The Land Cover component of the ESA Climate Change Initiative. Extending the series of global land cover maps to 2015 with PROBA-V: current achievements.

Essential Climate Variables (ECVs) were listed by the Global Climate Observing System (GCOS) as critical information to further understand the climate system and support climate modelling. In response, the European Space Agency (ESA) launched its Climate Change Initiative (CCI) in order to deliver global datasets matching the need for long-term satellite-based products for the climate domain. The ESA Land Cover CCI (LC_CCI) project, dedicated to the Land Cover ECV, built on the ESA-GlobCover experiences to revisit all algorithms required for the generation of global LC products from various Earth Observation (EO) instruments that meet the needs of key users of the climate modelling community. The first phase of the LC_CCI project delivered a new generation of satellite-derived global land cover products consisting in three maps at 300 m spatial resolution for three epochs centered on the years 2010 (2008-2012), 2005 (2003-2007) and 2000 (1998-2002). These maps were obtained from SPOT-Vegetation and ENVISAT-MERIS time series. Other significant outputs were (i) 7-day surface reflectance time series for the whole archive of MERIS Full and Reduced Resolution data (2002-2012), (ii) land surface seasonality products describing the seasonal variability of the land surface for the vegetation greenness, snow cover and burned areas and (iii) a global map of open water bodies at 300 m spatial resolution derived from Envisat ASAR and ancillary data. All products were delivered with an aggregation tool, enabling re-projection, re-sampling and translation from LC classes into Plant Functional Types for the different climate models. Three major Earth System models already investigated these new products as land surface information. During this second phase (2014-2016), one objective of the LC_CCI project is to generate new global LC products covering the 1990s and 2015. Extending the series of LC maps to 2015 will rely exclusively on PROBA-V time series. These time series are composited from daily to weekly, then to seasonal periods, using the mean compositing algorithm and used as input to the classification chain. The change detection algorithm, originally based on SPOT-Vegetation data, is updated to the specificities of the PROBA-V time series covering 2013 to 2015. The challenge is to ensure a consistency between the successive global LC maps while they are derived from different sensors.