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.
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 ). GLOBALALBEDO project 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.
OLCI's spectral definition permits a fine characterisation of the vegetation with three parameters: the FAPAR, the LAI and the OTCI (see full description below). Concerning the Leaf Area Index (LAI) and similarly to flood water extent, this parameter 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.
FAPAR - Fraction of Absorbed Photosynthetically Active Radiation (ECV)
In order to monitor the state and evolution of terrestrial vegetation cover, OLCI acquires multi-spectral imagery of the Earth. Defined as an OLCI standard Level-2 product, the FAPAR is derived from the radiation measured over land surfaces. FAPAR has been defined to advantageously replace the Normalised Difference Vegetation Index (NDVI), provided it is itself properly estimated. 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, FAPAR is an essential surface parameter for the provision of Earth climate system data.
Leaf Area Index (LAI)(ECV)
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.
OLCI Terrestrial Chlorophyll Index (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).
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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. https://doi.org/10.1109/JSTARS.2019.2899998
 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.
Harris, A., Dash, J., 2010. The potential of the MERIS Terrestrial Chlorophyll Index for carbon flux estimation. Remote Sens. Environ. 114, 1856–1862. https://doi.org/10.1016/j.rse.2010.03.010
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
Zhang, S., Liu, L., 2014. The potential of the MERIS Terrestrial Chlorophyll Index for crop yield prediction. Remote Sens. Lett. 5, 733–742. https://doi.org/10.1080/2150704X.2014.963734
For further information about land applications and services available, see: Copernicus website.