Abstract
Land surface temperature (LST) is an essential climate variable (ECV) for monitoring the Earth climate system. To ensure accurate retrieval from satellite data, it is important to validate satellite derived LSTs and ensure that they are within the required accuracy and precision thresholds. An emissivity-dependent split-window algorithm with viewing angle dependence and two dual-angle algorithms are proposed for the Sentinel-3 SLSTR sensor. Furthermore, these algorithms are validated together with the Sentinel-3 SLSTR operational LST product as well as several emissivity-dependent split-window algorithms with in-situ data from a rice paddy site. The LST retrieval algorithms were validated over three different land covers: flooded soil, bare soil, and full vegetation cover. Ground measurements were performed with a wide band thermal infrared radiometer at a permanent station. The coefficients of the proposed split-window algorithm were estimated using the Cloudless Land Atmosphere Radiosounding (CLAR) database: for the three surface types an overall systematic uncertainty (median) of −0.4 K and a precision (robust standard deviation) 1.1 K were obtained. For the Sentinel-3A SLSTR operational LST product, a systematic uncertainty of 1.3 K and a precision of 1.3 K were obtained. A first evaluation of the Sentinel-3B SLSTR operational LST product was also performed: systematic uncertainty was 1.5 K and precision 1.2 K. The results obtained over the three land covers found at the rice paddy site show that the emissivity-dependent split-window algorithms, i.e., the ones proposed here as well as previously proposed algorithms without angular dependence, provide more accurate and precise LSTs than the current version of the operational SLSTR product.
Highlights
This paper presents the adaptation of an split-window algorithms (SWAs) with explicit angular dependence, which was previously successfully applied to Spinning Enhanced Visible and InfraRed Imager (SEVIRI) data, to Sea and Land Surface Temperature Radiometer (SLSTR); the validation of the adapted SWA and its comparison with other SWAs with an explicit emissivity dependence; the adaptation of a dual-angle algorithms (DAAs) to SLSTR and its validation
The operational SLSTR Land surface temperature (LST) algorithm depends on biome, day/nighttime, vegetation fraction, and viewing zenith angle
From the validation results it is concluded that the operational Sentinel-3A SLSTR LST product is accurate for nighttime data, with an accuracy of 1.0 K and a precision of 1.0 K for the three investigated surfaces combined
Summary
Land surface temperature (LST)—like near-surface air temperature—is a key variable in a wide variety of studies, since it is linked to land–atmosphere energy transfer and flux balances [1,2]. It is required for monitoring evapotranspiration and climate change [3,4], as well as for providing estimates of fire size and temperature [5,6], volcanoes and lava flow [7,8], and vegetation health [9,10,11]. The Climate Change Initiative (CCI) was launched by the European Space Agency (ESA) for improving the prediction of climate change
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