Since 2005, the Land Surface Analysis Satellite Application Facility (LSA SAF) operationally retrieves Land Surface Temperature (LST) for the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board Meteosat Second Generation (MSG). The high temporal resolution of the Meteosat satellites and their long term availability since 1977 make their data highly valuable for climate studies. In order to ensure that the LSA SAF LST product continuously meets its target accuracy of 2 °C, it is validated with in-situ measurements from four dedicated LST validation stations. Three stations are located in highly homogenous areas in Africa (semiarid bush, desert, and Kalahari semi-desert) and typically provide thousands of monthly match-ups with LSA SAF LST, which are used to perform seasonally resolved validations. An uncertainty analysis performed for desert station Gobabeb yielded an estimate of total in-situ LST uncertainty of 0.8 ± 0.12 °C. Ignoring rainy seasons, the results for the period 2009–2014 show that LSA SAF LST consistently meets its target accuracy: the highest mean root-mean-square error (RMSE) for LSA SAF LST over the African stations was 1.6 °C while mean absolute bias was 0.1 °C. Nighttime and daytime biases were up to 0.7 °C but had opposite signs: when evaluated together, these partially compensated each other.

The European Organization for the Exploitation of Meteorological Satellites’ (EUMETSAT) Meteosat satellites provide the unique opportunity to compile a 30+ year land surface temperature (LST) climate data record. Since the Meteosat instrument on-board Meteosat 2–7 is equipped with a single thermal channel, single-channel LST retrieval algorithms are used to ensure consistency across Meteosat satellites. The present study compares the performance of two single-channel LST retrieval algorithms: (1) A physical radiative transfer-based mono-window (PMW); and (2) a statistical mono-window model (SMW). The performance of the single-channel algorithms is assessed using a database of synthetic radiances for a wide range of atmospheric profiles and surface variables. The two single-channel algorithms are evaluated against the commonly-used generalized split-window (GSW) model. The three algorithms are verified against more than 60,000 LST ground observations with dry to very moist atmospheres (total column water vapor (TCWV) 1–56 mm). Except for very moist atmospheres (TCWV > 45 mm), results show that Meteosat single-channel retrievals match those of the GSW algorithm by 0.1–0.5 K. This study also outlines that it is possible to put realistic uncertainties on Meteosat single-channel LSTs, except for very moist atmospheres: simulated theoretical uncertainties are within 0.3–1.0 K of the in situ root mean square differences for TCWV < 45 mm.

A new all-weather land surface temperature (LST) product derived at the Satellite Application Facility on Land Surface Analysis (LSA-SAF) is presented. It is the first all-weather LST product based on visible and infrared observations combining clear-sky LST retrieved from the Spinning Enhanced Visible and Infrared Imager on Meteosat Second Generation (MSG/SEVIRI) infrared (IR) measurements with LST estimated with a land surface energy balance (EB) model to fill gaps caused by clouds. The EB model solves the surface energy balance mostly using products derived at LSA-SAF. The new product is compared with in situ observations made at 3 dedicated validation stations, and with a microwave (MW)-based LST product derived from Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) measurements. The validation against in-situ LST indicates an accuracy of the new product between -0.8 K and 1.1 K and a precision between 1.0 K and 1.4 K, generally showing a better performance than the MW product. The EB model shows some limitations concerning the representation of the LST diurnal cycle. Comparisons with MW LST generally show higher LST of the new product over desert areas, and lower LST over tropical regions. Several other imagers provide suitable measurements for implementing the proposed methodology, which offers the potential to obtain a global, nearly gap-free LST product.