Here, we present a conceptual and quantitative model to describe the role of the Cytochrome b(6)f complex in controlling steady-state electron transport in C-3 leaves. The model is based on new experimental methods to diagnose the maximum activity of Cyt b(6)f in vivo, and to identify conditions under which photosynthetic control of Cyt b(6)f is active or relaxed. With these approaches, we demonstrate that Cyt b(6)f controls the trade-off between the speed and efficiency of electron transport under limiting light, and functions as a metabolic switch that transfers control to carbon metabolism under saturating light. We also present evidence that the onset of photosynthetic control of Cyt b(6)f occurs within milliseconds of exposure to saturating light, much more quickly than the induction of non-photochemical quenching. We propose that photosynthetic control is the primary means of photoprotection and functions to manage excitation pressure, whereas non-photochemical quenching functions to manage excitation balance. We use these findings to extend the Farquhar et al. (Planta 149:78-90, 1980) model of C-3 photosynthesis to include a mechanistic description of the electron transport system. This framework relates the light captured by PS I and PS II to the energy and mass fluxes linking the photoacts with Cyt b(6)f, the ATP synthase, and Rubisco. It enables quantitative interpretation of pulse-amplitude modulated fluorometry and gas-exchange measurements, providing a new basis for analyzing how the electron transport system coordinates the supply of Fd, NADPH, and ATP with the dynamic demands of carbon metabolism, how efficient use of light is achieved under limiting light, and how photoprotection is achieved under saturating light. The model is designed to support forward as well as inverse applications. It can either be used in a stand-alone mode at the leaf-level or coupled to other models that resolve finer-scale or coarser-scale phenomena.

High temperature and accompanying high vapor pressure deficit often stress plants without causing distinctive changes in plant canopy structure and consequential spectral signatures. Sun-induced chlorophyll fluorescence (SIF), because of its mechanistic link with photosynthesis, may better detect such stress than remote sensing techniques relying on spectral reflectance signatures of canopy structural changes. However, our understanding about physiological mechanisms of SIF and its unique potential for physiological stress detection remains less clear. In this study, we measured SIF at a high-temperature experiment, Temperature Free-Air Controlled Enhancement, to explore the potential of SIF for physiological investigations. The experiment provided a gradient of soybean canopy temperature with 1.5, 3.0, 4.5, and 6.0 degrees C above the ambient canopy temperature in the open field environments. SIF yield, which is normalized by incident radiation and the fraction of absorbed photosynthetically active radiation, showed a high correlation with photosynthetic light use efficiency (r = 0.89) and captured dynamic plant responses to high-temperature conditions. SIF yield was affected by canopy structural and plant physiological changes associated with high-temperature stress (partial correlation r = 0.60 and -0.23). Near-infrared reflectance of vegetation, only affected by canopy structural changes, was used to minimize the canopy structural impact on SIF yield and to retrieve physiological SIF yield (phi(F)) signals. phi(F) further excludes the canopy structural impact than SIF yield and indicates plant physiological variability, and we found that phi(F) outperformed SIF yield in responding to physiological stress (r = -0.37). Our findings highlight that phi(F) sensitively responded to the physiological downregulation of soybean gross primary productivity under high temperature. phi(F), if reliably derived from satellite SIF, can support monitoring regional crop growth and different ecosystems' vegetation productivity under environmental stress and climate change.

Our study suggests that the global CO2 fertilization effect (CFE) on vegetation photosynthesis has declined during the past four decades. The Comments suggest that the temporal inconsistency in AVHRR data and the attribution method undermine the results' robustness. Here, we provide additional evidence that these arguments did not affect our finding and that the global decline in CFE is robust.

Here, we describe a model of C-3, C-3-C-4 intermediate, and C-4 photosynthesis that is designed to facilitate quantitative analysis of physiological measurements. The model relates the factors limiting electron transport and carbon metabolism, the regulatory processes that coordinate these metabolic domains, and the responses to light, carbon dioxide, and temperature. It has three unique features. First, mechanistic expressions describe how the cytochrome b(6)f complex controls electron transport in mesophyll and bundle sheath chloroplasts. Second, the coupling between the mesophyll and bundle sheath expressions represents how feedback regulation of Cyt b(6)f coordinates electron transport and carbon metabolism. Third, the temperature sensitivity of Cyt b(6)f is differentiated from that of the coupling between NADPH, Fd, and ATP production. Using this model, we present simulations demonstrating that the light dependence of the carbon dioxide compensation point in C-3-C-4 leaves can be explained by co-occurrence of light saturation in the mesophyll and light limitation in the bundle sheath. We also present inversions demonstrating that population-level variation in the carbon dioxide compensation point in a Type I C-3-C-4 plant, Flaveriachloraefolia, can be explained by variable allocation of photosynthetic capacity to the bundle sheath. These results suggest that Type I C-3-C-4 intermediate plants adjust pigment and protein distributions to optimize the glycine shuttle under different light and temperature regimes, and that the malate and aspartate shuttles may have originally functioned to smooth out the energy supply and demand associated with the glycine shuttle. This model has a wide range of potential applications to physiological, ecological, and evolutionary questions.

Sun-induced chlorophyll fluorescence (SIF) measurements have shown unique potential for quantifying plant physiological stress. However, recent investigations found canopy structure and radiation largely control SIF, and physiological relevance of SIF remains yet to be fully understood. This study aims to evaluate whether the SIF-derived physiological signal improves quantification of crop responses to environmental stresses, by analyzing data at three different spatial scales within the U.S. Corn Belt, i.e. experiment plot, field, and regional scales, where ground-based portable, stationary and space-borne hyperspectral sensing systems are used, respectively. We found that, when controlling for variations in incoming radiation and canopy structure, crop SIF signals can be decomposed into non-physiological (i.e. canopy structure and radiation, 60% similar to 82%) and physiological information (i.e. physiological SIF yield, Phi(F), 17% similar to 31%), which confirms the contribution of physiological variation to SIF. We further evaluated whether Phi(F) indicated plant responses under high-temperature and high vapor pressure deficit (VPD) stresses. The plot-scale data showed that phi(F) responded to the proxy for physiological stress (partial correlation coefficient, r(p)= 0.40, p< 0.001) while non-physiological signals of SIF did not respond (p> 0.1). The field-scale Phi(F) data showed water deficit stress from the comparison between irrigated and rainfed fields, and Phi(F) was positively correlated with canopy-scale stomatal conductance, a reliable indicator of plant physiological condition (correlation coefficient r= 0.60 and 0.56 for an irrigated and rainfed sites, respectively). The regional-scale data showed Phi(F) was more strongly correlated spatially with air temperature and VPD (r= 0.23 and 0.39) than SIF (r= 0.11 and 0.34) for the U.S. Corn Belt. The lines of evidence suggested that Phi(F) reflects crop physiological responses to environmental stresses with greater sensitivity to stress factors than SIF, and the stress quantification capability of Phi(F) is spatially scalable. Utilizing Phi(F) for physiological investigations will contribute to improve our understanding of vegetation responses to high-temperature and high-VPD stresses.

Sun-induced chlorophyll fluorescence (SIF) is a promising new tool for remotely estimating photosynthesis. However, the degree to which incoming solar radiation and the structure of the canopy rather than leaf physiology contribute to SIF variations is still not well characterized. Therefore, we investigated relationships between SIF and variables that at least partly capture the canopy structure component of SIF. For this, we relied on high-quality SIF observations from ground-based instruments, high-resolution airborne SIF imagery and the most recent satellite SIF products to cover large ranges in spatial and temporal resolution and diverse ecosystems. We found that the canopy structure-related near-infrared reflectance of vegetation multiplied by incoming sunlight (NIRvP) is a robust proxy for far-red SIF across a wide range of spatial and temporal scales. Our findings indicate that contributions from leaf physiology to SIF variability are small compared to the structure and radiation components. Also, NIRvP captured spatio-temporal patterns of canopy photosynthesis better than SIF, which seems to be mostly due to the greater retrieval noise of SIF. Compared to other relevant structural SIF proxies, NIRvP showed more robust relationships to SIF, especially at the global scale. Our results highlight the promise of using widely available NIRvP data for vegetation monitoring and also indicate the potential of using SIF and NIRvP in combination to extract physiological information from SIF.

Remote sensing of solar-induced fluorescence (SIF) opens a new window for quantifying a key ecological variable, the terrestrial ecosystem gross primary production (GPP), because of the revealed strong SIF-GPP correlation. However, similar to many other remotely sensed metrics, SIF observations suffer from the sun-sensor geometry effects, which may have important impacts on the SIF-GPP relationship but remain poorly understood. Here we used remotely sensed SIF, globally distributed tower GPP data, and a mechanistic model to provide a systematic analysis. Our results reveal that leaf physiology, canopy structure, and sun-sensor geometries all affect the SIF-GPP relationship. In particular, we found that SIF observations in the sun-tracking hotspot direction can be a better proxy of GPP due to the similar responses of light use efficiency and SIF escaping probability in the hotspot direction to the increasing incoming solar radiation. Such conclusions are supported by a variety of modeling simulations and satellite observations over various plant function types, at different time scales and with satellite observational modes. This study demonstrates the potential and advantage of normalizing SIF observations to the hotspot direction for better global GPP estimations. This study also demonstrates the great potentials of current and future spaceborne sun-tracking satellite missions for a significant improvement in measuring and monitoring, at a wide range of spatial and temporal scales, the changes in terrestrial ecosystem GPP in response to anticipated changes in the Earth's environmental conditions.

Solar-induced chlorophyll fluorescence (SIF) provides us with new opportunities to understand the physiological and structural dynamics of vegetation from leaf to global scales. However, the relationships between SIF and gross primary productivity (GPP) are not fully understood, which is mainly due to the challenges of decoupling structural and physiological factors that control the relationships. Here, we report the results of continuous observations of canopy-level SIF, GPP, absorbed photosynthetically active radiation (APAR), and chlorophyll: carotenoid index (CCI) in a temperate evergreen needleleaf forest. To understand the mechanisms underlying the relationship between GPP and SIF, we investigated the relationships of light use efficiency (LUEp), chlorophyll fluorescence yield (?F), and the fraction of emitted SIF photons escaping from the canopy (fesc) separately. We found a strongly non-linear relationship between GPP and SIF at diurnal and seasonal time scales (R2 = 0.91 with a hyperbolic regression function, daily). GPP saturated with APAR, while SIF did not. Also, there were differential responses of LUEp and ?F to air temperature. While LUEp reached saturation at high air temperatures, ?F did not saturate. We found that the canopy-level chlorophyll: carotenoid index was strongly correlated to canopy-level ?F (R2 = 0.84) implying that ?F could be more closely related to pigment pool changes rather than LUEp. In addition, we found that the fesc contributed to a stronger SIF-GPP relationship by partially capturing the response of LUEp to diffuse light. These findings can help refine physiological and structural links between canopy-level SIF and GPP in evergreen needleleaf forest.

Disentangling the individual contributions from vegetation and soil in measured canopy reflectance is a grand challenge to the remote sensing and ecophysiology communities. Since Solar Induced chlorophyll Fluorescence (SIF) is uniquely emitted from vegetation, it can be used to evaluate how well reflectance-based vegetation indices (VIs) can separate the vegetation and soil components. Due to the residual soil background contributions, Near-infrared (NIR) reflectance of vegetation (NIRv) and Difference Vegetation index (DVI) present offsets when compared to SIF (i.e., the value of NIRv or DVI is non-zero when SIF is zero). In this study, we proposed a simple framework for estimating the true NIR reflectance of vegetation from Hyperspectral measurements (NIRvH) with minimal soil impacts. NIRvH takes advantage of the spectral shape variations in the red-edge region to minimize the soil effects. We evaluated the capability of NIRvH, NIRv and DVI in isolating the true NIR reflectance of vegetation using the data from both the model-based simulations and Hyperspectral Plant imaging spectrometer (HyPlant) measurements. Benchmarked by simultaneously measured SIF, NIRvH has the smallest offset (0-0.037), as compared to an intermediate offset of 0.047-0.062 from NIRv, and the largest offset of 0.089-0.112 from DVI. The magnitude of the offset can vary with different soil reflectance spectra across spatio-temporal scales, which may lead to bias in the downstream NIRv-based photosynthesis estimates. NIRvH and SIF measurements from the same sensor platform avoided complications due to different geometry, footprint and time of observation across sensors when studying the radiative transfer of reflected photons and SIF. In addition, NIRvH was primarily determined by canopy structure rather than chlorophyll content and soil brightness. Our work showcases that NIRvH is promising for retrieving canopy structure parameters such as leaf area index and leaf inclination angle, and for estimating fluorescence yield with current and forthcoming hyperspectral satellite measurements.

Reliable spatiotemporal crop data are vital for sustainable agricultural management. However, efficient algorithms that can be automatically applied to large regions are scarce, especially for cash crops, since it is hard to distinguish their uniqueness merely from temporal profiles of traditional vegetation indices. The efficiency of knowledge-based temporal features and red-edge pigment indices in characterizing crop growth has been reported in the literature, but the potential of combined applications in identifying crops has not been validated yet. This study fills this gap by developing a knowledge-based automated Peanut mapping Algorithm with a combined consideration of crop Phenology and Pigment content variations (PAPP). Peanut crop has earlier and longer flowering stages compared to other crops such as paddy rice and maize. Peanut fields are distinguished with less variations in anthocyanin and chlorophyll as well as higher carotenoid concentrations. Herein, three phenology and pigment-based indicators were proposed for peanut mapping by exploring the concentration and variations of the chlorophyll, anthocyanin and carotenoid indices, respectively. This PAPP algorithm was validated over large regions (around 250 thousand km(2) cropland) covering three provinces of Northeast China using Sentinel-2 time-series images. The results reported that there was 8,371 km(2) peanut area in Northeast China in 2018, concentrated in the western Jilin and Liaoning provinces. Validation from the 1,102 field survey sites revealed overall accuracies of 94%, with a kappa index of 0.87 and F-1 score of 0.91. The PAPP algorithm was not sensitive to thresholding, and a high classification accuracy could be obtained once the threshold of one indicator was roughly defined. The thresholds could be determined based on the proportions of staple crops (i.e. paddy rice and maize) using the historical agricultural statistical data since peanut fields either show the least or largest values in these three proposed indicators. The PAPP algorithm demonstrates the capabilities of automatic peanut mapping over large regions with no requirements of further training and modifications. This study makes contributions to a sustainable agricultural management society given the potential significant role of legume crops in co-delivering food security and adapting to climate change.