Global Land cover

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  • Global Land coverWeb App
    Updated: 4 March 2021

    Data source All the Land Cover Layers are based on PROBA-V satellite observations, offered by the Belgian Science Policy Office (BELSPO). Based on the current, independent validation dataset, the overall accuracy of the main, discrete Land Cover Classification remains just above 80%. Hence, the cover depicted here may differ from the present reality on the ground. For more information on accuracy, please read the Product User Manual. For the permanent and seasonal water layers, water seasonality and maximum water extent information was used from the European Commission Joint Research Centre’s, Global Surface Water History Record, that is described in this article and available from this app. The built-up (urban) class was generated by down-sampling the World Settlement Footprint (WSF) layer, offered by the German Aerospace Centre DLR, and OpenStreetMap data, offered by the OpenStreetMap contributors, to the PROBA-V resolution. A 30m shoreline vector from the US Geological Survey was resampled for land-sea masking. The land cover layers were produced using other ancillary data sources, including: Integrated Multi-satellitE Retrievals for GPM (IMERG), offered by NASA Goddard Space Flight Center, Global Precipitation Measurement (GPM) mission team Circum-Polar Arctic Vegetation Map, offered by the U.S. CPAVM team The backdrop images of the map are offered by Mapbox, Inc.

Source Copernicus Global Land Cover Layers-Collection 2
Contributor
Date of Dataset January 01, 2015-September 19, 2021
Updated 4 March 2021
Expected Update Frequency As needed
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Methodology

For these global 100m resolution maps, the main inputs are PROBA-V satellite observations, organized into millions of Sentinel-2 equivalent tiles of 110x110km. The processing in this tiling grid, and with UTM projection, ensures high quality and facilitates the continuity with Sentinel-2 observations.

These satellite observations are put through an innovative algorithm, that includes:

its own satellite data pre-processing, with geometric and atmospheric corrections;
data cleaning by sensor specific status masks and (temporal) outlier detection techniques;
calculation of the input data density indicator
data fusion between 5-daily 100m resolution and daily 300m resolution data;
extraction of 183 metrics, including the base reflectances, vegetation indicators, time series harmonics and descriptive statistics.
the use of 168K training points, collected at 10m resolution, from GeoWIKI’s crowd-sourcing, for year 2015
the use of well-established, external datasets for the shoreline masking, ecological regionalisation, built-up (urban) cover, permanent and seasonal water cover, arctic vegetation, weather and topography;
supervised classification and regression.

The classified metrics are calculated over a three year period (epoch), in three processing modes:

base maps for epoch 2015, that serves as reference for the classifier and regression models,
consolidated maps (epochs 2016, 2017 and 2018) with full years of prior and pastor data and
the near-real time (nrt, 2019) maps with one year prior and only three months pastor data.

Time series break maps are computed using firstly a BFAST break detection algorithm on a time series of MODIS Near-Infrared Reflectance of Vegetation(NIRv) input data and secondly a Hidden Markov Model. These break maps show the areas where changes occurred between years and are used in temporal post-processing rules to improve the cover fraction time series. The changes in land cover maps are aligned with these break predictions as well.

The final maps are validated using an independent set of 21.7K validation points, also sourced from Geo-WIKI.

The processing continued to use the innovative Big Data techniques on the PROBA-V Mission Exploitation Platform.

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