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opencc-by-4.0Dec 2019View details →

SWIR abyssal peridotites EBSD and microprobe datasets

The crystallographic preferred orientation (CPO) of olivine, pyroxenes, spinel, and amphibole was measured in 17 selected samples using a JEOL JSM 5600 at the Geosciences Montpellier EBSD facility, with 17 kV acceleration voltage and 24 mm working distance. For each thin section, CPO maps covered areas of 4 to 13 cm2 with a grid step of 15 or 35 µm, depending on olivine minimum grain size. Because of the large size of olivine porphyroclasts in most samples, CPO were measured on several thin sections and then combined. Higher-resolution CPO maps (grid step 0.5 to 10 µm depending on the dominant grain size) of fine-grained zones were also obtained for 7 samples using the CamScan Crystal Probe X500-FEG SEM, also at the Geosciences Montpellier EBSD facility. Post-acquisition treatment consisted in deleting wild spikes and filling non-indexed pixels with ≥ 6 neighbors with coherent orientations with their average orientation.  In situ major element concentrations of amphiboles were measured using a Cameca SX-100 electron microprobe (CAMPARIS service, Paris). The accelerating voltage was fixed at 15 kV and beam current at 10 nA. The spot size was 1μm. Counting times were 10 s. Compositions are presented in TableS3.

Doodleverse/Segmentation Zoo Res-UNet models for 4-class (water, whitewater, sediment and other) segmentation of Sentinel-2 and Landsat-7/8 5-band (RGB+NIR+SWIR) images of coasts.

These Residual-UNet model data are based on 5-band RGB+NIR+SWIR (red, green, blue, near-infrared, and short-wave infrared) images of coasts and associated labels.   Models have been created using Segmentation Gym* using the following dataset**: <a href="https://doi.org/10.5281/zenodo.7344571">https://doi.org/10.5281/zenodo.7344571 </a> Classes: {0=water, 1=whitewater, 2=sediment, 3=other} File descriptions For each model, there are 5 files with the same root name: 1. '.json' config file: this is the file that was used by Segmentation Gym* to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse. 2. '.h5' weights file: this is the file that was created by the Segmentation Gym* function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym* function  `seg_images_in_folder.py`. Models may be ensembled. 3. '_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model 4. '_model_history.npz' model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function `train_model.py` 5. '.png' model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function `train_model.py` Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU   References *Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. <a href="https://doi.org/10.1029/2022EA002332">https://doi.org/10.1029/2022EA002332</a> See: <a href="https://github.com/Doodleverse/segmentation_gym">https://github.com/Doodleverse/segmentation_gym</a> ** Buscombe, Daniel. (2022). Images and 4-class labels for semantic segmentation of Sentinel-2 and Landsat RGB, NIR, and SWIR satellite images of coasts (water, whitewater, sediment, other) (v1.0) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.7344571">https://doi.org/10.5281/zenodo.7344571</a>

Images and 4-class labels for semantic segmentation of Sentinel-2 and Landsat RGB, NIR, and SWIR satellite images of coasts (water, whitewater, sediment, other)

Images and 4-class labels for semantic segmentation of Sentinel-2 and Landsat RGB, NIR, and SWIR satellite images of coasts (water, whitewater, sediment, other) Description 579 images and 579 associated labels for semantic segmentation of Sentinel-2 and Landsat RGB satellite images of coasts. The 4 classes are 0=water, 1=whitewater, 2=sediment, 3=other These images and labels have been made using the Doodleverse software package, Doodler*. These images and labels could be used within numerous Machine Learning frameworks for image segmentation, but have specifically been made for use with the Doodleverse software package, Segmentation Gym**. Some (422) of these images and labels were originally included in the Coast Train*** data release, and have been modified from their original by reclassifying from the original classes to the present 4 classes. The label images are a subset of the following data release**** <a href="https://doi.org/10.5281/zenodo.7335647">https://doi.org/10.5281/zenodo.7335647</a> Imagery comes from the following 10 sand beach sites: <ol> <li>Duck, NC, Hatteras NC, USA</li> <li>Santa Cruz CA, USA</li> <li>Galveston TX, USA</li> <li>Truc Vert,France</li> <li>Sunset State Beach CA, USA</li> <li>Torrey Pines CA, USA</li> <li>Narrabeen, NSW, Australia</li> <li>Elwha WA, USA</li> <li>Ventura region, CA, USA</li> <li>Klamath region, CA USA</li> </ol> Imagery are a mixture of 10-m Sentinel-2 and 15-m pansharpened Landsat 7, 8, and 9 visible-band imagery of various sizes. Red, Green, Blue, NIR, and SWIR bands only File descriptions <ol> <li>classes.txt, a file containing the class names</li> <li>images.zip, a zipped folder containing the 3-band RGB images of varying sizes and extents</li> <li>nir.zip, a zipped folder containing the corresponding near-infrared (NIR) imagery</li> <li>swir.zip, a zipped folder containing the corresponding shortwave-infrared (SWIR) imagery</li> <li>labels.zip, a zipped folder containing the 1-band label images</li> <li>overlays.zip, a zipped folder containing a semi-transparent overlay of the color-coded label on the image (blue=0=water, red=1=whitewater, yellow=2=sediment, green=3=other)</li> <li>resized_images.zip, RGB images resized to 512x512x3 pixels</li> <li>resized_nir.zip, NIR images resized to 512x512x3 pixels</li> <li>resized_swir.zip, SWIR images resized to 512x512x3 pixels</li> <li>resized_labels.zip, label images resized to 512x512 pixels</li> </ol> References *Doodler: Buscombe, D., Goldstein, E.B., Sherwood, C.R., Bodine, C., Brown, J.A., Favela, J., Fitzpatrick, S., Kranenburg, C.J., Over, J.R., Ritchie, A.C. and Warrick, J.A., 2021. Human‐in‐the‐Loop Segmentation of Earth Surface Imagery. Earth and Space Science, p.e2021EA002085<a href="https://doi.org/10.1029/2021EA002085">https://doi.org/10.1029/2021EA002085</a>. See <a href="https://github.com/Doodleverse/dash_doodler">https://github.com/Doodleverse/dash_doodler.</a> **Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. <a href="https://doi.org/10.1029/2022EA002332">https://doi.org/10.1029/2022EA002332</a> See: <a href="https://github.com/Doodleverse/segmentation_gym">https://github.com/Doodleverse/segmentation_gym</a> ***Coast Train data release: Wernette, P.A., Buscombe, D.D., Favela, J., Fitzpatrick, S., and Goldstein E., 2022, Coast Train--Labeled imagery for training and evaluation of data-driven models for image segmentation: U.S. Geological Survey data release, <a href="https://doi.org/10.5066/P91NP87I">https://doi.org/10.5066/P91NP87I</a>. See <a href="https://coasttrain.github.io/CoastTrain/">https://coasttrain.github.io/CoastTrain/ </a>for more information **** Buscombe, Daniel, Goldstein, Evan, Bernier, Julie, Bosse, Stephen, Colacicco, Rosa, Corak, Nick, Fitzpatrick, Sharon, del Jesús González Guillén, Anais, Ku, Venus, Paprocki, Julie, Platt, Lindsay, Steele, Bethel, Wright, Kyle, & Yasin, Brandon. (2022). Images and 4-class labels for semantic segmentation of Sentinel-2 and Landsat RGB satellite images of coasts (water, whitewater, sediment, other) (v1.0) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.7335647">https://doi.org/10.5281/zenodo.7335647</a>

opencc-by-4.0Jun 2023View details →

June 2023 Supplement Images and 4-class labels for semantic segmentation of Sentinel-2 and Landsat RGB, NIR, and SWIR satellite images of coasts (water, whitewater, sediment, other)

June 2023 Supplement of Images and 4-class labels for semantic segmentation of Sentinel-2 and Landsat RGB, NIR, and SWIR satellite images of coasts (water, whitewater, sediment, other) Description Supplementary dataset to: Buscombe, Daniel, Goldstein, Evan, Bernier, Julie, Bosse, Stephen, Colacicco, Rosa, Corak, Nick, Fitzpatrick, Sharon, del Jesús González Guillén, Anais, Ku, Venus, Paprocki, Julie, Platt, Lindsay, Steele, Bethel, Wright, Kyle, & Yasin, Brandon. (2022). Images and 4-class labels for semantic segmentation of Sentinel-2 and Landsat RGB satellite images of coasts (water, whitewater, sediment, other) (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7335647 This supplemental dataset consists of 283 RGB images and 283 associated labels for semantic segmentation of Sentinel-2 and Landsat RGB satellite images of coasts. Of these, 77 images-label pairs also have a corresponding NIR and SWIR satellite image. The 4 classes are 0=water, 1=whitewater, 2=sediment, 3=other These images and labels have been made using the Doodleverse software package, Doodler*. These images and labels could be used within numerous Machine Learning frameworks for image segmentation, but have specifically been made for use with the Doodleverse software package, Segmentation Gym**. Imagery are a mixture of 10-m Sentinel-2 and 15-m pansharpened Landsat 7, 8, and 9 visible-band imagery of various sizes. NIR, SWIR, Red, Green, and Blue bands only File descriptions <ol> <li>classes.txt, a file containing the class names</li> <li>images.zip, a zipped folder containing the 3-band images of varying sizes and extents</li> <li>labels.zip, a zipped folder containing the 1-band label images</li> <li>overlays.zip, a zipped folder containing a semi-transparent overlay of the color-coded label on the image (blue=0=water, red=1=whitewater, yellow=2=sediment, green=3=other)</li> <li>nir.zip</li> <li>swir.zip</li> </ol> References Buscombe, Daniel, Goldstein, Evan, Bernier, Julie, Bosse, Stephen, Colacicco, Rosa, Corak, Nick, Fitzpatrick, Sharon, del Jesús González Guillén, Anais, Ku, Venus, Paprocki, Julie, Platt, Lindsay, Steele, Bethel, Wright, Kyle, & Yasin, Brandon. (2022). Images and 4-class labels for semantic segmentation of Sentinel-2 and Landsat RGB satellite images of coasts (water, whitewater, sediment, other) (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7335647 *Doodler: Buscombe, D., Goldstein, E.B., Sherwood, C.R., Bodine, C., Brown, J.A., Favela, J., Fitzpatrick, S., Kranenburg, C.J., Over, J.R., Ritchie, A.C. and Warrick, J.A., 2021. Human‐in‐the‐Loop Segmentation of Earth Surface Imagery. Earth and Space Science, p.e2021EA002085<a href="https://doi.org/10.1029/2021EA002085">https://doi.org/10.1029/2021EA002085</a>. See <a href="https://github.com/Doodleverse/dash_doodler">https://github.com/Doodleverse/dash_doodler.</a> **Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. <a href="https://doi.org/10.1029/2022EA002332">https://doi.org/10.1029/2022EA002332</a> See: <a href="https://github.com/Doodleverse/segmentation_gym">https://github.com/Doodleverse/segmentation_gym</a>

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Epipremnum aureum VIS-SWIR Hyperspectral Image from HYPERIA

The spectral image show two Epipremnum aureum leaves measured in the visible and short wave infrared (400-1700 nm) using the HERAVISSWIR device developed during the HYPERIA project. The leaf on the right is healty while the leaf on the left has evident stress signatures. The image was recorded from a 1m distance using a combination of a LED and halogen lamps to cover the full spectral range.

opencc-by-4.0Jul 2024View details →

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Doodleverse/Segmentation Zoo Res-UNet models for 4-class (water, whitewater, sediment and other) segmentation of Sentinel-2 and Landsat-7/8 7-band (RGB+NIR+SWIR+NDWI+MNDWI) images of coasts.

Doodleverse/Segmentation Zoo Res-UNet models for 4-class (water, whitewater, sediment and other) segmentation of Sentinel-2 and Landsat-7/8 7-band (RGB+NIR+SWIR+NDWI+MNDWI) images of coasts. Models have been created using Segmentation Gym* using the following dataset**: https://doi.org/10.5281/zenodo.7344571 Classes: {0=water, 1=whitewater, 2=sediment, 3=other} File descriptions For each model, there are 5 files with the same root name: 1. '.json' config file: this is the file that was used by Segmentation Gym* to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse. 2. '.h5' weights file: this is the file that was created by the Segmentation Gym* function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym* function  `seg_images_in_folder.py`. Models may be ensembled. 3. '_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model 4. '_model_history.npz' model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function `train_model.py` 5. '.png' model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function `train_model.py` Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU References *Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 See: https://github.com/Doodleverse/segmentation_gym ** Buscombe, Daniel. (2022). Images and 4-class labels for semantic segmentation of Sentinel-2 and Landsat RGB, NIR, and SWIR satellite images of coasts (water, whitewater, sediment, other) (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7344571

opencc-by-4.0Apr 2022View details →

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Leads without swirly cloud

Same as other link but the big set of pixels in the top left are removed.

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Images and 2-class labels for semantic segmentation of Sentinel-2 and Landsat RGB, NIR, and SWIR satellite images of coasts (water, other)

Images and 2-class labels for semantic segmentation of Sentinel-2 and Landsat RGB, NIR, and SWIR satellite images of coasts (water, other) Images and 2-class labels for semantic segmentation of Sentinel-2 and Landsat 5-band (R+G+B+NIR+SWIR) satellite images of coasts (water, other) Description 3649 images and 3649 associated labels for semantic segmentation of Sentinel-2 and Landsat 5-band (R+G+B+NIR+SWIR) satellite images of coasts. The 2 classes are 1=water, 0=other. Imagery are a mixture of 10-m Sentinel-2 and 15-m pansharpened Landsat 7, 8, and 9 visible-band imagery of various sizes. Red, Green, Blue, near-infrared, and short-wave infrared bands only These images and labels could be used within numerous Machine Learning frameworks for image segmentation, but have specifically been made for use with the Doodleverse software package, Segmentation Gym**. Two data sources have been combined Dataset 1 * 579 image-label pairs from the following data release**** https://doi.org/10.5281/zenodo.7344571 * Labels have been reclassified from 4 classes to 2 classes. * Some (422) of these images and labels were originally included in the Coast Train*** data release, and have been modified from their original by reclassifying from the original classes to the present 2 classes. * These images and labels have been made using the Doodleverse software package, Doodler*. Dataset 2 <ul> <li>3070 image-label pairs from the Sentinel-2 Water Edges Dataset (SWED)***** dataset, https://openmldata.ukho.gov.uk/, described by Seale et al. (2022)******</li> <li>A subset of the original SWED imagery (256 x 256 x 12) and labels (256 x 256 x 1) have been chosen, based on the criteria of more than 2.5% of the pixels represent water</li> </ul> File descriptions <ul> <li>    classes.txt, a file containing the class names</li> <li>    images.zip, a zipped folder containing the 3-band RGB images of varying sizes and extents</li> <li>    labels.zip, a zipped folder containing the 1-band label images</li> <li>    nir.zip, a zipped folder containing the 1-band near-infrared (NIR) images</li> <li>    swir.zip, a zipped folder containing the 1-band shorttwave infrared (SWIR) images</li> <li>    overlays.zip, a zipped folder containing a semi-transparent overlay of the color-coded label on the image (red=1=water, blue=0=other)</li> <li>    resized_images.zip, RGB images resized to 512x512x3 pixels</li> <li>    resized_labels.zip, label images resized to 512x512x1 pixels</li> <li>    resized_nir.zip, NIR images resized to 512x512x1 pixels</li> <li>    resized_swir.zip, SWIR images resized to 512x512x1 pixels</li> </ul> References *Doodler: Buscombe, D., Goldstein, E.B., Sherwood, C.R., Bodine, C., Brown, J.A., Favela, J., Fitzpatrick, S., Kranenburg, C.J., Over, J.R., Ritchie, A.C. and Warrick, J.A., 2021. Human‐in‐the‐Loop Segmentation of Earth Surface Imagery. Earth and Space Science, p.e2021EA002085https://doi.org/10.1029/2021EA002085. See https://github.com/Doodleverse/dash_doodler. **Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 See: https://github.com/Doodleverse/segmentation_gym ***Coast Train data release: Wernette, P.A., Buscombe, D.D., Favela, J., Fitzpatrick, S., and Goldstein E., 2022, Coast Train--Labeled imagery for training and evaluation of data-driven models for image segmentation: U.S. Geological Survey data release, https://doi.org/10.5066/P91NP87I. See https://coasttrain.github.io/CoastTrain/ for more information ****Buscombe, Daniel. (2022). Images and 4-class labels for semantic segmentation of Sentinel-2 and Landsat RGB, NIR, and SWIR satellite images of coasts (water, whitewater, sediment, other) (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7344571 *****Seale, C., Redfern, T., Chatfield, P. 2022. Sentinel-2 Water Edges Dataset (SWED) https://openmldata.ukho.gov.uk/ ******Seale, C., Redfern, T., Chatfield, P., Luo, C. and Dempsey, K., 2022. Coastline detection in satellite imagery: A deep learning approach on new benchmark data. Remote Sensing of Environment, 278, p.113044.

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Doodleverse/Segmentation Zoo Res-UNet models for 2-class (water, other) segmentation of Sentinel-2 and Landsat-7/8 5-band (RGB+NIR+SWIR) images of coasts.

Doodleverse/Segmentation Zoo Res-UNet models for 2-class (water, other) segmentation of Sentinel-2 and Landsat-7/8 5-band (RGB+NIR+SWIR) images of coasts. These Residual-UNet model data are based on RGB+NIR+SWIR (red, green, blue, near infrared and shortwave infrared) images of coasts and associated labels. Models have been created using Segmentation Gym* using the following dataset**: <a href="https://doi.org/10.5281/zenodo.7384263">https://doi.org/10.5281/zenodo.7384263</a> Classes: {0=other, 1=water} File descriptions For each model, there are 5 files with the same root name: 1. '.json' config file: this is the file that was used by Segmentation Gym* to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse. 2. '.h5' weights file: this is the file that was created by the Segmentation Gym* function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym* function  `seg_images_in_folder.py`. Models may be ensembled. 3. '_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model 4. '_model_history.npz' model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function `train_model.py` 5. '.png' model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function `train_model.py` Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU   References *Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. <a href="https://doi.org/10.1029/2022EA002332">https://doi.org/10.1029/2022EA002332</a> See: <a href="https://github.com/Doodleverse/segmentation_gym">https://github.com/Doodleverse/segmentation_gym</a> ** Buscombe, Daniel. (2022). Images and 2-class labels for semantic segmentation of Sentinel-2 and Landsat RGB, NIR, and SWIR satellite images of coasts (water, other) (v1.0) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.7384263">https://doi.org/10.5281/zenodo.7384263</a>

closedIPD-NOFeb 2026View details →

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SWIR Otoscopy Study

ClinicalTrials.gov study NCT06944795. IPD Sharing: NO. Countries: 1. Publications: 6.

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SWIR Camera in Walnut site 120 meters

Open the record for dataset details and reuse information.

publicJun 2018View details →

ASTER L2 Surface Radiance VNIR and SWIR V004

The Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Surface Radiance VNIR and SWIR (AST_09) contains atmospherically corrected data for the Visible and Near Infrared (VNIR) and Shortwave Infrared (SWIR) sensors. Both the VNIR and SWIR data are atmospherically corrected and are generated using the bands of the corresponding ASTER [Level 1B](https://doi.org/10.5067/ASTER/AST_L1B.004) image.The AST_09 product has a spatial resolution of 15 meters (m) for the VNIR bands and 30 m for the SWIR bands.Known Issues* Level 2 products that are on the international date line/anti-meridian have incorrect bounding coordinates for the UTM zone defined in the file metadata.* SWIR data acquired after April 2008 may exhibit anomalous saturation and striping. Users should consult the ASTER SWIR User Advisory for additional details.Improvements/Changes from Previous Versions* Enhanced Atmospheric Correction: Version 4 uses Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) data as the primary source for atmospheric parameters (ozone, water vapor, pressure, and temperature), improving the precision of emissivity calculations. Fallback Mechanisms: When MERRA-2 data are unavailable, the product employs Global Data Assimilation System (GDAS) data as a backup, with climatology data serving as a final fallback to ensure continuous processing. Radiometric Calibration Update: Version 4 applies Radiometric Calibration Coefficient Version 5 (RCC V5) to improve the radiometric accuracy of the raw DNs, based on research by [Tsuchida and others (2020)](https://doi.org/10.3390/rs12030427), published in Remote Sensing.

ASTER L2 Surface Radiance VNIR and SWIR V003

The Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Surface Radiance VNIR and SWIR (AST_09) is a multi-file product that contains atmospherically corrected data for both the Visible and Near Infrared (VNIR) and Shortwave Infrared (SWIR) sensors. The AST_09 product has a spatial resolution of 15 meters (m) for the VNIR bands and 30 m for the SWIR bands. Each product delivery includes two Hierarchical Data Format - Earth Observing System (HDF-EOS) files: one for the VNIR, and the other for the SWIR. They are distinguished from one another by a one-second difference in the production time that appears as part of the file name. The more obvious distinguishing feature is the file size; the VNIR file is always the larger of the two. Both the VNIR and SWIR data are atmospherically corrected and are generated using the bands of the corresponding [ASTER Level 1B](https://doi.org/10.5067/ASTER/AST_L1B.003) image.The ASTER L2 Surface Radiance VNIR and SWIR data product is only available through [NASA's Earthdata Search](https://search.earthdata.nasa.gov/search?q=C1299783574-LPDAAC_ECS). The ASTER Order Instructions provide step-by-step directions for ordering this product.Known Issues* Data Anomalies: Users are advised that ASTER SWIR data acquired from April 2008 to the present exhibit anomalous saturation of values and anomalous striping. This effect is also present for some prior acquisition periods. Please refer to the ASTER SWIR User Advisory for more details.* Data acquisition gaps: On November 28, 2024, one of Terra's power-transmitting shunt units failed. As a result, there was insufficient power to maintain functionality of the ASTER instrument. ASTER resumed acquisitions for the VNIR bands on January 18, 2025, and for the TIR bands on April 15, 2025. Users should note the data gap in ASTER acquisitions from November 28, 2024, through January 16, 2025, for VNIR observations, and a gap from November 28, 2024, through April 15, 2025, for TIR acquisitions.Improvements/Changes from Previous Version* The Science Scalable Scripts-based Science Processor for Missions (S4PM) Version 3.4 algorithm, which is used to generate L2 Product Generation Executables (PGEs), is relying on a new ancillary input for atmospheric parameters. Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) is global atmospheric reanalysis that combines remote sensing observations and interactions with the climate system. It will be one of the primary ozone and water vapor, pressure, and temperature inputs for L2 PGEs. MERRA-2 will provide a finer geographic resolution grid since it is a 3-dimensional, 3-hourly data collection with 50-km (latitudinal direction) spatial resolution.* The fallback options for L2 PGEs are as follows: * Ozone: [TOVS Ozone (OZ_DLY ) > AURA Ozone Monitoring Instrument (AURAOMI) > Total Ozone Analysis from Stratospheric and Tropospheric (TOAST) > Earth Probe-Total Ozone Mapping Spectrometer (EPTOMS)] or [MERRA-2] > National Centers for Environmental Prediction (NCEP)/Global Data Assimilation System (GDAS) > Climatology * Water Vapor, Pressure, and Temperature: [MOD07_L2] or [MERRA-2] > NCEP/GDAS > Climatology* Caveat: The temporal range for MERRA-2 covers 1980 to present; however, there is latency of ~3 weeks after the end of a month. Hence, NCEP/GDAS > Climatology fallback sequence will be applied for on-demand requests that fall outside of MERRA-2's temporal range or if the data is not science grade.* Starting June 23, 2021, radiometric calibration coefficient Version 5 (RCC V5) will be applied to newly observed ASTER data and archived ASTER data products. Details regarding RCC V5 are described in the following journal article. * Tsuchida, S., Yamamoto, H., Kouyama, T., Obata, K., Sakuma, F., Tachikawa, T., Kamei, A., Arai, K., Czapla-Myers, J.S., Biggar, S.F., and Thome, K.J., 2020, Radiometric Degradation Curves for the ASTER VNIR Processing Using Vicarious and Lunar Calibrations: Remote Sensing, v. 12, no. 3, at https://doi.org/10.3390/rs12030427.* As of December 15, 2021, the LP DAAC has implemented changes to ASTER PGE Version 3.4, which will affect all ASTER Level 2 on-demand products. Changes include: * Aura Ozone Monitoring Instrument (OMI) has been added as one of the ancillary ozone inputs for any observations made after May 27, 2020. The sequence of fallbacks for ozone will remain the same. * Toolkit has been updated from Version 5.2.17 to 5.2.20. Users may notice minor differences between the two versions. Differences may include minuscule changes in digital numbers around the peripheral of the granule and boundaries of a cloud for Surface Reflectance and Surface Radiance (AST07 and AST09) QA Data Plane depending on the Operating System and libraries being used by the user to process the data.* Additionally, Climatology, which is one of the inputs for Ozone and Moisture, Temperature and Pressures (MTP) will be removed from

ASTER L2 Surface Reflectance VNIR and Crosstalk Corrected SWIR V004

The Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Surface Reflectance VNIR and Crosstalk Corrected SWIR (AST_07XT) dataset contains measures of the fraction of incoming solar radiation reflected from the Earth’s surface to the ASTER instrument corrected for atmospheric effects and viewing geometry for both the Visible and Near Infrared (VNIR) and Shortwave Infrared (SWIR) sensors. Both the VNIR and SWIR data are atmospherically corrected and are generated using the bands of the corresponding ASTER [L1B](https://doi.org/10.5067/ASTER/AST_L1B.004) image. The AST_07XT product has a spatial resolution of 15 meters (m) for the VNIR bands and 30 m for the SWIR bands.Known Issues* Level 2 products that are on the international date line/anti-meridian have incorrect bounding coordinates for the UTM zone defined in the file metadata.* SWIR data acquired after April 2008 may exhibit anomalous saturation and striping. Users should consult the ASTER SWIR User Advisory for additional details.Improvements/Changes from Previous Versions* SWIR Crosstalk Correction: Applies a correction to reduce stray light effects in the SWIR bands, enhancing data quality by minimizing artifacts. Enhanced Atmospheric Correction: Version 4 uses Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) data as the primary source for atmospheric parameters (ozone, water vapor, pressure, and temperature), improving the precision of emissivity calculations. Fallback Mechanisms: When MERRA-2 data are unavailable, the product employs Global Data Assimilation System (GDAS) data as a backup, with climatology data serving as a final fallback to ensure continuous processing. Radiometric Calibration Update: Version 4 applies Radiometric Calibration Coefficient Version 5 (RCC V5) to improve the radiometric accuracy of the raw DNs, based on research by [Tsuchida and others (2020)](https://doi.org/10.3390/rs12030427), published in Remote Sensing.

ASTER L2 Surface Radiance VNIR and Crosstalk Corrected SWIR V004

The Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Surface Radiance VNIR and Crosstalk Corrected SWIR (AST_09XT) contains atmospherically corrected data for the Visible and Near Infrared (VNIR) and Shortwave Infrared (SWIR) sensors. The AST_09XT product has a spatial resolution of 15 meters (m) for the VNIR bands and 30 m for the SWIR bands. Both the VNIR and the SWIR data are atmospherically corrected using the corresponding bands from an ASTER [Level 1B](https://doi.org/10.5067/ASTER/AST_L1B.004) image.Known Issues* Level 2 products that are on the international date line/anti-meridian have incorrect bounding coordinates for the UTM zone defined in the file metadata.* SWIR data acquired after April 2008 may exhibit anomalous saturation and striping. Users should consult the ASTER SWIR User Advisory for additional details.Improvements/Changes from Previous Versions* SWIR Crosstalk Correction: Applies a correction to reduce stray light effects in the SWIR bands, enhancing data quality by minimizing artifacts. Enhanced Atmospheric Correction: Version 4 uses Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) data as the primary source for atmospheric parameters (ozone, water vapor, pressure, and temperature), improving the precision of emissivity calculations. Fallback Mechanisms: When MERRA-2 data are unavailable, the product employs Global Data Assimilation System (GDAS) data as a backup, with climatology data serving as a final fallback to ensure continuous processing. Radiometric Calibration Update: Version 4 applies Radiometric Calibration Coefficient Version 5 (RCC V5) to improve the radiometric accuracy of the raw DNs, based on research by [Tsuchida and others (2020)](https://doi.org/10.3390/rs12030427), published in Remote Sensing.

Sentinel-5P TROPOMI Irradiance product SWIR module L1B V2 (S5P_L1B_IR_SIR) at GES DISC

The Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm).TROPOMI Level-1B (L1B) product is generated by the Koninklijk Nederlands Meteoroligisch Instituut (KNMI) TROPOMI L01B processor from Level-0 input data and auxiliary data products with the netCDF-4 enhanced model. It provides users with radiance, irradiance, calibration and engineering products.

Sentinel-5P TROPOMI SNPP cloud product band 7 (SWIR detector) 1-Orbit L2 5.5km x 7km V1 (S5P_L2__NP_BD7_HiR) at GES DISC

Starting from August 6th in 2019, Sentinel-5P TROPOMI along-track high spatial resolution (~5.5km at nadir) has been implemented.For data before August 6th of 2019, please check S5P_L2__NP_BD7_1 data collection.The Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite mission is one of the European Space Agency's (ESA) new mission family - Sentinels, and it is a joint initiative between the Kingdom of the Netherlands and the ESA. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (UV-VIS, 270nm to 495nm), near infrared (NIR, 675nm to 775nm), and shortwave infrared (SWIR, 2305nm-2385nm). Sentinel-5P is the first of the Atmospheric Composition Sentinels and is expected to provide measurements of ozone, NO2, SO2, CH4, CO, formaldehyde, aerosols and cloud at high spatial, temporal and spectral resolutions.Copernicus Sentinel-5P is flying in a loose formation with U.S. Suomi National Polar-orbiting Partnership (SNPP) so that S5P is able to utilize the high spatial resolution capability of the Visible Infrared Imager Radiometer Suite (VIIRS) instrument. S5P_L2_NP_BDx product contains VIIRS cloud information for each S5P across-track observation in a given band (i.e. band 3, band 6 and band 7). In addition to the nominal filed-of-view (FOV), the S5P_NPPC products are also generated for three scaled FOVs both in along and across-track directions to account for the presence of cloud covering a more extended area than the nominal FOV. The main output of S5P_L2_NP_BDx are the number of VIIRS pixels classified as confidently cloudy, probably cloudy, probably clear, and confidently clear; and the VIIRS sun-normalized radiance information in band M7, M9, and M11 such as mean, standard deviation, as well as number of valid radiance contributions.

ASTER L2 Surface Reflectance VNIR and SWIR V004

The Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Surface Reflectance VNIR and SWIR (AST_07) data product contains measures of the fraction of incoming solar radiation reflected from the Earth’s surface to the ASTER instrument corrected for atmospheric effects and viewing geometry for both the Visible and Near Infrared (VNIR) and Shortwave Infrared (SWIR) sensors. The AST_07 product has a spatial resolution of 15 meters (m) for the VNIR bands and 30 m for the SWIR bands.Known Issues* Level 2 products that are on the international date line/anti-meridian have incorrect bounding coordinates for the Universal Transverse Mercator (UTM) zone defined in the file metadata.* SWIR data acquired after April 2008 may exhibit anomalous saturation and striping. Users should consult the ASTER SWIR User Advisory for additional details.Improvements/Changes from Previous Versions* Enhanced Atmospheric Correction: Version 4 uses Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) data as the primary source for atmospheric parameters (ozone, water vapor, pressure, and temperature), improving the precision of emissivity calculations. Fallback Mechanisms: When MERRA-2 data are unavailable, the product employs Global Data Assimilation System (GDAS) data as a backup, with climatology data serving as a final fallback to ensure continuous processing. Radiometric Calibration Update: Version 4 applies Radiometric Calibration Coefficient Version 5 (RCC V5) to improve the radiometric accuracy of the raw DNs, based on research by [Tsuchida and others (2020)](https://doi.org/10.3390/rs12030427), published in Remote Sensing.

Sentinel-5P TROPOMI SNPP cloud product band 7 (SWIR detector) 1-Orbit L2 5.5km x 7km V2 (S5P_L2__NP_BD7_HiR) at GES DISC