Combined modelling of annual and diurnal land surface temperature cycles

IF 11.1 1区 地球科学 Q1 ENVIRONMENTAL SCIENCES Remote Sensing of Environment Pub Date : 2023-11-02 DOI:10.1016/j.rse.2023.113892
Lluís Pérez-Planells, Frank-M. Göttsche
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Abstract

The land surface's thermal dynamics follows annual and diurnal cycles that are to a large extent controlled by solar geometry. Therefore, annual and diurnal variations of land surface temperature (LST) can be modelled with relatively simple functions controlled by a small number of parameters, typically from three to six. The parameter values of the models can be determined by fitting the respective functions to time series of LST observations. Commonly either annual or diurnal LST variations are modelled or they are modelled sequentially in a two-step process. Here, we combine an annual temperature cycle (ATC) model controlled by the solar zenith angle (ATCsza) with a four-parameter version of the diurnal temperature cycle (DTC) model GOT09: this yields a new annual-diurnal temperature cycle (ADTC) model that simultaneously describes the annual and diurnal surface temperature dynamics. The proposed ADTC model is controlled by physically meaningful parameters: annual minimum temperature, annual temperature amplitude, annual maximum of daily temperature amplitude, mean time of thermal noon and time lag of maximum temperature with respect to summer solstice. Thus, the entire annual-diurnal LST dynamics is described with only five parameters. The new model was tested by fitting it to one year of LST observations obtained for five globally representative tiles of the Moderate Resolution Imaging Spectroradiometer (MODIS), onboard EOS – TERRA and EOS – AQUA satellites. For these tiles, the mean of the root mean square error (RMSE) was 4.2 K. ADTC modelled LSTs were also compared against those obtained with the standard ATC model for the four MODIS overpass times at five representative sites: for these, an overall RMSE of 1.2 K between the two models was obtained. The ADTC derived LSTs were validated against in-situ measurements from three different sites, which yielded an overall RMSE of 3.4 K. Additional investigations over five areas with different land covers (i.e. urban, lake, forest, mountain area and desert) revealed the potential of the ADTC parameters to describe the corresponding surface and climate properties. Since it is driven by solar geometry, the ADTC model reproduces double LST peaks in the tropics naturally. Furthermore, all available observations are modelled simultaneously, which means that a single set of parameters is obtained for each pixel and year.

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年和日地表温度循环的组合建模
陆地表面的热动力学遵循年周期和日周期,在很大程度上受太阳几何结构的控制。因此,地表温度(LST)的年变化和日变化可以用相对简单的函数建模,这些函数由少量参数控制,通常为三到六个。模型的参数值可以通过将各自的函数拟合到LST观测的时间序列来确定。通常,对年或日LST变化进行建模,或者在两步过程中对其进行顺序建模。在这里,我们将由太阳天顶角(ATCsza)控制的年温度循环(ATC)模型与日温度循环(DTC)模型GOT09的四参数版本相结合:这产生了一个新的年日温度循环模型,该模型同时描述了年和日表面温度动态。所提出的ADTC模型由物理上有意义的参数控制:年最低温度、年温度振幅、年最高日温度振幅、热中午平均时间和最高温度相对于夏至的时滞。因此,仅用五个参数来描述整个年度昼夜LST动力学。通过将新模型与中分辨率成像光谱仪(MODIS)、EOS–TERRA和EOS–AQUA卫星上五个具有全球代表性的瓦片的一年LST观测结果进行拟合,对其进行了测试。对于这些瓦片,均方根误差(RMSE)的平均值为4.2K。还将ADTC建模的LST与标准ATC模型在五个代表性地点的四个MODIS立交桥时间获得的LST进行了比较:对于这些瓦片而言,两个模型之间的总均方根误差为1.2K。根据来自三个不同地点的现场测量,对ADTC衍生的LST进行了验证,得出了3.4K的总体RMSE。对具有不同土地覆盖的五个地区(即城市、湖泊、森林、山区和沙漠)的额外调查揭示了ADTC参数描述相应地表和气候特性的潜力。由于它是由太阳几何结构驱动的,ADTC模型自然地再现了热带地区的双LST峰值。此外,所有可用的观测值都是同时建模的,这意味着每个像素和年份都可以获得一组参数。
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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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