Bicheron P.,French National Center for Space Studies |
Amberg V.,MAGELLIUM |
Bourg L.,ACRI ST |
Petit D.,MAGELLIUM |
And 8 more authors.
IEEE Transactions on Geoscience and Remote Sensing | Year: 2011
The GlobCover project has developed a service dedicated to the generation of multiyear global land cover maps at 300-m spatial resolution using as its main source of data the full-resolution full-swath (300 m) data (FRS) acquired by the MERIS sensor on-board the ENVISAT satellite. As multiple single daily orbits have to be combined in one single data set, an accurate relative and absolute geolocation of GlobCover orthorectified products is required and needs to be assessed. We describe in this paper the main steps of the orthorectification pre-processing chain as well as the validation methodology and geometric performance assessments. Final results are very satisfactory with an absolute geolocation error of 77-m rms and a relative geolocation error of 51-m rms. © 2011 IEEE.
Gade M.,University of Hamburg |
Melchionna S.,University of Hamburg |
Stelzer K.,Brockmann Consult |
Estuarine, Coastal and Shelf Science | Year: 2014
We demonstrate that Synthetic Apertur Radar (SAR) data have great potential to improve an existing monitoring system based on optical data for intertidal flats and to complement the classification of sediments, macrophytes, and mussels in the German Wadden Sea. Multi-satellite SAR data acquired at different radar bands (L, C, and X band, from ALOS PALSAR, from ERS SAR, Radarsat-2 and ENVISAT ASAR, and from TerraSAR-X, respectively) were used to investigate whether they can be jointly used for crude sediment classification on dry-fallen intertidal flats and for detecting benthic fauna such as blue mussel or oyster beds. In this respect, we show that both multi-satellite and multi-temporal analyses provide valuable input for the routine monitoring of exposed intertidal flats on the German North Sea coast, the latter already improving the identification of the spatial extent of mussel (oyster) beds. In addition, we demonstrate that high-resolution SAR is capable of detecting residuals of historical land use in areas that were lost to the sea during major storm surges in the 14th and 17th centuries. © 2014 Elsevier Ltd.
Radoux J.,Catholic University of Louvain |
Lamarche C.,Catholic University of Louvain |
Van Bogaert E.,Catholic University of Louvain |
Bontemps S.,Catholic University of Louvain |
And 2 more authors.
Remote Sensing | Year: 2014
Land cover is one of the essential climate variables of the ESA Climate Change Initiative (CCI). In this context, the Land Cover CCI (LC CCI) project aims at building global land cover maps suitable for climate modeling based on Earth observation by satellite sensors. The challenge is to generate a set of successive maps that are both accurate and consistent over time. To do so, operational methods for the automated classification of optical images are investigated. The proposed approach consists of a locally trained classification using an automated selection of training samples from existing, but outdated land cover information. Combinations of local extraction (based on spatial criteria) and self-cleaning of training samples (based on spectral criteria) are quantitatively assessed. Two large study areas, one in Eurasia and the other in South America, are considered. The proposed morphological cleaning of the training samples leads to higher accuracies than the statistical outlier removal in the spectral domain. An optimal neighborhood has been identified for the local sample extraction. The results are coherent for the two test areas, showing an improvement of the overall accuracy compared with the original reference datasets and a significant reduction of macroscopic errors. More importantly, the proposed method partly controls the reliability of existing land cover maps as sources of training samples for supervised classification. © 2014 by the authors.
Kwiatkowska E.J.,EUMETSAT |
Ruddick K.,Royal Belgian Institute Of Natural Sciences |
Ramon D.,HYGEOS |
Vanhellemont Q.,Royal Belgian Institute Of Natural Sciences |
And 3 more authors.
Ocean Science | Year: 2016
Ocean colour applications from medium-resolution polar-orbiting satellite sensors have now matured and evolved into operational services. These applications are enabled by the Sentinel-3 OLCI space sensors of the European Earth Observation Copernicus programme and the VIIRS sensors of the US Joint Polar Satellite System programme. Key drivers for the Copernicus ocean colour services are the national obligations of the EU member states to report on the quality of marine, coastal and inland waters for the EU Water Framework Directive and Marine Strategy Framework Directive. Further applications include CO2 sequestration, carbon cycle and climate, fisheries and aquaculture management, near-real-time alerting to harmful algae blooms, environmental monitoring and forecasting, and assessment of sediment transport in coastal waters. Ocean colour data from polar-orbiting satellite platforms, however, suffer from fractional coverage, primarily due to clouds, and inadequate resolution of quickly varying processes. Ocean colour remote sensing from geostationary platforms can provide significant improvements in coverage and sampling frequency and support new applications and services. EUMETSAT's SEVIRI instrument on the geostationary Meteosat Second Generation platforms (MSG) is not designed to meet ocean colour mission requirements, however, it has been demonstrated to provide valuable contribution, particularly in combination with dedicated ocean colour polar observations. This paper describes the ongoing effort to develop operational ocean colour water turbidity and related products and user services from SEVIRI. SEVIRI's multi-temporal capabilities can benefit users requiring improved local-area coverage and frequent diurnal observations. A survey of user requirements and a study of technical capabilities and limitations of the SEVIRI instruments are the basis for this development and are described in this paper. The products will support monitoring of sediment transport, water clarity, and tidal dynamics by providing hourly coverage and long-term time series of the diurnal observations. Further products and services are anticipated from EUMETSAT's FCI instruments on Meteosat Third Generation satellites (MTG), including potential chlorophyll a products. © Author(s) 2016.
Muller D.,Helmholtz Center Geesthacht |
Krasemann H.,Helmholtz Center Geesthacht |
Brewin R.J.W.,Plymouth Marine Laboratory |
Brockmann C.,Brockmann Consult |
And 12 more authors.
Remote Sensing of Environment | Year: 2015
The established procedure to access the quality of atmospheric correction processors and their underlying algorithms is the comparison of satellite data products with related in-situ measurements. Although this approach addresses the accuracy of derived geophysical properties in a straight forward fashion, it is also limited in its ability to catch systematic sensor and processor dependent behaviour of satellite products along the scan-line, which might impair the usefulness of the data in spatial analyses. The Ocean Colour Climate Change Initiative (OC-CCI) aims to create an ocean colour dataset on a global scale to meet the demands of the ecosystem modelling community. The need for products with increasing spatial and temporal resolution that also show as little systematic and random errors as possible, increases. Due to cloud cover, even temporal means can be influenced by along-scanline artefacts if the observations are not balanced and effects cannot be cancelled out mutually. These effects can arise from a multitude of results which are not easily separated, if at all. Among the sources of artefacts, there are some sensor-specific calibration issues which should lead to similar responses in all processors, as well as processor-specific features which correspond with the individual choices in the algorithms. A set of methods is proposed and applied to MERIS data over two regions of interest in the North Atlantic and the South Pacific Gyre. The normalised water leaving reflectance products of four atmospheric correction processors, which have also been evaluated in match-up analysis, is analysed in order to find and interpret systematic effects across track. These results are summed up with a semi-objective ranking and are used as a complement to the match-up analysis in the decision for the best Atmospheric Correction (AC) processor. Although the need for discussion remains concerning the absolutes by which to judge an AC processor, this example demonstrates clearly, that relying on the match-up analysis alone can lead to misjudgement. © 2015 Elsevier Inc.