This section provides access to various datasets published by the Land Cover ECV project.

Products at moderate spatial resolution 150-1000 m:

10-20m demonstration prototypes using Sentinel-2:

MRLC maps v207

24 consistent global land cover maps at 300m spatial resolution, on an annual basis from 1992 to 2015.

The annual MRLC maps v2.0.7 for years 2000, 2005 and 2010 replace completely the "v1.6.1 epoch-based" dataset as the annual MRLC maps v2.0.7 have been improved both in the representation of the areas stable over time and in the characterization of change.

Each pixel value corresponds to the label of a land cover class defined based on the UN Land Cover Classification System (LCCS). LCCS classifiers support the further conversion into Plant Functional Types distribution required by the Earth System Models. The typology counts 22 classes.

The 24 MRLC maps series is delivered along with 4 quality flags which document the products:

These 4 quality flags document the full time series and are not year specific.

These maps are derived from a unique baseline MRLC map which is generated thanks to a classification chain applied on the entire MERIS FR and RR archive from 2003 to 2012.

Independently from this baseline, MRLC changes are detected at 1 km based on a time series of annual global classifications generated from AVHRR HRPT (1992 - 1999), SPOT-Vegetation (1999 - 2012) and PROBA-V (2013 - 2015). Systematic analysis of the temporal trajectory of each pixel allowed depicting the major changes for a simplified land cover typology matching the IPCC classes. These classes are: cropland, forest, grassland, wetlands, settlements and other lands; the latter class being further split into shrubland, sparse vegetation, bare area and water.

When MERIS FR or PROBA-V time series are available, the changes detected at 1km are re-mapped at 300 meters. The last step consists in back- and up-dating the 10-year baseline MRLC map to produce the 24 annual MRLC maps from 1992 to 2015.

Global Water Bodies 4.0

Global map of open permanent water bodies at 300m spatial resolution derived from the full ENVISAT-ASAR dataset between 2005 and 2010

In an attempt to improve the characterization of inland water bodies in global LC products, a SAR-based approach has been implemented. Multi-temporal acquisitions of Envisat ASAR Wide Swath Mode with local gap fillers based on Image Mode and Global Monitoring Mode from the years 2005 to 2010, MERIS data and auxiliary datasets have been used to generate a single epoch map of permanent open water bodies at 300m.

The water pixels of this map correspond to the class "Water Bodies" of the CCI-LC Maps.

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CCI-LC User Tool

Dedicated user tool for allowing fitting land cover products to climate models by sub-setting, projection resampling re-projecting and re-sampling and by converting land cover classes into Plant Functional Types according to default or user-defined cross-walking tables.


Note that the current version of the user tool (v3.3) is not working with the CCI-LC Water Bodies product yet. This will be included in the next user tool version and this is reason why it can still be fine tuned.

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Seasonality Products

Three global climatological 7-day time series describing the natural variability of the vegetation, the snow cover and the burned areas

On a per pixel basis, these LC seasonality products reflect, along the year, the average dynamics and the inter-annual variability of the land surface over the 1998-2012 period. They are expressed as 7-day time profiles of the average and standard deviation for the vegetation greenness (NDVI) or as temporal series of occurrence probabilities for the snow and the burned areas.

Although they are built from existing and independent datasets, they were found to be quite consistent among themselves and with the land cover classes. These products are complementary to the three global CCI-LC maps products characterizing the same period.

Each climatology product is delivered in 52 files (1 file per 7-day time interval) and each file is made of measurements and quality flags.

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MERIS surface reflectance time series

The surface reflectance (SR) products consist of MERIS global time series covering the 2003-2012 period . The spectral content encompasses the 13 surface reflectance channels – the atmospheric bands 11 and 15 being removed – and the spatial resolution is of 300 m for the Full Resolution (FR) and 1000 m for the Reduced Resolution (RR). The time series are made of temporal syntheses obtained over a 7-day compositing period. In order to simplify the handling and analysis of global datasets, the MERIS SR time series are delivered in 5°x5° tiles.

Pre-processing: The pre-processing chain generates global SR time series by a series of pre-processing steps, including radiometric corrections, geometric correction, pixel identification, atmospheric correction with aerosol retrieval, BRDF corrections as well as compositing and mosaicking.

Quality control of input products: The MERIS dataset is very valuable and the use of the full mission dataset in the CCI-LC project in a consistent manner is a major effort. It requires advanced techniques for the development of specific quality checks related to the input data

The quality of each global multispectral SR composite is described, on a per-pixel basis, by a set of flags and values: uncertainties for each spectral band, current status of surface, uncertainties for each spectral band, number of observations with clear sky land coverage, water coverage, clear sky snow and ice coverage, cloudy coverage and cloud shadow coverage for each pixel. The uncertainties of the surface directional reflectance value is calculated from the contributions of each error source, assuming a negligible correlation between the different error sources.

The obtained values are compared with in-situ data from CEOS LandNet sites and with reflectance products available from other sensors and other projects. Besides assessing the quality of individual composites, the quality of the global SR time series is also documented, with the aim of quantifying their discrimination potential.

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CCI-LC Prototypes using Sentinel 2

These were generated within the CCI MRLC initiative using Sentinel-2 data at 20m over the whole Africa and at 10m over Mesoamerica.