## 1 Introduction  
Vegetation photosynthesis is a key driver of the global carbon cycle. Sun-induced chlorophyll fluorescence (SIF) is closely related to vegetation photosynthesis. Therefore, SIF is considered a promising remote sensing indicator to estimate gross primary production at the regional and global scales. Although, in recent years, SIF has been retrieved from airborne [1; 2; 3; 4] and satellite-based remote sensing systems [5; 6; 7], ground-based measurements of SIF are essential to validate the results of airborne and satellite-based systemsand to study the relationship between SIF and vegetation photosynthesis [8]. Therefore, an increasing number of long-term automatic field observation systems of SIF have been set up around the world [9; 10]. In ground-based observations, the majority of SIF inversion algorithms require not only the upwelling radiance spectrum (\(L\)) on top of the canopy, but also the simultaneous incident irradiance spectrum (\(E\)) at the surface [11]. Considering that a spectrometer can only receive the signals from a single optical channel at one time, two types of SIF observation systems, including the simultaneous system and the sequential system [12], have been developed. In a simultaneous system, two spectrometers measure \(E\) and \(L\) simultaneously [13]. As the performance of each spectrometer can change differently with time, temperature, humidity, and other factors [14], the two spectrometers must be intercalibrated frequently. Thus, simultaneous systems are not widely used in field measurements. In a sequential system, a spectrometer is connected to an optical path switcher to measure \(E\) and \(L\) in sequence [15]. Currently, sequential systems are widely used due to their ease of maintenance. To retrieve SIF accurately with the data acquired by sequential systems, the measured \(E\) (\(E_{M}\)) should match the actual irradiance (\(E_{L}\)) during \(L\) measurement. However, in field measurements, rapid changes in illumination can cause a temporal mismatch between \(E\) and \(L\) measurements. In other words, \(E_{M}\) is different from \(E_{L}\). During SIF retrieval in the field, temporal mismatch is one of the primary causes of errors [16]. To reduce the possibility of a temporal mismatch between \(E\) and \(L\), several methods have been proposed to shorten the measurement interval between \(E\) and \(L\), such as increasing the light flux of the fiber [17], improving the integration time optimization method [18], and improving the measurement process [9]. The above methods, however, cannot completely prevent the occurrence of a temporal mismatch when illumination rapidly changes in the field. To ensure the accuracy of the analyses, some researchers only use data collected under clear skies [15], which renders a great deal of field data useless. [PERSON] et al. [11] proposed a data quality indicator (named \(DQ_{S}\)) to account for the stability of \(E\) during the sandwich measurement section, in which two irradiance spectra (\(E1\) and \(E2\)) are acquired just before and immediately after the \(L\) measurement. \(DQ_{S}\) is calculated as the percentage difference between \(E1\) and \(E2\). \(DQ_{S}\) is currently widely used to filter out abnormal data caused by temporal mismatches between \(E\) and \(L\). However, this indicator does not account for variations in illumination between irradiance measurements, which may also cause inversion errors. If a quantum sensor is added to the SIF measurement system, PAR values can be recorded at a higher frequency than that of E measured by the spectrometer in the system, enabling a more accurate evaluation of illumination stability. Therefore, in this study, we use a SIF observation system equipped with a quantum sensor to perform observation experiments on a maize canopy.