## 1 Introduction  
Sun-induced chlorophyll fluorescence (SIF) can serve as a dynamic indicator of photosynthetic activity [1], and it is used for estimating gross primary productivity (GPP) and monitoring vegetation stress [2; 3; 4]. SIF is emitted in the red and near-infrared regions (640-850 nm), which include multiple atmospheric absorption bands and Fraunhofer lines (FLs). Compared to atmospheric absorption bands, SIF retrievals in FLs do not require accurate estimates of atmospheric transmittance spectra and are minimally affected by aerosol scattering [5; 6; 7]. This provides significant advantages in SIF retrieval accuracy. The insensitivity to elastic aerosol scattering results in more relaxed cloud filtering thresholds for FLs [8; 9; 10], leading to more effective SIF retrievals and clearer intensity distribution images [11].  
The FLs near 757 nm in the far-red region are close to the strongest SIF emission peak and are not affected by chlorophyll absorption, resulting in a good correlation between SIF retrievals in this band and photosynthetic activity [12]. The data-driven method avoids explicit modeling of complex radiative transfer, making it suitable for SIF retrievals based on high-spectral-resolution instruments (in FLs) or gas absorption bands [5; 6; 7; 13], and it is widely used in satellite-based SIF retrievals [14]. These methods are particularly useful for hyperspectral instruments or wider fitting windows in SIF inversion. This method treats satellite spectra as a combination of non-SIF radiation spectra and top-of-atmosphere (TOA) SIF spectra, represented by a linear combination of singular vectors and state vectors of steady-state fluorescence (Fs).  
When using data-driven methods for SIF retrieval in the 757 nm FLs, the SIF spectrum can sometimes be confused with the slope features of satellite spectra during the inversion process, leading to overfitting issues [13]. The forward model in data-driven methods is generated using non-SIF satellite spectra, so the Doppler effect-induced spectral shifts between satellite spectra should be considered. Random Doppler shifts increase the redundancy of information in non-SIF spectra, hindering the extraction of their features. And spectral shifts between the forward model and satellite spectra directly impact SIF signal retrieval. To our knowledge, no studies have discussed the correction and impact of the Doppler effect in data-driven methods.  
The GOSAT satellite, with its excellent spectral resolution, includes numerous far-red FLs in its FTS-1 band, providing ample opportunities for improving retrieval windows. In this study, we propose a retrieval window to address the overfitting issue in data-driven methods, enabling better decoupling of far-red FLs SIF signals from singular vectors. Additionally, we propose a spectral shift correction algorithm based on standard reference spectra to eliminate spectral shifts between satellite spectra and discuss the Doppler effect's impact on SIF retrieval. We performed SIF retrieval in the aforementioned window using the Doppler shift-corrected GOSAT spectra, generated the corresponding SIF product, and validated it. The structure of this paper is as follows. Section 2 presents the retrieval window, spectral shift correction algorithm, forward model, and strategies for generating and selecting singular vectors. It also introduces the data used and the evaluation methods for retrieval results. Section 3 validates the retrieval results using vegetation indices (VI), GPP, and contemporaneous Caltech GOSAT SIF products. Section 4 discusses the validation results and the impact of the Doppler effect. Finally, Section 5 concludes the paper.