## 1 Introduction  
Traditional vegetation monitoring processes commonly make use of indicators like vegetation indices [1], leaf area index (LAI) [2], and aboveground biomass [3], etc. Recently, solar-induced chlorophyll fluorescence (SIF) has been regarded as a new way of estimating gross primary productivity (GPP) due to its close correlation with photosynthesis [4]. SIF retrieved from satellites has wide coverage and a large number of long-term measurements, which are expanding its application field [5, 6, 7, 8, 9, 10]. As a common method used for SIF retrieval, the data-driven algorithm has been widely applied for SIF retrieval from satellite-based hyperspectral data. [PERSON] et al. [11] first presented a singular vector decomposition (SVD) data-driven algorithm to retrieve SIF from the Greenhouse gases Observing Satellite (GOSAT) space measurements with high spectral resolution (\(\sim\)0.04 nm). Following this, [PERSON] et al. [12] designed a principal component analysis (PCA) data-driven algorithm to map SIF from the Global Ozone Monitoring Experiment-2 (GOME-2) satellite data with moderate spectral resolution (\(\sim\)0.5 nm). Furthermore, [PERSON] et al. [13] presented a simplified forward linear model by estimating the atmospheric upward transmittance in advance and used a backward elimination algorithm to automatically determine the parameters of the data-driven algorithm, which was successfully applied for accurately retrieving satellite-based SIF from GOME-2 and the Scanning Imaging Absorption SpectroMeter for Atmospheric Chattorography (SCIA-MACHY) data. Moreover, a series of global satellite-based SIF products were generated by the data-driven algorithm using spectral measurements from the TROPOspheric Monitoring Instrument (TROPOMI) [14], Orbiting Carbon Observatory 2 (CXO-2) [15], OCO-3 [16], and Chinese Carbon Dioxide Observation Satellite Mission (TanSat) [17]. However, as was mentioned by [PERSON] et al. [13], there are inconsistencies between different products, even between products generated from the same satellite. Therefore, the accuracy of the satellite-based SIF datasets is dependent on both the specifications of sensors (spectral resolution, spectral range, and the signal-to-noise ratio (SNR), etc.) and the empirical parameters in the data-driven algorithms (fitting window, number of feature vectors, and polynomial order, etc.). Thus, the setting of the algorithm parameters for a specific satellite is very important. To date, simulated satellite data have been extensively used to verify the accuracy and precision of the SIF retrieval algorithm [12; 13; 18; 19; 20; 21], but it is still not well understood how and how much the empirical parameters in the data-driven algorithm affect satellite-based SIF retrievals. In this study, we aimed to investigate the influences of the empirical parameters of the data-driven algorithm on satellite-based SIF retrieval and develop an optimized data-driven algorithm for the upcoming First Terrestrial Ecosystem Carbon Inventory Satellite (TECIS-1) with a SIF Imaging Spectrometer (SIFIS) onboard, which will be launched at the beginning of 2022 (details listed in [PERSON] et al., 2020 [18]). For this purpose, we generated simulation datasets according to the spectral characteristics of SIFIS (Table 1). So far, little research has focused on the optimization of empirical parameters for SIF retrieval on TECIS-1, especially for red band SIF retrieval, and a detailed evaluation of empirical parameters is necessary to provide an accurate algorithm and generate satellite-based SIF products after the launch of TECIS-1. For this purpose, the simulation datasets were generated (Section 2.1) to investigate the influence of the empirical parameters of the data-driven algorithm on satellite-based SIF retrieval, including the influence of the fitting window (Section 3.1.1), the number of feature vectors, and polynomial order (Section 3.1.2), as well as the fluorescence spectrum function.