The Sentinel-2 mission is dedicated to land monitoring, emergency management and security. It serves for monitoring of land-cover change and biophysical variables related to agriculture and forestry. The mission is also used to monitor coastal and inland waters and is useful for risk and disaster mapping. The Sentinel-2 mission is fully operating since June 2017 with a constellation of two polar orbiting satellite units. Both Sentinel-2A and Sentinel-2B are equipped with an optical imaging sensor MSI (Multi-Spectral Instrument) which acquires optical data products with spatial resolution up to 10 m. Accurate atmospheric correction of satellite observations is a precondition for the development and delivery of high quality applications. Therefore the atmospheric correction processor Sen2Cor was developed with the objective of delivering land surface reflectance products. Sen2Cor is designed to process monotemporal single tile Level-1C products, providing Level-2A surface (Bottom-of-Atmosphere) reflectance product together with Aerosol Optical Thickness (AOT), Water Vapour (WV) estimation maps and a Scene Classification (SCL) map for further processing. The paper will give an overview of the Level-2A product content and up-to-date information about the data quality of the Level-2A products generated with Sen2Cor 2.8 in terms of Cloud Screening and Atmospheric Correction. In addition the paper gives an outlook on the next updates of Sen2Cor and their impact on Level-2A Data Quality.
Airborne infrared limb-viewing sensors may be used as surveillance devices in order to detect dim military targets. These
systems' performances are limited by the inhomogeneous background in the sensor field of view which impacts strongly
on target detection probability. Consequently, the knowledge of the radiance small-scale angular fluctuations and their
statistical properties is required to assess the sensors' detection capacity. In the stratosphere and in clear-sky conditions,
the structured background is mainly due to inertia-gravity-wave and turbulence-induced temperature and density spatial
fluctuations. Moreover, in the particular case of water vapor absorption bands, the mass fraction fluctuations play a non
negligible role on the radiative field. Thereby, considering as a first approximation the temperature field and the water
vapor field as stationary stochastic processes, the radiance autocorrelation function (ACF) can be expressed as a function
of the temperature ACF and the water vapor mass fraction ACF.
This paper presents the model developed to compute the two-dimensional radiance angular ACF. This model requires the
absorption coefficients and their temperature derivatives, which were calculated by a line-by-line code dedicated to water
vapor absorption bands. An analytical model was also developed for a simple homogeneous case, in order to validate the
average values and the radiance fluctuation variance. The numerical model variance and variance distribution are also
compared to SAMM2 outputs, the AFRL radiance structure computation code. The influence of water vapor fluctuations
on radiance fluctuations is also discussed.
Atmospheric radiance structures are induced by local temperature and density fluctuations. The design, development, and use of electro-optical surveillance systems require the knowledge of atmospheric radiance clutter at small spatial scales. A model is developed to study radiance fluctuations observed by an airborne IR sensor. This model uses intermediate results of the Air Force Research Laboratory background radiance code SAMM-2 and synthesizes an image of atmospheric background clutter according to the sensor characteristics. Inputs are a 3-D grid of temperature fluctuations and SAMM-2 transfer functions. We extend this code to calculate atmospheric limb viewing irradiance images for airborne and satellite sensors. We detail the irradiance extension of the clutter calculation code and present some results for satellite and airborne viewing conditions.
Infrared (IR) detectors can be used as airborne limb-viewing surveillance systems for missile detection. These systems'
performances are impacted by the atmospheric inhomogeneous background. In fact, the probability of target detection
can be heavily affected. Consequently, the knowledge of these radiance small-scale fluctuations and their statistical
properties is required to assess these systems' detection capability. A model of two-dimensional radiance spatial
fluctuations autocorrelation function (ACF) is developed. This model is dedicated to airborne limb-viewing conditions in
the thermal IR. In the stratosphere and in clear-sky conditions, the structured background is mainly due to
internal-gravity-wave-induced temperature and density spatial fluctuations. Moreover, in the particular case of water vapour
absorption bands, the mass fraction fluctuations play a non negligible role on the radiative field. Thereby, considering
the temperature field and the water vapour field as stochastic processes, the radiance ACF can be expressed as a function
of the temperature ACF and the water vapor mass fraction ACF. A local thermodynamic equilibrium model is sufficient
for stratospheric conditions and sunlight scattering is neglected in the thermal IR. In addition, determination of the
radiance fluctuations ACF requires the knowledge of the absorption coefficient and its first derivatives with respect to
the temperature and water vapour mass fraction. Thus, a line-by-line model specific to water vapor absorption bands has
been developed. This model is used to precalculate the absorption coefficients and their derivatives. This look-up table
method allows circumventing the computational cost of a line-by-line calculation. A detailed description of the radiance
fluctuations ACF model is presented and first results are discussed.
Airborne infrared limb-viewing detectors may be used as surveillance sensors in order to detect dim military targets. These systems' performances are limited by the inhomogeneous background in the sensor field of view which impacts strongly on target detection probability. SAMM-2 is an existing code able to model atmospheric structures and their impact on infrared limb-observed radiance. The AFRL background radiance code can be used to predict the radiance fluctuation as a result of a normalized temperature fluctuation, along a given line of sight (LOS). The existing code SIG was designed to compute the cluttered background which would be observed from a spaceborne sensor. However, this code was not able to compute accurate scenes as seen by an airborne sensor especially for LOS close to the horizon. Recently, we developed a new code called BRUTE3D adapted to airborne viewing conditions. This BRUTE3D code inputs a three-dimensional grid of temperature fluctuations and SAMM-2 transfer functions to synthesize an image of the atmospheric background clutter according to the sensor characteristics. This paper details the working principles of the code and presents some output results. The effects of the small-scale temperature fluctuations on infrared limb radiance as seen by an airborne sensor are highlighted.