global circulation and other models to predict future conditions more accurately.PREFIRE_SAT1_2B-MSK is derived from mid-infrared and far-infrared measurements collected globally by TIRS-PREFIRE aboard PREFIRE-SAT1 using a Machine Learning technique. Specifically, inputs are PREFIRE Spectral Radiance (PREFIRE_SAT1_1B-RAD) from PREFIRE Satellite 1 (PREFIRE_SAT1_2B-RAD) and PREFIRE Auxiliary Meteorology Data created for PREFIRE Satellite 1 (PREFIRE_SAT1_AUX-MET). PREFIRE_SAT1_2B-MSK is used to identify cloudy and clear scenes in TIRS-PREFIRE data, which supports the production of other Level 2 PREFIRE products. These include variables such as top of atmosphere spectral flux, surface spectral emissivity, and atmospheric properties such as temperature and water vapor profiles. Science data retrieval started July 24, 2024 and is ongoing. Geographic coverage is global, with the greatest concentration of data in the polar regions. Within the orbital swath there are eight distinct tracks of data associated with the eight separate spatial scenes for each PREFIRE-TIRS. At the beginning of the mission, the approximate scene footprint sizes were 11.8 km x 34.8 km (cross-track x along-track), with gaps between each scene of approximately 24.2 km. The entire swath was ~264 km across. Note that the scene footprint and swath sizes quoted here are for the orbit altitude soon after launch. However, the footprint size will slowly become smaller as the orbit altitude decreases with time. This data has a temporal resolution of 0.707 seconds and is available in netCDF-4.The cloud identification data for the sister instrument aboard PREFIRE-SAT2 can be found in the PREFIRE_SAT2_2B-MSK collection.
AIRS-AMSU variables-CloudSat cloud mask, radar reflectivities, and cloud classification matchups V3.2 (AIRSM_CPR_MAT) at GES DISC
This is AIRS-CloudSat collocated subset, in NetCDF 4 format. These data contain collocated: AIRS/AMSU retrievals at AMSU footprints, CloudSat radar reflectivities, and MODIS cloud mask. These data are created within the frames of the MEaSUREs project.The basic task is to bring together retrievals of water vapor and cloud properties from multiple "A-train" instruments (AIRS, AMSR-E, MODIS, AMSU, MLS, CloudSat), classify each "scene" (instrument look) using the cloud information,and develop a merged, multi-sensor climatology of atmospheric water vapor as afunction of altitude, stratified by the cloud classes. This is a large scienceanalysis project that will require the use of SciFlo technologies to discover and organize all of the datasets, move and cache datasets as required, findspace/time "matchups" between pairs of instruments, and process years ofsatellite data to produce the climate data records.The short name for this collection is AIRSM_CPR_MATParameters contained in the data files include the following:Variable Name|Description|Units CH4_total_column|Retrieved total column CH4| (molecules/cm2) CloudFraction|CloudSat/CALIPSO Cloud Fraction| (None) CloudLayers| Number of hydrometeor layers| (count) clrolr|Clear-sky Outgoing Longwave Radiation|(Watts/m**2) CO_total_column|Retrieved total column CO| (molecules/cm2) CPR_Cloud_mask| CPR Cloud Mask |(None) Data_quality| Data Quality |(None) H2OMMRSat|Water vapor saturation mass mixing ratio|(gm/kg) H2OMMRStd|Water Vapor Mass Mixing Ratio |(gm/kg dry air) MODIS_Cloud_Fraction| MODIS 250m Cloud Fraction| (None) MODIS_scene_var |MODIS scene variability| (None) nSurfStd|1-based index of the first valid level|(None) O3VMRStd|Ozone Volume Mixing Ratio|(vmr) olr|All-sky Outgoing Longwave Radiation|(Watts/m**2) Radar_Reflectivity| Radar Reflectivity Factor| (dBZe) Sigma-Zero| Sigma-Zero| (dB*100) TAirMWOnlyStd|Atmospheric Temperature retrieved using only MW|(K) TCldTopStd|Cloud top temperature|(K) totH2OStd|Total precipitable water vapor| (kg/m**2) totO3Std|Total ozone burden| (Dobson) TSurfAir|Atmospheric Temperature at Surface|(K) TSurfStd|Surface skin temperature|(K)End of parameter information
S-NPP CrIS IMG: Collocated VIIRS level 1 / cloud mask statistical summary V2 (SNDRSNCrISL1BIMG) at GES DISC
The Cross-track Infrared Sounder (CrIS) Level 1B Normal Spectral Resolution (NSR) data files contain radiance measurements along with ancillary spacecraft, instrument, and geolocation data of the CrIS instrument on the Suomi National Polar-orbiting Partnership Project (SNPP). In December 2014, the CrIS instrument on the SNPP satellite doubled the spectral resolution of shortwave infrared data being transmitted to the ground. In November 2015, additional points were included at the ends of the longwave and shortwave interferograms to improve the quality of the calibration. Prior to November 2, 2015 the data are only available in Normal Spectral Resolution, after November 2, 2015 at 16:06 UTC, the data are available in both NSR and Full Spectral Resolution (FSR). The NSR files have 1,317 channels: 163 shortwave channels from 3.9 to 4.7 microns (2555 to 2150 cm-1), 437 midwave channels from 5.7 to 8.05 microns (1752.5 to 1242.5 cm-1), and 717 longwave channels from 9.1 to 15.41 microns (1096.25 to 648.75 cm-1). Each CrIS field-of-regard (FOR) contains 9 field-of-views (FOVs) arranged in a 3X3 array. The Level 1B files contain 30 FORs in the cross track direction and 45 in the along track direction. Data products are constructed on six minute boundaries.The Visible Infrared Imaging Radiometer Suite (VIIRS) has 22 imaging and radiometric bands covering wavelengths from 0.41 to 12.5 microns. It provides the sensor data records for clouds, sea surface temperature, ocean color, and others. This IMG product primarily contains statistics of the VIIRS cloud mask and VIIRS L1B data within each CrIS footprint..
SWOT Level 2 Water Mask Pixel Cloud Auxiliary Data Product, Version C
Auxiliary information for pixel cloud product indicating to which water bodies the pixels are assigned in river and lake products. Also includes height-constrained pixel geolocation after reach- or lake-scale averaging. Point cloud over tile (approx 64x64 km2); half swath (left or right side of full swath). Available in netCDF-4 file format.<br>Please note that this collection contains SWOT Version C science data products.
JPSS-1 CrIS IMG: Collocated VIIRS level 1 / cloud mask statistical summary V2 (SNDRJ1CrISL1BIMG) at GES DISC
The Cross-track Infrared Sounder (CrIS) Level 1B Full Spectral Resolution (FSR) data files contain radiance measurements along with ancillary spacecraft, instrument, and geolocation data of the CrIS instrument on the Joint Polar Satellite System-1 (JPSS-1) platform. This platform is also know as NOAA-20 (National Oceanic and Atmospheric Administration). The JPSS-1 mission with CrIS instrumentation is a follow-on to the Suomi National Polar-orbiting Partnership (SNPP) mission. The CrIS instrumentation and data processing system is nearly identical to that of the SNPP satellite. /The FSR files have 2,223 channels (*2211 apodized channels): 637 (*633) shortwave channels from 3.9 to 4.7 microns (2555 to 2150 cm-1), 869 (*865) midwave channels from 5.7 to 8.05 microns (1752.5 to 1242.5 cm-1), and 717 (*713)longwave channels from 9.1 to 15.41 microns (1096.25 to 648.75 cm-1). Each CrIS field-of-regard (FOR) contains 9 field-of-views (FOVs) arranged in a 3X3 array. The Level 1B files contain 30 FORs in the cross track direction and 45 in the along track direction. Data products are constructed on six minute boundaries.The Visible Infrared Imaging Radiometer Suite (VIIRS) has 22 imaging and radiometric bands covering wavelengths from 0.41 to 12.5 microns. It provides the sensor data records for clouds, sea surface temperature, ocean color, and others. This IMG product primarily contains statistics of the VIIRS cloud mask and VIIRS L1B data within each CrIS footprint.
SWOT Level 2 Water Mask Pixel Cloud Data Product, Version D
The SWOT Level 2 Water Mask Pixel Cloud Product (SWOT_L2_HR_PIXC_D) provides high-resolution, geolocated observations of terrestrial surface water pixels detected by the Ka-band Radar Interferometer (KaRIn) onboard the Surface Water and Ocean Topography (SWOT) satellite. This product contains the foundational “pixel cloud” data from which hydrologic features such as rivers, lakes, floodplains, and wetlands are later identified and analyzed in higher-level products.The PIXC product provides the unstructured point cloud of water detections at full instrument resolution (~15–25 m cross-track, ~5–10 m along-track), supporting fine-scale hydrologic analyses and applications such as water body delineation, flood mapping, and change detection. Each granule represents one KaRIn high-rate tile (~64 × 64 km²), corresponding to either the left or right half of the instrument swath. It includes individual water pixel detections with associated geodetic coordinates, ellipsoidal heights, normalized radar backscatter, and classification flags indicating surface type (e.g., land, water, ice, or layover). Pixel heights are referenced to the WGS84 ellipsoid and are not corrected for solid Earth tides, ocean tides, or pole tides like KaRIn low-rate oceanography products. Users may apply additional processing or corrections depending on the intended scientific use. Data are distributed in NetCDF-4 format and follow Climate and Forecast (CF) metadata conventions.The PIXC product is complemented by an auxiliary pixel cloud product (PIXCVec) that provides a less noisy, height-constrained geolocation of the observed water pixels and identifies associated river and lake features in the SWOT Prior River & Lake Databases (PRD & PLD). Together they support the derivation of higher-level, featured based River and Lake hydrology products from SWOT.
AIRS-CloudSat cloud mask and radar reflectivities collocation indexes V4.0 (AIRS_CPR_IND) at GES_DISC
Version 4.1 is the current version of the data set. Previous versions are no longer available and have been superseded by Version 4.1.This is AIRS-AMSU-CloudSat collocation indexes, in netCDF-4 format. These data map CloudSat profile indexes to the collocated AMSU field of views, and AIRS IR footprints, per AIRS 6-min granule time. Hence it can be considered as Level 1. These data are created within the frames of the MEaSUREs project.The basic task is to bring together retrievals of water vapor and cloud properties from multiple "A-train" instruments (AIRS, AMSR-E, MODIS, AMSU, MLS, & CloudSat), classify each "scene" (instrument look) using the cloud information, and develop a merged, multi-sensor climatology of atmospheric water vapor as a function of altitude, stratified by the cloud classes. This is a large science analysis project that will require the use of SciFlo technologies to discover and organize all of the datasets, move and cache datasets as required, find space/time "matchups" between pairs of instruments, and process years of satellite data to produce the climate data records.The short name for this collection is AIRS_CPR_IND
AIRS-CloudSat cloud mask, radar reflectivities, and cloud classification matchups V3.2 (AIRS_CPR_MAT) at GES DISC
This is AIRS-CloudSat collocated subset, in NetCDF-4 format. These data contain collocated: AIRS Level 1b radiances spectra, CloudSat radar reflectivities, and MODIS cloud mask. These data are created within the frames of the MEaSUREs project. The basic task is to bring together retrievals of water vapor and cloud properties from multiple "A-train" instruments (AIRS, AMSR-E, MODIS, AMSU, MLS, CloudSat), classify each "scene" (instrument look) using the cloud information, and develop a merged, multi-sensor climatology of atmospheric water vapor as a function of altitude, stratified by the cloud classes. This is a large science analysis project that will require the use of SciFlo technologies to discover and organize all of the datasets, move and cache datasets as required, find space/time "matchups" between pairs of instruments, and process years of satellite data to produce the climate data records.The short name for this collection is AIRS_CPR_MATParameters contained in the data files include the following:Variable Name|Description|Units CldFrcStdErr|Cloud Fraction|(None) CloudLayers| Number of hydrometeor layers| (count) CPR_Cloud_mask| CPR Cloud Mask| (None) DEM_elevation| Digital Elevation Map| (m) dust_flag|Dust Flag|(None) latAIRS|AIRS IR latitude|(deg) Latitude|CloudSat Latitude |(degrees) LayerBase| Height of Layer Base| (m) LayerTop| Height of layer top| (m) lonAIRS|AIRS IR longitude|(deg) Longitude|CloudSat Longitude| (degrees) MODIS_cloud_flag| MOD35_bit_2and3_cloud_flag| (None) Radar_Reflectivity| Radar Reflectivity Factor| (dBZe) radiances|Radiances|(milliWatts/m**2/cm**-1/steradian) Sigma-Zero| Sigma-Zero| (dB*100) spectral_clear_indicator|Spectral Clear Indicator|(None) Vertical_binsize|CloudSat vertical binsize| (m)End of parameter information
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.