OLCI is the successor to MERIS aboard ENVISAT, the primary objective of which was to screen the ocean and land surface to harvest information related to biology (e.g. phenology of marine and terrestrial biomass). OLCI also provides reliable information on the atmosphere, especially on the aerosols characterisation. All applications of OLCI including contributions to climate study are presented in this applications section.
Table 1: Mapping between OLCI geophysical products and LAND applications
OLCI Geophysical Products
Mapping and monitoring of Land Use and Cover (LUC)
Climate Change Monitoring
Land Use and Cover (LUC) mapping and monitoring
Mapping and monitoring of Land Use and Cover (LUC) is defined as a priority research item in Europe (particularly in regards to Copernicus). Agricultural and environmental applications require reliable and actual information on LUC. The environment in Europe is constantly changing due to a combination of socio-economic and climatic processes. Extensive and various legal mechanisms have been defined at national and international level to protect the environment and ensure viable use of natural resources. These legal mechanisms are the basis for different activities in monitoring the environment and include the Amsterdam Treaty (1997), EU Habitats Directive, EU Common Agricultural Policy and the Kyoto Protocol.
For accurately monitoring large areas, and Europe in particular, remote sensing appears to be an appropriate tool. Previously in the Coordination of Information on the Environment (CORINE) land cover project, visual interpretation from LANDSAT-TM and SPOT-XS hard copies at a landscape level were used to produce an ecological legend . The CORINE Land Cover (CLC) database was updated during the CLC 2006 project. Other approaches are used (automatic pixel-wise) as digital classification of the same type of pictures creating national land cover maps [2, 3, 4].
However, these approaches are costly and time-consuming, especially if applied at a European scale, as they use high spatial resolution images. Using coarse spatial resolution data, such as that provided by the NOAA-AVHRR sensor, is an alternative.
However, this imagery restricts use for monitoring purposes because the majority of European land cover changes occur at fine scale. According to different case studies, a compromise between LANDSAT/SPOT and NOAA can be achieved using medium resolution images (i.e. from MERIS and MODIS). The OLCI mission's land applications are designed to provide continuity with MERIS and MODIS.
ESA's GLOBCOVER initiative aims to develop and demonstrate a service for the generation of global land cover maps. This map is based on Envisat MERIS fine resolution (300 m) mode data. Presently, GLOBCOVER 2009 is considered as the most detailed and recent global land cover map available.
Figure 1: At 300 m resolution, GLOBCOVER Land Cover v2 provides a revealing portrait of global land use. To build this map, a colour representing one of 22 different land classifications is associated with each pixel. The associated land classification is defined according to the predominant type of vegetation found at that location [Credits: ESA GLOBCOVER project]
The land surface albedo is the proportion of the incident light that is reflected by the land surface. This information is required for the the entire Earth's land surface (snow and snow-free) for initialisation and verification of Global Climate Model. To generate such a global map by temporal composition requires both sufficient directional looks and the very precise correction of top of atmosphere radiances to "at Surface" Directional Reflectances (SDRs). In addition, such a map requires precise radiometric calibration and inter-calibration of different sensors and computation of radiative transfer coefficients to derive broadband SDRs from different input narrowband SDRs and, given sufficient angular sampling from all the directional looks within a given temporal window, derive a suitable Bidirectional Reflection Distribution Function (BRDF ). http://www.globalbedo.org/ has been set up by ESA to create a 15 year time series by employing SPOT-VEGETATION as well as MERIS. A gap-filling method has been put in place by using 10 year mean estimates derived from equivalent BRDFs from MODIS and to complement the dataset. It is likely that reflectances from OLCI would be used for such albedo derivation.
Moreover, OLCI's spectral definition permits a fine characterisation of the vegetation with three parameters: the Green Instantaneous Fraction of Absorbed Photosynthetically Active Radiation (GI-FAPAR), the Leaf Area Index (LAI) and the OLCI Terrestrial Chlorophyll Index(OTCI).
LAI is not a 'core product' but can be derived from rectified reflectances. The two other products are defined as Essential Climate Variables (ECV), designated by the Global Climate Observing System (GCOS) and specifically monitored as relevant indicators for climate evolution studies and trend analysis:
GI-FAPAR: it has been defined to advantageously replace the Normalised Difference Vegetation Index (NDVI). Essential in the plant photosynthetic process, this bio-geophysical product is often used in diagnostic and predictive models computing primary productivity of the vegetation canopies. In addition, this parameter is also an input for the estimation of assimilation of CO2 in vegetation. According to international organisations including GCOS, GI-FAPAR is an essential surface parameter for the provision of Earth climate system data.
LAI: For a given unit area, the LAI is defined as the ratio of upper leaf surface area to ground area, in the case of broadleaf canopies, and as projected conifer needle surface area to ground area in the case of coniferous plants. As LAI directly characterises canopy structure, it appears to be a good predictor of primary productivity and crop growth. In addition, because of its substantial influence on energy exchange, water vapour and CO2 exchange between plants and the atmosphere, it is often used in ecosystem models. LAI is therefore required as an input for several ecosystem process models.
LAI can be an input for models of primary productivity or fire dynamics but can also be a parameter of interest on its own. Since direct LAI measurements would require taking all leaves from an area and quantifying their surface area per unit ground, the LAI estimates obtained by remote sensing are considered as approximations of true LAI. There are different mathematical models for calculating LAI, each of them containing specific assumptions and requiring specific inputs. Comprehension of the model assumptions and evaluation of its suitability in relation to available data are essential. In the same way, it is important to know how the model characterises the vegetation in function of field measurements and desired output. Since the majority of models are fine-tuned for a specific scale and for a specific ecosystem type, application of an existing model to another location may imply modifications of this model.
Even though LAI can be obtained from spectral vegetation indices, NDVI for instance, no single equation combining a set of coefficients and different surface types has been found. Using satellite imaging to estimate LAI requires a corrective process for atmospheric effects, topography and diurnal variations. In addition, values fluctuate quickly during a season with varying phenology. On the other hand, using visible/near-infra-red images to estimate LAI requires a cloudless and clear image and when these conditions are fulfilled, LAI values are extracted from the best quality images over a multiple day period (usually 8 – 10 days). In case of continually cloudy areas, using LIDAR or radar is a good alternative to evaluate vegetation characteristics.
Figure 2: Worldwide Leaf Area Index (LAI) [Credits: ESA, data user element]
OTCI: Vegetation canopy spectral response is characterised by two distinctive elements. First, low reflectance in the visible range of the spectrum (400-675 nm) as a result of chlorophyll absorption. Second, a relative high reflectance of NIR radiation (750-1350 nm) because of incident light scattering by leaf cell walls and intracellular air spaces. The narrow transitional region formed between these two features is known as the Red-Edge (RE). The RE position (REP) responds to increasing levels of chlorophyll by shifting towards longer wavelengths. Therefore, the REP can be successfully exploited for the remote sensing of canopy chlorophyll content (CCC).
The optical configuration of MERIS facilitated the development of the MERIS Chlorophyll Index (MTCI), a ratio of the difference of bands centred at 753 and 708 nm and the difference between bands centred at 708 and 681 nm. This simple, yet efficient arithmetic combination of MERIS spectral bands is strongly correlated to a wide range of CCC. The simplicity and sensitivity to CCC made the MTCI suitable for automation and to be adopted as an ESA Level-2 land product. Operational availability of MERIS MTCI data enabled terrestrial applications including monitoring land surface phenology (He et al., 2015; Rodriguez-Galiano et al., 2015), estimating gross primary productivity (Chiwara et al., 2018; Harris and Dash, 2010), identify crop health and production (Dash and Curran, 2007; Zhang and Liu, 2014), thus making MTCI a key product in vegetation monitoring.
OLCI was designed to replicate the optical capabilities of MERIS. This facilitated the development of a homologous index to continue the legacy of MERIS, the OLCI chlorophyll index (OTCI) which is available near real time from Sentinel-3. Despite instruments similarities, it is necessary to conduct verifications to evaluate the products' consistency to ensure continuity and to give confidence to the user community. Preliminary results suggest that, overall OTCI is in agreement with the expected temporal and spatial patterns that was observed with 10 years MTCI data. In principle, combination of MTCI and OTCI is providing a longer time series of data on terrestrial canopy chlorophyll content, thus offering opportunity to investigate land surface changes over the last two decades. Future work should complement the product's evaluation through systematic ground validation (e.g. Brown et al., 2019).
 Thunnissen H A M, M N Jaarsma & O F Schouwmans (1992) Land cover inventory in the Netherlands using remote sensing; application in a soil and groundwater vulnerability assessment system. International Journal of Remote Sensing, 13: 1693-1708
 Thunnissen H A M & E Noordman (1997) National land cover database of The Netherlands: classification methodology and operational implementation. BCRS report 96-20 (BCRS, Delft) 95 pp.
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 Project final document – "The science needs for land and solid Earth Sentinel 1-2-3 products", 08/05/2012, Department of geography - University of Zurich
 Brown, L.A., Dash, J., Lidon, A.L., Lopez-Baeza, E., Dransfeld, S., 2019. Synergetic Exploitation of the Sentinel-2 Missions for Validating the Sentinel-3 Ocean and Land Color Instrument Terrestrial Chlorophyll Index over a Vineyard Dominated Mediterranean Environment. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 12, 2244–2251. ieeexplore
 Chiwara, P., Ogutu, B.O., Dash, J., Milton, E.J., Ardö, J., Saunders, M., Nicolini, G., 2018. Estimating terrestrial gross primary productivity in water limited ecosystems across Africa using the Southampton Carbon Flux (SCARF) model. Sci. Total Environ. 630, 1472–1483. https://doi.org/10.1016/j.scitotenv.2018.02.314
 Dash, J., Curran, P.J., 2007. Relationship between the MERIS vegetation indices and crop yield for the state of South Dakota, USA, in: European Space Agency, (Special Publication) ESA SP.
 He, Y., Bo, Y., de Jong, R., Li, A., Zhu, Y., Cheng, J., 2015. Comparison of vegetation phenological metrics extracted from GIMMS NDVIg and MERIS MTCI data sets over China. Int. J. Remote Sens. 36, 300–317. https://doi.org/10.1080/01431161.2014.994719
 Rodriguez-Galiano, V.F., Dash, J., Atkinson, P.M., 2015. Characterising the land surface phenology of Europe using decadal MERIS data. Remote Sens. 7, 9390–9409. https://doi.org/10.3390/rs70709390
Measuring ocean colour from space allows information to be gathered about marine biological constituents. This measurement relates to water colouration (visible spectrum), which is affected by elements present in the water and especially by population with phytoplankton biomass (as indexed by Chl-a) which constitutes the first element of the trophic chain, and associated detrital material. For coastal and shallow waters, colouration of waters can also be the result of the release of terrestrial waters loaded with suspended sediment and organic matter, as well as re-suspension due to wave agitation. Initially designed for research studies in marine biology and carbon cycle, this observation technique has spawned a number of applications oriented toward marine area management and coastal zone management. Known reliable applications making use of ocean colour that benefit from OLCI, are summarised in the following sections.
Figure 3: GlobColour Chlorophyll Monthly Product from OLCI-A [Credits: ACRI-ST]
Ocean Net Primary Production mapping
One of the important and unique applications of basin to global-scale Chl-a maps is to calculate global ocean primary production. Using algorithms that incorporate satellite-based Chl-a to calculate regional to global-scale estimates of annual Net Primary Production  (NPP) provides important insights into the function of ocean ecosystems and biogeochemical processes. A key finding from NPP calculation based on satellite data is that ocean and terrestrial NPP contributes more or less equally to global productivity . As a result of the continuity over time of international ocean colour missions since 1998, NPP is monitored on a global scale. The essential function of carbon sink in the ocean is therefore regularly estimated. Climatic trends in such carbon uptake will be calculated once the time series of ocean colour observations is long enough, reinforcing the crucial need to maintain continuity in ocean colour missions over the next decades.
Figure 4: Map of the primary production for February 2019 in mgC.m-2.d-1. [Credits: CMEMS]
Algal Bloom and Water Quality Monitoring
Algal bloom detection has been the subject of a number of intensive research works during the last decade. The results of these works have been transformed into an operational capacity to trigger alerts for some invasive micro-algae. Within the framework of the GMES Service Element (e.g. Coastwatch and Marcoast) operational services have been set up and are still operating. The next scientific challenge is, whenever possible, to identify the type of algae and the harmfulness of the detected species together with the bloom strength and extent.
Figure 5: Algal bloom in the North Sea, S3-A/OLCI, 27/05/2017 [Credits: ESA]
Mesoscales Process Monitoring
As for SST, ocean colour provides spatial repartition of front and eddies at the ocean surface. The detection and identification of surface patterns can be achieved either using one of the available water-leaving reflectances at a given wavelength, or by a combination of them. Depending on the purpose, it can also be done with high level products such as water transparency, giving an integrated estimate of vertical visibility through the ocean upper layer. When oriented toward mesoscale circulations analysis or biological productivity (e.g. of large marine ecosystems, such as up-welling zones), these analyses are often performed by associating other Earth Observation measurements, such as SST and Sea Surface Height (SSH) (both available and collocated with ocean colour observations on Sentinel-3). In this case, marine habitats have been analysed for some species as a function of these three components (Chl-a, SST, SSH) and indications can be provided in NRT (less than 3 hours after measurement by satellite) to optimise fish catches and/or to avoid fishing near protected species (e.g. turtles). Although still experimental, fish stocks and their evolution can then be assessed in productive zones (Large Marine Ecosystem).
Figure 6: GlobColour Monthly Chlorophyll Regional Product (July 2019 - from a high concentration of 30 mg/m3 in red to 0.01 mg in purple) [Credits: ACRI-ST]
Phytoplankton size class and types estimation
Although limited to surface observation (the upper sea layer of one optical depth), several techniques, ranging from statistical to numerical, allow expansion of this information to greater depths and provide a better 3D description of the biological field. Besides this vertical extension, phytoplankton size class and types (e.g. diatoms) can also be determined from ocean colour through robust algorithms .
The assimilation of Chl-a measurements from space, or of Inherent Optical Properties (IOP) in biogeochemical modelling, is under way.
Sedimentary Processes Monitoring
Providing reliable atmospheric correction, ocean colour gives access to geographical extent and composition of turbid plumes, allowing monitoring of flood expansion and its impact at sea. This also provides a means of establishing the fluxes of terrestrial material at sea and of monitoring their fluctuations in space and time. This is particularly useful for the study of large estuaries.
Figure 7: River discharge off Korean peninsula, S3B/OLCI, 21/05/2019 [Credits: ESA]
 Yoder, J.A., S.C. Doney, D.A. Siegel and C. Wilson. 2010. Study of marine ecosystems and biogeochemistry now and in the future: Examples of the unique contributions from space. Oceanography 23(4):104-117, doi:10.5670/oceanog.2010.09.
 Field, C.B., M.J. Behrenfeld, J.T. Randerson and P. Falkowski. 1998. Primary Production of the biosphere: Integrating terrestrial and oceanic components. Science 281:237-240
 Uitz, J., D. Stramski, B. Gentili, F. D'Ortenzio, and H. Claustre (2012), Estimates of phytoplankton class-specific and total primary production in the Mediterranean Sea from satellite ocean color observations, Global Biogeochemical Cycles, 26, GB2024, doi:10.1029/2011GB004055.
 Alvain S., Moulin C., Dandonneau Y., and H. Loisel (2008) Seasonal distribution and succession of fominant phytoplankton groups in the global ocean : A satellite view., Global Biogeochem. Cycles, 22.
Three essential data can be extracted from OLCI observations related to atmosphere:
the atmospheric composition (mainly aerosols and water vapour): essential information for climate studies and weather forecasting
illumination condition (fraction of available natural light) of the observed area: a key input for biological studies
downwelling solar radiation (long- and short-wave) at the Earth's surface and top-of-atmosphere: critical information in estimating and monitoring the Earth Surface Radiation Budget (SRB).
Water vapour is both radiatively and chemically active, and so plays a key role in the atmosphere. It is the strongest greenhouse gas (GHGs), even if it is influenced more indirectly than directly by anthropogenic activity. It is an essential indicator for convection and radiative forcing in the Upper Troposphere (UT) and Lower Troposphere (LT). In addition, in the stratosphere, water vapour is a source gas for hydroxide, a chemically active gas in the ozone budget.
Scientific evidence confirms that the ascending branch of Brewer Dobson Circulation, controlling the balance of water vapour in the UT and in the LS, is modifying because of climate change. Using OLCI, scientists and weather forecasters have access to the Integrated Water Vapour Column measured over land and ocean.
By mass, atmospheric aerosols are minor components of the atmosphere, however, they are a crucial constituent of climate and particularly of climate change. Global radiation is impacted by aerosol which directly scatters solar radiation and indirectly influences cloud reflectivity, cloud cover and cloud life time.
Tropospheric aerosols can be formed in two different ways: either directly from the surface, e.g. sea salt from oceans or dust, smoke and soot from continents, or in the atmosphere through complex (photo-) chemical processes and reactions between gaseous components. These gaseous constituents came themselves from the surface, for example dimethyl sulphide (DMS) over oceans or sulphur and nitrogen oxides over continents. Most stratospheric aerosols originate in volcanic eruptions, powerful enough to inject Sulphur Dioxide (SO2) into this layer. Apart from volcanic eruptions, stratospheric aerosols can arise from Oceanic Carbonyl Sulphide (OCS), from low SO2 emissions (from Kilauea-type volcanoes) and other anthropogenic sources (industrial and aircraft operations). Climate is also affected by radiative effects induced by changes in cirrus cloud amounts, particle size and/or lifetime.
OLCI provides Aerosol Optical Thickness and Angstrom Exponents to scientists and weather forecasters.
Photosynthetically Active Radiation (PAR) is defined as the spectral range of solar radiation (in terms of wave band, from 400 to 700 nm) which photosynthetic organisms can use in the process of photosynthesis.
Agriculture, forestry and oceanography represent the main scientific fields using PAR measurements, to compute the euphotic depth in the ocean, for instance. OLCI products include PAR measurements for both land and ocean applications. Over land, this parameter ensures a link between plant status and available radiation. Over ocean, its value is necessary to compute primary production.
Surface Radiation Budget (SRB):
The Surface Radiation Budget is an essential component of surface energy budget . It is important to almost all aspects of climate and, therefore, required to be monitored systematically. SRB is composed of upward and downward solar and thermal infra-red irradiances. To be used in climate applications, these components require complex strategies of measurement due to their high fluctuation over the electromagnetic spectrum, over time and position.
In order to be a relevant part of climate research and assessment, it is essential to have well-analysed and planned measurement approaches of surface irradiance observations.
Radiation quantities are possibly responsible for forcing climate change, however these climate variations will alternately change observable radiation fields. As a consequence, it is required to proceed to a complex analysis of radiation observations for application to climate. Information on surface radiation budget is available from OLCI product.
Climate Change Monitoring
The work of the United Nations Framework Convention on Climate Change (UNFCCC) and of the Intergovernmental Panel on Climate Change (IPCC) are supported by the https://gcos.wmo.int/en/essential-climate-variables/table . All ECV are designed to be technically and economically viable for systematic observation. International exchanges are required to fulfil databases with present and historical observations. There are 54 ECVs currently defined.
Supporting this international effort, a substantial number of OLCI products include some ECVs (as shown in the following table).
ESA's Climate Change Initiative (CCI) comprises 23 parallel R&D projects generating ECVs across the land, atmosphere and ocean. Its global, consistent climate data records span several decades, have fully characterised uncertainties, and are available from the https://climate.esa.int/en/odp/#/dashboard .