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Evaluating degradations of TANSO-FTS/GOSAT using principal component analysis

机译：使用主成分分析评估Tanso-FTS / GOSAT的降解

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It is reported that the sensitivities of the short wavelength infrared bands of Thermal And Near-infrared Sensor for carbon Observation (TANSO) - Fourier Transform Spectrometer (FTS) on Greenhouse gases Observing SATellite (GOSAT) have been degraded with the wavenumber dependencies. These degradations affect to the XCO_2 and XCH_4 retrievals, so they have to be correctly considered in the retrieval algorithm. In this work, we developed a new algorithm to evaluate these degradations from on-orbit solar calibration spectra using principal component analysis. The effectiveness of this algorithm is to be able to distinguish the time-dependent components of the spectral variability from the independent ones. This possibly enables us to evaluate the temporal change of sensitivity more precisely. The degradation models were constructed by decomposing spectra, fitting principal components scores using the appropriate functions, and reconstructing those functions. The first components of the decomposed eigenvectors have less spectral dependencies for each band, because they are due to the angular dependency of reflectance of the diffuser plate. On the other hand, the other components have significant spectral dependencies and their temporal variabilities do not correspond to that of the first component. This fact indicates that the components have to be separately considered to construct the degradation models of TANSO-FTS.
机译：据报道，在观察卫星（GOSAT）的温室气体上的碳观察（丹科） - 傅立叶变换光谱仪（FTS）的热和近红外传感器的短波红外传感器的敏感性已经降低了波数依赖性。这些劣化影响到XCO_2和XCH_4检索，因此必须在检索算法中正确考虑它们。在这项工作中，我们开发了一种新的算法，可以使用主成分分析来评估从轨道太阳校准光谱的这些降级。该算法的有效性是能够区分从独立的频谱变异性的时间依赖性组件。这可能使我们能够更精确地评​​估灵敏度的时间变化。通过分解光谱构成劣化模型，使用适当的功能拟合主要成分分数，并重建这些功能。分解的特征向量的第一组件对每个频带具有较少的光谱依赖性，因为它们是由于扩散板的反射率的角度依赖性。另一方面，其他组件具有显着的频谱依赖性，并且它们的时间变性不对应于第一组件的时间。这一事实表明，该组件必须单独考虑构建TANSO-FT的降级模型。

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