Abstract. Ecosystem water use efficiency (WUE), defined as the ratio of carbon gain to water loss, is significantly affected by drought. Elucidating the coupling relationship between WUE and drought is essential for understanding the carbon–water trade-off strategies of vegetation under drought stress. Most existing studies mainly evaluated coupling relationship using correlation coefficients or regression slopes. However, the optimal drought timescale governing WUE responses to drought has not yet been systematically investigated. To fill these gaps, this study investigated the spatiotemporal patterns of the WUE–drought coupling relationship (characterized by the maximum correlation coefficient, Rmax, and optimal lag time, Topt) across global terrestrial ecosystems from 1982 to 2018, and further explored the potential causal mechanisms. The results revealed a delaying of the drought-response timescale of WUE, accompanied by a weakening in the WUE–drought correlation at the optimal timescale, as evidenced by a decrease in Rmax at a rate of -0.0003/year and an increase in Topt at a rate of 0.0155 months/year, indicating a globally weakened coupling relationship. Moreover, pronounced heterogeneity in the changes of coupling relationships changes was observed across different drought gradients and vegetation types. Attribution analysis indicated that CO2 fertilization was the primary factor contributing to the weakening of the coupling relationship. Surface soil moisture (SMsurf) was the most critical hydrothermal driver, exhibiting nearly opposite effects and significant threshold effects on Rmax and Topt. Causality diagnosis was further employed to construct direct and indirect causal networks of hydrothermal factors affecting Rmax and Topt across different vegetation types and drought gradients. This study highlights the weakened coupling between WUE and drought, suggesting that vegetation's carbon-water trade-off is evolving toward drought adaptation, which is crucial for understanding the adaptive strategies of vegetation in response to climate change.

Abstract Accurately representing photosynthetic optimum temperature (Topt) is essential for predicting terrestrial carbon uptake, yet conventional ecosystem-scale estimates based on peak gross primary productivity (GPP) are confounded by concurrent variations in radiation, moisture and phenology. Here, we develop a physiologically grounded approach that defines Topt as the air temperature where light use efficiency (LUE) reaches its maximum, thereby isolating the intrinsic thermal response of photosynthesis. We derived efficiency-based Topt (Topt-LUE) from 131 flux observation sites. By replacing biome-based Topt used in Vegetation Photosynthesis Model (VPM), we significantly improved GPP estimation (R2 = 0.71, RMSE = 1.93 gC m−2 d−1) compared to the biome-based approach (R2 = 0.62, RMSE = 2.84 gC m−2 d−1). We then used a Random Forest framework to generate global and time-varying Topt-LUE fields (2001–2020). The results revealed that biome-based Topt systematically overestimated Topt-LUE across ∼94% of global vegetated areas, with a mean bias of ∼10°C. The global Topt-LUE exhibit clear latitudinal gradients and biome-specific contrasts, providing evidence for widespread thermal acclimation of ecosystem photosynthesis. This acclimation is reflected in a mean increase in Topt-LUE of 0.021 ± 0.102 °C per year, underscoring a measurable response to long-term climate changes. When integrated into VPM, the dynamic Topt-LUE fields reshape the spatial pattern of simulated carbon uptake, mitigating overestimation in tropical areas (∼5 gC m−2 d−1) and enhancing underestimation in frigid areas and temperate regions including China, India and Europe. This study established a mechanistically grounded framework for quantifying ecosystem thermal acclimation, advancing the representation of temperature responses in terrestrial carbon cycle models.

Accurate measurement of canopy-scale solar-induced chlorophyll fluorescence (SIF) is essential for linking near-surface measurements with satellite observations and for reliably constraining terrestrial photosynthetic carbon uptake. However, the optimal ground observation scale (height and footprint) required to capture spatially representative SIF signals remains poorly defined. Here, we develop a physically based Optimal Observation Scale (OOS) model that integrates canopy height (Htoc) and fractional vegetation cover (FVC) to determine the optimal observation height (Hopt) and footprint size (Sopt) for maize. The model was parameterized using 3-D radiative transfer simulations (DART) and validated with a UAV-based hyperspectral SIF system across eight flight altitudes (5–50 m) over a full growing season. The results show that the spatial coefficient of variation (CV) of canopy SIF decreased significantly with increasing scale and stabilizes at Sopt of approximately 91.5 m2 (Hopt of approximately 26.6 m). Application of the OOS model reduced the CV from 17.3% to 7.9%, a 54.4% reduction in spatial uncertainty. The SIF–GPP (gross primary productivity) coupling strengthened with observation heights (Hobs) and approached saturation above the OOS-derived threshold (Hopt), highlighting the importance of observation scale for capturing photosynthetic dynamics. Validation against TROPOspheric Monitoring Instrument (TROPOMI) SIF further revealed that the strongest correlations (r ≈ 0.8) were achieved when UAV SIF observations were conducted at Hobs exceeding the Hopt calculated by the OOS model and spatially matched with satellite footprints. The OOS model provides a transferable, physically-based framework for optimizing ground SIF observations across scales. Its structure-based design also provides a pathway toward generalized SIF measurement strategies that can be extended to other ecosystems and future satellite validation efforts.

Accurate estimation of gross primary productivity (GPP) is fundamental for understanding ecosystem carbon cycling. Solar-induced chlorophyll fluorescence (SIF) and carbonyl sulfide (COS) provide complementary proxies of photosynthesis, yet their relative performance across temporal scales and sky conditions remains uncertain. Using continuous eddy covariance COS fluxes and ground-based SIF observations in a rice paddy, we assess their relationships with GPP at half-hourly and daily scales under clear and cloudy conditions. SIF shows stronger correlations with GPP at the half-hourly scale, whereas COS–GPP associations strengthen after temporal aggregation, particularly under cloudy skies. Partial correlations indicate that SIF captures short-term variability, while COS becomes more informative at the daily scale. Integrating SIF and COS improves GPP estimation across conditions, especially at the daily scale and under cloudy skies. These results demonstrate scale-dependent complementarity and highlight the value of multi-proxy approaches for robust ecosystem GPP estimation.

Accurate representation of plant photosynthesis in terrestrial biosphere models (TBMs) is critical for reliable carbon-cycle simulations. Most TBMs employ the Farquhar-von Caemmerer-Berry (FvCB) model, which uses an empirical representation of electron transport that limits the simulation of gross primary productivity (GPP) and solar-induced chlorophyll fluorescence (SIF) under variable conditions. Here, we present BEPS-CB6F, an improved model that incorporates the mechanistic cytochrome b6f (Cyt b6f) scheme (CB6F) of Johnson and Berry into the Biosphere-atmosphere Exchange Process Simulator (BEPS). This implementation replaces empirical formulations with a process-based energy-allocation framework and links GPP and SIF through shared physiological parameters, including the maximum Cyt b6f activity (Vqmax) and the fraction of total leaf absorbance allocated to photosystem II (PSII) (β₂). BEPS-CB6F also integrates key photoprotective processes, including cyclic electron flow around photosystem I (CEF), non-photochemical quenching of photosystem II (NPQ), and photosynthetic control of Cyt b6f, within a two-leaf canopy scheme that differentiates sunlit and shaded responses. The results show that across flux-tower sites, BEPS-CB6F substantially improves SIF simulations, with RMSE and rRMSE reductions at more than 90% of sites, and yields moderate but consistent improvements in GPP, including higher R2 and reduced RMSE at over 80% of sites. The model alleviates GPP overestimation under low light, particularly in shaded leaves, and markedly reduces SIF overestimation under high irradiance in sunlit leaves. BEPS-CB6F further enhances performance during heat and high-VPD conditions. By explicitly representing temperature-responsive CEF and NPQ, it captures the strong midday suppression of GPP and SIF, including reductions in GPP and SIF during heatwaves. Sensitivity analyses indicate that Vqmax and β2 strongly influence GPP simulations, while β2 is the primary driver of SIF simulations. These results highlight the importance of mechanistic electron-transport representation and demonstrate the potential of CB6F to improve terrestrial biosphere model predictions of carbon uptake and fluorescence.

Satellite sun-induced chlorophyll fluorescence (SIF) has emerged as a powerful proxy with great potential for tracking global vegetation photosynthesis. Yet, structural heterogeneity within vegetation canopies complicates the translation of SIF into gross primary productivity (GPP). In this study, we integrate global multi-source datasets with a SIF angular normalization theory incorporating a 2-leaf modeling strategy to investigate how forest height regulates the SIF-GPP linkage at the global scale. We show that increasing forest height systematically decreases the escape probability of SIF photons, thereby weakening the observed SIF signals. Simultaneously, taller canopies shift the balance between sunlit and shaded leaf contributions, affecting both SIF emission and GPP partitioning. These findings highlight the dual role of forest height in modulating both the radiative transfer of SIF and the physiological partitioning of GPP. Our results emphasize the necessity of integrating forest height into satellite-based photosynthesis models and provide a theoretical and observational basis for refining GPP estimates from spaceborne SIF measurements.

Solar-induced chlorophyll fluorescence (SIF) is a promising tool to estimate gross primary production (GPP), but the retrieval of SIF is commonly noisy and highly sensitive to various interference factors. Particularly, the retrieval of SIF in the red band (RSIF) is more challenging than in the far-red SIF (FRSIF) due to the weaker fluorescence signal and the weaker absorption depth of oxygen at the red band compared with the far-red band. A comprehensive evaluation of all factors will allow a reproducible interpretation of SIF signals and advance the estimation of GPP from SIF. Recent studies have assessed the sensitivity of SIF retrieval to sensor characteristics, retrieval methods, and hardware specifications. However, none of these studies have systematically investigated the directional retrieval error of SIF resulting from the mismatch between irradiance measured above the canopy and the true irradiance reaching the canopy components viewed by a sensor. This study illustrated the effect of mismatched irradiance on the retrieval of RSIF using the commonly used standard 3FLD method based on SCOPE model simulations. The retrieval accuracy was highest in the hotspot direction, but it decreased as the observation direction was away from the hotspot. The relative root mean square error (RRMSE) was generally higher than 20 % in the forward directions. To reduce the retrieval error due to the mismatch effect, we proposed a modified 3FLD method (MFLD) by calculating the true irradiance reaching the canopy in a given direction based on geometric optical theory. The results showed that MFLD clearly improved the retrieval accuracy for RSIF, especially in the forward directions where RRMSE decreased by 10 % in most cases. For example, the RRMSE was reduced from 19.26 % to 5.50 % after mitigating the mismatch between the measured and actual solar irradiance, when the solar zenith angle was 40 degrees and viewing zenith angle was 30 degrees in the forward solar principal plane. Even at the nadir observation, the RRMSE was also reduced from 12.84 % to 5.64 %. In summary, MFLD can effectively mitigate the irradiance mismatch effect on the retrieval of RSIF. These results will improve our interpretation of the relationship between GPP and RSIF at different observation directions.

Objective Solar-induced chlorophyll fluorescence (SIF) is a valuable metric for assessing photosynthesis and vegetation stress. However, as SIF radiance constitutes less than 3% of the reflected canopy radiance, the spectral resolution of a spaceborne SIF detector should be below 0.3 nm. To ensure adequate signal-to-noise ratio (SNR), current spaceborne SIF imagers typically achieve spatial resolutions above 1 km. European Space Agency' FLEX (Fluorescence explorer) mission recommends spatial resolution below 300 m, particularly for monitoring field and forest areas in Europe. The complex terrain and vegetation types in China, however, demand even higher spatial resolutions. In this paper, we propose a mid-resolution ultraspectral imager (MIRUS), designed for satellite-based SIF detection at a spatial resolution of 100 m. To evaluate the SIF retrieval performance of MIRUS, we develop a model that leverages SIF imaging spectrometer (SIFIS) data to calculate SIF retrieval accuracy. Methods Given the weak SIF radiance relative to canopy reflectance, the design specifications for MIRUS are shown in Table 1. MIRUS employs a low F# optic and a Littrow-Offner spectrometer to improve irradiance on the focal plane array (FPA) and reduce chromatic aberration, as shown in Fig. 2. The modulation transfer function (MTF) is optimized to be greater than 0.9, as shown in Fig. 3, while smile and keystone distortions are controlled to below 1.0% pixels and 3.3% pixels, respectively, as shown in Table 3. The spectral resolution is set at 0.3 nm, with a convex grating designed using rigorous coupled-wave analysis (RWCA) (Fig. 5), achieving an average diffraction efficiency of 0.7 (Fig. 7). The mechanism is designed as shown in Fig. 8. A prototype of MIRUS is produced, combining a telescope, a spectrometer, and an FPA. The prototype's performance, including instrument line shape (ILS), spectral resolution, smile, keystone, and SNR, is tested, with results shown in Figs. 9-13. Results and Discussions To assess MIRUS's SIF detection performance, we build a model relating SIF retrieval accuracy to the SNR of the spectral imager. The spectral range and resolution of the SIFIS on the Goumang satellite are comparable to the designed performance of MIRUS, with SIFIS achieving a maximum spatial resolution of 0.375 kmx0.800 km in non-binning mode. In addition, we analyze 48 SIFIS image orbits from January to October in 2023, covering diverse environments such as tropical rainforests, savannas, deserts, and polar regions. Using singular value decomposition (SVD) in the 743-758 nm range, we measure retrieval errors across different radiance levels (Fig. 17) and develop an SNR model for SIFIS data (Fig. 16). The radiance of MIRUS within the same wavelength range is simulated using the MODTRAN 6.0 model, based on MIRUS's orbital parameters and typical atmospheric, aerosol, and ground albedo parameters, as shown in Table 4. Subsequently, the SNR for MIRUS is calculated from this radiance, as shown in Fig. 16. Finally, the SIF retrieval accuracy for MIRUS is evaluated using a polynomial function of SNR, with results shown in Fig. 18. The relative errors for SIF retrieval are 1.22%-1.38% for 100 m GSD mode and 0.69%-0.86% for the 200 m GSD mode. Conclusions SIF serves as a "probe" for photosynthetic activity. "The remote sensing of chlorophyll fluorescence is a rapidly advancing front in terrestrial vegetation science, with emerging capability in space-based methodologies and prospects for diverse applications," as noted by G. H. Mohammed. Due to the weak SIF radiance, it must be captured with an ultraspectral imager. Considering the imaging SNR, the spatial resolution of SIF radiance retrieved by spaceborne instruments typically exceeds 1 km. In this paper, we propose a mid-resolution spaceborne SIF detector featuring a small F# TMA and a Littrow-Offner spectrometer. The high-groove-density convex grating is designed using rigorous coupled-wave analysis (RCWA), resulting in significantly greater irradiance on the FPA compared to SIFIS. A prototype is produced and tested, achieving a full width at half maximum (FWHM) of 0.3 nm, a spectral sampling interval (SSI) of 0.1 nm, a smile distortion of less than 0.0035 nm, a keystone of under 0.06 pixel, and an SNR exceeding 206 at 10 mWm(-2)sr(-1)nm(-1). To evaluate the SIF retrieval accuracy of MIRUS, we develop a method to estimate accuracy based on the instrument's SNR. The designed spectral performance of SIFIS matches that of MIRUS, and the spatial resolution of SIFIS is comparable to MIRUS. A polynomial relationship between SNR and SIF retrieval accuracy is established using radiance data from SIFIS. After calculating the typical radiance received by MIRUS, we use the SNR model to determine its typical SNR. Finally, the SIF retrieval accuracy is calculated using the polynomial, yielding relative errors of 1.22%-1.38% for the 100 m GSD mode and 0.69%-0.86% for the 200 m GSD mode, comparable to SIFIS performance.

Solar-induced chlorophyll fluorescence (SIF) has emerged as a valuable tool for estimating gross primary production (GPP). However, the mechanism linking SIF to GPP under waterlogging stress remains unclear. Here, we investigated the GPP-SIF relationship and their responses to waterlogging stress using three years of continuous ground measurements in a maize field. Our results revealed a significant decoupling in the GPP-SIF relationship under waterlogging stress, as evidenced by a decline in R2 values from 0.87 and 0.79 (non-waterlogging years: 2020 and 2021) to 0.20 (waterlogging year:2022), consistent with SCOPE model simulations. We examined the underlying mechanisms independently regulating SIF and GPP fluctuations. Our analysis suggested a considerable transition in dominating factors influencing both parameters, shifting from photosynthetically active radiation (PAR) under non-waterlogging conditions to soil water content (SWC) under waterlogging stress. Notably, we quantified the impact of elevated SWC on GPP and SIF, finding that the effect was more pronounced on GPP (62.41% reduction) than on SIF (54.3% reduction). We observed the weakened significance of SIF mutiscattering components induced by alterations in soil background spectra due to increased SWC affecting SIF radiative transfer processes. Complemented by SCOPE simulations, our analysis suggested that the significant decoupling of SIF and GPP physiological components, along with asymmetrical responses to SWC, collectively contribute to the reduced GPP-SIF relationship under waterlogging stress. Overall, our study provides valuable insights into GPP and SIF dynamics under waterlogging stress in a maize field, emphasizing the effectiveness of radiative transfer models for understanding plant photosynthetic responses to waterlogging stress.

Terrestrial gross primary production (GPP) quantifies the rate of CO 2 fixation in ecosystems through photosynthesis. The optimal temperature ( T opt ) is one of fundamental determinants in the vegetation photosynthesis model (VPM) model that constrains the temperature response of light use efficiency (LUE) under changing environmental conditions. However, the VPM model uses the biome‐specific T opt without considering the spatial variation in T opt . Therefore, this study estimated the global optimal temperature ( T opt ) from the fluorescence efficiency (Φ F , a proxy of LUE) and adjusted the VPM model by using the spatially resolved T opt . Our results reported that Φ F ‐derived T opt is well consistent with LUE‐derived T opt . In addition, the improvement in R 2 reached up to 0.05 for sites with a difference between the biome‐specific T opt and Φ F ‐derived T opt larger than 5°C. Our results highlight the potential of the adjusted VPM model for studying the global carbon cycle and their responses to the future warming.

Heat and water stress induce structural and physiological changes in plants that can become decoupled within a diurnal cycle due to faster physiological responses. Understanding these physiological responses can improve the large-scale modeling of photosynthesis and evapotranspiration. Satellite solar-induced chlorophyll fluorescence observations (SIFobs) provide both structural and physiological information and are recognized as a reliable indicator for monitoring plans heat and water stresses at large scales. However, the diurnal responses of large-scale SIFobs and its physiological component, fluorescence efficiency (Φf), to heat and water stresses remain unclear. In this study, we used data from Orbiting Carbon Observatory-3 (OCO-3) and combined a machine learning technique with the near-infrared radiance of vegetation (NIRvR) approach to model four years of hourly SIFobs and Φf data for summer seasons across mainland China. Statistical analyses of the modeled outputs were conducted to investigate the diurnal variations of SIFobs and Φf under varying water and heat stress conditions. Additionally, by comparing modeled SIFobs variations at different times of the day, we also attempted to investigate the uncertainties in assessing changes in the daily average SIFobs (ΔSIFdaily) when using daily correction factors to convert polar-orbiting satellite SIFobs into the daily averages (SIFdaily). Our results revealed that SIFobs and Φf at different times of day exhibited different variations under water and heat stress conditions, both in magnitude and sign, especially in forests. Morning and afternoon SIFobs generally exhibited larger positive or smaller negative responses than the midday period. In contrast, the morning Φf also exhibited larger positive or smaller negative responses than the midday period, but the opposite pattern was found for the afternoon Φf. Such diurnal differences in SIFobs and Φf responses became more pronounced on days with higher water and heat stresses. Additionally, morning polar-orbiting satellite SIF observations tended to overestimate ΔSIFdaily, whereas midday observations tended to underestimate it. Such biases also intensified with rising daily water and heat stress levels. Our findings broaden the understanding of the diurnal responses of SIF and especially Φf to varying heat and water stresses. The results also highlight the importance of observation time in monitoring plant water and heat stresses from polar-orbiting satellite SIF observations.

Forest biodiversity plays a critical role in sustaining ecosystem functioning and buffering the effects of increased extreme weather events on forests. A global assessment of the relationship between biodiversity and photosynthesis in natural forest ecosystems, however, remains elusive. We used a large dataset of the richness of tree species from a large number of globally distributed forest plots combined with satellite retrievals of sun-induced chlorophyll fluorescence, a novel proxy for photosynthesis, to evaluate the relationship between forest biodiversity and photosynthesis and its biological mechanisms at the global scale. We found that species richness and photosynthesis were often positively correlated at the global scale, with stronger relationships in tropical forests but weaker associations in high-latitude regions. This positive relationship was mainly driven by a larger role of species richness in increasing maximal photosynthesis than in prolonging the growing season. We also found that higher light capture by increasing the complexity of community structure was the basis of this increase in forest photosynthesis. Forests with high species richness also showed higher foliar nitrogen concentrations and the maximum rate of ribulose 1,5-bisphosphate carboxylase/oxygenase carboxylation, which are two crucial traits determining photosynthetic capacity. Our observation-based findings of ecosystem carbon uptake responses to changes in biodiversity suggest that the loss of biodiversity may jeopardize ecosystem carbon uptake and the terrestrial carbon sink, and will provide important constraints to Earth-system models. Forests with higher tree species richness show greater photosynthesis by capturing more sunlight, highlighting the essential role of biodiversity in enhancing carbon uptake and supporting the global carbon sink.

Gross primary productivity (GPP) is more accurately estimated by total canopy solar-induced chlorophyll fluorescence (SIFtotal) compared to raw sensor observed SIF signals (SIFobs). The use of two-leaf strategy, which distinguishes between SIF from sunlit (SIFsunlit) and shaded (SIFshaded) leaves, further improves GPP estimates. However, the two-leaf strategy, along with SIF corrections for bidirectional effects, has not been applied to transpiration (T) estimation. In this study, we used the angular normalization method to correct the bidirectional effects and separate SIFsunlit and SIFshaded. Then we developed SIFsunlit and SIFshaded driven semi-mechanistic and hybrid models, comparing their T estimates with those from a SIFobs driven semi-mechanistic model at both site and global scales. All three types of SIF-driven T models integrate canopy conductance (g(c)) with the Penman-Monteith model, differing in how g(c) is derived: from a SIFobs driven semi-mechanistic equation, a SIFsunlit and SIFshaded driven semi-mechanistic equation, and a SIFsunlit and SIFshaded driven machine learning model. When evaluated against partitioned T using the underlying water use efficiency method at 72 eddy covariance sites and two global T remote sensing products, a consistent pattern emerged: SIFsunlit and SIFshaded driven hybrid model > SIFsunlit and SIFshaded driven semi-mechanistic model > SIFobs driven semi-mechanistic model. The SIFsunlit and SIFshaded driven hybrid model demonstrated a notable proficiency under high vapor pressure deficit and low soil water content conditions. The SIFobs driven semi-mechanistic model tends overestimate T at low T values, and this issue is significantly alleviated by the SIFsunlit and SIFshaded driven semi-mechanistic and hybrid models. Our findings demonstrate that correcting the bidirectional effects and using the two-leaf strategy on GPP estimation can improve T estimation and provide a new global T product incorporating vegetation physiological signal.

In recent years, solar‐induced chlorophyll fluorescence (SIF) has shown great potential for monitoring terrestrial photosynthesis, but existing satellite SIF retrievals typically feature coarse spatial resolutions on the order of kilometers or larger. Recently, the Chinese Terrestrial Ecosystem Carbon Inventory Satellite, Goumang, launched in August 2022, carries a unique SIF Imaging Spectrometer (SIFIS), the first spaceborne sensor especially designed for global SIF retrieval. SIFIS provides a high spatial resolution (370 × 800 m) and high spectral resolution (0.24, 664–786 nm), enabling the same order of SIF retrieval error (∼0.48 mW/m 2 /nm/sr) as other satellite SIF products. The SIFIS radiance measurement and SIF retrievals were first validated using the airborne AisaIBIS data. SIFIS SIF showed high spatial and temporal agreement with independent satellite SIF data sets and high correlations with flux tower estimates of gross primary production (R 2 = 0.87). This new SIF product opens new avenues for studying fine‐scale photosynthesis from space.

Satellite observations of solar-induced chlorophyll fluorescence (SIF) offer a promising approach for monitoring plant heat and water stresses across spatial scales. Most studies have focused on seasonal responses of satelliteobserved SIF and its physiological component, fluorescence efficiency (hf), to heat and water stresses. However, their diurnal responses remain poorly understood. Besides, polar-orbiting satellites typically use a daily correction factor to upscale fixed-time SIF observations into daily averages (SIFdaily). Given the diurnal SIF variations, the reliability of this approach under stress conditions is uncertain. In this study, we used the ratio of SIF to near-infrared radiance of vegetation (NIRvR) as a a linear approximation of hf. Then, we applied the eXtreme Gradient Boosting (XGBoost) algorithm to model hourly observations of summer (June-August) SIF and hf from Orbiting Carbon Observatory-3 (OCO-3) data. Our modeling was constrained to mainland China from 2019 to 2022. We calculated anomalies in SIF and hf at different times of the day and conducted linear regressions on them with daily air temperature or soil moisture anomalies. Our results showed that the responses of SIF and hf to stresses varied significantly with time of day and vegetation type. On days experiencing high water and heat stresses, morning and afternoon SIF exhibited weaker declines than midday. In contrast, morning hf generally exhibited stronger increases than midday, whereas afternoon hf showed weaker increases. Such diurnal differences in SIF and hf responses were more pronounced in forests than in grasslands and intensified with rising water and heat stress levels. Additionally, we found that morning polar-orbiting satellite SIF observations tended to overestimate SIFdaily changes, whereas midday observations tended to underestimate them. These biases also intensified with rising stress levels. Our findings emphasize the importance of diurnal satellite SIF observations in deepening our understanding of plant responses to water and heat stress, as well as in improving the monitoring of plant stresses.

Understanding how ecosystems respond to ubiquitous microplastic (MP) pollution is crucial for ensuring global food security. Here, we conduct a multiecosystem meta-analysis of 3,286 data points and reveal that MP exposure leads to a global reduction in photosynthesis of 7.05 to 12.12% in terrestrial plants, marine algae, and freshwater algae. These reductions align with those estimated by a constructed machine learning model using current MP pollution levels, showing that MP exposure reduces the chlorophyll content of photoautotrophs by 10.96 to 12.84%. Model estimates based on the identified MP-photosynthesis nexus indicate annual global losses of 4.11 to 13.52% (109.73 to 360.87 MT·y −1 ) for main crops and 0.31 to 7.24% (147.52 to 3415.11 MT C·y −1 ) for global aquatic net primary productivity induced by MPs. Under scenarios of efficient plastic mitigation, e.g., a ~13% global reduction in environmental MP levels, the MP-induced photosynthesis losses are estimated to decrease by ~30%, avoiding a global loss of 22.15 to 115.73 MT·y −1 in main crop production and 0.32 to 7.39 MT·y −1 in seafood production. These findings underscore the urgency of integrating plastic mitigation into global hunger and sustainability initiatives.