Solar-induced chlorophyll fluorescence (SIF) provides a new and direct way of monitoring photosynthetic activity. However, current SIF products are limited by low spatial resolution or sparse sampling. In this paper, we present a data-driven method of generating a global, spatially continuous TanSat SIF product. Firstly, the key explanatory variables for modelling canopy SIF were investigated using in-situ and satellite observations. According to theoretical and experimental analysis, the solar radiation intensity was found to be a dominant driving environmental variable for the SIF yield at both the canopy and global scales; this has, however, been neglected in previous research. The cosine value of the solar zenith angle at noon (cos (SZA₀)), a proxy for solar radiation intensity, was found to be a dominant abiotic factor for the SIF yield. Next, a Random Forest (RF) approach was employed for SIF prediction based on Moderate Resolution Imaging Spectroradiometer (MODIS) visible-to-NIR reflectance data, the normalized difference vegetation (NDVI), cos (SZA₀), and air temperature. The machine learning model performed well at predicting SIF, giving R² values of 0.73, an RMSE of 0.30 mW m⁻² nm⁻¹ sr⁻¹ and a bias of 0.22 mW m⁻² nm⁻¹ sr⁻¹ for 2018. If cos (SZA₀) was not included, the accuracy of the RF model decreased: the R² value was then 0.65, the RMSE 0.34 mW m⁻² nm⁻¹ sr⁻¹ and an bias of 0.26 mW m⁻² nm⁻¹ sr⁻¹, further verifying the importance of cos (SZA₀). Finally, the globally continuous TanSat SIF product was developed and compared to the TROPOspheric Monitoring Instrument (TROPOMI) SIF data. The results showed that the globally continuous TanSat SIF product agreed well with the TROPOMI SIF data, with an R² value of 0.73. Thus, this paper presents an improved approach to modelling satellite SIF that has a better accuracy, and the study also generated a global, spatially continuous TanSat SIF product with a spatial resolution of 0.05°.

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.

Satellite-derived solar-induced chlorophyll fluorescence (SIF) has been proven to be a valuable tool for monitoring vegetation’s photosynthetic activity at regional or global scales. However, the coarse spatiotemporal resolution or discrete space coverage of most satellite SIF datasets hinders their full potential for studying carbon cycle and ecological processes at finer scales. Although the recent TROPOspheric Monitoring Instrument (TROPOMI) partially addresses this issue, the SIF still has drawbacks in spatial insufficiency and spatiotemporal discontinuities when gridded at high spatiotemporal resolutions (e.g., 0.05°, 1-day or 2-day) due to its nonuniform sampling sizes, swath gaps, and clouds contaminations. Here, we generated a new global SIF product with Seamless spatiotemporal coverage at Daily and 0.05° resolutions (SDSIF) during 2018–2020, using the random forest (RF) approach together with TROPOMI SIF, MODIS reflectance and meteorological datasets. We investigated how the model accuracy was affected by selection of explanatory variables and model constraints. Eventually, models were trained and applied for specific continents and months given the similar response of SIF to environmental variables within closer space and time. This strategy achieved better accuracy (R² = 0.928, RMSE = 0.0597 mW/m²/nm/sr) than one universal model (R² = 0.913, RMSE = 0.0653 mW/m²/nm/sr) for testing samples. The SDSIF product can well preserve the temporal and spatial characteristics in original TROPOMI SIF with high temporal correlations (mean R² around 0.750) and low spatial residuals (less than ±0.081 mW/m²/nm/sr) between them two at most regions (80% of global pixels). Compared with the original SIF at five flux sites, SDSIF filled the temporal gaps and was better consistent with tower-based SIF at the daily scale (the mean R² increased from 0.467 to 0.744. Consequently, it provided more reliable 4-day SIF averages than the original ones from sparse daily observations (e.g., the R² at Daman site was raised from 0.614 to 0.837), which resulted in a better correlation with 4-day tower-based GPP. Additionally, the global coverage ratio and local spatial details had also been improved by the reconstructed seamless SIF. Our product has advantages in spatiotemporal continuities and details over the original TROPOMI SIF, which will benefit the application of satellite SIF for understanding carbon cycle and ecological processes at finer spatial and temporal scales.

Solar-induced chlorophyll fluorescence (SIF) has been recognized as a proxy of gross primary production (GPP) across various terrestrial biomes. However, the effects of low temperature on SIF and GPP among different plant function types (PFTs) have not yet been well-explored. To gain a better understanding of the relationship between SIF and GPP, we investigated the variation in the GPP/SIF ratio in response to low-temperature conditions using satellite and tower-based datasets. Based on the TROPOMI SIF product and FLUXCOM GPP data, we found that the SIF and GPP exhibited consistent seasonal and spatial patterns, while the GPP/SIF ratio differed for different PFTs. The GPP/SIF ratio for forest types was generally higher than 10 gC·d⁻¹·mw⁻¹·nm·sr, whereas the GPP/SIF ratio for grass and crop types was generally lower than 10 gC·d⁻¹·mw⁻¹·nm·sr. In addition, there were noticeable differences in the seasonal pattern of the GPP/SIF ratio between the selected samples that experienced low-temperature stress (below 10 °C, defined as group A) and those that grew under relatively warm conditions (above 10 °C throughout the year, defined as group B). The GPP/SIF ratio for group A generally exhibited a “hump-shaped” seasonal pattern, and that for group B showed a slightly “bowl-shaped” seasonal pattern, which means it is important to consider the effects of temperature on the SIF-GPP relationship. Through linear regression and correlation analysis, we demonstrate that there was a positive correlation between the GPP/SIF ratio and temperature for group A, with a wide temperature range including low-temperature conditions, indicating that, in this case, temperature affected the SIF–GPP relationship; however, for group B—with a temperature higher than 10 °C throughout the year—the GPP/SIF ratio was not consistently affected by temperature. The response of GPP/SIF to low temperature stress was confirmed by tower-based observations at a C3 cropland (C3CRO) site and a boreal evergreen needleleaf forest (BoENF) site. Although the relationship between the GPP/SIF ratio and temperature differed among PFTs, the GPP/SIF ratio decreased under low-temperature conditions for PFTs. Therefore, the GPP/SIF ratio was not constant and was largely influenced by low temperature for different PFTs, thus highlighting the importance of incorporating temperature into SIF-based GPP estimation.

Solar-induced chlorophyll fluorescence (SIF) is closely related to the light-reaction process and has been recognized as a good indicator for tracking gross primary productivity (GPP). Nevertheless, it has not been widely examined how SIF and GPP respond to temperature. Here, we explored the linkage mechanisms between SIF and GPP in winter wheat based on continuous measurements of canopy SIF ($cSIF$), GPP, and meteorological data. To separately explore the structural and physiological mechanisms underlying the SIF–GPP relationship, we studied the temperature responses of the estimated light use efficiency ($LU{E}_p$), canopy-level chlorophyll fluorescence yield ($cSI{F}_{yield}$) and photosystem-level chlorophyll fluorescence yield (${\mathsf{\Phi }}_F$) estimated using canopy-scale remote sensing measurements. We found that GPP, red canopy SIF ($cSI{F}_{688}$) and far-red canopy SIF ($cSI{F}_{760}$) all exhibited a decreasing trend during overwintering periods. However, GPP and $cSI{F}_{688}$ showed relatively more obvious changes in response to air temperature ($T_a$) than $cSI{F}_{760}$ did. In addition, the $LU{E}_p$ responded sensitively to $T_a$ (the correlation coefficient, r = 0.83, p-value < 0.01). The $cSI{F}_{yield\_688}$ and ${\mathsf{\Phi }}_{F\_688}$ (${\mathsf{\Phi }}_F$ at 688 nm) also exhibited significantly positive correlations with $T_a$ (r > 0.7, p-value < 0.05), while $cSI{F}_{yield\_760}$ and ${\mathsf{\Phi }}_{F\_760}$ (${\mathsf{\Phi }}_F$ at 760 nm) were weakly correlated with $T_a$ (r < 0.3, p-value > 0.05) during overwintering periods. The results also show that $LU{E}_p$ was more sensitive to $T_a$ than ${\mathsf{\Phi }}_F$, which caused changes in the $LU{E}_p/{\mathsf{\Phi }}_F$ ratio in response to $T_a$. By considering the influence of $T_a$, the GPP estimation based on the total SIF emitted at the photosystem level ($tSIF$) was improved (with R² increased by more than 0.12 for $tSI{F}_{760}$ and more than 0.05 for $tSI{F}_{688}$). Therefore, our results indicate that the $LU{E}_p/{\mathsf{\Phi }}_F$ ratio is affected by temperature conditions and highlights that the SIF–GPP model should consider the influence of temperature.