Abstract

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.

Highlights

  • Land surface temperature (LST) is one of the main quantities governing the energy exchange between surface and atmosphere

  • Within the Land Surface Analysis Satellite Application Facility (LSA Satellite Application Facility (SAF)), LST is estimated through the application of a Generalized Split-Window (GSW) formulation similar to the one first proposed for AVHRR and Moderate Resolution Imaging Spectroradiometer (MODIS), which was adapted to the response functions of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) channels [2]: LST “ pA1 ` A2

  • Having described the LST validation stations and their specific characteristics in terms of site and instrumentation, the in-situ LST obtained with the methods described in Section 4 are used to validate Land Surface Analysis (LSA) SAF’s operational LST product for Meteosat Second Generation (MSG)/SEVIRI

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Summary

Introduction

Land surface temperature (LST) is one of the main quantities governing the energy exchange between surface and atmosphere. LSA SAF retrieves 15-min LST estimates from the SEVIRI channels at 10.8 μm and 12.0 μm using a generalized split-window (GSW) algorithm [2,3]. Relative accuracy can be assessed by cross-validation between LST products obtained with different retrieval algorithms and/or for different sensors [8]. The size of the area that needs to be viewed by the validation instrument at the ground depends on the within-pixel variability of the surface and on how well measurements of several “end members” can be mixed in order to obtain a representative value for the satellite pixel [16]. LST retrieval methods based on measurements in two pseudo-contiguous channels in the thermal infra-red, i.e., split-window algorithms, generally rely on the differential absorption of the two bands to improve the atmospheric correction [2,3]

Generalized Split Window Algorithm
Vegetation Cover Method for LSE Retrieval
In-Situ Measurements and LST Determination
LST Derivation from in-Situ
Land Surface Emissivity Determination
Uncertainty of in-Situ LST
LST Validation Stations
Estimation of Land Surface Cover and Representative in-Situ LSTs
Land Surface Emissivity at Dahra
Land Surface Emissivity at Gobabeb
Land Surface Emissivity at Farm Heimat
Results and Discussion
Results for Dahra
Discussion for bias
Results for Gobabeb
Monthly statistics at Gobabeb
Discussion for Gobabeb
Results for Farm Heimat
Discussion
Conclusions
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