Retrieving Soil and Vegetation Temperatures From Dual-Angle and Multipixel Satellite Observations

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 multi-angle 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 multi-pixel observations together. The Sea and Land Surface Temperature Radiometer (SLSTR) onboard ESA's Sentinel-3 satellite allows for quasi-synchronous dual-angle observations, from which LSCTs can be retrieved using dualangle and multi-pixel algorithms. The better performance of the combined algorithm is demonstrated using a sensitivity analysis based on a synthetic dataset. The spatial errors in oblique view due to different spatial resolution can reach 4.5 K and have a large effect on the multi-angle algorithm. The introduction of multi-pixel information in a window can reduce the effect of such spatial errors, and the retrieval results of LSCTs can be further improved by using multi-angle information for a pixel. In the validation, the proposed combined algorithm performed better, with LSCT root mean squared errors (RMSEs) 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 pixelaverage 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.

Satellite Remote Sensing of Global Land Surface Temperature: Definition, Methods, Products, and Applications

“…(2020) applied a DTC model and spatial correlation, and Bian et al. (2020) used dual‐angle observations and spatial information, to retrieve component temperatures. Although the availability of separation methods continues to be improved, the estimation error was still remarkable, larger than 2 K (X. Liu, Tang, Li, Zhou, et al., 2020), which cannot support the accurate angular correction of LST.…”