Angular normalization of GOES-16 and GOES-17 land surface temperature over overlapping region using an extended time-evolving kernel-driven model

IF 11.1 1区 地球科学 Q1 ENVIRONMENTAL SCIENCES Remote Sensing of Environment Pub Date : 2024-11-30 DOI:10.1016/j.rse.2024.114532
Boxiong Qin , Shuisen Chen , Biao Cao , Yunyue Yu , Peng Yu , Qiang Na , Enqing Hou , Dan Li , Kai Jia , Yingpin Yang , Tian Hu , Zunjian Bian , Hua Li , Qing Xiao , Qinhuo Liu
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Abstract

Land surface temperature (LST) is an important parameter that critically contributes to Earth’ s climate. Thermal anisotropy is a major challenge that must be addressed while generating long-term LST products from satellites. For instance, the differences between GOES-16 and GOES-17 LST products caused by thermal anisotropy have not yet been resolved, which impacts the high-frequency monitoring of the land surface. The coupled contributions of the gap fraction and hotspot effects in the thermal infrared domain result in the existence of thermal anisotropy effect. The time-evolving kernel-driven model (TEKDM) is a recently proposed practical tool for conducting LST angular normalization for geostationary satellites. However, the existing six-parameter TEKDM considers only the hotspot effect and ignores the gap fraction effect, which may limit the TEKDM-based angular normalization method. In this study, we proposed an extended seven-parameter TEKDM considering both the gap fraction and hotspot effects and normalized the angular effect of GOES-16 and GOES-17 LST products over the overlapping region using this model. The accuracy of this seven-parameter TEKDM was evaluated using a physically based discrete anisotropic radiative transfer (DART) simulation dataset. Subsequently, the seven-parameter TEKDM-based angular normalization method was evaluated using the GOES-16 and GOES-17 LST products of the overlapping region for one year against ten AmeriFlux sites. The results showed that the seven-parameter TEKDM had a RMSE (MBE) of 0.36 K (0.0019 K). Compared with the RMSE of the NOAA-released GOES LST products, the seven-parameter TEKDM-based normalization method could reduce the RMSE of GOES-16 and GOES-17 LST products from 2.2 K and 2.6 K to 1.7 K, respectively, with a reduction of 0.5 K (22.7 %) and 0.9 K (34.6 %), respectively. Furthermore, the RMSE/MBE of GOES-17 LST exhibited a different diurnal variation pattern than that of GOES-16 LST, which could be explained by the different illumination-viewing geometries of the two satellites. This emphasizes the necessity of conducting angular normalization of current geostationary satellite LST products. The seven-parameter TEKDM provides a feasible method for generating long-term high-quality LST datasets for remote sensing communities.
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基于扩展时变核驱动模型的GOES-16和GOES-17重叠区地表温度角归一化
地表温度(LST)是影响地球气候的一个重要参数。热各向异性是卫星产生长期地表温度产品时必须解决的主要挑战。例如,由于热各向异性导致的GOES-16和GOES-17地表温度产品差异尚未得到解决,这影响了对地表的高频监测。热红外区间隙分数和热点效应的耦合作用导致了热各向异性效应的存在。时间演化核驱动模型(TEKDM)是最近提出的一种用于地球静止卫星LST角归一化的实用工具。然而,现有的六参数TEKDM只考虑了热点效应,忽略了间隙分数效应,这可能会限制基于TEKDM的角归一化方法。在本研究中,我们提出了一个同时考虑间隙分数和热点效应的扩展的七参数TEKDM模型,并利用该模型对重叠区域上GOES-16和GOES-17 LST产品的角效应进行了归一化。使用基于物理的离散各向异性辐射传输(DART)模拟数据集评估了这种七参数TEKDM的准确性。随后,利用10个AmeriFlux站点的GOES-16和GOES-17重叠区域1年的LST产品,对基于tekdm的7参数角归一化方法进行了评估。结果表明,7参数TEKDM的RMSE (MBE)为0.36 K (0.0019 K),与noaa释放的GOES LST产品的RMSE相比,基于TEKDM的7参数归一化方法可将GOES-16和GOES-17 LST产品的RMSE分别从2.2 K和2.6 K降低到1.7 K,分别降低0.5 K(22.7%)和0.9 K(34.6%)。此外,GOES-17 LST的RMSE/MBE呈现出不同于GOES-16 LST的日变化模式,这可能与两颗卫星不同的光照观测几何形状有关。这就强调了对现有地球静止卫星LST产品进行角度归一化的必要性。7参数TEKDM为遥感群落长期高质量地表温度数据集的生成提供了一种可行的方法。
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来源期刊
Remote Sensing of Environment
Remote Sensing of Environment 环境科学-成像科学与照相技术
CiteScore
25.10
自引率
8.90%
发文量
455
审稿时长
53 days
期刊介绍: Remote Sensing of Environment (RSE) serves the Earth observation community by disseminating results on the theory, science, applications, and technology that contribute to advancing the field of remote sensing. With a thoroughly interdisciplinary approach, RSE encompasses terrestrial, oceanic, and atmospheric sensing. The journal emphasizes biophysical and quantitative approaches to remote sensing at local to global scales, covering a diverse range of applications and techniques. RSE serves as a vital platform for the exchange of knowledge and advancements in the dynamic field of remote sensing.
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