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首页> 外文期刊>Journal of the Meteorological Society of Japan >An Algorithm for Land Surface Temperature Retrieval Using Three Thermal Infrared Bands of Himawari-8
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An Algorithm for Land Surface Temperature Retrieval Using Three Thermal Infrared Bands of Himawari-8

机译:Himari-8三热红外频段陆地温度检索算法

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This paper presents a method for estimating the land surface temperature (LST) from Himawari-8 data. The Advanced Himawari Imager onboard Himawari-8 has three thermal infrared bands in the spectral range of 10-12.5 mu m. We developed a nonlinear three-band algorithm (NTB) that makes the best use of these bands to estimate the LST. The formula of the algorithm includes 10 coefficients. The optimum values of these coefficients were derived using a statistical regression method from the simulated data, as obtained by a radiative transfer model. The simulated data sets correspond to a variety of values of LST, as well as surface emissivity, type and season of temperature and water vapor profiles. Viewing zenith angles (VZAs) from 0 degrees to 60 degrees were considered. For the coefficients obtained in this way, we verified the root-mean-square error (RMSE) in terms of the VZA, LST and precipitable water dependence. We showed that the NTB can accurately estimate the LST with an RMSE less than 0.9 K compared with the nonlinear split-window algorithm developed by Sobrino and Romaguera (2004). Moreover, we evaluated the sensitivities of the LST algorithms to the uncertainties in input data by using the dataset independent of the dataset used to obtain coefficients. Consequently, we showed that the NTB has the highest robustness against the uncertainties in input data. Finally, the stepwise LST retrieval method was constructed. This method includes a simple cloud mask procedure and the land surface emissivity estimation. The LST product was evaluated using in-situ data over the Tibetan Plateau, and the validity was confirmed.
机译:本文介绍了一种估算HIMAWARI-8数据的陆地温度(LST)的方法。 Himawari-8的高级Himawari Imager Imageer在频谱范围内有三个热红外条带10-12.5 mu m。我们开发了一种非线性三频段算法(NTB),可以最好地使用这些频段来估计LST。算法的公式包括10个系数。通过辐射传输模型获得的来自模拟数据的统计回归方法导出这些系数的最佳值。模拟数据集对应于LST的各种值,以及表面发射率,温度和水蒸气剖面的型号和季节。观察从0度到60度的Zenith角度(vzas)。对于以这种方式获得的系数,我们在VZA,LST和可降水依赖方面验证了根均方误差(RMSE)。与由Sobrino和Romaguera开发的非线性分裂窗口算法相比,NTB可以准确地估计LST,而不是0.9 k(2004)。此外,我们通过使用独立于用于获得系数的数据集的数据集评估LST算法对输入数据中的不确定性的敏感性。因此,我们表明NTB对输入数据中的不确定性具有最高的鲁棒性。最后,构建了逐步的LST检索方法。该方法包括简单的云掩模程序和陆地表面发射率估计。使用原位数据在藏高原上使用原位数据进行评估,确认有效性。

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