Solar-induced chlorophyll fluorescence (SIF) is an indicator of vegetation photosynthesis, and multiple satellite SIF products have been generated in recent years. However, current SIF products are limited for applications toward vegetation photosynthesis monitoring because of low spatial resolution or spatial discontinuity. This study uses a spatial downscaling method to obtain a redistribution of the original TROPOspheric Monitoring Instrument (TROPOMI) SIF (OSIF). As a result, a downscaled SIF dataset (TroDSIF) with fine spatio-temporal resolutions (500 m, 16 days) was generated. Compared with a machine learning (ML) SIF product and OSIF, TroDSIF can better reproduce the OSIF signals with higher R2, lower root mean square error (RMSE), and nearly zero residuals at different latitudes. Direct validation on TroDSIF using tower-based SIF measurements demonstrated a good consistency between them. However, TroDSIF is dependent on the linear hypothesis between OSIF and the ML-predicted SIF used in the redistribution process. Nonetheless, we believe TroDSIF is anticipated to be beneficial to conducting global vegetation photosynthesis and climate change studies at precise scales.

Solar-induced chlorophyll fluorescence (SIF) is essential for monitoring vegetation photosynthesis. The water vapor absorption band, the broadest absorption window, has a deeper absorption line than the O2-B band, providing significant potential for SIF retrieval; however, substantial variation in atmospheric water vapor column concentrations limits research on SIF retrieval using this band. This study evaluates seven common SIF retrieval algorithms, including sFLD, 3FLD, iFLD, pFLD, SFM, SVD, and DOAS, using simulated datasets under varying atmospheric water vapor concentrations, spectral resolution (SR), and signal-to-noise ratios (SNRs). Additionally, the SIF retrieval results from the H2O, O2-B, and O2-A absorption bands are compared and analyzed to explore the fluorescence retrieval potential of the water vapor band. Furthermore, the potential of commonly used spectrometers, including Ocean Optics QE Pro and ASD FieldSpec 3, for SIF retrieval using the water vapor absorption band was evaluated. The results were further validated using ground-based tower observations. The results show that sFLD consistently overestimates SIF in the water vapor band, limiting its reliability, while SFM performs best across varying conditions. In comparison, 3FLD and pFLD, along with SVD, are accurate at high resolutions but less effective at lower ones. iFLD performs relatively poorly overall, whereas DOAS excels in low SR retrievals. At the same time, our study also shows that the water vapor band offers higher accuracy in ground-based SIF retrieval compared to the O2-B band, demonstrating strong application potential and providing valuable references for selecting SIF retrieval algorithms.

Solar-induced chlorophyll fluorescence (SIF) has long been regarded as a proxy for photosynthesis and has shown superiority in estimating gross primary production (GPP) compared to traditional vegetation indices, especially in evergreen ecosystems. However, current SIF-based GPP estimations regard the canopy as a large leaf and seldom consider the impact of interactions among light, canopy structure, and leaf physiology. In this study, we proposed GPP estimation models with different descriptions of light–structure–physiology interactions (including the layered model, the two-leaf model, and the layered two-leaf model) and compared their performances with the big-leaf model using half-hourly (or hourly) observations at evergreen needleleaf forest sites. First, we found that the big-leaf model underestimated GPP, especially at noon. All models showed higher accuracy than that of the big-leaf model. Second, we investigated the diurnal dynamics of GPP estimations in each canopy layer and found that models with a two-leaf assumption captured the diurnal variations in GPP better than that with the layered assumption. We also deduced that the poor performance of the big-leaf model was related to its overestimation of the overall light stress on the redox state of PSII reaction centers (qL). Finally, we noticed that the qL at the canopy scale had lower sensitivity to light change than the single-leaf qL and that the light response of canopy-scale qL was influenced by the leaf area index during seasonal cycles. Overall, this study describes methods to accurately estimate sub-daily GPP from SIF in evergreen needleleaf forests and demonstrates that the interactions among light, canopy structure, and leaf physiology regulate the SIF-GPP relationship at the canopy scale. Further, it indicates the need to consider the description of light distribution within the canopy in next-generation terrestrial biosphere models, even if they incorporate SIF to constrain their parameterization. Thus, upscaling the established leaf-scale mechanistic SIF-GPP relationship or findings to canopy-scale applications still requires much work, especially when there are significant changes in environmental conditions and their within-canopy distributions.

As the largest carbon reservoir within terrestrial ecosystems, forest ecosystems play a major role as carbon sinks in the global carbon cycle. There are still some uncertainties regarding the responses of different carbon fluxes to environmental changes in cold temperate climate forest ecosystems. Here, 14 cold temperate forest flux sites for at least ten years were investigated, including carbon fluxes and environmental variables such as temperature, precipitation, shortwave radiation, and vapor pressure deficit. By calculating the Spearman correlation coefficient, there was a congruence between photosynthetic productivity (i.e., gross primary productivity, GPP) and carbon sequestration (i.e., net ecosystem productivity, NEP) at thirteen forest sites, and at one forest site, GPP and NEP were decoupled. Annual GPP and NEP displayed a consistent trend when temperature and precipitation had significantly opposite trends and when temperature had a significantly positive correlation with VPD. But when VPD was significantly negatively correlated with both temperature and SW in spring and when temperature was negatively correlated with both SW and VPD in summer, a decoupling of GPP and NEP occurred. The impacts of various environmental factors on the annual carbon fluxes were calculated for each year and season using the path analysis method. At forest sites with consistent trends in GPP and NEP, annual, spring, and summer temperatures had significant positive correlations with GPP and ecosystem respiration (RE). While at the decoupled forest site, environmental factors had a stronger effect on RE, which then contributed to the observed decoupling of GPP and NEP. Finally, the Partial Least Squares method was used to analyze the relative contribution of each environmental factor to annual carbon fluxes. The results revealed that temperature and summer precipitation were the key environmental factors affecting forest ecosystems. This study provides important insights into the different responses of carbon fluxes in forest ecosystems undergoing environmental changes.

Satellite-based solar-induced chlorophyll fluorescence (SIF) serves as a valuable proxy for monitoring the photosynthesis of vegetation globally. The Global Ozone Monitoring Experiment-2A (GOME-2A) SIF product has gained widespread popularity, particularly due to its extensive global coverage since 2007. However, serious temporal degradation of the GOME-2A instrument is a problem, and there is currently a lack of time-consistent GOME-2A SIF products that meet the needs of temporal trend analysis. In this paper, the GOME-2A instrument's temporal degradation was first calibrated using a pseudo-invariant method, which revealed 16.21 % degradation of the GOME-2A radiance at the near-infrared (NIR) band from 2007 to 2021. Based on the calibration results, the temporal degradation of the GOME-2A radiance spectra was successfully corrected by using a fitted quadratic polynomial function whose determination coefficient (R2) was 0.851. Next, a data-driven algorithm was applied for SIF retrieval at the 735–758 nm window. Also, a photosynthetically active radiation (PAR)-based upscaling model was employed to upscale the instantaneous clear-sky observations to monthly average values to compensate for the changes in cloud conditions and atmospheric scattering. Accordingly, a global temporally consistent GOME-2A SIF dataset (TCSIF) for 2007 to 2021 with the correction of temporal degradation was successfully generated, and the spatiotemporal pattern of global SIF was then investigated. Corresponding trend maps of the global temporally consistent GOME-2A SIF showed that 62.91 % of vegetated regions underwent an increase in SIF, and the global annual averaged SIF exhibited a trend of increasing by 0.70 % yr−1 during the 2007–2021 period. The TCSIF dataset is available at https://doi.org/10.5281/zenodo.8242928 (Zou et al., 2023).

Accurate monitoring of the leaf area index (LAI) and aboveground biomass (AGB) using remote sensing at a fine scale is crucial for understanding the spatial heterogeneity of vegetation structure in mountainous ecosystems. Understanding discrepancies in various retrieval strategies considering topographic effects or not is necessary to improve LAI and AGB estimations over mountainous areas. In this study, the performances of the look-up table method (LUT) using radiative transfer model (RTM), machine learning algorithms (MLAs), and hybrid RTM integrating RTM and MLAs based on Landsat surface reflectance (SR) before and after topographic correction were compared and analyzed. The results show that topographic correction improves the accuracies of retrieval methods involving RTM more significantly than the MLAs, meanwhile, it reduces the performance variability of different MLAs. Based on the topographically corrected Landsat SR, the random forest (RF) combined with RTM improves the retrieval accuracy of RTM-based LUT by 7.7% for LAI and 13.8% for AGB, and reduces the simulation error of MLA by 15.1% for LAI and 20.1% for AGB. Compared with available remote sensing products, the hybrid RTM based on Landsat SR with topographic correction has better feasibility to capture LAI and AGB variation at 30 m scale over mountainous areas.

The strong correlation between gross primary production (GPP) and sun-induced chlorophyll fluorescence (SIF) has been reported in many studies and is the basis of the SIF-based GPP estimation. However, GPP and SIF are not fully synchronous under various environmental conditions, which may destroy a stable GPP–SIF relationship. Therefore, exploring the difference between responses of GPP and SIF to the environment is essential to correctly understand the GPP–SIF relationship. As the common driver of GPP and SIF, the incident radiation could cause GPP and SIF to have similar responses to the environment, which may obscure the discrepancies in the responses of GPP and SIF to the other environmental variables, and further result in the ambiguity of the GPP–SIF relationship and uncertainties in the application of SIF. Therefore, we tried to exclude the dominant role of radiation in the responses of GPP and SIF to the environment based on the binning method, in which continuous tower-based SIF, satellite SIF, and eddy covariance GPP data from two growing seasons were used to investigate the differences in the responses of GPP and SIF to radiation, air temperature (Ta), and evaporation fraction (EF). We found that the following: (1) At both the site and satellite scales, there were divergences in the light response speeds between GPP and SIF which were affected by Ta and EF. (2) SIF and its light response curves were insensitive to EF and Ta compared to GPP, and the consistency in GPP and SIF light responses was gradually improved with the improvement of Ta and EF. (3) The dynamic slope values of the GPP–SIF relationship were mostly caused by the different sensitivities of GPP and SIF to EF and Ta. Our results highlighted that GPP and SIF were not highly consistent, having differences in environmental responses that further confused the GPP–SIF relationship, leading to complex SIF application.

Solar-induced chlorophyll fluorescence (SIF) has shown promise in estimating gross primary production (GPP); however, there is a lack of global GPP datasets directly utilizing SIF with models possessing clear expression of the biophysical and biological processes in photosynthesis. This study introduces a new global 0.05° SIF-based GPP dataset (CMLR GPP, based on Canopy-scale Mechanistic Light Reaction model) using TROPOMI observations. A modified mechanistic light response model was employed at the canopy scale to generate this dataset. The canopy qL (opened fraction of photosynthesis II reaction centers), required by the CMLR model, was parameterized using a random forest model. The CMLR GPP estimates showed a strong correlation with tower-based GPP (R2 = 0.72) in the validation dataset, and it showed comparable performance with other global datasets such as Boreal Ecosystem Productivity Simulator (BEPS) GPP, FluxSat GPP, and GOSIF (global, OCO-2-based SIF product) GPP at a global scale. The high accuracy of CMLR GPP was consistent across various normalized difference vegetation index, vapor pressure deficit, and temperature conditions, as well as different plant functional types and most months of the year. In conclusion, CMLR GPP is a novel global GPP dataset based on mechanistic frameworks, whose availability is expected to contribute to future research in ecological and geobiological regions.

Since the initiation of the Grain for Green Project (GFGP) in 1999, dramatic change in vegetation status on the Loess Plateau. Spatially, geographical detector was employed to detect dominant variables influencing the spatial arrangement of normalized difference vegetation index (NDVI). Temporally, lagged or accumulated monthly precipitation, temperature and standardized precipitation evapotranspiration indices (SPEIs) sensitive to the monthly NDVI were first detected for every individual pixel, and the correlation between the NDVI and meteorological elements with time-lag effects was established a random forest model of unchanged land cover, followed by attributing impacts of climatic alterations and human interventions through residual examination across changed land cover. The findings indicate that (1) precipitation, slope, and soil dominantly influence the spatial arrangement of the NDVI. (2) Precipitation in current the month and cumulative temperatures of the previous 1-2 months steadily affect vegetation growth significantly, the optimal accumulation time interval for SPEI around 2000 are 8 months and 4 months, respectively. (3) Increases in the average NDVI within woodland and meadow vegetation on the Loess Plateau were primarily driven by climate change before 2000, accounting for 76.2%, whereas after 2000 it was dominantly driven by human activities, accounting for 64.16%.

Solar-induced chlorophyll fluorescence (SIF) has shown great potential in estimating gross primary production (GPP). However, their quantitative relationship is not invariant, which undermines the reliability of empirical SIF-based GPP estimation at fine spatiotemporal scales, especially under extreme conditions. In this study, we developed a parsimonious mechanistic model for SIF-based GPP estimation in evergreen needle forests (ENF) by employing the Mechanistic Light Response framework and Eco-Evolutionary theory to describe the light and dark reactions during photosynthesis, respectively. Specifically, we found that considering the seasonal variation in a key parameter of the MLR framework, the maximum photochemical efficiency of photosystem II (Phi(PSIImax)), can avoid the GPP overestimation in winter and early spring due to the relatively low environmental sensitivity of SIF. Compared to the estimates from other benchmark models, our GPP estimates were closer to the 1: 1 line and had higher accuracy (average R-2 = 0.86, RMSE=1.99 mu mol m(-2) s(-1)) across sites. Furthermore, the changes in the relationship between SIF and J (refers to the electron transport rate) contribute a lot to the dynamic SIF-GPP relationship in this study, while the J-GPP relationship is less variant when the temperature drops. The seasonal variation in the SIF-J relationship, especially the reduction in its slope at low temperatures, is found largely explained by the Phi(PSIImax). These results indicate the importance of the uncertainty caused by the variation in the SIF-J relationship for SIF-based GPP estimation, and the consideration of changes in Phi(PSIImax) under extreme conditions (such as severe winter in this study) is crucial for the improvement of GPP estimation via SIF.

Solar-induced chlorophyll fluorescence (SIF) provides a promising approach to monitoring plant photosynthesis. To this end, numerous retrieval algorithms have emerged and been developed to estimate ground, airborne, and satellite SIF; however, the accuracy of SIF retrieval methods in the red band is still somewhat limited. One potential obstacle that hinders the accuracy of retrieval of red SIF is the difficulty of modeling the true shape of the reflectance in the O-2-B band. To overcome this issue, herein, an improved spectral-fitting method (SFM) using principal component analysis (PCA) data-driven reflectance reconstruction method, SFM-PCA, is proposed based on a novel SIF-free reflectance dataset to improve the accuracy of SIF retrieval in the O-2-B band. In this work, the SFM-PCA method was validated using a field leaf dataset, canopy simulations, and tower-based canopy measurements. Compared to the true red SIF values at either the leaf or canopy levels, the SFM-PCA method was found to perform better than previous methods, with R-2 values of 0.97 and 0.999 for leaf measurements and canopy simulations, respectively, and corresponding normalized root-mean-square error (NRMSE) values of 6.98% and 2.335%. For the tower-based measurements, the red SIF retrieved using the SFM-PCA method was also more consistent with the O-2-A SIF. This indicates that it is feasible to make use of principal components (PCs) derived from SIF-free reflectance measurements to accurately model the true shape of the reflectance spectrum in the O-2-B band and to improve ground-based SIF retrieval in the red band. This also has the potential to be applied to the satellite-based measurements.

TCSIF: A temporally consistent global GOME-2A SIF dataset with correction of sensor degradation Description: The global monthly GOME-2A SIF dataset (2007–2021) with correction of temporal degradation. The corrected global GOME-2 SIF dataset can be obtained in two types. The daily level2 dataset is provided in hdf5 format(compressed in the zip files named "{Year}{Quater}.zip"). The name of the hdf5 files was SIF_daily_YYYYMMDD.h5, YYYY, MM, and DD represent the year, month, and date, respectively. The level3 datasets which were aggregated monthly from the level2 dataset, have a spatial resolution of 0.5°and were saved in TIFF format in chronological order from 2007 to 2021 (compressed in the file "Level3.zip"). The name of the files was SIFpar_evi_monthly _YYYYMM.tif, where SIF was product type, par, and evi represented upscaled parameters, monthly represented temporal scale, YYYY and MM was the year and month, respectively. The SIF output was stored in the hdf5 files along with other variables of interest for further processing and visualization. See the appendix for the structure of the hdf5 file. cloud_fraction[float]: Description: Effective cloud fraction derived from GOME-2 Level1B product. Units: none latitude[float]: Description: Pixel center latitude. Units: degrees N longitude[float]: Description: Pixel center longitude. Units: degrees E latitude_bounds[float]: Description: Latitude of the boundary corners for each pixel. Units: degrees N longitude _bounds[float]: Description: Longitude of the boundary corners. Units: degrees E SIF_740[float]: Description: SIF signal at 740nm retrieved using the 735–758 nm fitting window. Units: mW m-2 nm-1 sr-1 SIF_daily [float]: Description: SIF signal at 740nm with correction of day-length. Units: mW m-2 nm-1 sr-1 Sigma_i[float]: Description: The squre of single retrieval error of SIF_740. Units: (mW m-2 nm-1 sr-1)2 Solar_zenith_angle [float]: Description: Solar zenith angle. Units: degrees Solar_azimuth_angle [float]: Description: Solar azimuth angle. Units: degrees Viewing _zenith_angle [float]: Description: Viewing zenith angle. Units: degrees Viewing_azimuth_angle[float]: Description: Viewing azimuth angle. Units: degrees chi2[float]: Description: The reduced chi-square value calculated based on the the fitting residuals. Units: None Rad_NIR[float]: Description: The average radiance within the 735~758 nm window Units: mW m-2 nm-1 sr-1 ps_NIR[float]: Description: The average reflectance within at around 780 nm. Units: None ps_red[float]: Description: The average reflectance within the 665~680 nm window Units: None NDVI[float]: Description: Calculated by the TOA reflectance at red band (around 680 nm) and near-infrared band (around 780nm). Units: None

Spaceborne solar-induced chlorophyll fluorescence (SIF) products have emerged over the past decade. However, limited by the spatial-temporal mismatch between satellite-based and ground measurements or the absence of sufficient in situ SIF observations, no direct validation results of satellite-derived SIF products have been provided to date. The Orbiting Carbon Observatoty-2/3 (OCO-2/3) platforms allow us to validate SIF products using in situ SIF observations benefiting from comparable spatial resolution (1.3 km × 2.25 km and 1.6 km × 2.2 km, respectively) to ground measurements. In addition, the automated in situ SIF measurement technique facilitates accuracy validation of spaceborne SIF dataset. In this communication, we describe a direct validation of OCO-2/3 SIF products using tower-based SIF measurements acquired at six sites across China. There were 1–7 and 1–18 successful observations overpassing six in situ sites for OCO-2 and OCO-3, respectively. An overall consistent temporal variation pattern was observed between the OCO-2/3 SIF products and tower-based SIF measurements. The validation accuracies of OCO-2 and OCO-3 SIF products were 0.26 and 0.33 mW m−2 sr−1 nm−1, respectively. To best use satellite-based SIF products in a wide range of applications, we suggest that further accuracy validation using more in situ SIF observations is required, which in turn requires increased availability of public in situ SIF observations.

日 光诱导叶绿素荧光(Solar-Induced Chlorophyll Fluorescence,SIF)与植被光合作用关系密切,能够揭示植被真实生理状况,准确获取SIF信息对陆地生态碳循环和全球植被监测具有重要意义.以3FLD算法的SIF反演结果和NIRvR为参考,开展了基于塔基平台的数据驱动SIF反演算法性能评价研究.首先利用塔基连续冠层光谱观测数据,分析了 SVD算法在不同大气窗口的SIF反演效果;其次利用大气校正前后的实测数据,探究大气因素对SVD算法反演SIF的影响程度;最后,将实测数据按照光照条件区分,对比了天气稳定与天气波动条件下,基于SVD算法的SIF反演结果的稳定性.结果表明:(1)SVD算法在735～759 nm(不包含大气吸收波段)、745～780 nm(包含大气吸收波段)窗口的SIF反演精度较高;(2)SVD算法的SIF反演精度受大气影响远小于3FLD算法;(3)当光照条件剧烈变化时,使用SVD算法可以有效克服FLD类SIF反演算法对同步太阳光谱观测的依赖;即使光照迅速变化,基于SVD算法依旧能够得到稳定可靠的SIF反演结果.综上所述,SVD算法对于塔基SIF反演来说具有非常大的应用潜力.

Abstract. Satellite-based solar-induced chlorophyll fluorescence (SIF) provides a direct way of monitoring the photosynthesis of vegetation globally. Global Ozone Monitoring Experiment-2A (GOME-2A) SIF product has become the most popular SIF dataset given its capacity for global coverage since 2007. However, serious temporal degradation of the GOME-2A instrument is a problem, and no temporally consistent GOME-2A SIF products are yet available. In this paper, the GOME-2A instrument’s temporal degradation was first calibrated using a pseudo-invariant method, which revealed 16.21 % degradation of the GOME-2A radiance at the near-infrared (NIR) band from 2007 to 2021. Based on the calibration results, the temporal degradation of the GOME-2A radiance spectra was successfully corrected by using a fitted quadratic polynomial function whose determination coefficient (R2) is 0.851. Next, a data-driven algorithm was applied for SIF retrieval at the 735–758 nm window. Besides, a photosynthetically active radiation (PAR)-based upscaling model was employed to upscale the instantaneous clear-sky observations to monthly average values to compensate for the changes in weather conditions. Accordingly, a global GOME-2A SIF dataset (TCSIF) with correction of temporal degradation was successfully generated from 2007 to 2021, and the spatiotemporal pattern of global SIF was then investigated. Corresponding trend maps of the global temporally consistent GOME-2A SIF showed that 62.91 % of vegetated regions underwent an increase in SIF, and the global annual averaged SIF exhibited a trend of increasing by 0.70 % yr−1 during the 2007–2021 period. The TCSIF dataset is available at https://doi.org/10.5281/zenodo.8242928 (Zou et al., 2023).

The product of near-infrared radiation reflected by vegetation (NIRv) and PAR (NIRvP) is a promising proxy for the remote estimation of gross primary production (GPP). However, the efficiency of NIRvP in estimating the GPP and its limitations across multiple biomes and climate zones remain unclear. In this study, we aimed to evaluate the performance and limitations of NIRvP in estimating the GPP in comparison to absorbed photosynthetically active radiation (APAR), solar-induced chlorophyll fluorescence (SIF), and the MOD17A2H GPP product. Overall, the correlation between NIRvP and eddy covariance (EC) GPP was stronger than that of APAR, SIF, and MOD17A2H GPP across most biomes with usually similar seasonal variations in radiation, air temperature (TA), and precipitation. The near-​infrared (NIR) reflectance (ρNIR) and light use efficiency (LUE) exhibited a covarying relationship under these environmental conditions, which suggested that the ρNIR contributed positively to the NIRvP-GPP relationship under such climatic conditions. However, the performance of NIRvP was poor in some biomes and climate zones, which exhibited different variations in the seasonal patterns of radiation, TA, and precipitation. The resulting inconsistencies between ρNIR and LUE implied that the ρNIR contributed negatively to the NIRvP-GPP relationship in these regions. Altogether, the findings demonstrated that the NIRvP-GPP relationship was robust but attained a moderate overall relationship across ecosystems (R2 < 0.50) in the majority of biomes and climate zones. In addition, this study also elucidated the limitations of NIRvP as a GPP proxy in certain climate zones, which was attributed to the synergistic contributions of APAR and ρNIR in the NIRvP-GPP relationship.

Extensive research suggests that sun-induced chlorophyll fluorescence (SIF) and gross primary productivity (GPP) have a near-linear relationship, providing a promising avenue for estimating the carbon uptake of ecosystems. However, the factors influencing the relationship are not yet clear. This study examines the roles of SIF's radiative, structural, and physiological information in predicting GPP, based on four years of field observations of a corn canopy at various temporal scales. We quantified SIF's radiative component by measuring the intensity of incident photosynthetically active radiation (iPAR), and separated the structural and physiological components from SIF observations using the fluorescence correction vegetation index (FCVI). Our results show that the R2 values between SIF and GPP, as estimated by linear models, increased from 0.66 at a half-hour resolution to 0.86 at a one-month resolution. In comparison, the product of FCVI and iPAR, representing the non-physiological information of SIF, performed consistently well in predicting GPP with R2>0.84 at various temporal scales, suggesting a limited contribution of the physiological information of SIF for GPP estimation.. The results further reveal that SIF's radiative and structural components positively impacted the SIF-GPP linearity, while the physiological component had a negative impact on the linearity for most cases, changing from 0.6 % to -27.5 %. As for the temporal dependency, the controls of the SIF-GPP relationship moved from radiation at diurnal scales to structure at the seasonal scales. The structural contribution changed from 14.8 % at a half-hour resolution to 92.4 % at a one-month resolution, while the radiative contribution decreased from 118.0 % at a half-hour resolution to 11.7 % at a one-month resolution. This study contributes to enhancing our understanding of the physiological information conveyed by SIF and the factors influencing the temporal dependency of the SIF-GPP relationship.

Quantifying and monitoring land surface evapotranspiration (ET) is an essential task for understanding the earth’s water, energy, and carbon cycles. ET, specifically plant transpiration ( $T$ ), is closely linked to the photosynthesis, which is coupled through stomatal function. However, the mechanistic links between sun-induced chlorophyll fluorescence (SIF) information indicating canopy photosynthetic activity and $T$ are complex and difficult to derive empirically. An empirical SIF- $T$ relationship at ecosystem scale was developed and coupled to the two-source energy balance model (TSEB-SIF) to estimate the ET and its components, $T$ and soil evaporation, $E$ . By comparing model predictions with observations from an irrigated cropland site located in a semiarid region, the TSEB-SIF model shows a slightly better performance to the TSEB model in estimating ET, especially under water deficit conditions. Moreover, the TSEB-SIF model more reliably partitioned the $T$ from ET, while the TSEB model tended to overestimate the contribution of $T$ to ET.

Satellite-based data of solar-induced chlorophyll fluorescence (SIF) and the near-infrared radiation reflected by vegetation (NIRvP) are being increasingly used for the estimation of vegetation gross primary product (GPP) at the global scale. Although SIF contains more physiological information than NIRvP, NIRvP can have higher data quality and spatio-temporal resolution. Therefore, the two variables can be considered complementary for GPP monitoring. Here, we propose a simple framework to combine SIF and NIRvP data from different data sources to generate an enhanced SIF product (eSIF). The original SIF data comes from the TROPOMI instrument onboard the Sentinel-5P mission, whereas NIRvP data are derived from MODIS spectral reflectance and ERA5 reanalysis data. The resulting eSIF product has a spatial resolution of 0.05 degrees and a temporal resolution of 8 days, as well as a higher signal-to-noise ratio and a lower angular dependency than the original TROPOMI SIF data. Our results demonstrate that eSIF has similar spatial patterns to the original SIF but is more spatially continuous and less noisy. Comparisons with the FLUXCOM global GPP product show that eSIF has a more universal relationship with GPP than NIRvP for different grass/crop plant functional types (the coefficients of variation are 18.9% for slopes of GPP to eSIF and 27.3% for slopes of GPP to NIRvP), but NIRvP outperforms eSIF for tracking GPP for forest PFTs exclude BoENF. Moreover, eSIF is able to better track the seasonal variations in GPP related to environmental stresses. This study highlights that our methodology based on the combination of SIF and NIRvP is a promising approach for better monitoring of GPP.