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An Algorithm for Land Surface Temperature Retrieval Using Three Thermal Infrared Bands of Himawari-8

机译:利用Himawari-8的三个热红外波段进行地表温度反演的算法

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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 μm. 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° to 60° 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:本文提出了一种从Himawari-8数据估算陆面温度(LST)的方法。 Himawari-8上的Advanced Himawari成像仪具有10-12.5μm光谱范围内的三个红外热波段。我们开发了一种非线性三波段算法(NTB),该算法可以充分利用这些波段来估算LST。该算法的公式包括10个系数。这些系数的最佳值是使用统计回归方法从辐射传输模型获得的模拟数据中得出的。模拟数据集对应于各种LST值,以及表面发射率,温度和水汽剖面的类型和季节。考虑了从0°到60°的视角天顶角(VZA)。对于以这种方式获得的系数,我们根据VZA,LST和可沉淀水依赖性验证了均方根误差(RMSE)。我们证明,与Sobrino和Romaguera(2004)开发的非线性拆分窗口算法相比,NTB可以用RMSE小于0.9 K准确估计LST。此外,我们通过使用独立于用于获取系数的数据集的数据集,评估了LST算法对输入数据不确定性的敏感性。因此,我们证明了NTB对输入数据的不确定性具有最高的鲁棒性。最后,构建了逐步LST检索方法。该方法包括简单的云遮罩程序和地面发射率估计。使用青藏高原的原位数据评估了LST产品,并确认了其有效性。

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