Modelling sun-induced fluorescence and photosynthesis with a land surface model at local and regional scales in northern Europe

Recent satellite observations of sun-induced chlorophyll fluorescence (SIF) are thought to provide a large-scale proxy for gross primary production (GPP), thus providing a new way to assess the performance of land surface models (LSMs). In this study, we assessed how well SIF is able to predict GPP in the Fenno-Scandinavian region and what potential limitations for its application exist. We implemented a SIF model into the JSBACH LSM and used active leaf-level chlorophyll fluorescence measurements (Chl F) to evaluate the performance of the SIF module at a coniferous forest at Hyytiala, Finland. We also compared simulated GPP and SIF at four Finnish micrometeorological flux measurement sites to observed GPP as well as to satellite-observed SIF. Finally, we conducted a regional model simulation for the Fenno-Scandinavian region with JSBACH and compared the results to SIF retrievals from the GOME-2 (Global Ozone Monitoring Experiment-2) space-borne spectrometer and to observation-based regional GPP estimates. Both observations and simulations revealed that SIF can be used to estimate GPP at both site and regional scales. At regional scale the model was able to simulate observed SIF averaged over 5 years with r(2) of 0.86. The GOME-2-based SIF was a better proxy for GPP than the remotely sensed fA-PAR (fraction of absorbed photosynthetic active radiation by vegetation). The observed SIF captured the seasonality of the photosynthesis at site scale and showed feasibility for use in improving of model seasonality at site and regional scale.Peer reviewe

New Methods for Measurements of Photosynthesis from Space

Our ability to close the Earth's carbon budget and predict feedbacks in a warming climate depends critically on knowing where, when, and how carbon dioxide (CO2) is exchanged between the land and atmosphere. In particular, determining the rate of carbon fixation by the Earth's biosphere (commonly referred to as gross primary productivity, or GPP) and the dependence of this productivity on climate is a central goal. Historically, GPP has been inferred from spectral imagery of the land and ocean. Assessment of GPP from the color of the land and ocean requires, however, additional knowledge of the types of plants in the scene, their regulatory mechanisms, and climate variables such as soil moisture—just the independent variables of interest! Sunlight absorbed by chlorophyll in photosynthetic organisms is mostly used to drive photosynthesis, but some can also be dissipated as heat or re‐radiated at longer wavelengths (660–800 nm). This near‐infrared light re‐emitted from illuminated plants is termed solarinduced fluorescence (SIF), and it has been found to strongly correlate with GPP. To advance our understanding of SIF and its relation to GPP and environmental stress at the planetary scale, the Keck Institute for Space Studies (KISS) convened a workshop—held in Pasadena, California, in August 2012—to focus on a newly developed capacity to monitor chlorophyll fluorescence from terrestrial vegetation by satellite. This revolutionary approach for retrieving global observations of SIF promises to provide direct and spatially resolved information on GPP, an ideal bottom‐up complement to the atmospheric net CO2 exchange inversions. Workshop participants leveraged our efforts on previous studies and workshops related to the European Space Agency’s FLuorescence EXplorer (FLEX) mission concept, which had already targeted SIF for a possible satellite mission and had developed a vibrant research community with many important publications. These studies, mostly focused on landscape, canopy, and leaf‐level interpretation, provided the ground‐work for the workshop, which focused on the global carbon cycle and synergies with atmospheric net flux inversions. Workshop participants included key members of several communities: plant physiologists with experience using active fluorescence methods to quantify photosynthesis; ecologists and radiative transfer experts who are studying the challenge of scaling from the leaf to regional scales; atmospheric scientists with experience retrieving photometric information from space‐borne spectrometers; and carbon cycle experts who are integrating new observations into models that describe the exchange of carbon between the atmosphere, land and ocean. Together, the participants examined the link between “passive” fluorescence observed from orbiting spacecraft and the underlying photochemistry, plant physiology and biogeochemistry of the land and ocean. This report details the opportunity for forging a deep connection between scientists doing basic research in photosynthetic mechanisms and the more applied community doing research on the Earth System. Too often these connections have gotten lost in empiricism associated with the coarse scale of global models. Chlorophyll fluorescence has been a major tool for basic research in photosynthesis for nearly a century. SIF observations from space, although sensing a large footprint, probe molecular events occurring in the leaves below. This offers an opportunity for direct mechanistic insight that is unparalleled for studies of biology in the Earth System. A major focus of the workshop was to review the basic mechanisms that underlie this phenomenon, and to explore modeling tools that have been developed to link the biophysical and biochemical knowledge of photosynthesis with the observable—in this case, the radiance of SIF—seen by the satellite. Discussions led to the identification of areas where knowledge is still lacking. For example, the inability to do controlled illumination observations from space limits the ability to fully constrain the variables that link fluorescence and photosynthesis. Another focus of the workshop explored a “top‐down” view of the SIF signal from space. Early studies clearly identified a strong correlation between the strength of this signal and our best estimate of the rate of photosynthesis (GPP) over the globe. New studies show that this observation provides improvements over conventional reflectance‐based remote sensing in detecting seasonal and environmental (particularly drought related) modulation of photosynthesis. Apparently SIF responds much more quickly and with greater dynamic range than typical greenness indices when GPP is perturbed. However, discussions at the workshop also identified areas where top‐down analysis seemed to be “out in front” of mechanistic studies. For example, changes in SIF based on changes in canopy light interception and the light use efficiency of the canopy, both of which occur in response to drought, are assumed equivalent in the top‐down analysis, but the mechanistic justification for this is still lacking from the bottom‐up side. Workshop participants considered implications of these mechanistic and empirical insights for large‐scale models of the carbon cycle and biogeochemistry, and also made progress toward incorporating SIF as a simulated output in land surface models used in global and regional‐scale analysis of the carbon cycle. Comparison of remotely sensed SIF with modelsimulated SIF may open new possibilities for model evaluation and data assimilation, perhaps leading to better modeling tools for analysis of the other retrieval from GOSAT satellite, atmospheric CO2 concentration. Participants also identified another application for SIF: a linkage to the physical climate system arising from the ability to better identify regional development of plant water stress. Decreases in transpiration over large areas of a continent are implicated in the development and “locking‐in” of drought conditions. These discussions also identified areas where current land surface models need to be improved in order to enable this research. Specifically, the radiation transport treatments need dramatic overhauls to correctly simulate SIF. Finally, workshop participants explored approaches for retrieval of SIF from satellite and ground‐based sensors. The difficulty of resolving SIF from the overwhelming flux of reflected sunlight in the spectral region where fluorescence occurs was once a major impediment to making this measurement. Placement of very high spectral resolution spectrometers on GOSAT (and other greenhouse gas–sensing satellites) has enabled retrievals based on infilling of solar Fraunhofer lines, enabling accurate fluorescence measurements even in the presence of moderately thick clouds. Perhaps the most interesting challenge here is that there is no readily portable ground‐based instrumentation that even approaches the capability of GOSAT and other planned greenhouse gas satellites. This strongly limits scientists’ ability to conduct ground‐based studies to characterize the footprint of the GOSAT measurement and to conduct studies of radiation transport needed to interpret SIF measurement. The workshop results represent a snapshot of the state of knowledge in this area. New research activities have sprung from the deliberations during the workshop, with publications to follow. The introduction of this new measurement technology to a wide slice of the community of Earth System Scientists will help them understand how this new technology could help solve problems in their research, address concerns about the interpretation, identify future research needs, and elicit support of the wider community for research needed to support this observation. Somewhat analogous to the original discovery that vegetation indices could be derived from satellite measurements originally intended to detect clouds, the GOSAT observations are a rare case in which a (fortuitous) global satellite dataset becomes available before the research community had a consolidated understanding on how (beyond an empirical correlation) it could be applied to understanding the underlying processes. Vegetation indices have since changed the way we see the global biosphere, and the workshop participants envision that fluorescence can perform the next indispensable step by complementing these measurements with independent estimates that are more indicative of actual (as opposed to potential) photosynthesis. Apart from the potential FLEX mission, no dedicated satellite missions are currently planned. OCO‐2 and ‐3 will provide much more data than GOSAT, but will still not allow for regional studies due to the lack of mapping capabilities. Geostationary observations may even prove most useful, as they could track fluorescence over the course of the day and clearly identify stress‐related down‐regulation of photosynthesis. Retrieval of fluorescence on the global scale should be recognized as a valuable tool; it can bring the same quantum leap in our understanding of the global carbon cycle as vegetation indices once did

Terrestrial vegetation-water interactions in observations and models

Im Zusammenhang mit dem globalen Klimawandel ist die Vegetation besonders wichtig, da sie die anthropogenen CO2-Emissionen aufnehmen und den Wasser- und Energiekreislauf regulieren kann. Während frühere Forschungsarbeiten wertvolle Einblicke in langfristige Veränderungen des Grüns der Vegetation und in Bezug auf die Reaktion der Vegetation auf steigende Temperaturen und atmosphärisches CO2 lieferten, sind die Wechselwirkungen zwischen Vegetation und Wasser noch immer nicht vollständig verstanden. Tatsächlich hat die Dynamik der Bodenfeuchte in der Wurzelzone einen grundlegenden Einfluss auf die Veränderung des Grüns und die Produktivität der Vegetation. Dennoch sind weder die die Empfindlichkeit der Vegetationsproduktivität gegenüber der Bodenwasserversorgung noch die funktionelle Reaktion der Vegetation (d. h. Photosynthese und Transpiration) auf Bodentrockenheitsepisoden auf globaler Ebene vollständig geklärt worden. Forschungsengpässe sind fehlende globale Beobachtungen von Vegetationsfunktion und Bodenwasservariabilität. Außerdem werden die statistischen Instrumente für die Analyse umfangreicher und vielschichtiger Daten nur unzureichend genutzt, was ein besseres Verständnis der globalen Reaktion der Vegetation auf Wasser verhindert. Gleichzeitig trägt eine bessere Kenntnis der Reaktion der Vegetation auf die Wasserversorgung zu einem besseren Verständnis des terrestrischen Wasserkreislaufs bei. Hydrologische Extremereignisse schädigen die Infrastruktur, können das menschliche Wohlergehen beeinträchtigen und treten Berichten zufolge in vielen Regionen der Welt immer häufiger und intensiver auf. Während ein Konsens über die Bedeutung meteorologischer Faktoren für die Regulierung des Wasserkreislaufs und der damit verbundenen Extremereignisse besteht, ist die Rolle der Vegetationsdynamik und -eigenschaften noch nicht ausreichend erforscht. Ihre stärkere Berücksichtigung in hydrologischen Studien bietet das Potenzial, die Prozesse, die hydrologische Extreme antreiben, genauer zu verstehen. Dadurch kann ein besseres Verständnis der Wechselwirkungen zwischen Vegetation und Wasser im Hinblick auf die Wasserempfindlichkeit der Vegetation und die Rückkopplung der Vegetation auf Klimaextreme die Genauigkeit der Landoberflächenmodellierung verbessern, was für die Verbesserung der Klimaprojektionen unerlässlich ist. Dank der jüngsten Entwicklungen im Bereich der Erdbeobachtung und der Anwendbarkeit leistungsfähiger statistischer Analysewerkzeuge ist es nun möglich, globale Wechselwirkungen zwischen Vegetation und Wasser mit noch nie dagewesener Genauigkeit zu untersuchen. In diesem Zusammenhang stützt sich diese Arbeit insbesondere auf (i) neuartige Datenprodukte wie sonneninduzierte Chlorophyllfluoreszenz oder globale Bodenfeuchte und Evapotranspiration, die aus der Hochskalierung von Stationsmessungen mit Algorithmen des maschinellen Lernens gewonnen wurden, (ii) längere Aufzeichnungen und aktualisierte Aufbereitungen etablierter Datenprodukte wie Blattflächenindex und terrestrische Wasserspeicherung und (iii) die Entwicklung erklärbarer Methoden des maschinellen Lernens, mit denen Informationen effizient aus multivariaten Datenströmen abgeleitet werden können und die darüber hinaus leicht implementier- und in ökohydrologischen Studien anwendbar sind. Basierend auf diesen Datensätzen und Werkzeugen, wird in dieser Arbeit die Empfindlichkeit der globalen Vegetation gegenüber der Bodenwasserversorgung über Raum und Zeit hinweg neu untersucht.:Summary 7 Zusammenfassung 11 1 Introduction 15 1.1 Motivation 16 1.2 Terrestrial vegetation and its relationship with water supply 18 1.2.1 Vegetation functioning 18 1.2.2 Hydro-meteorological drivers of evaporation and vegetation productivity 19 1.2.3 Vegetation structure and physiology 21 1.3 Terrestrial water cycle and its relationship with vegetation 24 1.3.1 Water balance 24 1.3.2 Vegetation regulating the water cycle 26 1.3.3 The relevance of vegetation on hydrological extremes 27 1.4 Advances in observations and models 30 1.4.1 Spaceborne remote sensing 30 1.4.2 Data-driven and physical-based models 34 1.5 Research questions and thesis outline 37 1.5.1 What is the relationship between vegetation productivity and water supply? 37 1.5.2 Can vegetation regulate hydrological extremes? 38 1.5.3 Can land surface models capture vegetation-water interplay? 40 1.5.4 Thesis outline 40 2 Global vegetation controls using multi-layer soil moisture 41 2.1 Introduction 42 2.2 Data and methods 43 2.3 Results and discussion 45 2.4 Conclusions 53 2.A Appendix 54 3 Widespread increasing vegetation sensitivity to soil moisture 70 3.1 Introduction 71 3.2 Data and methods 72 3.3 Results and discussion 78 3.4 Conclusions 85 3.A Appendix 86 4 The drought effect on vegetation physiology inferred from space 101 4.1 Introduction 102 4.2 Data and methods 104 4.3 Results and discussion 111 4.4 Conclusions 122 4.A Appendix 123 5 Drought propagation into the terrestrial water cycle 136 5.1 Introduction 137 5.2 Data and methods 139 5.3 Results and discussion 145 5.4 Conclusions 155 5.A Appendix 157 6 Drivers of high river flows in European near-natural catchments 171 6.1 Introduction 172 6.2 Data and methods 173 6.3 Results and discussion 179 6.4 Conclusion 184 6.A Appendix 186 7 Synthesis 193 7.1 What is the relationship between vegetation productivity and water supply? 194 7.2 Can vegetation regulate hydrological extremes? 197 7.3 Can land surface models capture the observed vegetation-water interplay? 199 7.4 Limitations 200 7.4.1 Difficulties in predicting SIF in tropical regions 200 7.4.2 Observing terrestrial photosynthesis and evaporation 201 7.4.3 Methods related to variable importance quantification 202 7.5 Outlook 202 7.5.1 Vegetation sensitivity to soil moisture and its implications 203 7.5.2 Vegetation functioning and related structure and physiology 203 7.5.3 Extreme events: floods and drought 204 References 206 Statement of authorship contributions 238 Acknowledgements 239 Curriculum Vitae 241 Scientific publications 242 IMPRS certificate 244In the context of global climate change, vegetation is particularly relevant as it can take up anthropogenic CO2 emissions and regulate water and energy cycling. While previous research provided valuable insights into long-term changes in vegetation greenness and in terms of the vegetation response to increasing temperature and atmospheric CO2, vegetation-water interactions are still not fully understood. In fact, root-zone soil moisture dynamics have a fundamental influence on modulating vegetation greenness and productivity. Nevertheless, neither the sensitivity of vegetation productivity to soil water supply nor the vegetation functional response (i.e., photosynthesis and transpiration) to soil drought episodes have been fully resolved at the global scale. Missing global observations of vegetation functioning and terrestrial water variability are bottlenecks, and statistical tools for analyzing large and multi-stream data are poorly exploited, preventing a better understanding of global vegetation water response. At the same time, a better knowledge of the vegetation response to the water supply in turn advances the understanding of the terrestrial water cycle. Hydrological extremes are damaging infrastructure and can affect human well-being, and have been reported to become more frequent and intense in many regions around the world. While a consensus exists regarding the importance of meteorological drivers for regulating the water cycle and related extreme events, the role of vegetation dynamics and characteristics is understudied. Its greater consideration in hydrological studies offers the potential to more accurately understand the processes driving hydrological extremes. Thereby, a better understanding on vegetation-water interactions in terms of vegetation water sensitivity and vegetation feedbacks on climate extremes can advance the accuracy of land surface modelling which is essential to improve climate projections. Thanks to recent developments in Earth observations and in the applicability of powerful statistical analyses tools, investigating global vegetation-water interactions is now possible with unprecedented accuracy. In this context, this thesis builds particularly on (i) novel data products such as Sun-induced chlorophyll fluorescence or global gridded soil moisture and evapotranspiration products obtained from upscaling station measurements with machine learning algorithms, (ii) longer records and updated processing of established data products such as leaf area index and terrestrial water storage, and (iii) the development of explainable machine learning methods which can efficiently derived information from multivariate data streams, and are furthermore implemented and readily applicable in ecohydrological studies. With these datasets and tools, this thesis revisits the sensitivity of global vegetation to soil water supply across space and time.:Summary 7 Zusammenfassung 11 1 Introduction 15 1.1 Motivation 16 1.2 Terrestrial vegetation and its relationship with water supply 18 1.2.1 Vegetation functioning 18 1.2.2 Hydro-meteorological drivers of evaporation and vegetation productivity 19 1.2.3 Vegetation structure and physiology 21 1.3 Terrestrial water cycle and its relationship with vegetation 24 1.3.1 Water balance 24 1.3.2 Vegetation regulating the water cycle 26 1.3.3 The relevance of vegetation on hydrological extremes 27 1.4 Advances in observations and models 30 1.4.1 Spaceborne remote sensing 30 1.4.2 Data-driven and physical-based models 34 1.5 Research questions and thesis outline 37 1.5.1 What is the relationship between vegetation productivity and water supply? 37 1.5.2 Can vegetation regulate hydrological extremes? 38 1.5.3 Can land surface models capture vegetation-water interplay? 40 1.5.4 Thesis outline 40 2 Global vegetation controls using multi-layer soil moisture 41 2.1 Introduction 42 2.2 Data and methods 43 2.3 Results and discussion 45 2.4 Conclusions 53 2.A Appendix 54 3 Widespread increasing vegetation sensitivity to soil moisture 70 3.1 Introduction 71 3.2 Data and methods 72 3.3 Results and discussion 78 3.4 Conclusions 85 3.A Appendix 86 4 The drought effect on vegetation physiology inferred from space 101 4.1 Introduction 102 4.2 Data and methods 104 4.3 Results and discussion 111 4.4 Conclusions 122 4.A Appendix 123 5 Drought propagation into the terrestrial water cycle 136 5.1 Introduction 137 5.2 Data and methods 139 5.3 Results and discussion 145 5.4 Conclusions 155 5.A Appendix 157 6 Drivers of high river flows in European near-natural catchments 171 6.1 Introduction 172 6.2 Data and methods 173 6.3 Results and discussion 179 6.4 Conclusion 184 6.A Appendix 186 7 Synthesis 193 7.1 What is the relationship between vegetation productivity and water supply? 194 7.2 Can vegetation regulate hydrological extremes? 197 7.3 Can land surface models capture the observed vegetation-water interplay? 199 7.4 Limitations 200 7.4.1 Difficulties in predicting SIF in tropical regions 200 7.4.2 Observing terrestrial photosynthesis and evaporation 201 7.4.3 Methods related to variable importance quantification 202 7.5 Outlook 202 7.5.1 Vegetation sensitivity to soil moisture and its implications 203 7.5.2 Vegetation functioning and related structure and physiology 203 7.5.3 Extreme events: floods and drought 204 References 206 Statement of authorship contributions 238 Acknowledgements 239 Curriculum Vitae 241 Scientific publications 242 IMPRS certificate 24

Sensitivity of estimated total canopy SIF emission to remotely sensed LAI and BRDF products

Remote sensing of solar-induced chlorophyll fluorescence (SIF) provides new possibilities to estimate terrestrial gross primary production (GPP). To mitigate the angular and canopy structural effects on original SIF observed by sensors (SIFobs), it is recommended to derive total canopy SIF emission (SIFtotal) of leaves within a canopy using canopy interception (i0) and reflectance of vegetation (RV). However, the effects of the uncertainties in i0 and RV on the estimation of SIFtotal have not been well understood. Here, we evaluated such effects on the estimation of GPP using the Soil-Canopy-Observation of Photosynthesis and the Energy balance (SCOPE) model. The SCOPE simulations showed that the R2 between GPP and SIFtotal was clearly higher than that between GPP and SIFobs and the differences in R2 (ΔR2) tend to decrease with the increasing levels of uncertainties in i0 and RV. The resultant ΔR2 decreased to zero when the uncertainty level in i0 and RV was ~30% for red band SIF (RSIF, 683 nm) and ~20% for far-red band SIF (FRSIF, 740 nm). In addition, as compared to the TROPOspheric Monitoring Instrument (TROPOMI) SIFobs at both red and far-red bands, SIFtotal derived using any combination of i0 (from MCD15, VNP15, and CGLS LAI products) and RV (from MCD34, MCD19, and VNP43 BRDF products) showed comparable improvements in estimating GPP. With this study, we suggest a way to advance our understanding in the estimation of a more physiological relevant SIF datasets (SIFtotal) using current satellite products

Understanding the land carbon cycle with space data: current status and prospects

Our understanding of the terrestrial carbon cycle has been greatly enhanced since satellite observations of the land surface started. The advantage of remote sensing is that it provides wall-to-wall observations including in regions where in situ monitoring is challenging. This paper reviews how satellite observations of the biosphere have helped improve our understanding of the terrestrial carbon cycle. First, it details how remotely sensed information of the land surface has provided new means to monitor vegetation dynamics and estimate carbon fluxes and stocks. Second, we present examples of studies which have used satellite products to evaluate and improve simulations from global vegetation models. Third, we focus on model data integration approaches ranging from bottom-up extrapolation of single variables to carbon cycle data assimilation system able to ingest multiple types of observations. Finally, we present an overview of upcoming satellite missions which are likely to further improve our understanding of the terrestrial carbon cycle and its response to climate change and extremes

Tracking Seasonal and Interannual Variability in Photosynthetic Downregulation in Response to Water Stress at a Temperate Deciduous Forest

The understanding and modeling of photosynthetic dynamics affected by climate variability can be highly uncertain. In this paper, we examined a well‐characterized eddy covariance site in a drought‐prone temperate deciduous broadleaf forest combining tower measurements and satellite observations. We find that an increase in spring temperature usually leads to enhanced spring gross primary production (GPP), but a GPP reduction in late growing season due to water limitation. We evaluated how well a coupled fluorescence‐photosynthesis model (SCOPE) and satellite data sets track the interannual and seasonal variations of tower GPP from 2007 to 2016. In SCOPE, a simple stress factor scaling of Vcmax as a linear function of observed predawn leaf water potential (ψ_(pd)) shows a good agreement between modeled and measured interannual variations in both GPP and solar‐induced chlorophyll fluorescence (SIF) from the Global Ozone Monitoring Experiment‐2 (GOME‐2). The modeled and satellite‐observed changes in SIF_(yield) are ~30% smaller than corresponding changes in light use efficiency (LUE) under severe stress, for which a common linear SIF to GPP scaling would underestimate the stress reduction in GPP. Overall, GOME‐2 SIF tracks interannual tower GPP variations better than satellite vegetations indices (VIs) representing canopy “greenness.” However, it is still challenging to attribute observed SIF variations unequivocally to greenness or physiological changes due to large GOME‐2 footprint. Higher‐resolution SIF data sets (e.g., TROPOMI) already show the potential to well capture the downregulation of late‐season GPP and could pave the way to better disentangle canopy structural and physiological changes in the future

P-model v1.0: an optimality-based light use efficiency model for simulating ecosystem gross primary production

Terrestrial photosynthesis is the basis for vegetation growth and drives the land carbon cycle. Accurately simulating gross primary production (GPP, ecosystem-level apparent photosynthesis) is key for satellite monitoring and Earth System Model predictions under climate change. While robust models exist for describing leaf-level photosynthesis, predictions diverge due to uncertain photosynthetic traits and parameters which vary on multiple spatial and temporal scales. Here, we describe and evaluate a gross primary production (GPP, photosynthesis per unit ground area) model, the P-model, that combines the Farquhar-von Caemmerer-Berry model for C3 photosynthesis with an optimality principle for the carbon assimilation- transpiration trade-off, and predicts a multi-day average light use efficiency (LUE) for any climate and C3 vegetation type. The model is forced here with satellite data for the fraction of absorbed photosynthetically active radiation and site-specific meteorological data and is evaluated against GPP estimates from a globally distributed network of ecosystem flux measurements. Although the P-model requires relatively few inputs and prescribed parameters, the R2 for predicted versus observed GPP based on the full model setup is 0.75 (8-day mean, 131 sites) – better than some state-of-the-art satellite data-driven light use efficiency models. The R2 is reduced to 0.69 when not accounting for the reduction in quantum yield at low temperatures and effects of low soil moisture on LUE. The R2 for the P-model-predicted LUE is 0.37 (means by site) and 0.53 (means by vegetation type). The P-model provides a simple but powerful method for predicting – rather than prescribing light use efficiency and simulating terrestrial photosythesis across a wide range of conditions. The model is available as an R package (rpmodel)

The effect of increasing temperature on crop photosynthesis: From enzymes to ecosystems

As global land surface temperature continues to rise and heatwave events increase in frequency, duration, and/or intensity, our key food and fuel cropping systems will likely face increased heat-related stress. A large volume of literature exists on exploring measured and modelled impacts of rising temperature on crop photosynthesis, from enzymatic responses within the leaf up to larger ecosystem-scale responses that reflect seasonal and interannual crop responses to heat. This review discusses (i) how crop photosynthesis changes with temperature at the enzymatic scale within the leaf; (ii) how stomata and plant transport systems are affected by temperature; (iii) what features make a plant susceptible or tolerant to elevated temperature and heat stress; and (iv) how these temperature and heat effects compound at the ecosystem scale to affect crop yields. Throughout the review, we identify current advancements and future research trajectories that are needed to make our cropping systems more resilient to rising temperature and heat stress, which are both projected to occur due to current global fossil fuel emissions