数字农科院2.0

An integrated method for angular and temporal reconstruction of land surface temperatures

文献类型： 外文期刊

作者： Zunjian Bian;Shouyi Zhong;J. L. Roujean;Xiangyang Liu;Sibo Duan;Hua Li;Biao Cao;Ruibo Li;Yongming Du;Qing Xiao;Qinhuo Liu

作者机构：

关键词： Angular normalization;Land surface temperature;Temporal change;VNIR and TIR data

期刊名称： Remote Sensing of Environment

ISSN： 0034-4257

年卷期： 2024 年 313 卷

页码：

收录情况： SCIE（2024版） ; ; EI（2024版）

摘要： Land surface temperature (LST) is an essential climate variable (ECV) which can be estimated from appropriate measurements of the surface thermal infrared (TIR) radiance. LST varies on a very short time scale and closely depends on the illumination and scan angles considered. To fully exploit LST products, a method for reconstructing the temporal profile and the angular dependence at the same time is proposed here. A combined visible-thermal envelope method (VT-KDTC) is built using kernel-driven (KD) and diurnal temperature cycle (DTC) models, referring to the surface structure and thermal factors, respectively. To demonstrate the reliability of the approach, TIR data from the geostationary satellite Himawari 8 are combined with visible and near-infrared (VNIR) data from the polar orbit satellite Sentinel-3A/3B. In addition to satellite observations, a synthetic dataset from the Soil Canopy Observation, Photochemistry and Energy Fluxes (SCOPE) model is also generated. Considering an anisotropy model in addition to the DTC model leads to a method displaying a better ability to simulate LSTs with a root mean squared error (RMSE) of 0.48 K against the original satellite results, compared to only the DTC model up to 1.44 K. By utilizing the field measurements as a reference, the reconstructed results are improved with a total bias of 0.72K and an RMSE of 2.58 K. Compared to the original results without correction, approximately 41% and 10% decreases are obtained in bias and RMSE, respectively. Our proposed method can also achieve LST downscaling supported by the higher spatial resolution of VNIR data when the temperature difference is assumed to be homogeneous within the coarse pixels. Thus, a simple achievable solution can be used for temperature reconstruction to enhance the quality of the LST product.

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