Global land cover is a key variable in the earth system with feedbacks on climate, biodiversity and natural resources. However, global land cover data sets presently fall short of user needs in providing detailed spatial and thematic information that is consistently mapped over time and easily transferable to the requirements of earth system models. In 2009, the European Space Agency launched the Climate Change Initiative (CCI), with land cover (LC_CCI) as 1 of 13 essential climate variables targeted for research development. The LC_CCI was implemented in three phases: first responding to a survey of user needs; developing a global, moderate-resolution land cover data set for three time periods, or epochs (2000, 2005, and 2010); and the last phase resulting in a user tool for converting land cover to plant functional type equivalents. Here we present the results of the LC_CCI project with a focus on the mapping approach used to convert the United Nations Land Cover Classification System to plant functional types (PFTs). The translation was performed as part of consultative process among map producers and users, and resulted in an open-source conversion tool. A comparison with existing PFT maps used by three earth system modeling teams shows significant differences between the LC_CCI PFT data set and those currently used in earth system models with likely consequences for modeling terrestrial biogeochemistry and land–atmosphere interactions. The main difference between the new LC_CCI product and PFT data sets used currently by three different dynamic global vegetation modeling teams is a reduction in high-latitude grassland cover, a reduction in tropical tree cover and an expansion in temperate forest cover in Europe. The LC_CCI tool is flexible for users to modify land cover to PFT conversions and will evolve as phase 2 of the European Space Agency CCI program continues.

The existing medium-resolution land cover time series produced under the European Space Agency's Climate Change Initiative provides 29 years (1992–2020) of annual land cover maps at 300 m resolution, allowing for a detailed study of land change dynamics over the contemporary era. Because models need two-dimensional parameters rather than two-dimensional land cover information, the land cover classes must be converted into model-appropriate plant functional types (PFTs) to apply this time series to Earth system and land surface models. The first-generation cross-walking table that was presented with the land cover product prescribed pixel-level PFT fractional compositions that varied by land cover class but that lacked spatial variability. Here we describe a new ready-to-use data product for climate modelling: spatially explicit annual maps of PFT fractional composition at 300 m resolution for 1992–2020, created by fusing the 300 m medium-resolution land cover product with several existing high-resolution datasets using a globally consistent method. In the resulting data product, which has 14 layers for each of the 29 years, pixel values at 300 m resolution indicate the percentage cover (0 %–100 %) for each of 14 PFTs, with pixel-level PFT composition exhibiting significant intra-class spatial variability at the global scale. We additionally present an updated version of the user tool that allows users to modify the baseline product (e.g. re-mapping, re-projection, PFT conversion, and spatial sub-setting) to meet individual needs. Finally, these new PFT maps have been used in two land surface models – Organising Carbon and Hydrology in Dynamic Ecosystems (ORCHIDEE) and the Joint UK Land Environment Simulator (JULES) – to demonstrate their benefit over the conventional maps based on a generic cross-walking table. Regional changes in the fractions of trees, short vegetation, and bare-soil cover induce changes in surface properties, such as the albedo, leading to significant changes in surface turbulent fluxes, temperature, and vegetation carbon stocks. The dataset is accessible at https://doi.org/10.5285/26a0f46c95ee4c29b5c650b129aab788 (Harper et al., 2023).

The Atmospheric Correction Inter-comparison eXercise (ACIX) is an international initiative with the aim to analyse the Surface Reflectance (SR) products of various state-of-the-art atmospheric correction (AC) processors. The Aerosol Optical Thickness (AOT) and Water Vapour (WV) are also examined in ACIX as additional outputs of AC processing. In this paper, the general ACIX framework is discussed; special mention is made of the motivation to initiate the experiment, the inter-comparison protocol, and the principal results. ACIX is free and open and every developer was welcome to participate. Eventually, 12 participants applied their approaches to various Landsat-8 and Sentinel-2 image datasets acquired over sites around the world. The current results diverge depending on the sensors, products, and sites, indicating their strengths and weaknesses. Indeed, this first implementation of processor inter-comparison was proven to be a good lesson for the developers to learn the advantages and limitations of their approaches. Various algorithm improvements are expected, if not already implemented, and the enhanced performances are yet to be assessed in future ACIX experiments.

This article presents the burned area (BA) product of the Copernicus Climate Change Service (C3S) of the European Commission. This product, named C3SBA10, is based on the adaptation to Sentinel-3 OLCI images of a BA algorithm developed within the Fire Climate Change Initiative (FireCCI) project, which used MODIS data. We first reviewed the adaptation process and then analysed the results of both products for common years (2017–2019). Comparisons were performed using four different grid sizes (0.05°, 0.10°, 0.25°, and 0.50°). Annual correlations between the two products ranged from 0.94 to 0.99. Global BA estimates were found to be more similar when the two Sentinel-3 satellites were active (2019), as the temporal resolution was closer to that of the MODIS sensor. Global validation was performed using reference data derived from Landsat-8 images, following a stratified random sampling design. The C3SBA10 showed commission errors between 16 and 21% and omission errors from 48 to 50%, similar to those found in the FireCCI product. The temporal reporting accuracy was also validated using 19 million active fires. In total, 87% of the detections were made within 10 days after the fire by both products. The high consistency between both products ensures global BA data provision from 2001 to the present. The datasets are freely available through the Copernicus Climate Data Store (CDS) repository.

In the framework of the European Copernicus programme, the European Space Agency (ESA) has launched the Sentinel-2 (S2) Earth Observation (EO) mission which provides optical high spatial resolution imagery over land and coastal areas. As part of this mission, a tool (named S2-RUT, from Sentinel-2 Radiometric Uncertainty Tool) has been developed. The tool estimates the radiometric uncertainty associated with each pixel in the top-of-atmosphere (TOA) reflectance factor images provided by ESA. This paper describes the design and development process of the initial version of the S2-RUT tool. The initial design step describes the S2 radiometric model where a set of uncertainty contributors are identified. Each of the uncertainty contributors is specified by reviewing the pre- and post-launch characterisation. The identified uncertainty contributors are combined following the guidelines in the ‘Guide to Expression of Uncertainty in Measurement’ (GUM) model and this combination model is further validated by comparing the results to a multivariate Monte Carlo Method (MCM). In addition, the correlation between the different uncertainty contributions and the impact of simplifications in the combination model have been studied. The software design of the tool prioritises an efficient strategy to read the TOA reflectance factor images, extract the auxiliary information from the metadata in the satellite products and the codification of the resulting uncertainty image. This initial version of the tool has been implemented and integrated as part of the Sentinels Application Platform (SNAP).

Earth Observation satellite data allows for the monitoring of the surface of our planet at predefined intervals covering large areas. However, there is only one medium resolution sensor family in orbit that enables an observation time span of 40 and more years at a daily repeat interval. This is the AVHRR sensor family. If we want to investigate the long-term impacts of climate change on our environment, we can only do so based on data that remains available for several decades. If we then want to investigate processes with respect to climate change, we need very high temporal resolution enabling the generation of long-term time series and the derivation of related statistical parameters such as mean, variability, anomalies, and trends. The challenges to generating a well calibrated and harmonized 40-year-long time series based on AVHRR sensor data flown on 14 different platforms are enormous. However, only extremely thorough pre-processing and harmonization ensures that trends found in the data are real trends and not sensor-related (or other) artefacts. The generation of European-wide time series as a basis for the derivation of a multitude of parameters is therefore an extremely challenging task, the details of which are presented in this paper.