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As part of the Copernicus programme of the European Commission (EC), the European Space Agency (ESA) has developed and is currently operating the Sentinel-2 mission that is acquiring high spatial resolution optical imagery. This article provides a description of the calibration activities and the status of the mission products validation activities after one year in orbit. Measured performances, from the validation activities, cover both Top-Of-Atmosphere (TOA) and Bottom-Of-Atmosphere (BOA) products. The presented results show the good quality of the mission products both in terms of radiometry and geometry and provide an overview on next mission steps related to data quality aspects.

Since 18 December 2004, the PARASOL satellite is a member of the so-called A-train atmospheric orbital observatory, flying together with Aqua, Aura, CALIPSO, CLOUDSAT, and OCO satellites. These satellites combine for the first time a full suite of instruments for observing aerosols and clouds, using passive radiometer complementarily with active lidar and radar sounders. The PARASOL payload is extensively derived from the instrument developed for the POLDER programs that performs measurements of bidirectionality and polarization for a very wide field-of-view and for a visible͞near-infrared spectral range. An overview of the results obtained during the commissioning phase and the reevaluation after one year in orbit is presented. In-flight calibration methods are briefly described, and radiometric and geometric performances are both evaluated. All algorithms are based on a panel of methods using mainly natural targets previously developed for POLDER missions and adapted or redeveloped in the PARASOL context. Regarding performances, all mission requirements are met except for band 443 (not recommended for use). After one year in orbit, a perfect geometrical stability was found while a slight decrease of the radiometric sensitivity was observed and corrected through an innovative multitemporal algorithm based on observations of bright and scattered convective clouds. The scientific exploitation of PARASOL has now begun, particularly by coupling these specific observations with other A-train sensor measurements.

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Abstract-POLDER is a CNES instrument on board NASDA's ADEOS polar orbiting satellite, which was successfully launched in August 1996. On October 30, 1996, POLDER entered its nominal acquisition phase and worked perfectly until ADEOS's early end of service on June 30, 1997. POLDER is a multispectral imaging radiometer/polarimeter designed to collect global and repetitive observations of the solar radiation reflected by the earth/atmosphere system, with a wide field of view (2400 km) and a moderate geometric resolution (6 km). The instrument concept is based on telecentric optics, on a rotating wheel carrying 15 spectral filters and polarizers, and on a bidimensional charge coupled device (CCD) detector array. In addition to the classical measurement and mapping characteristics of a narrow-band imaging radiometer, POLDER has a unique ability to measure polarized reflectances using three polarizers (for three of its eight spectral bands, 443 to 910 nm) and to observe target reflectances from 13 different viewing directions during a single satellite pass.One of POLDER's original features is that its in-flight radiometric calibration does not rely on any on-board device. Many calibration methods using well-characterized calibration targets have been developed to achieve a very high calibration accuracy. This paper presents the various methods implemented in the in-flight calibration plan and the results obtained during the instrument calibration phase: absolute calibration over molecular scattering, interband calibration over sunglint and clouds, multiangular calibration over deserts and clouds, intercalibration with Ocean Color and Temperature Scanner (OCTS), and water vapor channels calibration over sunglint using meteorological analysis. A brief description of the algorithm and of the performances of each method is given.

Multi-temporal images acquired at high spatial and temporal resolution are an important tool for detecting change and analyzing trends, especially in agricultural applications. However, to insure a reliable use of this kind of data, a rigorous radiometric normalization step is required. Normalization can be addressed by performing an atmospheric correction of each image in the time series. The main problem is the difficulty of obtaining an atmospheric characterization at a given acquisition date. In this paper, we investigate whether relative radiometric normalization can substitute for atmospheric correction. We develop an automatic method for relative radiometric normalization based on calculating linear regressions between unnormalized and reference images. Regressions are obtained using the reflectances of automatically selected invariant targets. We compare this method with an atmospheric correction method that uses the 6S model. The performances of both methods are compared using 18 images from of a SPOT 5 time series acquired over Reunion Island. Results obtained for a set of manually selected invariant targets show excellent agreement between the two methods in all spectral bands: values of the coefficient of determination (r2 exceed 0.960, and bias magnitude values are less than 2.65. There is also a strong correlation between normalized NDVI values of sugarcane fields (r2 = 0.959). Despite a relative error of 12.66% between values, very comparable NDVI patterns are observed.

Combining data from multiple sensors into a single seamless time series, also known as data interoperability, has the potential for unlocking new understanding of how the Earth functions as a system. However, our ability to produce these advanced data sets is hampered by the differences in design and function of the various optical remote-sensing satellite systems. A key factor is the impact that calibration of these instruments has on data interoperability. To address this issue, a workshop with a panel of experts was convened in conjunction with the Pecora 20 conference to focus on data interoperability between Landsat and the Sentinel 2 sensors. Four major areas of recommendation were the outcome of the workshop. The first was to improve communications between satellite agencies and the remote-sensing community. The second was to adopt a collections-based approach to processing the data. As expected, a third recommendation was to improve calibration methodologies in several specific areas. Lastly, and the most ambitious of the four, was to develop a comprehensive process for validating surface reflectance products produced from the data sets. Collectively, these recommendations have significant potential for improving satellite sensor calibration in a focused manner that can directly catalyze efforts to develop data that are closer to being seamlessly interoperable.

International audienceIn this study, we present a radiative transfer model, so-called OSOAA, that is able to predict the radiance and degree of polarization within the coupled atmosphere-ocean system in the presence of a rough sea surface. The OSOAA model solves the radiative transfer equation using the successive orders of scattering method. Comparisons with another operational radiative transfer model showed a satisfactory agreement within 0.8%. The OSOAA model has been designed with a graphical user interface to make it user friendly for the community. The radiance and degree of polarization are provided at any level, from the top of atmosphere to the ocean bottom. An application of the OSOAA model is carried out to quantify the directional variations of the water leaving reflectance and degree of polarization for phytoplankton and mineral-like dominated waters. The difference between the water leaving reflectance at a given geometry and that obtained for the nadir direction could reach 40%, thus questioning the Lambertian assumption of the sea surface that is used by inverse satellite algorithms dedicated to multi-angular sensors. It is shown as well that the directional features of the water leaving reflectance are weakly dependent on wind speed. The quantification of the directional variations of the water leaving reflectance obtained in this study should help to correctly exploit the satellite data that will be acquired by the current or forthcoming multi-angular satellite sensors