Integrating SIF and Clearness Index to Improve Maize GPP Estimation Using Continuous Tower-Based Observations

Solar-induced chlorophyll fluorescence (SIF) has been proven to be well correlated with vegetation photosynthesis. Although multiple studies have found that SIF demonstrates a strong correlation with gross primary production (GPP), SIF-based GPP estimation at different temporal scales has not been well explored. In this study, we aimed to investigate the quality of GPP estimates produced using the far-red SIF retrieved at 760 nm (SIF₇₆₀) based on continuous tower-based observations of a maize field made during 2017 and 2018, and to explore the responses of GPP and SIF to different meteorological conditions, such as the amount of photosynthetically active radiation (PAR), the clearness index (CI, representing the weather condition), the air temperature (AT), and the vapor pressure deficit (VPD). Firstly, our results showed that the SIF₇₆₀ tracked GPP well at both diurnal and seasonal scales, and that SIF₇₆₀ was more linearly correlated to PAR than GPP was. Therefore, the SIF₇₆₀-GPP relationship was clearly a hyperbolic relationship. For instantaneous observations made within a period of half an hour, the R² value was 0.66 in 2017 and 2018. Based on daily mean observations, the R² value was 0.82 and 0.76 in 2017 and 2018, respectively. and had an R² value of 0.66 (2017) and 0.66 (2018) for instantaneous observations made within a period of half an hour and 0.82 (2017) and 0.76 (2018) for daily mean observations. Secondly, it was found that the SIF_760-GPP relationship varied with the environmental conditions, with the CI being the dominant factor. At both diurnal and seasonal scales, the ratio of GPP to SIF₇₆₀ decreased noticeably as the CI increased. Finally, the SIF₇₆₀-based GPP models with and without the inclusion of CI were trained using 70% of daily observations from 2017 and 2018 and the models were validated using the remaining 30% of the dataset. For both linear and non-linear models, the inclusion of the CI greatly improved the SIF₇₆₀-based GPP estimates based on daily mean observations: the value of R² increased from 0.71 to 0.82 for the linear model and from 0.82 to 0.87 for the non-linear model. The validation results confirmed that the SIF₇₆₀-based GPP estimation was improved greatly by including the CI, giving a higher R² and a lower RMSE. These values improved from R² = 0.66 and RMSE = 7.02 mw/m²/nm/sr to R² = 0.76 and RMSE = 6.36 mw/m²/nm/sr for the linear model, and from R² = 0.71 and RMSE = 4.76 mw/m²/nm/sr to R² = 0.78 and RMSE = 3.50 mw/m²/nm/sr for the non-linear model. Therefore, our results demonstrated that SIF₇₆₀ is a reliable proxy for GPP and that SIF₇₆₀-based GPP estimation can be greatly improved by integrating the CI with SIF₇₆₀. These findings will be useful in the remote sensing of vegetation GPP using satellite, airborne, and tower-based SIF data because the CI is usually an easily accessible meteorological variable.