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Evaluation of Spectral Vegetation Index Translation Equations for the Development of Long-Term Data Records

机译:评估长期数据记录发展的光谱植被指数翻译方程

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Multi-sensor continuity/compatibility of spectral vegetation indices (VIs) is a complicated issue due to differences in both sensor characteristics and product generation algorithms. In this study, we focused on the spectral issue (spectral bandpass differences) and examined various functional forms to approximate the "isoline-based" translation equation of Yoshioka et al. [1] for the normalized difference vegetation index (NDVI). This Yoshioka translation equation was derived based on the physics of atmosphere-vegetation-photon interactions and, thus, is advantageous in accurately inter-relating and translating the NDVI across multiple sensors. The results indicated that a polynomial approximation to the Yoshioka translation equation was an appropriate form, in which the soil-adjusted vegetation index and aerosol optical thickness (AOT) were used as predictor variables. Radiometric variables that would be correlated well with atmospheric contaminations (e.g., AOT) need to be sought for the practical applications of the Yoshioka translation equation.
机译:频谱植被指数的多传感器连续性/兼容性(VI)是由于传感器特性和产品生成算法的差异是一种复杂的问题。在这项研究中,我们专注于光谱问题(光谱带通差异)并检查了各种功能形式,以近似Yoshioka等人的“Isolare的”平移方程。 [1]对于归一化差异植被指数(NDVI)。基于大气 - 植被 - 光子相互作用的物理来源的这种yoshioka翻译方程是有利的,在准确地相互关联和平移多个传感器的情况下是有利的。结果表明,与yoshioka平移等式的多项式近似是一种合适的形式,其中使用土壤调节的植被指数和气溶胶光学厚度(AOT)作为预测变量。需要与大气污染(例如,AOT)相关的辐射变量需要寻求Yoshioka翻译方程的实际应用。

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