Retreval of Solar-Induced Chlorohyll Fluoresence with Principal Component Ananlysis Method

Abstract

The Fraunhofer line discrimination (FLD) principle is widely used for retrieving solar-induced chlorophyll fluorescence (SIF), which assumes that the spectral reflectance is smooth and can be modeled using simply mathematical function. However, the changes in the sun and observation geometry and atmospheric properties result in the ‘hump’ or ‘dip’ of the reflectance spectrum in the oxygen A-band. This leads to overestimations or underestimations in the SIF retrieval. The principal component analysis (PCA) algorithm is one of the main approaches used for satellite-based SIF retrieval, which can acquire reflectance characteristic information due to directional effect with large datasets. This paper attempts to test whether the errors caused by FLD method can be eliminated using the PCA algorithm. The results show that the PCA algorithm performs well in all conditions, with root mean square error less than 0.005, indicating that the bias caused by the changes in sun and observation geometry could be eliminated with PCA algorithm.