The ESA Climate Change Initiative (CCI) has more than 20 projects targeting different Essential Climate Variables (ECV's), across a diverse range of disciplines from the oceans, ice sheets, land and the atmosphere. Each of the different ECV projects have a different core community, often with their own data standards and existing data formats; however these can be diverse, and to aid interoperability and use of the data outside of the individual communities the Climate Change Initiative has developed a set of CCI data standards to be used across all the projects. By their nature, the data produced across the full range of ECV's is very heterogeneous, and the data standards have to be flexible enough to accomodate vastly different data types from glacier grounding lines to 3D fields of atmospheric trace gases, while still providing a common framework across all data products.

The standards have been built drawing on existing standards where possible; in particular, use of the NetCDF format (for appropriate data) and the Climate and Forecast (CF) conventions, with in-file metadata attributes based on those from the Attribute Convention for Data Discovery (ACDD). Work is also ongoing towards interoperability with requirements for other climate data portals e.g. the Copernicus Climate Change Service (C3S), which is important as many CCI ECV’s are transitioning to operational production through C3S. In this poster we will describe the key features of the CCI data standards and how they have evolved over the course of the CCI programme as the standards have gained increasing community acceptance and new requirements have come to light. As an example, we will illustrate the importance of the use of the CCI data standards in integrating the full range of CCI datasets into infrastructures such as the CCI Open Data Portal and CCI Toolbox.

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Lakes and enclosed inland seas are integrators of environmental, anthropogenic and climatic changes occurring within their catchments. The factors that drive lake conditions vary widely across space and time, and lakes, in turn, impact their surrounding environments in important and diverse ways. Remote sensing can offer unique insights into the response of lakes to change, at a global level.

The ESA Climate Change Initiative Lakes (CCI-Lakes) project will provide the first consistent dataset of essential climate variables for a global selection of lakes. This is a multi-disciplinary task combining expertise in the remote observation of lake water extent, lake level, ice cover, surface water temperature and surface water reflectance.

It is crucial to reach a consistent dataset including these variables, which are observable at varying spatial resolutions and temporal intervals, and available from sensor records which do not always overlap in time. A consistent Lakes climate data record will be of value for climate scientists as well as lake hydrologists and limnologists with local to global interests. To best meet their diverse requirements, a user consultation and online survey is launched at the start of the project.

During this poster presentation, members of the CCI Lakes consortium will be available to jointly fill out the user requirements survey and to answer any questions. Please join us at the poster to ensure that your lakes are included in our work.

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The purpose of the European Space Agency’s Climate Change Initiative (CCI) programme is to ensure that the full potential of long-term global Earth Observation (EO) archives for a number of Essential Climate Variables (ECVs), defined by the United Nations Framework Convention on Climate Change (UNFCCC), are realised. The programme covers 22 ECVs (involving successful collaborations between more than 100 institutions from all over Europe), generating validated ECV datasets, from harmonised multi-sensor satellite data, which can be used to provide a solid basis for climate science and modelling, specialist application development and, ultimately, European and global policy making.

A single point-of-access (free and open) to these CCI ECV datasets, hosted by the Centre for Environmental Data Archival (CEDA)’s CCI Central Data Archive (CDA), is provided through the CCI Open Data Portal (ODP). The CCI CDA currently contains more than 150 datasets (incl. older versions, only 111 of which are available to users through the ODP) amounting to approximately 99 TB of data. The ODP allows for users to take advantage of a number of useful features, including:

Data Download Protocols
Users are provided with the opportunity to download data from the CDA, using the FTP, WMS, WCS and OpenDAP download protocols (depending on the format of the data).

Dashboard
Users are provided a novel visualisation interface which demonstrates a high level view of the ECV datasets contained within the CDA:
- Key information (including useful metadata) is provided for each dataset;
- Graphical representation of temporal coverage is provided for each dataset;
- Data download (via the aforementioned download protocols);
- Links to relevant user documentation (e.g. product guides, etc.).

CCI Search
Users are provided with the opportunity to use a faceted search of ECV datasets contained within the CDA (including data download via the aforementioned download protocols).

The ODP is also complemented by the CCI Toolbox, which allows for users to read and work (i.e. apply operations and transformations) with ECV data (retrieved from the CDA).

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Progress in data access provides new opportunities for characterising, quantifying and monitoring Land Cover, Land Use (LCLU) and theirs changes. Deriving information about land areas and area changes is essential for the annual inventory of national emissions of greenhouse gas (GHG) in order to comply with the United Nations Framework Convention on Climate Change (UNFCCC) and the Kyoto Protocol. This national inventory reports anthropogenic emissions and removals induced by human activities for a calendar year. The analysis of European GHG inventory indicates that countries are not making a direct use of remote sensing and mostly rely on statistics, census and survey data. The current Belgian methodology is not making an exception to this rule. It is based on a regular grid of points on which a diagnosis of Land Use is made automatically through the use of several national datasets. Following this processing, a certain number of points (~20%) remains unclassified and are then classified by the manual interpretation of aerial photography. This study proposes a wall-to-wall approach based on Sentinel-2 data for mapping GHG emissions and removals associated with the Land Use, Land Use Change and Forestry (LULUCF) sector in Wallonia, Belgium. The LCLU classification was performed following a per-pixel supervised classification method. The Land Use/Cover Area frame statistical Survey (LUCAS) was used as training data. We investigated the performance of four widely used classifiers under different sample sizes in order to compare their performance for LCLU classification. In addition, different time periods of images have been compiled to achieve the highest overall accuracy. The use of Sentinel-2 data and LUCAS survey supports a free of access, harmonised and consistent methodology that could be applied at the European scale. This exhaustive and spatially explicit approach will help Belgium to meet the evolving requirements of the UNFCCC and EU legislation. Our study could also stand for a prototype of LULUCF classification in Europe and thus enable us to draw comparisons across countries.

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The 21st Conference of the Parties (COP 21) exemplified the intense political, scientific and public debate about climate change. Observing the changing Earth is at the heart of ESA’s Living Planet Programme. Measurements made in support of this programme must be universally accepted, robust and of sufficient accuracy to ensure stability and trust of long-term records. TRUTHS provides this missing assurance by directly anchoring the space-based Earth observing (EO) systems to the internationally-accepted physical standards of the SI measurement system, thus guaranteeing the temporal consistency of data, even beyond the lifetime of individual systems, whilst simultaneously providing data of unprecedented quality to address key challenges across the Earth Science disciplines. This will enhance performance and ensure consistency and coherence of data from the instruments on-board the Sentinels and allow the upgrade of Copernicus and the global EO system to create a foundation for an “operational climate observing system”.

TRUTHS will be the scientific forerunner of a paradigm shift in how the Earth is observed, delivering data not constrained to a single discipline but deliberately specified to allow it to be configured to support applications in and at the boundaries of Land, Ocean and Atmosphere and meet the exacting needs of climate. It will be a standards laboratory in space, providing and enabling SI-traceable measurements of unequivocal accuracy and a fundamental enabling element of any sustainable space climate observatory.

TRUTHS will establish a fiducial data set of incoming and outgoing solar radiation with sufficient spatial and spectral resolution and unprecedented accuracy that will:

• Provide an SI-traceable benchmark of the state of the planet’s shortwave (SW) radiative balance, forcings and feedbacks from which human induced climate trends can be detected in as short a timescale as possible, limited only by natural variability.
• Facilitate an upgrade in performance and functionality of current and future (e.g. Sentinels) and some heritage Earth observing systems to meet the specific needs of climate through an SI-traceable anchored reference calibration in orbit, ensuring robust coherence and interoperability. This is the ‘lynch pin’ needed to establish an ‘integrated Earth observing system’ and associated Climate Data Records (CDRs). TRUTHS will also serve as an enabler for the growth of next-generation ‘micro-satellites’ by providing a reference calibration for sensors too small for robust calibration systems of their own.
• Deliver data of sufficient quality and flexibility to test and improve the retrieval of solar reflective Essential Climate Variables (ECVs), (particularly the carbon cycle on land and ocean) and other operational applications and services, following an Earth-system-science approach to mission design and utilisation.
• Provide a robust SI-traceable anchor to address the continued debate and uncertainty regarding the impact of solar radiation (spectral and total) on the atmosphere and, consequently, climate in the near and medium term.
• Initiate a space-based climate observing system as the European contribution to a coordinated international effort, underpinning CEOS and GEOSS.
• Be a science-led mission ready to transition to deliver sustainable long-term observations operationally.

With a high spectral resolution sensor, TRUTHS observes climate-relevant processes related to the atmosphere, oceans, land and cryosphere and provides new insight for societal understanding of the grand challenges: food, water, natural resources, health and disaster management. Many of the science challenges outlined in the ESA strategy lie in the interfaces between disciplines and are driven by uncertainty in our understanding of the Earth system. TRUTHS provides the data to test and constrain climate models and retrieval algorithms upgrading the capability of not only satellite systems but also surface-based observing systems. In this way it builds on and enhances the current research programme through injecting new and improved data into proven methods.

The IPCC concludes that the mix of natural variability and anthropogenic effects on decadal timescales is far from fully understood or measured, requiring significant improvements in accuracy. Unequivocal attribution and quantification of subtle fingerprint indicators from this noisy background are fundamental to our ability to predict future climate reliably and to adopt appropriate mitigation/adaptation strategies. The uncertainty in climate prediction lies in the complexity of the models, our inadequate understanding of the Earth system, its feedback mechanisms and the relatively poor quality of available data against which to test predictions on the necessary decadal timescales. TRUTHS accuracy has been optimised to be within 20 % of the perfect observing system; allowing detection of trends in key climate parameters, from the clutter of natural variability, in as short a time as possible. For example, it will detect a trend in Cloud Radiative Forcing, the dominant and most uncertain climate feedback, in half the time of other sensors.

TRUTHS will address these challenges through two complementary but distinct approaches: It will establish a fiducial data set which can then:
• be used as a direct sampling of climate signals
• improve the performance in other observing systems through reference calibrations

The latent potential of spectrometers for other than atmospheric missions has for many years remained to be substantiated, although new missions are starting to emerge they are still a relative rarity. TRUTHS will radically change this perception. By adding very high, SI traceable, radiometric accuracy to the spectrometer data, it creates as its level 1 product a ‘fiducial data set’ that serves not only as a benchmark of the state of the planet but also provides the spectral building blocks that can be reconfigured to the spectral signature of multitudes of bio-geo physical parameters, making the mission not only a climate mission, but also one for Land, Ocean, Atmosphere and Cryosphere- enhancing the potential of missions such as EnMap, PRISMA as well as the new space constellations of nano/micro Sats.

One of the most compelling arguments for TRUTHS, in addition to its own science outputs, is its ability to ‘upgrade’ and underpin the traceability of the international EO system as a whole. Data interoperability, the ‘virtual constellations’ of CEOS, the ESA Climate Change Initiative and the EU Copernicus Climate Change Service all require the biases of existing and future sensors to be understood and corrected so that unequivocal ‘quality indicators’ can be assigned to their delivered knowledge-information products. The ‘corner stone’ role TRUTHS has in establishing the architecture needed for long term climate monitoring is widely highlighted as ‘urgent’ by the international community.

TRUTHS has the spectral and spatial resolution to ensure its measurements can be re-convolved to match those of most sensors operating in the solar reflective domain. It will exploit the concept of SNO where, for many climate sensors, it will have hundreds of cross-over opportunities per annum. It will also enhance the emerging international calibration infrastructure to radically upgrade sensors like the Sentinels, which are currently limited by in-flight radiometric accuracy. For example, MSI of Sentinel 2 radiometric accuracy can be improved by a factor of five to ~ 0.5 %. These methods allow archived data from past sensors to be corrected and improved, such that, in some cases, TRUTHS-quality climate records can be extended into the past.

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The quantification of anthropogenic CO2 emissions from city to country scale requires the combination of observational information with systems for the interpretation of such observations. The Copernicus High Priority Candidate Mission for Anthropogenic CO2 Monitoring is a satellite constellation that could provide atmospheric CO2 observations with nearly global coverage at high spatial resolution. This data stream is expected to provide a significant enhancement of our observational in-situ and space-based capabilities. The interpretation of the available observational data streams in terms of CO2 fluxes will ultimately rely on comprehensive inverse modelling systems which include process modules that simulate fossil and biogenic fluxes.

We show first results from a Carbon Cycle Fossil Fuel Data Assimilation System (CCFFDAS) that is built around coupled process models of the terrestrial biosphere and of fossil fuel emissions. The CCFFDAS exploits several observational data streams, including atmospheric (X)CO2, nightlights, reported national totals for sectorial fossil fuel emissions, and emission estimates for individual power plants. The data streams are linked to the process models by dedicated observation operators and are used to constrain the models' process parameters. The calibrated model is then used to provide surface fluxes that are consistent with the observations and the model, together with estimated uncertainty ranges.

We quantify the added value of the potential observations from the Copernicus High Priority Candidate Mission for Anthropogenic CO2 Monitoring on the basis of a synthetic data set.
This added value is quantified in terms of uncertainty reduction of fossil fuel emissions on a global grid of 0.1 degree resolution relative to a prior. For example, with observations from one satellite and a conservative configuration of the CCFFDAS, the uncertainty reduction for the anthropogenic emissions over a 0.4° by 0.4° target region around Berlin roughly 13%.

We explore the sensitivity of this uncertainty reduction with respect to design aspects of the mission, i.e. the number of satellites flying in constellation, swath width, or systematic and random retrieval uncertainty, with respect to the configuration of the CCFFDAS, i.e. the definition of the control vector, prior and data uncertainties, and with respect to the spatial scale of the target region.

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Biomass burning (including forest, grassland, peatland and agricultural fires) have important impacts on global terrestrial and atmospheric systems, affecting land cover, surface albedo, and the atmospheric concentration of greenhouse gases, chemically reactive species and aerosols. Several products have been generated during the last years to estimate total burned area, but uncertainties remain, particularly those associated to small and low intensity fires.

The Fire_cci project of the European Space Agency Climate Change Initiative has generated consistent time series of burned area products to assess the extent of biomass burning, as well as their spatial and temporal characteristics. These datasets provide essential information to researchers studying fire ecology and management, and most importantly to climate and emissions researchers, as well as dynamic global vegetation modellers.

The products generated during the Fire_cci project include the first global burned area dataset at 250m spatial resolution, derived from the MODIS sensor for the years 2001-2017, which has allowed to detect a mean global burned area of 4.65 Mkm2 during those years, more than any other existing global product at moderate resolution to date. The project has also generated the first global AVHRR-LTDR-derived burned area product, extending the burned area information back to 1982. Complementary, a sub-Saharan Africa burned area product for 2016 was produced based on Sentinel-2 information at a spatial resolution of 20m. This dataset has allowed detecting small burned patches with a level of detail not achievable by medium resolution sensors, which derived in a total burned area detected for 2016 of 4.90 Mkm2, in Africa alone. These results indicate that total global burned area could be underestimated by more than 50 percent, with its significant implication in fire emissions inventory.

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The Emissions Database for Global Atmospheric Research (EDGAR) provides independent estimates of global anthropogenic emissions and emissions trends of greenhouse gases (GHG) and air pollutants on a 0.1°x 0.1° spatial grid from 1970 onwards. Estimates are produced for all countries in a consistent way using publicly available statistics and datasets and applying the same methodology, for use in atmospheric models and policy evaluation.
EDGAR is managed by the Joint Research Centre (JRC) of the European Commission and has been extensively used in international assessment efforts on Climate Change including the IPCC 5th Assessment Report.
At the moment, EDGAR does not contain information for the Land Use, Land Use Change, and Forestry (LULUCF) sector. However, previous studies highlighted the importance of this sector in terms of mitigation potential to meet the Paris Agreement goals.
The JRC is developing a new LULUCF component of EDGAR to include global estimates of emissions and removals from this sector calculated in a spatially-explicit way using a Tier1 Intergovernmental Panel on Climate Change (IPCC) default methodology. The production process makes use of different sources of data, such as EO data, maps and statistical datasets. The yearly global land cover maps produced within the ESA Climate Change Initiative (CCI) are used as activity data to estimate areas under different land uses. Land cover maps are reclassified to produce proxies of the standard IPCC land use categories and monitor the different land uses considered within the GHG inventories (forest land, cropland, grassland, wetlands, settlements, other land). Ancillary data are used to stratify according to climate, age classes, soils, and ecological zones and match every strata with the appropriate parameters.
The first results in Europe and North America in terms of areas of the "Forest Land remaining Forest Land" IPCC category as well as in terms of related carbon emissions and removals show a good agreement with the GHG inventories submitted by Annex I countries to the UNFCCC. The current development stage of the EDGAR LULUCF component is presented and the main limitations and challenges are discussed.
The LULUCF component is important to extend the sectors covered by EDGAR, thus improving its usefulness for global studies. At the same time, this simple and operational IPCC-compliant approach can be useful to be adopted in the production of GHG inventories in contexts where more complex estimation methods cannot be applied.

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Soil moisture Climate Data Records (CDRs) produced from active and passive microwave sensors are valuable for the study of the coupling between global water, energy and carbon cycles over land . As part of the European Space Agency (ESA) Climate Change Initiative (CCI) a multi-decadal CDR is produced by systematically combining Level-2 datasets from separate missions. The combination of individual Level 2 datasets into a single consistent long-term product gives us the opportunity to profit from the advantages of individual missions, and to obtain homogenised CDRs with improved spatial and temporal coverage.

The most recent version of the ESA CCI product (v04.4) provides a PASSIVE dataset from radiometer-based products (1978 – mid-2018), an ACTIVE dataset from scatterometer-based products (1991 – mid-2018) and a COMBINED product (1978 – mid-2018). These products are generated from Level 2 data after scaling them into a common climatology, by computing a weighted average of measurements from all sensors that are available at a certain point in time.

After scaling, inhomogeneities may remain at the sensor transition times in the merged product due to differences in sensor properties, like frequency or radiometric accuracy. We use relative, statistical methods for the detection and adjustment of such structural breaks in the CDR using land surface model simulations as a reference and in this way create a further homogenised product that is even more reliable for long-term studies.

A soil moisture CDR based on the ESA CCI algorithm is also produced within the European Commission’s Copernicus Climate Changes Services (C3S) programme and made available through their Climate Data Store. In addition to a daily product, monthly aggregates as well as a dekadal (10-days) products are produced in near-real-time (updated every ten days), providing continuous updates to the CDR.

The accuracy of each data product is assessed through comparison to in situ soil moisture observations using ground reference sites, particularly those included within the International Soil Moisture Network. This assessment is undertaken each time a new ESA CCI version is generated and regularly every three months for the C3S product (where new reference data are available), and the results are compared against previous product versions to assess the evolution of the product quality over time. The quality assessment also includes consideration of the stability of the product and a completeness / consistency check.

In this study, we will present an overview of the product generation including the break-detection and homogenisation techniques. Details of the quality assurance methodology and examples of how the product has been used will also be presented.

The development of the ESA CCI products has been supported by ESA’s Climate Change Initiative for Soil Moisture (Contract No. 4000104814/11/I-NB and 4000112226/14/I-NB) and the European Union’s FP7 EartH2Observe “Global Earth Observation for Integrated Water Resource Assessment” project (grant agreement number 331 603608). Funded by Copernicus Climate Change Service implemented by ECMWF through C3S 312a Lot 7 Soil Moisture service.

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