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

Abstract

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. Secondly, it was found that the SIF760–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.

The Links between Canopy Solar-Induced Chlorophyll Fluorescence and Gross Primary Production Responses to Meteorological Factors in the Growing Season in Deciduous Broadleaf Forest

“…SIF and GPP linear regression was larger on cloudy days that exhibit a higher proportion of diffuse PAR, which is consistent with findings for maize [21], rice paddy [19], and mixed broad-leaved forest [49]. Middle and lower parts of the canopy can receive more radiation under cloudy conditions [5,47], enabling the middle and lower canopy to participate in SIF emission and photosynthesis. However, the influence of measurement techniques must be taken into account in that tower-based spectrometers will predominantly receive the SIF signal that was emitted from the top canopy leaves and escaped from the inside canopy [57,58], meanwhile, GPP divided from EC measurements comes from a larger area, and the carbon released by the internal leaves will eventually escape from the canopy [59].…”

“…In addition, this study has inconsistent results with previous studies [21,47] and possible explanations are given in the following analysis. We found that with the aggregation of timescales, the correlation of SIF-GPP deteriorated.…”

“…Before each data acquisition, the instrument optimizes the integration time automatically according to the light intensity at that time. Therefore, the period of each measurement depends on the light conditions at the time, and it will be shorter at midday than at sunrise or sunset [47]. During the growing season, the canopy solar-induced chlorophyll fluorescence is continuously monitored, and the instrument is calibrated regularly.…”

Generation of a Global Spatially Continuous TanSat Solar-Induced Chlorophyll Fluorescence Product by Considering the Impact of the Solar Radiation Intensity

“…The seasonal trend in SIF at the Niwot Ridge site, where the majority of the forest consisted of needleaved evergreens, was investigated using both continuous TanSat and tower-based SIF measurements made by Magney et al [76], and also compared with the tower-based NDVI observations, as illustrated in Figure 10. Here, we used the daily mean clearness index (CI) (only CI > 0.6 was used) to determine whether it was clear skies [77]. We calculated the daily SIF under clear-sky conditions, if there are more than three samples a day.…”

Site Characteristics Mediate the Relationship Between Forest Productivity and Satellite Measured Solar Induced Fluorescence

“…At sub-diurnal or half-hourly timescales, GPP:SIF was reported to follow an asymptotic trend (Li et al, 2018a;Chen et al, 2020). Indeed, GPP saturates at high APAR, while SIF keeps increasing as APAR increases, leading to a hyperbolic relationship between SIF and GPP at the instantaneous timescale (Damm et al, 2015;Gu et al, 2019).…”