Find research datasets worth reusing

opencc-by-4.0Aug 2022View details →

Sentinel-3 NDVI ARD and Long Term Statistics (1999-2019) from the Copernicus Global Land Service over Lombardia

Sentinel-3 NDVI Analysis Ready Data (ARD)  (C_GLS_NDVI_20220101_20220701_Lombardia_S3_2.nc) product provided by the Copernicus Global Land Service [3]. The file C_GLS_NDVI_20220101_20220701_Lombardia_S3_2_masked.nc is derived from C_GLS_NDVI_20220101_20220701_Lombardia_S3_2.nc but values have been scaled (raw_value * ( 1/250)  - 0.08) and values lower then -0.08 and greater than 0.92 have been removed (set to missing values). The original dataset can also be discovered through the OpenEO API[5] from the CGLS distributor VITO [4]. Access is free of charge but an <a href="https://aai.egi.eu/">EGI registration</a> is needed. The file called Italy.geojson  has been created using the Global Administrative Unit Layers <a href="https://data.apps.fao.org/map/catalog/srv/eng/catalog.search#/metadata/9c35ba10-5649-41c8-bdfc-eb78e9e65654">GAUL G2015_2014</a> provided by FAO-UN (see <a href="https://data.apps.fao.org/map/catalog/srv/api/records/9c35ba10-5649-41c8-bdfc-eb78e9e65654/attachments/GAUL2015_Documentation.zip">Documentation</a>). It only contains information related to Italy.   Further info about drought indexes can be found in the Integrated Drought Management Programme [5] [1] <a href="https://www.sciencedirect.com/science/article/abs/pii/027311779500079T">Application of vegetation index and brightness temperature for drought detection</a> [2] <a href="https://en.wikipedia.org/wiki/Normalized_difference_vegetation_index">NDVI</a> [3] <a href="https://land.copernicus.eu/global/index.html">Copernicus Global Land Service</a> [4] <a href="https://vito.be/en">Vito</a> [5] <a href="https://openeo.org/">OpenEO</a> [5] <a href="https://www.droughtmanagement.info/indices">Integrated Drought Management</a>

opencc-by-4.0Nov 2019View details →

Surface deformation of the Mw 6.4 and Mw 7.1 Ridgecrest earthquakes measured from subpixel correlation of Copernicus Sentinel-2 optical images

Surface deformation of the Mw 6.4 and Mw 7.1 Ridgecrest earthquakes measured from subpixel correlation of Copernicus Sentinel-2 optical images

opencc-by-4.0May 2023View details →

Copernicus High Resolution Vegetation Phenology and Productivity for Doñana Natural Space

GeoTIFF rasters with the following phenometrics obtained from Sentinel 2 Data:.   * Start of the season Day of the Year (SOSD) * Maximun of the Season Day of the Year (MAXD) * End of the Season Day of the Year (EOSD) * Start of the season Value (SOSV) * Maximun of the Season Value (MAXV) * End of the Season Value (EOSV)   These rasters have been downloaded, mosaicked and croped with Doñana Natural Space DEIMS.ID through Pyvpp python package.

opencc-by-4.0Jul 2023View details →

Copernicus Global Land Cover data from 2015-01-01 to 2019-12-31 for Troms and Finnmark (Norway)

This dataset contains 100m x 100m maps of cover fraction expressed in % ground cover per pixel for 10 base classes including moss & lichen for years 2015 to 2019. The geographical area of interest corresponds to the Troms and Finnmark counties in Norway. Along with 10.5281/zenodo.8142713 this is to be used as input to forecast vegetation browning in Troms and Finnmark using machine learning.

opencc-by-4.0Jun 2024View details →

In situ dataset for initialization and validation of the Copernicus Med-MFC biogeochemical model system (MedBGCins)

The biogeochemical model system in use by the Mediterranean Monitoring Forecasting Centre (Med-MFC) of the EU Copernicus Marine Service requires several observational datasets for data assimilation and model initialization and validation (Coppini et al., 2023; Cossarini et al., 2021; Salon et al., 2019). The present MedBGCins dataset consists of the in situ measurements, coming from selected platforms, on which the initialization and validation of the biogeochemical model system are built. The MedBGCins dataset collects in situ measurements along the Mediterranean Sea water column and during the 1995-2023 time period for nutrients (i.e., nitrate, nitrite, phosphate, silicate, ammonium), dissolved oxygen, dissolved inorganic carbon, total alkalinity, total scale pH at 25°C. The dataset also provides pCO2 and total scale pH at in situ conditions, reconstructed by using the PyCO2SYS Python toolbox (Humpreys et al., 2024). The complete list of variables is indicated in Table 1. The largest subset of the original data are from EMODnet Chemistry Mediterranean Sea - Eutrophication and Acidity aggregated datasets 1911/2022 v2023 (reference in Table 2), including both profiles and time series, plus other documented cruises (same table). Additional information and references are included in the UserGuide file.

opencc-by-4.0Dec 2021View details →

PM2.5 4 days forecast from December, 22 2020 retrieved from Copernicus Monitoring Service

Dataset used in the Galaxy Pangeo tutorials on Xarray. Data is in netCDF format and is from <a href="https://ads.atmosphere.copernicus.eu/">Copernicus Air Monitoring Service</a> and more precisely PM2.5 (<a href="https://en.wikipedia.org/wiki/Particulates#Size,_shape_and_solubility_matter">Particle Matter < 2.5 μm</a>) 4 days forecast from December, 22 2021. This dataset is very small and there is no need to parallelize our data analysis. Parallel data analysis with Pangeo is not covered in this tutorial and will make use of another dataset.   This dataset is not meant to be useful for scientific studies.

opencc-by-4.0Jan 2022View details →

Copernicus Global Land Service: Global biome cluster layer for the 100m global land cover processing line

A map of 73 global biome clusters, geographic areas that were grouped to optimize the global 100m land cover processing. In order to group Earth Observation data for faster processing or adaptation of algorithms to specific regions, the 100m global land cover (CGLS-LC100) algorithm uses a Global Biome Cluster layer. The term biome cluster hereby refers to a geographic area which has similar bio-geophysical parameters and, therefore, can be grouped for processing. In other words, the biome cluster layer can be seen as an ecological regionalisation which outlines areas of similar environmental conditions, ecological processes, and biotic communities (Coops et al., 2018). There are already several global regionalisation layers existing, e.g. Ecoregions 2017 global dataset (Dinerstein et al., 2017), Geiger-Koeppen global ecozones after Olofsson update (Olofsson et al., 2012), Global ecological zones for FAO forest reporting with update 2010 (FAO, 2012). But several tests in the CGLS-LC100 workflow have shown that the existing layers did not provide the required global and continental classification accuracy. These findings go along with Coops et al. (2018) who stated that "Most regionalisations are made based on subjective criteria, and cannot be readily revised, leading to outstanding questions with respect to how to optimally develop and define them." Therefore, we decided to develop a customized ecological regionalisation layer which performs best with the given PROBA-V remote sensing data and the specifications of the CGLS-LC100 product. It groups spectral similar areas and helps to optimize the later classification/regression to regional patterns. Input into the layer creation were well-known existing datasets which were combined, re-grouped and advanced based on prior CGLS-LC100 classification results and local mapping knowledge of the workflow developer. To ensure that this layer is clearly separable from other existing regionalisations and not mistakenly interpreted as an eco-region layer, we decide to call it biome clusters layer. The following steps outline the global biome clusters layer generation: <ul> <li>Spatial union of Ecoregions 2017 dataset (Dinerstein et al., 2017), Geiger-Koeppen dataset (Olofsson et al., 2012) and Global FAO eco-regions datasets (FAO, 2012);</li> <li>Regrouping and dissolving by using experience from first global CGLS-LC100 mapping results and subjective mapping experience of the developer;</li> <li>Refinement of the biome clusters in the High North latitudes via incorporation of a Global tree-line layer (Alaska Geobotany Center, 2003);</li> <li>Manual improvement of borders between biome clusters to reduce classification artefacts by using a DEM and mapping experience from previous projects and continental test runs;</li> <li>Usage of a global land/sea mask, the Sentinel-2 tiling grid and PROBA-V imaging extent to extend the borders of the biome clusters into the sea to make sure that also small islands on the coastline are correctly processed.</li> </ul> When developing a regionalisation, the definition of the clusters and the boundaries that delineate them in time and space is the key challenge. Overall, the map distinguishes 73 global biome clusters.

opencc-by-4.0Nov 2018View details →

Country averages of Copernicus ERA5 hourly meteorological variables

Note: a new <a href="https://zenodo.org/record/2650191#.XMCUEBMzY3E">time-series dataset from ERA5</a> has been published — this one won't be updated/maintained anymore Country averages of meteorological variables generated using the R routines available in the package <a href="https://github.com/matteodefelice/panas">panas</a> based on the <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview">Copernicus Climate Change ERA5 reanalyses</a>. The time-series are at hourly resolution and the included variables are: <ul> <li>2-meter temperature (t2m),</li> <li>snow depth (snow_depth),</li> <li>mean sea-level pressure (mslp),</li> <li>runoff,</li> <li>surface solar radiation (ssrd),</li> <li>surface solar radiation with clear-sky (ssrdc),</li> <li>temperature at 850hPa (t850),</li> <li>total precipitation (total_prec),</li> <li>zonal (west-east direction) wind speed at 10m (u10) and 100m (u100),</li> <li>meridional (north-sud) wind speed at 10m (v10) and 100m (v100),</li> <li>dew point temperature (dew)</li> </ul> The original gridded data has been averaged considered the national borders of the following countries (European 2-letter country codes are used, i.e. ISO 3166 alpha-2 codes with the exception of GB->UK and GR->EL): AL, AT, BA, BE, BG, BY, CH, CY, CZ, DE, DK, DZ, EE, EL, ES, FI, FR, HR, HU, IE, IS, IT, LT, LU, LV, MD, ME, MK, NL, NO, PL, PT, RO, RS, SE, SI, SK, UA, UK. The unit measures here used are listed in the official page: https://cds.climate.copernicus.eu/cdsapp#!/dataset/era5-hourly-data-on-single-levels-from-2000-to-2017?tab=overview The script used to generate the files is available on github <a href="https://github.com/matteodefelice/era5-country-averages">here</a>

opencc-by-4.0Feb 2019View details →

Subpixel offsets of Copernicus Sentinel 2 data, related to the displacement field of the Sulawesi Earthquake (2018, Mw 7.5)

<a href="https://en.wikipedia.org/wiki/Sulawesi">Sulawesi</a> lies within a complex fault system located between the <a href="https://en.wikipedia.org/wiki/Australian_Plate">Australian</a>, <a href="https://en.wikipedia.org/wiki/Pacific_Plate">Pacific</a>, <a href="https://en.wikipedia.org/wiki/Philippine_Sea_Plate">Philippine</a> and <a href="https://en.wikipedia.org/wiki/Sunda_Plate">Sunda Plates</a>. The main active structure onshore at the western part of Central Sulawesi is the left-lateral NNW-SSE trending <a href="https://en.wikipedia.org/wiki/Palu-Koro_fault">Palu-Koro</a> <a href="https://en.wikipedia.org/wiki/Strike-slip_Fault">strike-slip fault</a> that forms the boundary between the North Sula and Makassar blocks. On 28 September 2018, a large tsunamigenic <a href="https://en.wikipedia.org/wiki/Earthquake">earthquake</a> (Mw 7.5) struck the <a href="https://en.wikipedia.org/wiki/Minahasa_Peninsula">Minahasa Peninsula</a>, Indonesia. The earthquake caused massive damages near Palu city, including onshore gravitational instabilities and a tsunami. These data are the result of subpixel image correlation on Copernicus Sentinel-2 data (17 September 2018 and 2 October 2018) to derive the two-dimensional (East-West and North-South) horizontal co-seismic displacement field. In these data, the displacement field is expressed in meters. These results show a dominant senextral strike-slip motion on the onshore part of the Palu-Koro fault. Maximum displacement at the surface reached more than 8 meters at the location of Palu city. Processing is performed with COSI-CORR (Leprince et al., 2007). Data value higher than | 10 | meters should be considered as noise and disregarded. I used a correlation window size of 32 pixels with a sampling step of 16 pixels. A ramp has been removed from (separately) the North-South offsets and from the East-West offsets. The files are rasters of floating point values, served with a header file readable by ENVI software. Sign conventions: -North-South offsets: positive values to the North. - East-West offsets:  positive values to the West.   <a href="http://www.esa.int/spaceinimages/Images/2018/10/Indonesia_earthquake_displacement_data">http://www.esa.int/spaceinimages/Images/2018/10/Indonesia_earthquake_displacement_data</a> Copyright: Contains modified Copernicus Sentinel data (2018), processed at the French Geological Survey (BRGM)

opencc-by-sa-4.0Sep 2018View details →

MAJA look-up tables for Sentinel-2 A&B sensors, for Copernicus Atmosphere Monitoring Service aerosol types

The archive contains the Look-up tables used by MAJA atmospheric correction software, used to process Sentinel-2 A&B sensors. These look-up tables correspond to the aerosol types used by Copernicus Atmosphere Monitoring Service (CAMS). However, the default continental model is also provided. Version 1.1 has new LUT for water vapour estimates, which corrects for a bias observed for large water vapour contents (above 2.5 g/cm2) Version 1.2 just changed the Folder name for a better integration with Start_maja. Version 1.3 added the Header files

opencc-by-4.0Jul 2019View details →

Subpixel optical correlation co-seismic offsets for the Mw 6.4 and Mw 7.1 Ridgecrest, California earthquakes, from Copernicus Sentinel 2 data

Two strong earthquakes (Mw 6.4 and Mw 7.1) took place near Ridgecrest, California, on July 4 2019 and July 6, respectively. <a href="https://earthquake.usgs.gov/earthquakes/eventpage/ci38443183/executive">https://earthquake.usgs.gov/earthquakes/eventpage/ci38443183/executive</a> <a href="https://earthquake.usgs.gov/earthquakes/eventpage/ci38457511/executive">https://earthquake.usgs.gov/earthquakes/eventpage/ci38457511/executive</a> In order to assess surface ruptures and the displacement field from the earthquakes, we used subpixel image correlation with Copernicus Sentinel-2 optical imagery (Band 4). MicMac and CosiCorr software was used to to extract the 2D (East-West and North-South) horizontal co-seismic displacement field. Four high-resolution figures are given per method and component (EW and NS). Road network (white lines - from OpenStreetMap) and Quaternary Faults (black polylines) from USGS (<a href="https://earthquake.usgs.gov/hazards/qfaults/">https://earthquake.usgs.gov/hazards/qfaults/</a>) are used for overlay. Rasters are given per software used (MICMAC_ for MicMac and COSI for CosiCorr), with a pixel resolution of 20m. Final product is corrected with detrending (to remove mostly registration errors) and filtered to remove noise. Stripes resulting from pushbroom scanner and orbit errors were not removed at this product (visible as WNW-ESE and NNE-SSW linear parallel stripes). -North-South displacement: positive values to the North. - East-West displacement:  positive values to the East. Raster files are projected in UTM Zone 11North WGS84 ( EPSG:32611)   A contribution to CEOS Working Group Disasters: Seismic Demonstrator Copyright: Contains modified Copernicus Sentinel data (2019), OpenStreetMap data (2019), Quaternary Fault and Fold Database of the United States - USGS (2019)

opencc-by-4.0Nov 2022View details →

SNAPPING PSI surface motion measurements over selected sites presented in MDPI Remote Sensing paper "SNAPPING Services on the Geohazards Exploitation Platform for Copernicus Sentinel-1 Surface Motion Mapping"

SNAPPING PSI surface motion measurements over selected sites as presented in the paper with the title "SNAPPING Services on the Geohazards Exploitation Platform for Copernicus Sentinel-1 Surface Motion Mapping"  by Michael Foumelis, Jose Manuel Delgado Blasco, Fabrice Brito, Fabrizio Pacini, Elena Papageorgiou, Panteha Pishehvar and Philippe Bally on Remote Sensing Open Access Journal. Whenever using this dataset, please cite its original paper (<a href="https://doi.org/10.3390/rs14236075">https://doi.org/10.3390/rs14236075</a>) and include the reference to this dataset (<a href="https://doi.org/10.5281/zenodo.7369653">https://doi.org/10.5281/zenodo.7369653</a>). This dataset includes average Line-of-Sight velocities for the following sites and dates: <table> <tbody> <tr> <td>Site name</td> <td>Country</td> <td>Period</td> <td>Relative orbit</td> <td>Orbit direction</td> </tr> <tr> <td>Cap-Haïtien</td> <td>Haiti</td> <td>Jan-2017 / Dec-2019</td> <td>106</td> <td>ascending</td> </tr> <tr> <td>Gran Renaissance Ethiopian Dam</td> <td>Ethiopia</td> <td>Jan-2019 / Jun-2021</td> <td>50</td> <td>descending</td> </tr> <tr> <td>La Palma Volcano</td> <td>Spain</td> <td>Jun-2019 / Dec-2021</td> <td>169</td> <td>descending</td> </tr> <tr> <td>Santorini Volcano</td> <td>Greece</td> <td>Apr-2015 / May-2021</td> <td>29</td> <td>ascending</td> </tr> <tr> <td>San Francisco</td> <td>USA</td> <td>Jan-2016 / Dec-2020</td> <td>115</td> <td>descending</td> </tr> <tr> <td>Thessaloniki International Airport (SKG)</td> <td>Greece</td> <td>Apr-2015 / Dec-2020</td> <td>102</td> <td>ascending</td> </tr> </tbody> </table>

opencc-by-4.0Sep 2023View details →

Observational datasets for validation of Mediterranean Biogeochemical Copernicus Modelling System, period 2018-2020

Datasets used for the validation of the biogeochemical component of the Mediterranean Analysis and Forecast center of the EU Copernicus Marine Service for the period 2018-2020. The list of datasets includes: 1) the Delay Mode Satellite chlorophyll from https://data.marine.copernicus.eu/product/OCEANCOLOUR_MED_BGC_L3_NRT_009_141/description after interpolation to the 1/24° horizontal resolution, weekly averages and quality check with internal climatology 2) the BGC-Argo float profiles of nitrate, chlorophyll and oxygen from Coriolis DAC (ftp://ftp.ifremer.fr/ifremer/argo; https://doi.org/10.17882/42182#76230) after an internal quality check procedure which is described in Salon et al., 2019.  3) the climatological profiles for 16 subbasins of nitrate, phosphate, silicate, oxygen, DIC, alkalinity, pCO2 and pH computed from the Emodnet 2018 data collection and additional scientific datasets as described in Salon et al., 2019.   Ref.: Salon, S., Cossarini, G., Bolzon, G., Feudale, L., Lazzari, P., Teruzzi, A., Solidoro, C. and Crise, A., 2019. Novel metrics based on Biogeochemical Argo data to improve the model uncertainty evaluation of the CMEMS Mediterranean marine ecosystem forecasts. Ocean Science, 15(4), pp.997-1022.

opencc-by-4.0Dec 2019View details →

Copernicus Global Land Service: Land Cover 100m: collection 3: epoch 2015: Globe

Base epoch 2015 from the Collection 3 of annual, global 100m land cover maps. Other available epochs: <a href="https://doi.org/10.5281/zenodo.3518026">2016</a>  <a href="https://doi.org/10.5281/zenodo.3518036">2017</a>  <a href="https://doi.org/10.5281/zenodo.3518038">2018</a>  <a href="https://doi.org/10.5281/zenodo.3939050">2019</a> Produced by the global component of the Copernicus Land Service, derived from PROBA-V satellite observations and ancillary datasets. The maps include <ul> <li>a main discrete classification with 23 classes aligned with UN-FAO's Land Cover Classification System,</li> <li>a set of versatile cover fractions: percentage (%) of ground cover for the 10 main classes</li> <li>a forest type layer</li> <li>quality layers on input data density</li> </ul> <a href="https://land.copernicus.eu/global/lcviewer">Click here to view the maps</a> <a href="https://land.copernicus.eu/global/lcviewer">More information about the land cover maps</a> <a href="https://doi.org/10.5281/zenodo.3606295">Product User Manual</a>

opencc-zeroJul 2016View details →

Subsidence of Beijing (China) mapped by Copernicus Sentinel-1 time series interferometry

RESULTS DESCRIPTION Recent reports from scientific and mainstream media have indicated that the city of Beijing, together with its surroundings, is subsiding at fast and alarming rate as result of the overexploitation of groundwater. The depletion of groundwater causes underlying soil to compact, creating a phenomenon called subsidence. The Beijing region has been experiencing this phenomenon since 1935, but in last years the rate of sinking has significantly increased. A team of researchers, within ESA sponsored, SEOM InSARap project performed an interferometric analysis of Copernicus Sentinel-1 data which confirms the reported findings also with current data. While the results speak for themselves, we can just once more reiterate on the usefulness of the Copernicus Programme, in this case for deformation monitoring applications. ANALYSIS SUMMARY <ul> <li>Data overview: <ul> <li>Sentinel-1 IW</li> <li>Track 47 descending</li> <li>Observation window December 2014 - June 2016</li> <li>Data download via Scientific Data Hub</li> </ul> </li> <li>Processing overview: <ul> <li>Time series analysis performed with Small Baseline Subset (SBAS) methodology</li> <li>Interferometric combinations of up to 96 days used</li> </ul> </li> </ul> More information and context available at insarap.org Terms and Conditions: All Sentinel-1 results that are available for download are Derived Works of Copernicus data (2014-2016), subject to the "TERMS AND CONDITIONS FOR THE USE AND DISTRIBUTION OF SENTINEL DATA AND SERVICE INFORMATION". Acknowledgments:  ESA SEOM InSARap project - Sentinel-1 InSAR Performance Study with TOPS Data, contract number 4000110680/14/I-BG-InSARap

opencc-by-4.0Nov 2023View details →

INTERACT arctic research stations: Historic weather data fetched and plotted from Copernicus Climate Data Store

Plots with historic weather data for arctic research stations covered by International Network for Terrestrial Research and Monitoring in the Arctic (INTERACT). Comparison with WMO climate normal periods 1961-1990 and 1991-2020, averages and stations deviations from the normal periods are calculated and plotted.The data source for historic data of temperature and precipitation is: Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., Thépaut, J-N. (2023): ERA5 monthly averaged data on single levels from 1940 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: 10.24381/cds.f17050d7  See the linked github repository https://zenodo.org/doi/10.5281/zenodo.10214962 for how data was fetched from Copernicus Climate Data Store, prepared and plotted. The github repository also contains CSVs with source data for the plots.

openNov 2024View details →

Processed Sentinel 1, Sentinel 2 and Copernicus Emergency Management Service data for fine tuning and predicting flood extent with IBM's granite-geospatial-uki-flood-detection model

This dataset contains processed Sentinel 1 Sentinel 2 imagery together with flood event labels extracted from the Copernicus Emergency Management Service. It has been assembled to demonstrate fine tuning and inference of flood event segmentation using granite geospatial foundation models developed by IBM Research. Please see <a href="https://huggingface.co/ibm-granite/granite-geospatial-uki-flooddetection">https://huggingface.co/ibm-granite/granite-geospatial-uki-flooddetection</a> for more information on models and use. Sentinel-1 The European Space Agency. 2014. Sentinel-1 Mission. <a href="https://sentinel.esa.int/web/sentinel/copernicus/sentinel-1">https://sentinel.esa.int/web/sentinel/missions/sentinel1</a>. Accessed: 2024-11-25. Sentinel-2 The European Space Agency. 2015. Sentinel-2 Mission. <a href="https://sentinel.esa.int/web/sentinel/copernicus/sentinel-2">https://sentinel.esa.int/web/sentinel/missions/sentinel2</a>. Accessed: 2024-11-25. Copernicus Emergency Management Service <a href="https://emergency.copernicus.eu/mapping/list-of-activations-rapid">https://emergency.copernicus.eu/mapping/list-of-activations-rapid</a>. Accessed: 2024-11-25.  Attribution Contains modified Copernicus Sentinel data [2019-2024] Contains modified Copernicus Service information [2019-2023]

opencc-byNov 2021View details →

Fire danger indices historical data from the Copernicus Emergency Management Service

June, July August consolidated data. Generated using Copernicus Climate Change Service information 2021.

opencc-by-4.0Aug 2022View details →

Selected boulders at Copernicus central peak from L. Sun and P. Lucey Lunar boulder study work

This file contains the location of 54 boulders and soils on Copernicus central peak that were studied by Sun L. and Lucey P. G. in their lunar boulder study paper. This file can be uploaded to the LROC Quickmap website (https://quickmap.lroc.asu.edu/) for checking the location of each boulder and their nearby soils.