The High Resolution (HR) Land Cover (LC) Essential Climate Variable (ECV) project involves the accurate description and analysis of land cover (LC) and land cover change (LCC) using Earth Observation (EO) data with high spatial resolution (10-30m).

Land Cover has a key role on surface energy, water and carbon fluxes variables. Moreover, LCC has an impact on radiative (albedo), aerodynamic, evaporative properties, carbon storage with different effects on surface temperature and precipitation. These factors have been taken into account by the climate modelling community to properly model Earth climate at a global scale using medium resolution EO data (i.e., 100m).

However, improvement of sensor technology achieved during the last decade makes EO data with high spatial resolution available. For these reasons, HR_LandCover_CCI will produce LC and LCC maps with high spatial resolution and investigate their impact and the crucial role of spatial resolution on climate models at regional scale.

The project will focus primarily on three areas:

  1. South America (Amazon forest)
  2. West Africa (Sahel band)
  3. Western Siberia.


The primary objective of HR_LandCover_CCI is to study and investigate the role of the spatial resolution of LC and LCC in supporting climate modelling research at regional scale. HR_LandCover_CCI aims at improving the understanding of the interaction between climate and land cover while increasing the spatial resolution of 1 order of magnitude (from 300m to 10-30m) with respect to the LandCover_CCI.

About the project

Land cover (LC) is defined by FAO as the observed physical cover of the Earth surface. It includes the vegetation and human-made infrastructures, like water, ice, rock, forest or urban areas. Its assessment and the monitoring of its dynamics are of extreme importance for the correct estimation of Earth radiation budget. Moreover, LC is correlated to land use and by proxy allows the study of how human activities impact on the Climate.

CCI+ aims at the cooperation between climate modelers, EO communities and industrial partners on the development of software systems for the pre-operational ECV production in a research context.

During the three years of the programme, the Consortium will generate and validate LC maps and LCC using EO data acquired over the last decades and continuously acquired today. This will enable the study of LC and LCC and their impact on Earth Climate at regional scale using high spatial resolution EO data (i.e., 10-30m). Indeed, with the evolution of technology, a higher spatial resolution and an increased operational availability of EO satellite data make it possible to overcome several limitations of LC mapping. The HR_LandCover_CCI variable is crucial for improving our understanding in relation to climate modelling and mitigation/adaptation strategies.

The primary objectives are:

  1. Examining the role of the spatial resolution to support climate research;
  2. Studying LCC in key regions exposed to extreme climate conditions or characterized by significant climate changes over the last decades
  3. Understanding classification variability across spatio-temporal scales.

The EO outputs will be:

  1. A static HRLC at subcontinental level at 10m as reference static input to the climate models
  2. The long-term record of regional HRLC maps at 30m in the regions identified for the historical analysis every 5 years
  3. The change information at 30m and yearly scale for supporting the updates of the HRLC maps


Above: a) Multi-scale and (b) multi-temporal LC concept highlighting the spatial and temporal consistency among products at different scales

HR_LandCover_CCI will keep spatio-temporal consistency with respect to the CCI Medium Resolution (MR) Land Cover. Several new challenges should be addressed that concerns EO science, validation, engineering and climate modelling. In particular, challenges related to the Climate studies are:

Challenges and novelty related to EO Science consist of:

High quality results will be guaranteed by several validation activities: