Remote sensing of solar-induced chlorophyll fluorescence (SIF) has been widely investigated with satellites/sensors covering the spectral range of SIF emission with fine spectral resolutions (SRs). The potential precision of SIF retrievals is limited by instruments’ spectral specifications. Here, the influence of spectral characteristics (SR, signal-to-noise ratio (SNR), and spectral coverage) on data-driven SIF retrieval was assessed using simulations, and the SIF retrieval capability of the obsolete, in-orbit, and planned satellites was evaluated from the spectral perspective. As a result, the 757–759- and 735–758-nm fitting windows (FWs) were found to be optimal for far-red SIF retrieval on satellites with fine (< 0.1 nm) and moderate (0.1–0.5 nm) SR. The 682–697-nm FW was found to perform better for red SIF retrievals. For far-red SIF retrievals, Orbiting Carbon Observatory 2 (OCO-2) and TanSat were found to have the best SIF retrieval performance, with a root-mean-square-error (RMSE $^{\ast}$ ) of lower than 0. 25 mW $\cdot \,\,\text{m}^{-2}\,\,\cdot $ sr $^{-1}\,\,\cdot $ nm-1, followed by satellites with moderate SRs (0.1–0.5 nm) such as CO2 monitoring (CO2M), TROPOspheric Monitoring Instrument (TROPOMI), and Global Ozone Monitoring Experiment 2 (GOME-2) (RMSE $^{\ast} < 0.5$ mW $\cdot \,\,\text{m}^{-2}\,\,\cdot $ sr $^{-1}\,\,\cdot $ nm-1). The high SNR of SCanning Imaging Absorption Spectro Meter for Atmospheric CHartographY (SCIAMACHY) did not improve the far-red SIF retrieval greatly (RMSE $^{\ast} =0.47$ mW $\cdot \,\,\text{m}^{-2}\,\,\cdot $ sr $^{-1}\,\,\cdot $ nm-1) but obtained a low RMSE $^{\ast} $ at the red band (0.43 mW $\cdot \,\,\text{m}^{-2}\,\,\cdot $ sr $^{-1}\,\,\cdot $ nm-1). The highest red SIF retrieval potential was found on TROPOMI, with an RMSE $^{\ast} $ of 0.41 mW $\cdot \,\,\text{m}^{-2}\,\,\cdot $ sr $^{-1}\,\,\cdot $ nm-1. Tropospheric Emissions Monitoring of Pollution (TEMPO) and FLuorescence Explorer (FLEX) gave poor performance (RMSE $^{\ast} >0.9$ mW $\cdot \,\,\text{m}^{-2}\,\,\cdot $ sr $^{-1}\,\,\cdot $ nm-1) with their current spectral specifications. Improvement of the spectral characteristics is still needed to obtain precise SIF retrievals.

Space-based solar-induced chlorophyll fluorescence (SIF) has been widely demonstrated as a great proxy for monitoring terrestrial photosynthesis and has been successfully retrieved from satellite-based hyperspectral observations using a data-driven algorithm. As a semi-empirical algorithm, the data-driven algorithm is strongly affected by the empirical parameters in the model. Here, the influence of the data-driven algorithm’s empirical parameters, including the polynomial order (n_p), the number of feature vectors (n_SV), the fluorescence emission spectrum function, and the fitting window used in the retrieval model, were quantitatively investigated based on the simulations of the SIF Imaging Spectrometer (SIFIS) onboard the First Terrestrial Ecosystem Carbon Inventory Satellite (TECIS-1). The results showed that the fitting window, n_p, and n_SV were the three main factors that influenced the accuracy of retrieval. The retrieval accuracy was relatively higher for a wider fitting window; the root mean square error (RMSE) was lower than 0.7 mW m⁻² sr⁻¹ nm⁻¹ with fitting windows wider than 735–758 nm and 682–691 nm for the far-red band and the red band, respectively. The RMSE decreased first and then increased with increases in n_p range from 1 to 5 and increased in n_SV range from 2 to 20. According to the specifications of SIFIS onboard TECIS-1, a fitting window of 735–758 nm, a second-order polynomial, and four feature vectors are the optimal parameters for far-red SIF retrieval, resulting in an RMSE of 0.63 mW m⁻² sr⁻¹ nm⁻¹. As for red SIF retrieval, using second-order polynomial and seven feature vectors in the fitting window of 682–697 nm was the optimal choice and resulted in an RMSE of 0.53 mW m⁻² sr⁻¹ nm⁻¹. The optimized parameters of the data-driven algorithm can guide the retrieval of satellite-based SIF and are valuable for generating an accurate SIF product of the TECIS-1 satellite after its launch.