Abstract. The high temporal resolution of data acquisition by geostationary satellites and their capability to resolve the diurnal cycle allows for the retrieval of a valuable source of information about geophysical parameters. In this paper, we implement a Kalman filter approach to apply temporal constraints on the retrieval of surface emissivity and temperature from radiance measurements made from geostationary platforms. Although we consider a case study in which we apply a strictly temporal constraint alone, the methodology will be presented in its general four-dimensional, i.e., space-time, setting. The case study we consider is the retrieval of emissivity and surface temperature from SEVIRI (Spinning Enhanced Visible and Infrared Imager) observations over a target area encompassing the Iberian Peninsula and northwestern Africa. The retrievals are then compared with in situ data and other similar satellite products. Our findings show that the Kalman filter strategy can simultaneously retrieve surface emissivity and temperature with an accuracy of ± 0.005 and ±0.2 K, respectively.

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Abstract. A Kalman filter-based approach for the physical retrieval of surface temperature and emissivity from SEVIRI (Spinning Enhanced Visible and Infrared Imager) infrared observations has been developed and validated against in situ and satellite observations. Validation for land has been provided based on in situ observations from the two permanent stations at Evora and Gobabeb operated by Karlsruhe Institute of Technology (KIT) within the framework of EU-METSAT's Satellite Application Facility on Land Surface Analysis (LSA SAF). Sea surface retrievals have been intercompared on a broad spatial scale with equivalent satellite products (MODIS, Moderate Resolution Imaging Spectroradiometer, and AVHRR, Advanced Very High Resolution Radiometer) and ECMWF (European Centre for MediumRange Weather Forecasts) analyses. For surface temperature, the Kalman filter yields a root mean square accuracy of ≈ ±1.5 • C for the two land sites considered and ≈ ±1.0 • C for the sea. Comparisons with polar satellite instruments over the sea surface show nearly zero temperature bias. Over the land surface the retrieved emissivity follows the seasonal vegetation cycle and permits identification of desert sand regions using the SEVIRI channel at 8.7 µm due to the strong quartz reststrahlen bands around 8-9 µm. Considering the two validation stations, we have found that emissivity retrieved in SEVIRI channel 10.8 µm over the gravel plains of the Namibian desert is in excellent agreement with in situ observations. Over Evora, the seasonal variation of emissivity with vegetation is successfully retrieved and yields emissivity values for green and dry vegetation that are in good agreement with spectral library data. The algorithm has been applied to the SEVIRI full disk, and emissivity maps on that global scale have been physically retrieved for the first time.

The problem of diurnal variation in surface emissivity over the Sahara Desert during non-raining days is studied and assessed with observations from the Infrared Atmospheric Sounding Interferometer (IASI). The analysis has been performed over a Sahara Desert dune target area during July 2010. Spinning Enhanced Visible and Infrared Imager observations from the European geostationary platform Meteosat-9 (Meteorological Satellite 9) have been also used to characterize the target area. Although the amplitude of this daily cycle has been shown to be very small, we argue that suitable nighttime meteorological conditions and the strong contrast of the reststrahlen absorption bands of quartz (8–14 μ m) can amplify its effect over the surface spectral emissivity. The retrieval of atmospheric parameters show that at nighttime an atmospheric temperature inversion occurs close to the surface yielding a thin boundary layer which acts like a lid, keeping normal convective overturning of the atmosphere from penetrating through the inversion. This mechanism traps water vapour close to the land and drives the direct adsorption of water vapour at the surface during the night. The diurnal variation in emissivity at 8.7 μ m has been found to be as large as 0.03 with high values at night and low values during the day. At 10.8 μ m and 12 μ m the variation has the same sign as that at 8.7 μ m, but with a smaller amplitude, 0.019 and 0.014, respectively. The impact of these diurnal variations on the retrieval of surface temperature and atmospheric parameters has been analyzed

A fully physical retrieval scheme for land surface emissivity spectra is presented, which applies to high spectral resolution infrared observations from satellite sensors. The surface emissivity spectrum is represented with a suitably truncated Principal Component Analysis (PCA) transform and PCA scores are simultaneously retrieved with surface temperature and atmospheric parameters. The retrieval methodology has been developed within the general framework of Optimal Estimation and, in this context, is the first physical scheme based on a PCA representation of the emissivity spectrum. The scheme has been applied to IASI (Infrared Atmospheric Sounder Interferometer) and the retrieved emissivities have been validated with in situ observations acquired during a field experiment carried out in 2017 at Gobabeb (Namib desert) validation station. It has been found that the retrieved emissivity spectra are independent of background information and in good agreement with in situ observations.