This dataset provides daily-mean sea surface temperatures (SST), presented on global 0.05° latitude-longitude grid, spanning 1980 to present. This is a Level 4 product, with gaps between available daily observations filled by statistical means.

The SST CCI Analysis product contains estimates of daily mean SST and sea ice concentration. Each SST value has an associated uncertainty estimate.

The dataset has been produced as part of the version 3 Climate Data Record (CDR) produced by the European Space Agency (ESA) Climate Change Initiative Sea Surface Temperature project (ESA SST_cci). The CDR accurately maps the surface temperature of the global oceans over the period 1980 to 2021 using observations from many satellites, with a high degree of independence from in situ measurements. The data provide independently quantified SSTs to a quality suitable for climate research.

Data from 2022 onwards are provided as an Interim Climate Data Record (ICDR) and will be updated daily at one month behind present. The Copernicus Climate Change Service (C3S) funded the development of the ICDR extension and production of the ICDR during 2022. From 2023 onwards the production of the ICDR is funded by the UK Earth Observation Climate Information Service (EOCIS) and Marine and Climate Advisory Service (MCAS).

This CDR Version 3.0 product supersedes the CDR v2.1 product. Compared to the previous version the major changes are:

• Longer time series: 1980 to 2021 (previous CDR was Sept 1981 to 2016)

• Improved retrieval to reduce systematic biases using bias-aware optimal methods (for single view sensors)

• Improved retrieval with respect to desert-dust aerosols

• Addition of dual-view SLSTR data from 2016 onwards

• Addition of early AVHRR/1 data in 1980s, and improved AVHRR processing to reduce data gaps in 1980s

• Use of full-resolution MetOp AVHRR data (previously used ‘global area coverage’ Level 1 data)

• Inclusion of L2P passive microwave AMSR data

Data are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/

When citing this dataset please also cite the associated data paper:

Embury, O., Merchant, C.J., Good, S.A., Rayner, N.A., Høyer, J.L., Atkinson, C., Block, T., Alerskans, E., Pearson, K.J., Worsfold, M., McCarroll, N., Donlon, C. Satellite-based time-series of sea-surface temperature since 1980 for climate applications. Scientific Data 11, 326 (2024). https://doi.org/10.1038/s41597-024-03147-w

The CCI-LC team has successfully produced and released its 3-epoch series of global land cover maps at 300m spatial resolution, where each epoch covers a 5-year period (2008-2012, 2003-2007, 1998-2002). These maps were produced using a multi-year and multi-sensor strategy in order to make use of all suitable data and maximize product consistency.

The entire 2003-2012 MERIS Full and Reduced Resolution (FR and RR) archive was used as input to generate a 10-year 2003-2012 global land cover map. This 10-year product has then served as a baseline to derive the 2010, 2005 and 2000 maps using back- and up-dating techniques with MERIS and SPOT-Vegetation time series specific to each epoch.

The Soil Moisture CCI ACTIVE dataset is one of the three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The ACTIVE product has been created by fusing scatterometer soil moisture products, derived from the active remote sensing instruments AMI-WS and ASCAT. PASSIVE and COMBINED products have also been created.

The v09.1 ACTIVE product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in percent of saturation [%] and covers the period (yyyy-mm-dd) 1991-08-05 to 2023-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.

The data set should be cited using the following references:

1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-2019

2. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001

3. Preimesberger, W., Scanlon, T., Su, C. -H., Gruber, A. and Dorigo, W., "Homogenization of Structural Breaks in the Global ESA CCI Soil Moisture Multisatellite Climate Data Record," in IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 4, pp. 2845-2862, April 2021, doi: 10.1109/TGRS.2020.3012896.

Context and methodology

Technical details

• rzsm1: 0-10 cm
• rzsm2: 10-40 cm
• rzsm3: 40-100 cm
• rzsm4: 0-100 cm

• The package can be installed via pip using "pip install esa_cci_sm"
• The documentation for this package is available here: https://esa-cci-sm.readthedocs.io/en/latest/
• The "parameter" argument (e.g., https://github.com/TUW-GEO/esa_cci_sm/blob/33a8a453bbccb55188804bce07a37315e9a3db43/src/esa_cci_sm/interface.py#L39) can be specified to any of the layer variables (rzsm1, rzsm2, ...)

Reference

Pasik, A., Gruber, A., Preimesberger, W., De Santis, D., and Dorigo, W.: Uncertainty estimation for a new exponential-filter-based long-term root-zone soil moisture dataset from Copernicus Climate Change Service (C3S) surface observations, Geosci. Model Dev., 16, 4957–4976, https://doi.org/10.5194/gmd-16-4957-2023, 2023

Additional citations

Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001.

Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture Climate Data Records and their underlying merging methodology. Earth System Science Data 11, 717-739, https://doi.org/10.5194/essd-11-717-2019

Preimesberger, W., Scanlon, T., Su, C. -H., Gruber, A. and Dorigo, W. (2021). Homogenization of Structural Breaks in the Global ESA CCI Soil Moisture Multisatellite Climate Data Record, in IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 4, pp. 2845-2862, April 2021, doi: 10.1109/TGRS.2020.3012896.

Related Records

The following records are all part of the Soil Moisture Climate Data Records from satellites community

The Climate Change Initiative (CCI) Land Cover (LC) project of the European Space Agency (ESA) delivers consistent global LC maps at 300 m spatial resolution on an annual basis from 1992 to 2015. Each pixel value corresponds to the label of a land cover class defined based on the UN Land Cover Classification System (LCCS). UN Biodiversity Lab shows only the data for year 2015.

For further information or to download the full dataset please visit the: European Space Agency Climate Change Innitiative.

Citation: European Space Agency Climate Change Initiative, Land Cover project. 2017. 300m Annual Global Land Cover Time Series from 1992 to 2015. Retrieved from http://maps.elie.ucl.ac.be/CCI/viewer/. Accessed through UN Biodiversity Lab, (date). www.unbiodiversitylab.org.

As part of the ESA Land Cover Climate Change Initiative (CCI) project a set of Global Land Cover Maps have been produced. These are available at 300m spatial resolution for three epochs centred on the year 2010 (2008-2012), 2005 (2003-2007) and 2000 (1998-2002), where each epoch covers a 5-year period.

Each pixel value corresponds to the label of a land cover class defined using UN-LCCS classifiers. For each epoch, the land cover map is delivered along with 4 quality flags which document the reliability of the classification. These are described further in the Product User Guides.

Further Land Cover CCI products, user tools and a product viewer are available at: http://maps.elie.ucl.ac.be/CCI/viewer/index.php

The Soil Moisture CCI COMBINED dataset is one of three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by directly merging Level 2 scatterometer and radiometer soil moisture products derived from the AMI-WS, ASCAT, SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2, SMOS, SMAP, FY-3B and GPM satellite instruments. PASSIVE and ACTIVE products have also been created.The v06.1 COMBINED product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2020-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.The data set should be cited using the following references:1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-20192. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001

Data products generated by the Ocean Colour component of the European Space Agency Climate Change Initiative project. These files are monthly composites of merged sensor (MERIS, Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua, Sea-Wide Field-of-View Sensor (SeaWiFS) Local Area Coverage (LAC) & Global Area Coverage (GAC), Visible and Infrared Imager/Radiometer Suite (VIIRS), OLCI) products. MODIS Aqua and SeaWiFS were band-shifted and bias-corrected to MERIS bands and values using a temporally and spatially varying scheme based on the overlap years of 2003-2007. VIIRS was band-shifted and bias-corrected in a second stage against the MODIS Rrs that had already been corrected to MERIS levels, for the overlap period 2012-2013; and at the third stage OLCI was bias corrected against already corrected MODIS, for overlap period 2016-07-01 to 2019-06-30. VIIRS, MODIS, SeaWiFS and MERIS Rrs were derived from a combination of NASA's l2gen (for basic sensor geometry corrections, etc) and HYGEOS Polymer v4.12 (for atmospheric correction). OLCI Rrs were sourced at L1b (already geometrically corrected) and processed with polymer. The Rrs were binned to a sinusoidal 4km level-3 grid, and later to 4km geographic projection, by Brockmann Consult's SNAP. Derived products were generally computed with the standard algorithmsthrough SeaDAS. QAA IOPs were derived using the standard SeaDAS algorithm but with a modified backscattering table to match that used in the bandshifting. The final chlorophyll is a combination of OCI, OCI2, OC2 and OCx, depending on the water class memberships. Uncertainty estimates were added using the fuzzy water classifier and uncertainty estimation algorithm of Tim Moore as documented in Jackson et al (2017). and updated accorsing to Jackson et al. (in prep).

Data products generated by the Ocean Colour component of the European Space Agency Climate Change Initiative project. These files are monthly composites of merged sensor (MERIS, Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua, Sea-Wide Field-of-View Sensor (SeaWiFS) Local Area Coverage (LAC) & Global Area Coverage (GAC), Visible and Infrared Imager/Radiometer Suite (VIIRS)) products. MODIS Aqua and MERIS were band-shifted and bias-corrected to SeaWiFS bands and values using a temporally and spatially varying scheme based on the overlap years of 2003-2007. VIIRS was band-shifted and bias-corrected in a second stage against the MODIS Rrs that had already been corrected to SeaWiFS levels, for the overlap period 2012-2013. VIIRS and SeaWiFS Rrs were derived from standard NASA L2 products; MERIS and MODIS from a combination of NASA's l2gen (for basic sensor geometry corrections, etc) and HYGEOS Polymer v3.5 (for atmospheric correction). The Rrs were binned to a sinusoidal 4km level-3 grid, and later to 4km geographic projection, by Brockmann Consult's BEAM. Derived products were generally computed with the standard SeaDAS algorithms. QAA IOPs were derived using the standard SeaDAS algorithm but with a modified backscattering table to match that used in the bandshifting. The final chlorophyll is a combination of OC4, Hu's CI and OC5, depending on the water class memberships. Uncertainty estimates were added using the fuzzy water classifier and uncertainty estimation algorithm of Tim Moore as documented in Jackson et al (2017).

This dataset provides Sea Ice Concentration (SIC) for the polar regions, derived from the Nimbus-5 Electrical Scanning Microwave Radiometer (ESMR), which operated between 1972 and 1977. It is processed with an algorithm using the single channel ESMR data (19.35 GHz), and has been gridded at 25 km grid spacing. This is the second version of the product, v1.1.

This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project.

The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.

This dataset contains their Version 6.0 chlorophyll-a product (in mg/m3) on a sinusoidal projection at 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day, monthly and yearly composites) covering the period 1997 - 2022. Note, the chlorophyll-a data are also included in the 'All Products' dataset.

This data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection.)

This dataset provides global daily estimates of Root-Zone Soil Moisture (RZSM) content at 0.25° spatial grid resolution, derived from gap-filled merged satellite observations of 14 passive satellites sensors operating in the microwave domain of the electromagnetic spectrum. Data is provided from January 1991 to December 2023.

This dataset was produced with funding from the European Space Agency (ESA) Climate Change Initiative (CCI) Plus Soil Moisture Project (CCN 3 to ESRIN Contract No: 4000126684/19/I-NB "ESA CCI+ Phase 1 New R&D on CCI ECVS Soil Moisture"). Project website: https://climate.esa.int/en/projects/soil-moisture/" target="_blank" rel="noopener">https://climate.esa.int/en/projects/soil-moisture/. Operational implementation is supported by the Copernicus Climate Change Service implemented by ECMWF through C3S2 312a/313c.

Studies using this dataset

This dataset is used by Hirschi et al. (2025) to assess recent summer drought trends in Switzerland.

Abstract

ESA CCI Soil Moisture is a multi-satellite climate data record that consists of harmonized, daily observations from various microwave satellite remote sensing sensors (Dorigo et al., 2017, 2024; Gruber et al., 2019). This version of the dataset uses the PASSIVE record as input, which contains only observations from passive (radiometer) measurements (scaling reference AMSR-E). The surface observations are gap-filled using a univariate interpolation algorithm (Preimesberger et al., 2025). The gap-filled passive observations serve as input for an exponential filter based method to assess soil moisture in different layers of the root-zone of soil (0-200 cm) following the approach by Pasik et al. (2023). The final gap-free root-zone soil moisture estimates based on passive surface input data are provided here at 4 separate depth layers (0-10, 10-40, 40-100, 100-200 cm) over the period 1991-2023.

Summary

• Gap-free root-zone soil moisture estimates from 1991-2023 at 0.25° spatial sampling from passive measurements
• Fields of application include: climate variability and change, land-atmosphere interactions, global biogeochemical cycles and ecology, hydrological and land surface modelling, drought applications, agriculture and meteorology
• More information: See Dorigo et al. (2017, 2024) and Gruber et al. (2019) for a description of the satellite base product and uncertainty estimates, Preimesberger et al. (2025) for the gap-filling, and Pasik et al. (2023) for the root-zone soil moisture and uncertainty propagation algorithm.

Programmatic Download

You can use command line tools such as wget or curl to download (and extract) data for multiple years. The following command will download and extract the complete data set to the local directory ~/Downloads on Linux or macOS systems.

#!/bin/bash

# Set download directory
DOWNLOAD_DIR=~/Downloads

base_url="https://researchdata.tuwien.ac.at/records/8dda4-xne96/files"

# Loop through years 1991 to 2023 and download & extract data
for year in {1991..2023}; do
  echo "Downloading $year.zip..."
  wget -q -P "$DOWNLOAD_DIR" "$base_url/$year.zip"
  unzip -o "$DOWNLOAD_DIR/$year.zip" -d $DOWNLOAD_DIR
  rm "$DOWNLOAD_DIR/$year.zip"
done

Data details

The dataset provides global daily estimates for the 1991-2023 period at 0.25° (~25 km) horizontal grid resolution. Daily images are grouped by year (YYYY), each subdirectory containing one netCDF image file for a specific day (DD), month (MM) in a 2-dimensional (longitude, latitude) grid system (CRS: WGS84). The file name has the following convention:

ESA_CCI_PASSIVERZSM-YYYYMMDD000000-fv09.1.nc

Data Variables

Each netCDF file contains 3 coordinate variables (WGS84 longitude, latitude and time stamp), as well as the following data variables:

• rzsm_1: (float) Root Zone Soil Moisture at 0-10 cm. Given in volumetric units [m3/m3].
• rzsm_2: (float) Root Zone Soil Moisture at 10-40 cm. Given in volumetric units [m3/m3].
• rzsm_3: (float) Root Zone Soil Moisture at 40-100 cm. Given in volumetric units [m3/m3].
• rzsm_4: (float) Root Zone Soil Moisture at 100-200. Given in volumetric units [m3/m3].
• uncertainty_1: (float) Root Zone Soil Moisture uncertainty at 0-10 cm from propagated surface uncertainties [m3/m3].
• uncertainty_2: (float) Root Zone Soil Moisture uncertainty at 10-40 cm from propagated surface uncertainties [m3/m3].
• uncertainty_3: (float) Root Zone Soil Moisture uncertainty at 40-100 cm from propagated surface uncertainties [m3/m3].
• uncertainty_4: (float) Root Zone Soil Moisture uncertainty at 100-200 cm from propagated surface uncertainties [m3/m3].

Additional information for each variable is given in the netCDF attributes.

Version Changelog

• v9.1
  • Initial version based on PASSIVE input data from ESA CCI SM v09.1 as used by Hirschi et al. (2025).

Software to open netCDF files

These data can be read by any software that supports Climate and Forecast (CF) conform metadata standards for netCDF files, such as:

• https://github.com/pydata/xarray" target="_blank" rel="noopener">Xarray (python)
• https://unidata.github.io/netcdf4-python/" target="_blank" rel="noopener">netCDF4 (python)
• https://github.com/TUW-GEO/esa_cci_sm">esa_cci_sm (python)
• Similar tools exists for other programming languages (Matlab, R, etc.)
• Software packages and GIS tools can open netCDF files, e.g. CDO, NCO, QGIS, ArCGIS
• You can also use the GUI software Panoply to view the contents of each file

References

• Dorigo, W., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, P. D., Hirschi, M., Ikonen, J., de Jeu, R., Kidd, R., Lahoz, W., Liu, Y. Y., Miralles, D., Mistelbauer, T., Nicolai-Shaw, N., Parinussa, R., Pratola, C., Reimer, C., van der Schalie, R., Seneviratne, S. I., Smolander, T., and Lecomte, P.: ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions, Remote Sensing of Environment, 203, 185-215, 10.1016/j.rse.2017.07.001, 2017
• Dorigo, W., Stradiotti, P., Preimesberger, W., Kidd, R., van der Schalie, R., Frederikse, T., Rodriguez-Fernandez, N., & Baghdadi, N. (2024). ESA Climate Change Initiative Plus - Soil Moisture Algorithm Theoretical Baseline Document (ATBD) Supporting Product Version 09.0. Zenodo. https://doi.org/10.5281/zenodo.13860922
• Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W.: Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-2019, 2019.
• Hirschi, M., Michel, D., Schumacher, D. L., Preimesberger, W., Seneviratne, S. I.: Recent summer soil moisture drying in Switzerland based on the SwissSMEX network, 2025 (paper submitted)
• Pasik, A., Gruber, A., Preimesberger, W., De Santis, D., and Dorigo, W.: Uncertainty estimation for a new exponential-filter-based long-term root-zone soil moisture dataset from Copernicus Climate Change Service (C3S) surface observations, Geosci. Model Dev., 16, 4957–4976, https://doi.org/10.5194/gmd-16-4957-2023, 2023
• Preimesberger, W., Stradiotti, P., and Dorigo, W.: ESA CCI Soil Moisture GAPFILLED: An independent global gap-free satellite climate data record with uncertainty estimates, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2024-610, in review, 2025.

Related Records

Please see the ESA CCI Soil Moisture science data records community for more records based on ESA CCI SM.

The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The values of the raster are the distances (in kilometres) from the cell centres to the nearest featureFilenames: arm_esaccilc_dst011_100m_2013 Distance to ESA-CCI-LC cultivated area edges 2013 arm_esaccilc_dst040_100m_2013 Distance to ESA-CCI-LC woody-tree area edges 2013 arm_esaccilc_dst130_100m_2013 Distance to ESA-CCI-LC shrub area edges 2013 arm_esaccilc_dst140_100m_2013 Distance to ESA-CCI-LC herbaceous area edges 2013 arm_esaccilc_dst150_100m_2013 Distance to ESA-CCI-LC sparse vegetation area edges 2013 arm_esaccilc_dst160_100m_2013 Distance to ESA-CCI-LC aquatic vegetation area edges 2013 arm_esaccilc_dst190_100m_2013 Distance to ESA-CCI-LC artificial surface edges 2013 arm_esaccilc_dst200_100m_2013 Distance to ESA-CCI-LC bare area edges 2013 Methodology: The geodesic distances have been calculated using the haversine formula and global input datasets to avoid edge effects at the country boundaries.Data Source: ESA (European Space Agency) CCI (Climate Change Initiative) Land Cover project 2017. "Land Cover CCI Product - Annual LC maps from 2000 to 2015 (v2.0.7)." http://maps.elie.ucl.ac.be/CCI/viewer/

Data products generated by the Ocean Colour component of the European Space Agency Climate Change Initiative project. These files are monthly composites of merged sensor (MERIS, Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua, Sea-Wide Field-of-View Sensor (SeaWiFS) Local Area Coverage (LAC) & Global Area Coverage (GAC), Visible and Infrared Imager/Radiometer Suite (VIIRS), OLCI) products. MODIS Aqua and SeaWiFS were band-shifted and bias-corrected to MERIS bands and values using a temporally and spatially varying scheme based on the overlap years of 2003-2007. VIIRS was band-shifted and bias-corrected in a second stage against the MODIS Rrs that had already been corrected to MERIS levels, for the overlap period 2012-2013; and at the third stage OLCI was bias corrected against already corrected MODIS, for overlap period 2016-07-01 to 2019-06-30. VIIRS, MODIS, SeaWiFS and MERIS Rrs were derived from a combination of NASA's l2gen (for basic sensor geometry corrections, etc) and HYGEOS Polymer v4.12 (for atmospheric correction). OLCI Rrs were sourced at L1b (already geometrically corrected) and processed with polymer. The Rrs were binned to a sinusoidal 4km level-3 grid, and later to 4km geographic projection, by Brockmann Consult's SNAP. Derived products were generally computed with the standard algorithmsthrough SeaDAS. QAA IOPs were derived using the standard SeaDAS algorithm but with a modified backscattering table to match that used in the bandshifting. The final chlorophyll is a combination of OCI, OCI2, OC2 and OCx, depending on the water class memberships. Uncertainty estimates were added using the fuzzy water classifier and uncertainty estimation algorithm of Tim Moore as documented in Jackson et al (2017). and updated accorsing to Jackson et al. (in prep). cdm_data_type=Grid comment=See summary attribute Conventions=CF-1.10, COARDS, ACDD-1.3 creation_date=Thu Apr 24 11:36:24 2025 Easternmost_Easting=179.97916666666663 geospatial_lat_max=89.97916666666667 geospatial_lat_min=-89.97916666666666 geospatial_lat_resolution=0.041666666666666664 geospatial_lat_units=degrees_north geospatial_lon_max=179.97916666666663 geospatial_lon_min=-179.97916666666666 geospatial_lon_resolution=0.04166666666666666 geospatial_lon_units=degrees_east git_commit_hash=ccd62ccbfe552e569fc0a9bbcb6f72fb5522843d grid_mapping_name=latitude_longitude history=Source data were: ESACCI-OC-L3S-OC_PRODUCTS-MERGED-1D_DAILY_4km_GEO_PML_OCx_QAA-20250201-fv6.0.nc, ESACCI-OC-L3S-OC_PRODUCTS-MERGED-1D_DAILY_4km_GEO_PML_OCx_QAA-20250202-fv6.0.nc, ESACCI-OC-L3S-OC_PRODUCTS-MERGED-1D_DAILY_4km_GEO_PML_OCx_QAA-20250203-fv6.0.nc, ESACCI-OC-L3S-OC_PRODUCTS-MERGED-1D_DAILY_4km_GEO_PML_OCx_QAA-20250204-fv6.0.nc, ESACCI-OC-L3S-OC_PRODUCTS-MERGED-1D_DAILY_4km_GEO_PML_OCx_QAA-20250205-fv6.0.nc, ESACCI-OC-L3S-OC_PRODUCTS-MERGED-1D_DAILY_4km_GEO_PML_OCx_QAA-20250206-fv6.0.nc, ESACCI-OC-L3S-OC_PRODUCTS-MERGED-1D_DAILY_4km_GEO_PML_OCx_QAA-20250207-fv6.0.nc, ESACCI-OC-L3S-OC_PRODUCTS-MERGED-1D_DAILY_4km_GEO_PML_OCx_QAA-20250208-fv6.0.nc, ESACCI-OC-L3S-OC_PRODUCTS-MERGED-1D_DAILY_4km_GEO_PML_OCx_QAA-20250209-fv6.0.nc, ESACCI-OC-L3S-OC_PRODUCTS-MERGED-1D_DAILY_4km_GEO_PML_OCx_QAA-20250210-fv6.0.nc, ESACCI-OC-L3S-OC_PRODUCTS-MERGED-1D_DAILY_4km_GEO_PML_OCx_QAA-20250211-fv6.0.nc, ESACCI-OC-L3S-OC_PRODUCTS-MERGED-1D_DAILY_4km_GEO_PML_OCx_QAA-20250212-fv6.0.nc, ESACCI-OC-L3S-OC_PRODUCTS-MERGED-1D_DAILY_4km_GEO_PML_OCx_QAA-20250213-fv6.0.nc, ESACCI-OC-L3S-OC_PRODUCTS-MERGED-1D_DAILY_4km_GEO_PML_OCx_QAA-20250214-fv6.0.nc, ESACCI-OC-L3S-OC_PRODUCTS-MERGED-1D_DAILY_4km_GEO_PML_OCx_QAA-20250215-fv6.0.nc, ESACCI-OC-L3S-OC_PRODUCTS-MERGED-1D_DAILY_4km_GEO_PML_OCx_QAA-20250216-fv6.0.nc, ESACCI-OC-L3S-OC_PRODUCTS-MERGED-1D_DAILY_4km_GEO_PML_OCx_QAA-20250217-fv6.0.nc, ESACCI-OC-L3S-OC_PRODUCTS-MERGED-1D_DAILY_4km_GEO_PML_OCx_QAA-20250218-fv6.0.nc, ESACCI-OC-L3S-OC_PRODUCTS-MERGED-1D_DAILY_4km_GEO_PML_OCx_QAA-20250219-fv6.0.nc, ESACCI-OC-L3S-OC_PRODUCTS-MERGED-1D_DAILY_4km_GEO_PML_OCx_QAA-20250220-fv6.0.nc, ESACCI-OC-L3S-OC_PRODUCTS-MERGED-1D_DAILY_4km_GEO_PML_OCx_QAA-20250221-fv6.0.nc, ESACCI-OC-L3S-OC_PRODUCTS-MERGED-1D_DAILY_4km_GEO_PML_OCx_QAA-20250222-fv6.0.nc, ESACCI-OC-L3S-OC_PRODUCTS-MERGED-1D_DAILY_4km_GEO_PML_OCx_QAA-20250223-fv6.0.nc, ESACCI-OC-L3S-OC_PRODUCTS-MERGED-1D_DAILY_4km_GEO_PML_OCx_QAA-20250224-fv6.0.nc, ESACCI-OC-L3S-OC_PRODUCTS-MERGED-1D_DAILY_4km_GEO_PML_OCx_QAA-20250225-fv6.0.nc, ESACCI-OC-L3S-OC_PRODUCTS-MERGED-1D_DAILY_4km_GEO_PML_OCx_QAA-20250226-fv6.0.nc, ESACCI-OC-L3S-OC_PRODUCTS-MERGED-1D_DAILY_4km_GEO_PML_OCx_QAA-20250227-fv6.0.nc, ESACCI-OC-L3S-OC_PRODUCTS-MERGED-1D_DAILY_4km_GEO_PML_OCx_QAA-20250228-fv6.0.nc; netcdf_compositor_cci composites Rrs_412, Rrs_412_bias, Rrs_443, Rrs_443_bias, Rrs_490, Rrs_490_bias, Rrs_510, Rrs_510_bias, Rrs_560, Rrs_560_bias, Rrs_665, Rrs_665_bias, adg_412, adg_412_bias, adg_443, adg_443_bias, adg_490, adg_490_bias, adg_510, adg_510_bias, adg_560, adg_560_bias, adg_665, adg_665_bias, aph_412, aph_412_bias, aph_443, aph_443_bias, aph_490, aph_490_bias, aph_510, aph_510_bias, aph_560, aph_560_bias, aph_665, aph_665_bias, atot_412, atot_443, atot_490, atot_510, atot_560, atot_665, bbp_412, bbp_443, bbp_490, bbp_510, bbp_560, bbp_665, chlor_a, chlor_a_log10_bias, kd_490, kd_490_bias, water_class1, water_class10, water_class11, water_class12, water_class13, water_class14, water_class2, water_class3, water_class4, water_class5, water_class6, water_class7, water_class8, water_class9 with --mean, Rrs_412_rmsd, Rrs_443_rmsd, Rrs_490_rmsd, Rrs_510_rmsd, Rrs_560_rmsd, Rrs_665_rmsd, adg_412_rmsd, adg_443_rmsd, adg_490_rmsd, adg_510_rmsd, adg_560_rmsd, adg_665_rmsd, aph_412_rmsd, aph_443_rmsd, aph_490_rmsd, aph_510_rmsd, aph_560_rmsd, aph_665_rmsd, chlor_a_log10_rmsd, kd_490_rmsd with --root-mean-square, and MERIS_nobs, MODISA_nobs, OLCI-A_nobs, OLCI-B_nobs, SeaWiFS_nobs, VIIRS_nobs, total_nobs - with --total id=ESACCI-OC-L3S-OC_PRODUCTS-MERGED-1M_MONTHLY_4km_GEO_PML_OCx_QAA-202502-fv6.0.nc infoUrl=https://esa-oceancolour-cci.org/ institution=Plymouth Marine Laboratory keywords_vocabulary=GCMD Science Keywords naming_authority=uk.ac.pml NCO=netCDF Operators version 4.7.5 (Homepage = http://nco.sf.net, Code = https://github.com/nco/nco) Northernmost_Northing=89.97916666666667 number_of_bands_used_to_classify=4 number_of_files_composited=28 number_of_optical_water_types=14 platform=Orbview-2,Aqua,Envisat,Suomi-NPP, Sentinel-3a, Sentinel-3b processing_level=Level-3 project=Climate Change Initiative - European Space Agency, the UK Earth Observation Climate Information Service, and Copernicus Climate Change Service references=https://esa-oceancolour-cci.org/ sensor=SeaWiFS,MODIS,MERIS,VIIRS,OLCI sensors_present=OLCIa OLCIb source=NASA SeaWiFS L1A and L2 R2018.0 LAC and GAC, MODIS-Aqua L1A and L2 R2018.0, MERIS L1B 3rd reprocessing inc OCL corrections, NASA VIIRS L1A and L2 R2018.0, OLCI L1B sourceUrl=https://www.oceancolour.org/thredds/dodsC/CCI_ALL-v6.0-MONTHLY Southernmost_Northing=-89.97916666666666 spatial_resolution=4km nominal at equator standard_name_vocabulary=CF Standard Name Table v70 time_coverage_duration=P1M time_coverage_end=2025-03-01T00:00:00Z time_coverage_resolution=P1M time_coverage_start=1997-09-04T00:00:00Z tracking_id=576e0fa8-124f-4e16-8c11-85fee96f1493 Westernmost_Easting=-179.97916666666666

This dataset contains Daily Snow Cover Fraction (snow on ground) from AVHRR, produced by the Snow project of the ESA Climate Change Initiative programme.

Snow cover fraction on ground (SCFG) indicates the area of snow observed from space over land surfaces, in forested areas corrected for the transmissivity of the forest canopy. The SCFG is given in percentage (%) per pixel.

The global SCFG product is available at about 5 km pixel size for all land areas, excluding Antarctica and Greenland ice sheets. The coastal zones of Greenland are included.

The SCFG time series provides daily products for the period 1979-2022.

The product is based on medium resolution optical satellite data from the Advanced Very High Resolution Radiometer (AVHRR). Clouds are masked using the CLARA-A3 cloud product.

The retrieval method of the snow_cci SCFG product from AVHRR data has been further developed and improved based on the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre-classification module. All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 0.63 µm and 1.61 µm (channel 3a or the reflective part of channel 3b (ref3b)), and an emissive band centred at about 10.8 µm. The snow_cci snow cover mapping algorithm is a three-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFG retrieval method is applied. Finally, a post-processing removes erroneous snow pixels caused either by falsely classified clouds in the tropics or by unreliable ref3b values at a global scale.

The following auxiliary data sets are used for product generation: i) ESA CCI Land Cover from 2000; water bodies and permanent snow and ice areas are masked based on this dataset. Both classes were separately aggregated to the pixel spacing of the SCF product. Water areas are masked if more than 50 percent of the pixel is classified as water, permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map; ii) Forest canopy transmissivity map; this layer is based on the tree cover classes of the ESA CCI Land Cover 2000 data set and the tree cover density map from Landsat data for the year 2000 (Hansen et al., Science, 2013, DOI: 10.1126/science.1244693). This layer is used to apply a forest canopy correction and estimate in forested areas the fractional snow cover on ground.

The SCFG product is aimed to serve the needs of users working in cryosphere and climate research and monitoring activities, including the detection of variability and trends, climate modelling and aspects of hydrology, meteorology, and biology.

The Remote Sensing Research Group of the University of Bern, in cooperation with Gamma Remote Sensing is responsible for the SCFG product development and generation. ENVEO (ENVironmental Earth Observation IT GmbH) developed and prepared all auxiliary data sets used for the product generation.

The SCFG AVHRR product comprises a few data gaps in 1979 – 1986 (1979: 22.-24.Feb.; 01.-07.Oct.; 03.-04.Nov.; 07.Nov.; 17.-18.Nov.; 1980: 22.-27.Feb.; 01.March; 03.March; 15.-20.March; 30.March – 02.April; 26.-29.June; 12.-19.July; 12.-18.Dec.; 1981: 09.-11.May; 01.-03.Aug.; 14.-23.Aug.; 1982: 28.- 31.May; 25.-26. Oct.; 1983: 27.- 31. July; 01.- 02. and 06. Aug.; 1984: 14.-15.Jan.; 06. Dec.; 1985: 01.- 24.Feb; 1986: 15. March), resulting in a 99% data coverage over the entire study period of 43 years.

As part of the European Space Agency's (ESA) Sea Level Climate Change Initiative (CCI) Project, Fundamental Climate Data Records (FCDRs) have been computed for all the altimeter missions used within the project. These FCDR's consist of along track values of sea level anomalies and altimeter standards for the period between 1993 and 2015. This version of the product is v2.0.The FCDR's are mono-mission products, derived from the respective altimeter level-2 products. They have been produced along the tracks of the different altimeters, with a resolution of 1Hz, corresponding to a ground distance close to 6km. The dataset is separated by altimeter mission, and divided into files by altimetric cycle corresponding to the repetivity of the mission. When using or referring to the Sea Level cci products, please mention the associated DOIs and also use the following citation where a detailed description of the Sea Level_cci project and products can be found:Ablain, M., Cazenave, A., Larnicol, G., Balmaseda, M., Cipollini, P., Faugère, Y., Fernandes, M. J., Henry, O., Johannessen, J. A., Knudsen, P., Andersen, O., Legeais, J., Meyssignac, B., Picot, N., Roca, M., Rudenko, S., Scharffenberg, M. G., Stammer, D., Timms, G., and Benveniste, J.: Improved sea level record over the satellite altimetry era (1993â 2010) from the Climate Change Initiative project, Ocean Sci., 11, 67-82, doi:10.5194/os-11-67-2015, 2015.For further information on the Sea Level CCI products, and to register for these projects please email: info-sealevel@esa-sealevel-cci.org

The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics. This dataset comprises Level 3 daily and monthly gridded aerosol products from the ATSR-2 instrument on the ENVISAT satellite, derived using the ORAC algorithm, version 4.01. The data covers the period from 1995 - 2003.For further details about these data products please see the linked documentation.

The Soil Moisture CCI PASSIVE dataset is one of three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by merging data from the SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2, SMOS, SMAP, FY-3B and GPM satellite instruments. ACTIVE and COMBINED products have also been created.

The v06.2 PASSIVE product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2021-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.

The data set should be cited using the following references:

1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-2019

2. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001

Data products generated by the Ocean Colour component of the European Space Agency Climate Change Initiative project. These files are daily composites of merged sensor (MERIS, MODIS Aqua, SeaWiFS LAC & GAC, VIIRS) products. MODIS Aqua and MERIS were band-shifted and bias-corrected to SeaWiFS bands and values using a temporally and spatially varying scheme based on the overlap years of 2003-2007. VIIRS was band-shifted and bias-corrected in a second stage against the MODIS Rrs that had already been corrected to SeaWiFS levels, for the overlap period 2012-2013. VIIRS, MODIS and SeaWiFS Rrs were derived from standard NASA L2 products; MERIS - from a combination of NASA's l2gen (for basic sensor geometry corrections, etc) and HYGEOS Polymer v4.8 (for atmospheric correction). The Rrs were binned to a sinusoidal 4km level-3 grid, and later to 4km geographic projection, by Brockmann Consult's BEAM. Derived products were generally computed with the standard SeaDAS algorithms. QAA IOPs were derived using the standard SeaDAS algorithm but with a modified backscattering table to match that used in the bandshifting. The final chlorophyll is a combination of OC4, Hu's CI and OC5, depending on the water class memberships. Uncertainty estimates were added using the fuzzy water classifier and uncertainty estimation algorithm of Tim Moore as documented in Jackson et al (2017).

This dataset contains monthly 1 degree averages of sea surface temperature data in Obs4MIPS format, from the European Space Agency (ESA)'s Climate Change Initiatve (CCI) Sea Surface Temperature (SST) v2.1 analysis.

The data covers the period from 1981-2017, with the data from 1981 to 2016 coming from the Sea Surface Temperature (SST) project of the ESA CCI project. The data for 2017 were generated using the same approach but under funding from the Copernicus Climate Change Service (C3S).

This particular product has been generated for inclusion in Obs4MIPs (Observations for Model Intercomparisons Project), which is an activity to make observational products more accessible for climate model intercomparisons.

Data are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/

When citing this dataset please also cite the associated data paper: Merchant, C.J., Embury, O., Bulgin, C.E., Block T., Corlett, G.K., Fiedler, E., Good, S.A., Mittaz, J., Rayner, N.A., Berry, D., Eastwood, S., Taylor, M., Tsushima, Y., Waterfall, A., Wilson, R., Donlon, C. Satellite-based time-series of sea-surface temperature since 1981 for climate applications, Scientific Data 6:223 (2019). http://doi.org/10.1038/s41597-019-0236-x