Abstract
Land surface component temperatures (LSCTs), i.e., the temperatures of soil and vegetation, are important parameters in many applications, such as estimating evapotranspiration and monitoring droughts. However, the multiangle algorithm is affected due to different spatial resolution between nadir and oblique views. Therefore, we propose a combined retrieval algorithm that uses dual-angle and multipixel observations together. The sea and land surface temperature radiometer onboard ESA's Sentinel-3 satellite allows for quasi-synchronous dual-angle observations, from which LSCTs can be retrieved using dual-angle and multipixel algorithms. The better performance of the combined algorithm is demonstrated using a sensitivity analysis based on a synthetic dataset. The spatial errors in the oblique view due to different spatial resolution can reach 4.5 K and have a large effect on the multiangle algorithm. The introduction of multipixel information in a window can reduce the effect of such spatial errors, and the retrieval results of LSCTs can be further improved by using multiangle information for a pixel. In the validation, the proposed combined algorithm performed better, with LSCT root mean squared errors of 3.09 K and 1.91 K for soil and vegetation at a grass site, respectively, and corresponding values of 3.71 K and 3.42 K at a sparse forest site, respectively. Considering that the temperature differences between components can reach 20 K, the results confirm that, in addition to a pixel-average LST, the combined retrieval algorithm can provide information on LSCTs. This article demonstrates the potential of utilizing additional information sources for better LSCT results, which makes the presented combined strategy a promising option for deriving large-scale LSCT products.
Highlights
L AND surface temperature (LST) is a key parameter in surface physical processes, such as the energy budget and water cycle, and plays an essential role in applications, such as weather prediction, surface drought monitoring and agricultural yield estimation [1]–[3]
Before the combined retrieval algorithm can be performed, an atmospheric correction is applied to the thermal infrared (TIR) input data: this is possible because sea and land surface temperature radiometer (SLSTR) observes the Earth in two thermal channels (8 and 9) under two viewing angles, which allows an LST retrieval performed with SW and dual-angle algorithms
And (b), the retrieved results were affected only by different spatial resolutions between nadir and oblique views. Such spatial errors can reach 4.5 K, and as they increased, the retrieval performance for multiangle algorithm significantly decreased with root mean squared errors (RMSEs) larger than 10.0 K
Summary
L AND surface temperature (LST) is a key parameter in surface physical processes, such as the energy budget and water cycle, and plays an essential role in applications, such as weather prediction, surface drought monitoring and agricultural yield estimation [1]–[3]. One of the main limitations of these products is that the retrieved LST reflects only pixel-average temperatures rather than physical temperatures of land surface components, such as soil and vegetation. For spatially coarse satellite data, knowledge of the land surface component temperatures (LSCTs) in a pixel seems more desirable for accurately recording the surface temperature state. There are numerous studies describing the usefulness of LSCT in applications, e.g., for improved estimates of evapotranspiration with a two-source energy balance model [9] drought monitoring with a modified temperature vegetation dryness index [10], and vegetation growth monitoring with a crop water stress index [11]
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