Solar-induced chlorophyll fluorescence (SIF) is regarded as a proxy for photosynthesis in terrestrial vegetation. Tower-based long-term observations of SIF are very important for gaining further insight into the ecosystem-specific seasonal dynamics of photosynthetic activity, including gross primary production (GPP). Here, we present the design and operation of the tower-based automated SIF measurement (SIFSpec) system. This system was developed with the aim of obtaining synchronous SIF observations and flux measurements across different terrestrial ecosystems, as well as to validate the increasing number of satellite SIF products using in situ measurements. Details of the system components, instrument installation, calibration, data collection, and processing are introduced. Atmospheric correction is also included in the data processing chain, which is important, but usually ignored for tower-based SIF measurements. Continuous measurements made across two growing cycles over maize at a Daman (DM) flux site (in Gansu province, China) demonstrate the reliable performance of SIF as an indicator for tracking the diurnal variations in photosynthetically active radiation (PAR) and seasonal variations in GPP. For the O₂–A band in particular, a high correlation coefficient value of 0.81 is found between the SIF and seasonal variations of GPP. It is thus concluded that, in coordination with continuous eddy covariance (EC) flux measurements, automated and continuous SIF observations can provide a reliable approach for understanding the photosynthetic activity of the terrestrial ecosystem, and are also able to bridge the link between ground-based optical measurements and airborne or satellite remote sensing data.

Solar-induced chlorophyll fluorescence (SIF), which can be used as a novel proxy for estimating gross primary production (GPP), can be effectively retrieved using ground-based, airborne and satellite measurements. Absorbed photosynthetically active radiation (APAR) is the key bridge linking SIF and GPP. Remotely sensed SIF at the canopy level ( $SI{F}_{canopy}$ ) is only a part of the total SIF emission at the photosystem level. An SIF-based model for GPP estimation would be strongly influenced by the fraction of SIF photons escaping from the canopy ( $f_{esc}$ ). Understanding the response of $SI{F}_{canopy}$ to the absorbed photosynthetically active radiation absorbed by chlorophyll ( $APA{R}_{chl}$ ) is a key step in estimating GPP but, as yet, this has not been well explored. In this study, we aim to investigate the relationship between remotely sensed $SI{F}_{canopy}$ and $APA{R}_{chl}$ based on simulations made by the Soil Canopy Observation Photosynthesis Energy fluxes (SCOPE) model and field measurements. First, the ratio of the fraction of the absorbed photosynthetically active radiation absorbed by chlorophyll ( ${\mathrm{fPAR}}_{chl}$ ) to the fraction of absorbed photosynthetically active radiation absorbed by green leaves ( ${\mathrm{fPAR}}_{green}$ ) is investigated using a dataset simulated by the SCOPE model. The results give a mean value of 0.722 for Cab at 5 μg cm⁻², 0.761 for Cab at 10 μg cm⁻² and 0.795 for other Cab content (ranging from 0.71 to 0.81). The response of $SI{F}_{canopy}$ to $APA{R}_{chl}$ is then explored using simulations corresponding to different biochemical and biophysical conditions and it is found that $SI{F}_{canopy}$ is well correlated with $APA{R}_{chl}$ . At the O₂-A band, for a given plant type, the relationship between $SI{F}_{canopy}$ and $APA{R}_{chl}$ can be approximately expressed by a linear statistical model even for different values of the leaf area index (LAI) and chlorophyll content, whereas the relationship varies with the LAI and chlorophyll content at the O₂-B band. Finally, the response of $SI{F}_{canopy}$ to $APA{R}_{chl}$ for different leaf angle distribution (LAD) functions is investigated using field observations and simulations; the results show that $f_{esc}$ is larger for a planophile canopy structure. The values of the ratio of $SI{F}_{canopy}$ to $APA{R}_{chl}$ are 0.0092 $\pm 0.0020$ , 0.0076 $\pm 0.0036$ and 0.0052 $\pm 0.0004$ μm⁻¹ sr⁻¹ for planophile vegetables/crops, planophile grass and spherical winter wheat, respectively, at the O₂-A band. At the O₂-B band, the ratios are 0.0063 $\pm 0.0014$ , 0.0049 $\pm 0.0030$ and 0.0033 $\pm 0.0004$ μm⁻¹ sr⁻¹, respectively. The values of this ratio derived from observations agree with simulations, giving values of 0.0055 $\pm 0.0002$ and 0.0068 $\pm 0.0001$ μm⁻¹ sr⁻¹ at the O₂-A band and 0.0032 $\pm 0.0002$ and 0.0047 $\pm 0.0001$ μm⁻¹ sr⁻¹ at the O₂-B band for spherical and planophile canopies, respectively. Therefore, both the simulations and observations confirm that the relationship between $SI{F}_{canopy}$ and ${\mathrm{APAR}}_{chl}$ is species-specific and affected by biochemical components and canopy structure, especially at the O₂-B band. It is also very important to correct for reabsorption and scattering of the SIF radiative transfer from the photosystem to the canopy level before the remotely sensed $SI{F}_{canopy}$ is linked to the GPP.

Solar-induced chlorophyll fluorescence (SIF) is closely linked to the photosynthesis of plants and has the potential to estimate gross primary production (GPP) at different temporal and spatial scales. However, remotely sensed SIF at a ground or space level is usually instantaneous, which cannot represent the daily total SIF. The temporal mismatch between instantaneous SIF (SIF_inst) and daily GPP (GPP_daily) impacts their correlation across space and time. Previous studies have upscaled SIF_inst to the daily scale based on the diurnal cycle in the cosine of the solar zenith angle ( $\cos \left(\mathrm{SZA}\right)$ ) to correct the effects of latitude and length of the day on the variations in the SIF-GPP correlation. However, the important effects of diurnal weather changes due to cloud and atmospheric scattering were not considered. In this study, we present a SIF upscaling method using photosynthetically active radiation (PAR) as a driving variable. First, a conversion factor (i.e., the ratio of the instantaneous PAR (PAR_inst) to daily PAR (PAR_daily)) was used to upscale in-situ SIF measurements from the instantaneous to daily scale. Then, the performance of the SIF upscaling method was evaluated under changing weather conditions and different latitudes using continuous tower-based measurements at two sites. The results prove that our PAR-based method can reduce not only latitude-dependent but also the weather-dependent variations in the SIF-GPP model. Specifically, the PAR-based method gave a more accurate prediction of diurnal and daily SIF (SIF_daily) than the $\cos \left(\mathrm{SZA}\right)$ -based method, with decreased relative root mean square error (RRMSE) values from 42.2% to 25.6% at half-hour intervals and from 25.4% to 13.3% at daily intervals. Moreover, the PAR-based upscaled SIF_daily had a stronger correlation with the daily absorbed PAR (APAR) than both the SIF_inst and $\cos \left(\mathrm{SZA}\right)$ -based upscaled SIF_daily, especially for cloudy days with a coefficient of determination (R²) that increased from approximately 0.5 to 0.8. Finally, the PAR-based SIF_daily was linked to GPP_daily and compared to the SIF_inst or $\cos \left(\mathrm{SZA}\right)$ -based SIF_daily. The results indicate that the SIF-GPP correlation can obviously be improved, with an increased R² from approximately 0.65 to 0.75. Our study confirms the importance of upscaling SIF from the instantaneous to daily scale when linking SIF with GPP and emphasizes the need to take diurnal weather changes into account for SIF temporal upscaling.

Fusing low-resolution hyperspectral images (HSI) with high-resolution multispectral images (MSI) has become a promising technique for generating high-resolution hyperspectral images (HHSI), effectively addressing low spatial resolution limitations of hyperspectral data. Deep learning-based fusion methods have emerged as the dominant approach in recent research. However, almost unsupervised deep fusion methods rely on degradation processes that may disrupt and lose the dominant spectral-spatial information in the original images. Moreover, they only construct spectral inverse mapping while lacking effective spectral-spatial interactions, resulting in insufficient details preservation, thereby affecting fusion performance. In this study, we propose a deep unsupervised pretraining fusion framework of spectral-spatial collaborative constraint (PFS2C2). Specifically, in the first stage, both spatial and spectral inverse modules are adaptively pretrained from the original HSI and MSI, providing initialize parameters for further optimization; in the second stage, the model learns spatial and spectral degradation modules to support the construction of inverse modules for subsequent stage; in the third stage, guided by pretrained parameters, we further optimize the inverse mapping and effectively extract spectral-spatial information at different resolution, thereby enhancing the fusion performance and applicability. Experimental results on simulated and real satellite data verify the superiority of our proposed method in recovering spatial details and preserving spectral fidelity.

The SCOPE (soil canopy observation of photochemistry and energy fluxes) model has been widely used to interpret solar-induced chlorophyll fluorescence (SIF) and investigate the SIF-photosynthesis links at different temporal and spatial scales in recent years. In the SCOPE model, the fluorescence quantum efficiency in dark-adapted conditions (FQE) for Photosystem II (fqe2) and Photosystem I (fqe1) were two key parameters of SIF emission, which have always been parameterized as fixed values derived from laboratory measurements. To date, only a few studies have focused on evaluating the SCOPE model for SIF interpretation, and the variation of FQE values in the field remains controversial. In this study, the accuracy of the SCOPE model to simulate the canopy SIF was investigated using diurnal experiments on winter wheat. First, ten diurnal experiments were conducted on winter wheat, and the canopy SIF emissions and the SCOPE model’s input parameters were directly measured or indirectly retrieved from the spectral radiances, gross primary productivity (GPP) data, and meteorological records. Second, the SCOPE-simulated SIF emissions with fixed FQE values were evaluated using the observed canopy SIF data. The results show that the SCOPE model can reliably interpret the diurnal cycles of SIF variation and provide acceptable results of SIF simulations at the O₂-B (SIF_B) and O₂-A (SIF_A) bands with RRMSEs of 24.35% and 23.67%, respectively. However, the SCOPE-simulated SIF_B and SIF_A still contained large systematical deviations at some growth stages of wheat, and the seasonal cycles of the ratio between SIF_B and SIF_A (SIF_A/SIF_B) cannot be credibly reproduced. Finally, the SCOPE-simulated SIF emissions with variable FQE values were evaluated using the observed canopy SIF data. The simulating accuracy of SIF_B and SIF_A can be improved greatly using variable FQE values, and the SCOPE simulations track well with the seasonal SIF_A/SIF_B values with an RRMSE of 20.63%. The results indicated a clear seasonal pattern of FQE values for unbiased SIF simulation: from the erecting to the flowering stage of wheat, the ratio of fqe1 to fqe2 (fqe1/fqe2) gradually increased from 0.05–0.1 to 0.3–0.5, while the fqe2 value decreased from 0.013 to 0.007. Our quantitative results of the model assessment and the FQE adjustment support the use of the SCOPE model as a powerful tool for interpreting the SIF emissions and can serve as a significant reference for future applications of the SCOPE model.

Solar-induced chlorophyll fluorescence (SIF) has been proven to be an efficient indicator of vegetation photosynthesis. To investigate the relationship between SIF and Gross Primary Productivity (GPP), tower-based continuous spectral observations coordinated with eddy covariance (EC) measurements are needed. As the strong absorption effect at the O₂-A absorption bands has an obvious influence on SIF retrieval based on the Fraunhofer Line Discrimination (FLD) principle, atmospheric correction is required even for tower-based SIF observations made with a sensor tens of meters above the canopy. In this study, an operational and simple solution for atmospheric correction of tower-based SIF observations at the O₂-A band is proposed. The aerosol optical depth (AOD) and radiative transfer path length (RTPL) are found to be the dominant factors influencing the upward and downward transmittances at the oxygen absorption band. Look-up tables (LUTs) are established to estimate the atmosphere transmittance using AOD and RTPL based on the MODerate resolution atmospheric TRANsmission 5 (MODTRAN 5) model simulations, and the AOD is estimated using the ratio of the downwelling irradiance at 790 nm to that at 660 nm (E₇₉₀/E₆₆₀). The influences of the temperature and pressure on the atmospheric transmittance are also compensated for using a corrector factor of RTPL based on an empirical equation. A series of field measurements were carried out to evaluate the performance of the atmospheric correction method for tower-based SIF observations. The difference between the SIF retrieved from tower-based and from ground-based observations decreased obviously after the atmospheric correction. The results indicate that the atmospheric correction method based on a LUT is efficient and also necessary for more accurate tower-based SIF retrieval, especially at the O₂-A band.