During the commissioning of the Sentinel-3B satellite, a single lunar observation was performed to assess the possible use of the moon for characterization and validation of onboard instruments. The observation was carried out in stable orientation after a roll maneuver, allowing the moon to be imaged by the Earth view of instruments. Data acquired by the Ocean Land Color Instrument (OLCI) allowed inflight verification of stray-light correction (SLC) performed by the Mission Performance Centre (MPC), and assessment of radiometric behavior of instrument in comparison with lunar irradiance models performed in cooperation between European Space Research and Technology Centre (ESTEC) and European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT). This paper describes the results of those activities along with the proposed update of stray-light correction developed with the use of lunar data.

An original method is presented in this paper for the joint retrieval of the mean daily total column aerosol optical depth and surface BRF from the daily accumulated Meteosat Second Generation–Spinning Enhanced Visible and Infrared Imager (MSG/SEVIRI) observations in the solar channels. The proposed algorithm is based on the optimal estimation (OE) theory, a one‐dimensional variational retrieval scheme that seeks an optimal balance between information that can be derived from the observations, and the one that is derived from prior knowledge of the system. The forward radiative transfer model explicitly accounts for the surface anisotropy and its coupling with the atmosphere. The low rate of change in the surface reflectance is used to derive the prior information on the surface state variables. The reliable estimation of the measurement system error is one of the most critical aspects of the OE method as it strongly determines the likelihood of the solution. An important effort in the proposed method has thus been dedicated to this issue, where the actual radiometric performances of SEVIRI are dynamically taken into account.

An original method, based on optimal estimation, was presented in a part one of this paper for the joint retrieval of the mean daily total column aerosol optical depth and the surface Bidirectional Reflectance Factor (BRF) from the daily accumulated Meteosat Second Generation–Spinning Enhanced Visible and Infrared Imager (MSG/SEVIRI) observations in the solar channels. The objective of this paper is to evaluate the benefits of the proposed approach and to document the limits of the algorithm assumptions in the context of its implementation in an operational ground segment. A twofold approach is followed. In a first step, by looking at the posterior correlation error matrix the capability of the so‐called Land Daily Aerosol (LDA) algorithm to decouple the surface‐atmosphere signal is analyzed. In particular, the impact of the prior information is investigated in detail. In a second step, the results of the algorithm are compared with independent data sets of aerosol optical depth and surface reflectance. In this phase, the accuracy of the algorithm is evaluated against ground observations from the AERONET network. LDA is shown to be in good agreement with these data, especially when the prior update mechanism is activated. Comparisons with the MODIS surface product showed that the bihemispherical reflectance derived from the LDA products is consistent with the equivalent MODIS white‐sky albedo. Aerosol spatial distributions are comparable in terms of geographical location and intensity, in particular for aerosol episodes with a limited daily variation.

The purpose of the presented new Land Daily Aerosol algorithm developed at EUMETSAT is to derive simultaneously the mean daily tropospheric aerosol load and the land surface reflectance from MSG/SEVIRI observations. The algorithm is based on the Optimal Estimation theory, where the forward radiative transfer model explicitly accounts for the Surface anisotropy and its coupling with the atmosphere. A priori information on surface reflectance results from its temporal stability. Results of comparisons with AERONET data are presented to validate the modelling approach and the algorithm that resolves the inversion problem.

Using the principle of reciprocity, observations acquired by the SEVIRI radiometer on-board the Meteosat Second Generation satellites provide multi-angular and multi-spectral measurements that can be used for retrieving information on both the atmospheric aerosol load, and the Earth surface. The purpose of the presented new Land Daily Aerosol algorithm developed at EUMETSAT is to derive simultaneously the mean daily tropospheric aerosol load and the land surface properties from the SEVIRI observations. The algorithm is based on the Optimal Estimation theory. The aerosol load is calculated through the optical depth parameter, for various classes of aerosols over land surfaces, and is inferred from the inversion of a forward radiative transfer model against daily-accumulated observations in the 0.6, 0.8 and 1.6 SEVIRI bands. These daily time series provide the angular sampling used to discriminate the radiative effects that result from the surface anisotropy, from those caused by the aerosol scattering. Results of comparisons with AERONET data are presented to validate the modelling approach and the algorithm that resolves the inversion problem. The retrieval error is analysed, together with the effects on the retrieval quality of updating in time the prior information.