A Physical-Based Method for Pixel-by-Pixel Quantifying Uncertainty of Land Surface Temperature Retrieval From Satellite Thermal Infrared Data Using the Generalized Split-Window Algorithm

文献类型： 外文期刊

第一作者： Gui, Yang

作者： Gui, Yang;Duan, Si-Bo;Li, Zhao-Liang;Huang, Cheng;Liu, Meng;Liu, Xiangyang;Gao, Caixia

作者机构：

关键词： Uncertainty; Atmospheric modeling; Land surface temperature; MODIS; Land surface; Temperature sensors; Quality control; Generalized split-window (GSW) algorithm; land surface temperature (LST); uncertainty quantification

期刊名称：IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING （ 影响因子：8.2； 五年影响因子：8.8 ）

ISSN： 0196-2892

年卷期： 2023 年 61 卷

页码：

收录情况： SCI

摘要： Land surface temperature (LST) is an important physical parameter at the interface between the Earth's surface and the atmosphere. Accurately quantifying LST uncertainty is essential for the generation of a long-term and consistent LST Climate Data Record (CDR) or Earth System Data Record (ESDR) from either multiple sensors or algorithms. In this study, a physical-based method was proposed to quantify the uncertainty of LST retrieval from satellite thermal infrared (TIR) data using the generalized split-window (GSW) algorithm. LST uncertainties were parameterized as a function of brightness temperature at the top of the atmosphere (TOA) and surface emissivity in two split-window channels, which are two key input parameters in the GSW algorithm, as well as their uncertainties. The performance of the parameterized uncertainty model was evaluated according to the simulation dataset at six prescribed viewing zenith angles (VZAs) of 0 degrees, 33.56 degrees, 44.42 degrees, 51.32 degrees, 56.25 degrees, and 60 degrees, with a root mean squared error (RMSE) of 0.001 K. The coefficients of the parameterized uncertainty model at arbitrary VZA within a sensor's field of view (FOV) can be obtained by linear interpolation of the coefficients at the six prescribed VZAs. Once the coefficients of the parameterized uncertainty model for each pixel are available, total LST uncertainties can be quantified on a pixel-by-pixel basis. As an example, the parameterized uncertainty model was applied to actual MODIS data for displaying the spatial distribution of LST uncertainties. The results indicate that the parameterized uncertainty model can characterize the spatial variation in LST uncertainties well over various land cover types.

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