Find research datasets worth reusing

opencc-by-4.0Jul 2020View details →

ESA-WOC North Atlantic Sea Surface Salinity maps from a multivariate combination of satellite and in situ surface measurements (2010-2018)

<p>We deliver here the daily sea surface salinity level 4 (SSS L4) product developed in the framework of the  European Space Agency World Ocean Circulation project (ESA-WOC), covering the period 2010-2018. This product was obtained by adapting to a 1/10° North Atlantic grid the multidimensional optimal interpolation algorithm used within the Copernicus Marine Environment Monitoring Service to retrieve the global SSS multi-year dataset (<a href="http://marine.copernicus.eu/services-portfolio/access-to-products/">http://marine.copernicus.eu/services-portfolio/access-to-products/</a>, product_id: MULTIOBS_GLO_PHY_REP_015_002, dataset_id: dataset-sss-ssd-rep-weekly). This algorithm interpolates SMOS observations and in situ SSS observations considering a space-time-thermal decorrelation function, estimated by including information from high-pass filtered daily SST data (Droghei et al., 2016; Buongiorno Nardelli, 2012). Here, we ingested the L3OS 2Q debiased daily valid ocean salinity values product from SMOS satellite, produced and disseminated by the Centre Aval de Traitement des Données SMOS (CATDS, 2017), OSTIA SST data (CMEMS, <a href="http://marine.copernicus.eu/services-portfolio/access-to-products/">http://marine.copernicus.eu/services-portfolio/access-to-products/</a>, product_id=SST_GLO_SST_L4_REP_OBSERVATIONS_010_011) and CORA5.2 surface salinity values (<a href="http://marine.copernicus.eu/services-portfolio/access-to-products/">http://marine.copernicus.eu/services-portfolio/access-to-products/</a>, product_id: INSITU_GLO_TS_REP_OBSERVATIONS_013_001_b, doi: 10.17882/46219TS1, Szekely et al., 2019) as input data, and used CMEMS weekly SSS dataset to build our background field (linearly interpolating it in time between the two closest analysis dates, and upsizing to the 1/10° grid through a cubic spline). All other interpolation parameters were set as in Droghei et al. (2018).            </p> <p> </p> <p><em>References:</em></p> <p>Buongiorno Nardelli, B.: A Novel Approach for the High-Resolution Interpolation of In Situ Sea Surface Salinity, J. Atmos. Ocean. Technol., 29(6), 867–879, doi:10.1175/JTECH-D-11-00099.1, 2012.</p> <p>CATDS (2017). CATDS-PDC L3OS 2Q - Debiased daily valid ocean salinity values product from SMOS satellite. CATDS (CNES, IFREMER, LOCEAN, ACRI). http://dx.doi.org/10.12770/12dba510-cd71-4d4f-9fc1-9cc027d128b0</p> <p>Droghei, R., Buongiorno Nardelli, B. and Santoleri, R.: Combining in-situ and satellite observations to retrieve salinity and density at the ocean surface, J. Atmos. Ocean. Technol., 33, 1211–1223, doi:10.1175/JTECH-D-15-0194.1, 2016.</p> <p>Droghei, R., Buongiorno Nardelli, B. and Santoleri, R.: A New Global Sea Surface Salinity and Density Dataset From Multivariate Observations (1993–2016), Front. Mar. Sci., 5(March), 1–13, doi:10.3389/fmars.2018.00084, 2018.</p> <p>Szekely, T., Gourrion, J., Pouliquen, S. and Reverdin, G.: The CORA 5.2 dataset for global in situ temperature and salinity measurements: Data description and validation, Ocean Sci., 15(6), 1601–1614, doi:10.5194/os-15-1601-2019, 2019.</p>

opencc-by-4.0Jun 2024View details →

ESA Cryo-TEMPO - Northern hemisphere land/ocean flag and distance to coast at resolution of 250 m.

<p>Land/Ocean flag nd distance to coast at high spatial resolution (250m) in the northern hemisphere. The land/ocean flag is computed from merged Open Street Map and Natural Earth land polygons. The shapefiles were rasterized and reprojected to northern hemisphere using gdal. All land mass with the exception of Greenland are based on Open Street Map. Distance to coast was computed with gdal (gdal_proximity.py). </p> <p>The file format is netCDF-4 and the datafile contains two variables (land_ocean_flag & distance_to_coast). The coordinate reference system of the variables is defined by EPSG:6931 (WGS 84 / NSIDC EASE-Grid 2.0 North) and the bounds of the data set are supplied as xc and yc variables in the data file. </p> <p>The file is used in the ESA CryoSat-2 Thematic Products (Cryo-TEMPO) Polar Ocean and Sea Ice products in the northern hemisphere. </p> <p> </p>

opencc-by-4.0Sep 2024View details →

Marine heatwaves and cold spells events based on ESA-CCI SSTs (experimental product)

<p>This repository contains an extension of the catalogues of marine heatwaves (MHWs) and cold spells (MCSs) prepared by the National Research Council - Institute of Marine Sciences (CNR-ISMAR, Italy) within the ESA-funded CAREHeat project. The catalogues are based on the ESA-CCI sea surface temperature (SST) dataset (available from https://doi.org/10.24381/cds.cf608234) for the period 1982-2022, on a regular 1°x1° longitude-latitude grid.</p> <p>Events are identified for each pixel following the methodology of Hobday et al. (2016) after preprocessing. Event categories are provided as daily maps and metrics are given by event. Results are <strong>experimental</strong> since the post-processing procedure effectively removes interannual variability from the SST record, so please use having consulted the documentation and not for operational purposes. </p> <p><br>Please cite the reference paper "Serva, F., et al.: Detection of Satellite Sea Surface Temperature Extremes: Low Frequency Variability and Climate Change, JGR:Oceans, 10.1029/2025JC022886, 2025" when using the dataset in your work.</p>

opencc-by-4.0Apr 2023View details →

Global Crop Type Validation Data Set for ESA WorldCereal System

<p>This dataset was created by using a new IIASA tool, called “Street Imagery validation” (<a href="https://svweb.cloud.geo-wiki.org/">https://svweb.cloud.geo-wiki.org/</a>) where users could check street level images (e.g., Google Street Level images, Mapillary etc.) and identify the crop type where it is possible. The advantage of this tool is that there are plenty of georeferenced images with dates, going back in time. The disadvantage is that users need to check plenty of images where only few will clearly show cropland fields that are mature enough to be identified. To make the data collection more efficient, we provided our experts with preliminary maps of points in agricultural areas where street level images are available for the year 2021. Then, the experts checked those locations in an opportunistic way. The dataset is completely independent from all the existing maps and the reference datasets.</p> <p>There are 3 main data records uploaded:</p> <ol> <li>sv_croptype_poly.zip – an archive with a shapefile containing all the collected polygons with crop type information. Not all the polygons correspond to actual field boundaries.</li> <li>sv_croptype_validations.csv – a table with crop type observations with centroid coordinates in WGS84</li> <li>sv_worldcereal_validation.csv – a table with a subset of crop type observations used in validation of WorldCereal crop type maps for 2021.</li> </ol> <p>Fields:</p> <ul> <li>"id" – unique observation identifier;</li> <li>"imgSource" – source of imagery used for visual inspection;</li> <li>"imgLoc" – image location;</li> <li>"svImgDate" – image date;</li> <li>"imageIdKey" – image unique identifier;</li> <li>"submitedAt" – date of submission of crop type observation;</li> <li>"cropType"  - crop type observation;</li> <li>"irrType" – irrigation type;</li> <li>"x", "y" – centroids of submitted polygons in WGS84.</li> </ul>

opencc-by-4.0Dec 2022View details →

ESA WorldCereal 10 m 2021 v100

<p><strong>ESA WorldCereal 2021 products v100</strong></p> <p>The European Space Agency (ESA) WorldCereal 10m 2021 product suite consist of global-scale annual and seasonal crop maps and (where applicable) their related confidence. Every file in this repository contains up to 106 agro-ecological zone (AEZ) products which were all processed with respect to <a href="https://www.tandfonline.com/doi/full/10.1080/15481603.2022.2079273">their own regional seasonality</a> and should be considered as independent products.</p> <p>Naming convention of the ZIP files is as follows:</p> <p><strong>WorldCereal_{year}_{season}_{product}_{classification|confidence}.zip</strong></p> <p>The actual AEZ-based GeoTIFF files inside each ZIP are named according to following convention:</p> <p><strong>{AEZ_id}_{season}_{product}_{startdate}_{enddate}_{classification|confidence}.tif</strong></p> <p>The seasons are defined in Table 1. Note that <strong>cereals</strong> as described by WorldCereal include wheat, barley and rye, which belong to the <em>Triticeae</em> tribe. Next to the actual WorldCereal products, this repository contains the files "<strong>WorldCereal_AEZ.geojson</strong>" that contains the AEZ description and outline, as well as "<strong>QGIS_stylefiles.zip</strong>"<strong> </strong>which contains QGIS style files (.qml) for product visualization purposes.</p> <table> <tbody> <tr> <th>Season</th> <th>Description</th> </tr> </tbody> <tbody> <tr> <td>tc-annual</td> <td>A one-year cycle being defined in a region by the end of the last considered growing season</td> </tr> <tr> <td>tc-wintercereals</td> <td>The main cereals season defined in a region</td> </tr> <tr> <td>tc-springcereals</td> <td>Optional springcereals season, only defined in certain AEZ</td> </tr> <tr> <td>tc-maize-main</td> <td>The main maize season defined in a region</td> </tr> <tr> <td>tc-maize-second</td> <td>Optional second maize season, only defined in certain AEZ.</td> </tr> </tbody> </table> <p><strong>Note</strong>: AEZs for which no irrigation product is available were not processed because of the unavailability of thermal Landsat data.</p> <p>A scientific paper describing the WorldCereal products and the methodology behind them is available through the link below:</p> <p><a href="https://essd.copernicus.org/articles/15/5491/2023/essd-15-5491-2023.html" target="_blank" rel="noopener">Van Tricht, K., Degerickx, J., Gilliams, S., Zanaga, D., Battude, M., Grosu, A., Brombacher, J., Lesiv, M., Bayas, J. C. L., Karanam, S., Fritz, S., Becker-Reshef, I., Franch, B., Mollà-Bononad, B., Boogaard, H., Pratihast, A. K., Koetz, B., and Szantoi, Z.: WorldCereal: a dynamic open-source system for global-scale, seasonal, and reproducible crop and irrigation mapping, Earth Syst. Sci. Data, 15, 5491–5515, https://doi.org/10.5194/essd-15-5491-2023, 2023.</a></p> <p><em>This work was supported by the European Space Agency under contract N°4000130569/20/I-NB.</em></p>

opencc-by-4.0Apr 2020View details →

ERT data collected at the Corona volcano (Lanzarote, Canary Islands) during the European Space Agency (ESA) testing campaign PANGAEA-X 2017

<p>This dataset contains the ERT (Electrical Resistivity Tomography) data collected between 22 and 23 November 2017 at the Corona volcano (Lanzarote, Canary Islands, Fig. 1) for the detection of lava tubes and the stratigraphic investigation of planetary volcanic analogues. This geophysical survey was carried out within the European Space Agency (ESA) testing campaign PANGAEA-X 2017 (Bessone et al., 2018), aimed at integrating astronaut training-data collection, documentation, analogue field geology procedures with remote sensing and in situ geophysical methods. </p> <p>Two ERT profiles were acquired in NE-SW and NNE-SSW orientations (Fig. 1). These were located roughly orthogonal to the Corona lava tube system and as far as possible on top of the main lava tube axes. The longer profile, profile D, is 470 m in length and was obtained using 48 electrodes spaced 10 m apart. The profile orientation is from SW to NE (electrode 1 to 48). The profile was acquired to detect lava tubes in test site D (sub-area south) where the exact location of a lava tube was known thanks to a LiDAR TLS (Terrestrial Laser Scan) subsurface survey (Santagata et al., 2018). A shorter profile, profile E, is 235 m long and was obtained using 48 electrodes 5 m apart. The profile orientation is from SSW to NNE (electrode 1 to 48). This profile was acquired in test site E (sub-area north) to provide a more detailed investigation of the potential existence of inaccessible sections of the tube whose location could be indicated by the evidence of closely-spaced aligned collapse structures.</p> <p>Each profile was collected using measure sequences compounded by 276 Wenner-Schlumberger array quadrupoles which ensure high vertical resolution and signal amplitude and 328 dipole-dipole array quadrupoles which provide enhanced lateral resolution. A fully automatic multi-electrode resistivity meter SYSCAL Jr Switch-48 by IRIS Instruments (400 V max output voltage, 1200 mA max output current, 100 W max output power, <a href="http://www.iris-instruments.com/syscal-juniorsw.html">http://www.iris-instruments.com/syscal-juniorsw.html</a>), was used for data collection.</p> <p>At most of the measurement points, it was necessary to drill the basalt using a hand drilling machine in order to place the tips of the electrodes into the ground at a depth of approximately 40 cm. The electrodes also needed to kept moist to reduce contact resistance between the electrode and the ground. A large amount of water (up to 2 liters per point) was needed for profile D, situated in an area above the lava tubes with very porous dry soil cover.</p> <p>The dataset is presented as a spreadsheet format which has the "space" as separator and the ".txt" extension. The structure of such a file is the following one:</p> <p>#, El array, Spa1/4, Rho, Dev, M, Sp, Vp, In, Time, Spa5/12, M1/20</p> <p>- #: Data point number</p> <p>- El array: Electrode array</p> <p>- Spa. 1/4: four spacing parameters (corresponding to the electrode array – in m)</p> <p>- Rho: resistivity value (in Ohm.m)</p> <p>- Dev: standard deviation (quality factor, in %)</p> <p>- M: global chargeability value (induced polarization parameter (in mV/V – "=0" if only-resistivity data))</p> <p>- Sp: spontaneous polarization (measured just before the injection, in mV)</p> <p>- Vp: measured primary voltage (in mV)</p> <p>- In: injected current intensity (in mA)</p> <p>- Time: injection time (pulse duration, in s)</p> <p>- Spa. 5/8: other spacing parameters (in m)</p> <p>- Spa. 9/12: electrode elevation (in m)</p> <p>- M1/M20: partial chargeability values (induced polarization window (in mV/V – "=0" if only-resistivity data))</p> <p> </p> <p>Acknowledgements</p> <p>The authors are grateful to ESA and all PANGAEA-X 2017 staff, particularly Loredana Bessone, Matthias Maurer, Herve Stevenin and Igor Drozdovskiy for their participation in data collection during some of the experiments and to the MilesBeyond Team, particularly Francesco Maria Sauro for his logistical support. Regional and local remote sensing data were obtained by the Spanish Instituto Geográfico Nacional (https://www.ign.es) and Gobierno de Canarias (https://www.grafcan.es, <a href="https://opendata.sitcan.es/">https://opendata.sitcan.es</a>).</p> <p> </p> <p>References</p> <p>Bessone, L., et al., 2018, Testing technologies and operational concepts for field geology exploration of the Moon and beyond: the ESA PANGAEA-X campaign, Geophysical Research Abstract, #EGU2018-4013.</p> <p>Santagata, T., Sauro, F., Massironi, M., Pozzobon, R., Del Vecchio, U., Lazzaroni, M., Damiano, N., Tonello, M., Tomasi, I., Martínez-Frìas, J. and Mateo Medero, E., 2018. Subsurface laser scanning and photogrammetry in the Corona Lava Tube System, Lanzarote, Spain, EGU General Assembly 2018, pp. EGU2018-5290.</p>

opencc-zeroOct 2020View details →

Validation of ESA CCI SM combined v04.7 vs ESA CCI SM combined v05.2 vs ISMN 20191211 global

QA4SM validation of soil moisture data: ESA CCI SM combined v04.7 vs ESA CCI SM combined v05.2 vs ISMN 20191211 global. URL: https://qa4sm.eu/result/ccfcee99-1d2a-4ee6-9919-8a3d87601d0b/. Produced on QA4SM (https://qa4sm.eu)

Validation of ESA CCI SM combined v05.2 vs ISMN 20191211 global - Anomalies and no ISMN flags

QA4SM validation of soil moisture data: ESA CCI SM combined v05.2 vs ISMN 20191211 global. URL: https://qa4sm.eu/result/5f6ae4c5-5115-4022-b489-99f4dda1089f/. Produced on QA4SM (https://qa4sm.eu)

Validation of ESA CCI SM combined v05.2 vs ISMN 20191211 global - Anomalies and ISMN flagged

QA4SM validation of soil moisture data: ESA CCI SM combined v05.2 vs ISMN 20191211 global. URL: https://qa4sm.eu/result/d8b5f409-2bb3-4580-be0b-0c9c2d71c968/. Produced on QA4SM (https://qa4sm.eu)

Validation of ESA CCI SM combined v05.2 vs ISMN 20191211 global - without Anomalies and ISMN flagged

QA4SM validation of soil moisture data: ESA CCI SM combined v05.2 vs ISMN 20191211 global. URL: https://qa4sm.eu/result/73ed1e31-eaa5-469a-ab9b-4451e4e4d4df/. Produced on QA4SM (https://qa4sm.eu)

Validation of ESA CCI SM combined v05.2 vs ISMN 20191211 global - without anomalies and without ISMN flags

QA4SM validation of soil moisture data: ESA CCI SM combined v05.2 vs ISMN 20191211 global. URL: https://qa4sm.eu/result/bbf7f693-74ff-4b03-8f5d-792b7b846b40/. Produced on QA4SM (https://qa4sm.eu)

opencc-by-4.0Nov 2018View details →

ESA SEOM-IAS – Measurement and ACS database O3 UV region

<p>The database contains measurements and absorption cross sections generated within the framework of the ESA project SEOM-IAS (Scientific Exploitation of Operational Missions - Improved Atmospheric Spectroscopy Databases), ESA/AO/1-7566/13/I-BG. Details on the project can be found at http://www.wdc.dlr.de/seom-ias/.</p> <p>The measurements were recorded at the German Aersopace Center (DLR) to provide a new absorption cross section database for ozone according to the needs of the TROPOMI instrument aboard the Sentinel 5-P satellite. The data are compiled in 2  zip files, one for the measurements (O3_UV_region_measurement_database_13112018.zip), one for the absorption cross sections (O3_UV_region_absorption_cross_section_database_13112018.zip) and a readme file (ESA_SEOM_IAS_spectra_O3UVRegion_readme.docx).</p> <p>The file “ACS_pTpoly_2nd+1st_order_BGcoefffit_constant_offset_V2.asc” in version II replaces the older version which contained an error in the wavelength axis. The readme file was updated, too.</p>

opencc-by-4.0Nov 2018View details →

ESA SEOM-IAS – Measurement and line parameter database O3 MIR region

<p>The database contains measurements and line parameters generated within the framework of the ESA project SEOM-IAS (Scientific Exploitation of Operational Missions - Improved Atmospheric Spectroscopy Databases), ESA/AO/1-7566/13/I-BG. Details on the project can be found at http://www.wdc.dlr.de/seom-ias/.</p> <p>The measurements were recorded and analysed at the German Aerospace Center (DLR) and the University of Reims (URCA) to provide a new line position and intensity database for ozone fundamentals in the mid infrared region. The data are compiled in five zip files, three for the measurements (O3_MIR_region_DLR_measurement_database_20112018.zip, O3_MIR_region_DLR_measurement_N2O2broad_database_07012021.zip, O3_MIR_region_URCA_measurement_database_20112018.zip), two for the line parameter databases (O3_MIR_region_DLR_parameter_database_08012021.zip, O3_MIR_region_URCA_parameter_database_20112018.zip) and a readme file (ESA_SEOM_IAS_O3MIRRegion_readme_V3.docx).</p> <p>Note: The DLR parameter database is replaced by a newer version.</p>

opencc-by-4.0Nov 2023View details →

CLaMS results used for age of air analysis in the report of assessment of the ESA Earth Explorer candidate mission CAIRT

<p>Results of the Chemical Lagrangian Model of the Stratosphere (CLaMS) used for age of air analysis in the report of assessment of the ESA Earth Explorer candidate mission CAIRT. Days of results: 2011-01-01, 2011-04-01, 2011-07-01, 2011-10-01 and 2019-09-23. Included trace gases: SF6, CFC-11, CFC-12, HCFC-22, N2O and CH4. The results also include the clock tracer BA, which can be converted into the precise mean age of or air of the model environment with the attached python script 'AOA2age_years.py'.</p>

opencc-by-4.0Jul 2022View details →

Engineering Dust Coma Model (EDCM) for ESA's Comet Interceptor mission to a dynamically new comet

<p>This data-set contains all results from the Engineering Dust Coma Model (EDCM) for ESA's Comet Interceptor (CI) mission to a dynamically new comet.</p> <p>A full description of the model behind the data can be found in the peer-reviewed paper <strong>Marschall, Zakharov et al. (2022), <a href="https://doi.org/10.1051/0004-6361/202243648">https://doi.org/10.1051/0004-6361/202243648</a>.</strong> Please cite this data-set and the paper when using the data.</p> <p>Contemporary numerical models of dusty-gas coma are used to obtain spatial distribution of dust for a given set of parameters. By varying parameters within a range of possible values we obtain an ensemble of possible dust distributions. Then, this ensemble is statistically evaluated in order to define the most probable cases and hence reduce the dispersion. This ensemble can be used to estimate not only the likely dust abundance along e.g. a fly-by trajectory of a spacecraft but also quantify the associated uncertainty.</p> <p>The dust environment assessment for the case when the target comet is not known beforehand (or when its parameters are known with large uncertainty) is critical for spacecraft safety and planning. The EDCM provides an assessment of dust environment for the CI mission.</p>

opencc-by-4.0Jul 2017View details →

Derivation of plant functional type (PFT) maps from the ESA CCI Land Cover product

<p><em>This package supplements the following paper submitted to ESSD: <strong>Gross and net land cover changes of the main plant functional types derived from the annual ESA CCI land cover maps (1992-2015).</strong></em></p> <p><em>Li, W., MacBean, N., Ciais, P., Defourny, P., Lamarche, C., Bontemps, S., Houghton, R. A. and Peng, S.: Gross and net land cover changes based on plant functional types derived from the annual ESA CCI land cover maps, Earth Syst. Sci. Data Discuss., 1–23, doi:10.5194/essd-2017-74, 2017.</em></p> <p><em>This package contains the protocol of converting the original annual ESA CCI Land Cover product into plant functional types (PFTs) that can be used by land surface models and the corresponding cross-walking table.</em></p> <p><em>The original ESA LC class data and translated PFTs in 2000 as an example are attached in the .zip file. The annual ESA CCI PFT maps from 1992 to 2015 at half degree resolution are also added in a .zip file.</em></p>

opencc-by-4.0May 2024View details →

Historical time-series reconstruction benchmark dataset of Landsat bi-monthly aggregates from GLAD ARD-2 at 30-m resolution with stratified sampling based on ESA CCI

<h2>Description</h2> <p>Historical time-series reconstruction benchmark dataset presented here is designed for evaluating and comparing the performance of time series reconstruction methods in the context of land cover change detection. The dataset is based on the European Space Agency Climate Change Initiative (ESA CCI) land cover dataset, which has been aggregated into 18 classes to facilitate analysis. The dataset includes information on land cover dynamics from 2000 to 2020, focusing on identifying and characterizing changes in land cover over time.</p> <h3><strong>Data Collection and Processing:</strong></h3> <p>The dataset is derived from the ESA CCI land cover dataset, which provides information on land cover classes at a global scale. The original dataset, containing 37 land cover classes, was aggregated into 18 classes based on similarity. Pixels with stable land cover over the study period and pixels with one or multiple land cover changes were identified and grouped into strata for sampling purposes.</p> <p>Sampling points were selected using a stratified sampling design, ensuring representation across different land cover classes and change scenarios. Approximately 2600 points were selected from each stratum, resulting in a total of 51,978 sampling points. The selected points were uniformly distributed along the strata, with spatial variations accounted for.</p> <p>Bimonthly time series data were extracted for each sampling point from 1997 to 2022, capturing temporal dynamics in land cover. Artificial gaps were introduced into the time series data to simulate real-world data loss, allowing for the evaluation of time series reconstruction methods under varying gap densities.</p> <p>The time series values were extracted from Landsat GLAD imagery using the specified spectral bands, including blue, green, red, NIR, SWIR1, SWIR2, and thermal bands. Additionally, a clear quality band was also extracted.</p> <h3>Data Details</h3> <ul> <li><strong>Time Period:</strong> 1997-01-01 to 2022-12-31</li> <li><strong>Type of Data: </strong>R data frame / Geopackage points.</li> <li><strong>Collection/Derivation:</strong> Derived from Landsat ARD v2, processed with Scikit-map.</li> <li><strong>Coordinate Reference System:</strong> EPSG:4326</li> <li><strong>Bounding Box:</strong> All the globe</li> <li><strong>File Format:</strong> RDS</li> </ul> <p> </p> <h3><strong>Reclassified Classes of ESA CCI Land Cover Dataset</strong></h3> <table> <tbody> <tr> <td> <div> <div> <p><strong>Aggregated Class Code</strong></p> </div> </div> </td> <td> <div> <div> <p><strong>Aggregated Class Label</strong></p> </div> </div> </td> <td> <div> <div> <p><strong>Original ESA CCI Classes</strong></p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>10</p> </div> </div> </td> <td> <div> <div> <p>Cropland rainfed</p> </div> </div> </td> <td> <div> <div> <p>10, 11, 12</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>30</p> </div> </div> </td> <td> <div> <div> <p>Mosaic cropland | natural vegetation</p> </div> </div> </td> <td> <div> <div> <p>30, 40</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>50</p> </div> </div> </td> <td> <div> <div> <p>Tree cover broadleaved evergreen</p> </div> </div> </td> <td> <div> <div> <p>50</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>60</p> </div> </div> </td> <td> <div> <div> <p>Tree cover broadleaved deciduous</p> </div> </div> </td> <td> <div> <div> <p>60, 61, 62</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>70</p> </div> </div> </td> <td> <div> <div> <p>Tree cover needleleaved evergreen</p> </div> </div> </td> <td> <div> <div> <p>70, 71, 72</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>80</p> </div> </div> </td> <td> <div> <div> <p>Tree cover needleleaved deciduous</p> </div> </div> </td> <td> <div> <div> <p>80, 81, 82</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>90</p> </div> </div> </td> <td> <div> <div> <p>Tree cover mixed leaf type</p> </div> </div> </td> <td> <div> <div> <p>90</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>100</p> </div> </div> </td> <td> <div> <div> <p>Mosaic tree and shrub | herbaceous cover</p> </div> </div> </td> <td> <div> <div> <p>100, 110</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>120</p> </div> </div> </td> <td> <div> <div> <p>Shrubland</p> </div> </div> </td> <td> <div> <div> <p>120, 121, 122</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>150</p> </div> </div> </td> <td> <div> <div> <p>Sparse vegetation</p> </div> </div> </td> <td> <div> <div> <p>150, 151, 152, 153</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>160</p> </div> </div> </td> <td> <div> <div> <p>Tree cover flooded</p> </div> </div> </td> <td> <div> <div> <p>160, 170</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>180</p> </div> </div> </td> <td> <div> <div> <p>Shrub or herbaceous cover flooded</p> </div> </div> </td> <td> <div> <div> <p>180</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>200</p> </div> </div> </td> <td> <div> <div> <p>Bare areas</p> </div> </div> </td> <td> <div> <div> <p>200, 201, 202</p> </div> </div> </td> </tr> </tbody> </table> <p>In the table, each row represents a reclassified land cover class, identified by a unique code. The 'Original ESA CCI Classes' column lists the specific land cover classes from the European Space Agency Climate Change Initiative dataset that are grouped together to form each broader category. Note that land cover classes not listed in this table were retained in their original value and were not reclassified.</p> <h3><strong>File Format</strong></h3> <p>The dataset comprises observations spanning from January 1997 to November 2022, capturing data for 51,978 samples.</p> <ul> <li>blue.rds: Time series data for the blue spectral band.</li> <li>green.rds: Time series data for the green spectral band.</li> <li>red.rds: Time series data for the red spectral band.</li> <li>nir.rds: Time series data for the near-infrared (NIR) spectral band.</li> <li>swir1.rds: Time series data for the shortwave infrared 1 (SWIR1) spectral band.</li> <li>swir2.rds: Time series data for the shortwave infrared 2 (SWIR2) spectral band.</li> <li>thermal.rds: Time series data for the thermal infrared band.</li> <li>clear.rds: Time series data for the clear quality band, used for masking out cloudy observations.</li> </ul> <p>How open the files in R:</p> <p><code>blue <- readRDS("blue.rds")</code></p> <p>To open the files in Python, you need to the <code>pyreadr</code> library:</p> <p><code>import pyreadr</code><br><code>blue = pyreadr.read_r('blue.rds')</code></p> <p> </p>