Using field spectroscopy to assess the potential of statistical approaches for the retrieval of sun-induced chlorophyll fluorescence from ground and space

Abstract

Sun-induced chlorophyll fluorescence (F_s) is an electromagnetic signal emitted in the 650–800 nm spectral window by the chlorophyll-a of green leaves. Previous studies demonstrated the retrieval of F_s on a global scale using high spectral resolution measurements by the Fourier Transform Spectrometer (FTS) on board the greenhouse gases observing satellite (GOSAT). The retrieval of F_s from GOSAT-FTS data is based on the modeling of the in-filling of solar Fraunhofer lines by F_s. The first F_s retrieval methods for GOSAT-FTS measurements were based on physical formulations of the radiative transfer between the atmosphere, the surface and the instrument including the F_s emission. As an alternative, a statistical method was also successfully applied to GOSAT data. This method is based on a singular vector decomposition (SVD) technique producing a basis of spectral functions able to model the contribution of the reflected solar radiation to the top-of-atmosphere measurement in a linear way. The F_s signal is included in the forward model as an extra parameter adding to the reflected solar radiation. Here, we use field spectroscopy measurements to provide further experimental evidence on the retrieval of F_s with statistical approaches in both Fraunhofer lines and atmospheric oxygen and water vapor bands. The statistical retrieval method used with GOSAT-FTS data has been adapted to a set of ground-based spectro-radiometer measurements in the 717–780 nm range. Retrieval results in the 745–759 nm window, which contains only Fraunhofer lines, support the overall approach of estimating F_s from space measurements in that spectral window. Furthermore, the application of the method to broader fitting windows including both Fraunhofer lines and and (oxygen and water vapor) atmospheric bands atmospheric bands has been proven to be very effective to reduce the retrieval noise and has also shown a good comparison with reference O_2A-based retrievals. This allows consideration of statistical methods as a powerful option for F_s retrieval from broad-band space-based measurements in the near-infrared.

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