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首页> 外文会议>IEEE International Geoscience and Remote Sensing Symposium >An improved physical method with linear spectral emissivity constraint to retrieve land surface temperature, emissivity and atmospheric profiles from satellite-based hyperspectral thermal infrared data
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An improved physical method with linear spectral emissivity constraint to retrieve land surface temperature, emissivity and atmospheric profiles from satellite-based hyperspectral thermal infrared data

机译：一种改进的具有线性光谱发射率约束的物理方法，从基于卫星的高光谱热红外数据检索陆地温度，发射率和大气谱

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摘要

In this paper, an improved method is proposed to simultaneously retrieve land surface temperature (LST), emissivity (LSE) and atmospheric profiles. This method employed the linear spectral emissivity constraint to efficiently reduce the number of retrieved variables. The proposed method was validated with some simulations. The initial guesses were derived from a neural network model. This method could greatly improve the accuracies of LST, LSE and atmospheric profiles. The RMSE of LST was decreased from 5.12 K (the initial guesses) to 1.59 K (the physical retrieved). The retrieved emissivity spectrum was in good agreement with the actual spectrum. An improvement of 1K in the tropospheric temperature was also been found. Those results showed that the proposed method is capable of improving the retrieval accuracies of land surface and atmospheric parameters with the remotely sensed thermal infrared data.
机译：本文提出了一种改进的方法，同时检索陆地温度（LST），发射率（LSE）和大气谱。该方法采用线谱发射率约束来有效地减少检索变量的数量。验证了所提出的方法有一些模拟。初始猜测来自神经网络模型。这种方法可以大大提高LST，LSE和大气谱的准确性。 LST的RMSE从5.12 k（最初猜测）降低到1.59 k（物理检索）。检索到的发射率谱与实际光谱吻合良好。还发现了对流层温度的1K改善。那些结果表明，该方法能够通过远程感测的热红外数据来提高地面和大气参数的检索精度。

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