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Suboptimal temperatures at sowing and emergence affect the early development of maize, with potentially irreversible effects later in the growing season. We studied recovery from cold stress of an inbred line (B73) and 13 Doubled Haploid lines derived from a European flint maize landrace. After a cold treatment (20–12 °C, day–night) from sowing to seedling establishment, seedlings were transplanted and grown in the greenhouse until the V8 stage (eight leaves fully developed), when we measured agronomically relevant plant traits and spectral indices of mature leaves. Survival rates of transplanted seedlings after cold treatment ranged from 10% to 100%. After a strong delay in early development due to cold, the surviving plants were able to compensate for this delay at later stages of recovery. They reached the V8 stage after only five more growing degree days than plants grown under the control treatment (25–18 °C, day–night). Plants from the most cold-tolerant genotypes (PE0401 and PE0100) accumulated more root and shoot biomass at the end of the recovery phase compared with the same genotypes exposed to the control treatment. The genotypes with the most plastic leaf morphological traits (PE0161 and PE0072) had little reduction in leaf biomass at the end of the recovery phase in comparison with less responsive genotypes such as PE0171. We conclude that genotypes that survived cold treatment with minimal cold damage of seedling leaves can be candidates for further cold recovery studies and breeding. Nevertheless, such studies must take trait acclimation for other suboptimal conditions into consideration. [ABSTRACT FROM AUTHOR]

Background: The leaf angle distribution (LAD) is an important structural parameter of agricultural crops that influences light interception, radiation fluxes and consequently plant performance. Therefore, LAD and its parametrized form, the Beta distribution, is used in many photosynthesis models. However, in field cultivations, these parameters are difficult to assess and cereal crops in particular pose challenges since their leaves are thin, flexible, and often bent and twisted around their own axis. To our knowledge, there is only a very limited set of methods currently available to calculate LADs of field-grown cereal crops that explicitly takes these special morphological properties into account. Results: In this study, a new processing pipeline is introduced that allows for the generation of realistic leaf surface models and the analysis of LADs of field-grown cereal crops from 3D point clouds. The data acquisition is based on a convenient stereo imaging setup. The approach was validated with different artificial targets and results on the accuracy of the 3D reconstruction, leaf surface modeling and calculated LAD are given. The mean error of the 3D reconstruction was below 1 mm for an inclination angle range between 0° and 75° and the leaf surface could be quantified with an average accuracy of 90%. The concordance correlation coefficient (CCC) of 99.6% (p-value = 1.5 ∗ 10 - 29 ) indicated a high correlation between the reconstructed inclination angle and the identity line. The LADs for bent leaves were reconstructed with a mean error of 0.21° and a standard deviation of 1.55°. As an additional parameter, the insertion angle was reconstructed for the artificial leaf model with an average error < 5°. Finally, the method was tested with images of field-grown cereal crops and Beta functions were approximated from the calculated LADs. The mean CCC between reconstructed LAD and calculated Beta function was 0.66. According to Cohen, this indicates a high correlation. Conclusion: This study shows that our image processing pipeline can reconstruct the complex leaf shape of cereal crops from stereo images. The high accuracy of the approach was demonstrated with several validation experiments including artificial leaf targets. The derived leaf models were used to calculate LADs for artificial leaves and naturally grown cereal crops. This helps to better understand the influence of the canopy structure on light absorption and plant performance and allows for a more precise parametrization of photosynthesis models via the derived Beta distributions. [ABSTRACT FROM AUTHOR]

In the context of climate change and global sustainable development goals, future wheat cultivation has to master various challenges at a time, including the rising atmospheric carbon dioxide concentration ([CO2]). To investigate growth and photosynthesis dynamics under the effects of ambient (-434 ppm) and elevated [CO2] (-622 ppm), a Free-Air CO2 Enrichment (FACE) facility was combined with an automated phenotyping platform and an array of sensors. Ten modern winter wheat cultivars (Triticum aestivum L.) were monitored over a vegetation period using a Light-induced Fluorescence Transient (LIFT) sensor, ground-based RGB cameras and a UAV equipped with an RGB and multispectral camera. The LIFT sensor enabled a fast quantification of the photosynthetic performance by measuring the operating efficiency of Photosystem II (Fq'/Fm') and the kinetics of electron transport, i.e. the reoxidation rates Fr1' and Fr2'. Our results suggest that elevated [CO2] significantly increased Fq'/Fm' and plant height during the vegetative growth phase. As the plants transitioned to the senescence phase, a pronounced decline in Fq'/Fm' was observed under elevated [CO2]. This was also reflected in the reoxidation rates Fr1' and Fr2'. A large majority of the cultivars showed a decrease in the harvest index, suggesting a different resource allocation and indicating a potential plateau in yield progression under e[CO2]. Our results indicate that the rise in atmospheric [CO2] has significant effects on the cultivation of winter wheat with strong manifestation during early and late growth. [ABSTRACT FROM AUTHOR]

Although our observing capabilities of solar‐induced chlorophyll fluorescence (SIF) have been growing rapidly, the quality and consistency of SIF datasets are still in an active stage of research and development. As a result, there are considerable inconsistencies among diverse SIF datasets at all scales and the widespread applications of them have led to contradictory findings. The present review is the second of the two companion reviews, and data oriented. It aims to (1) synthesize the variety, scale, and uncertainty of existing SIF datasets, (2) synthesize the diverse applications in the sector of ecology, agriculture, hydrology, climate, and socioeconomics, and (3) clarify how such data inconsistency superimposed with the theoretical complexities laid out in (Sun et al., 2023) may impact process interpretation of various applications and contribute to inconsistent findings. We emphasize that accurate interpretation of the functional relationships between SIF and other ecological indicators is contingent upon complete understanding of SIF data quality and uncertainty. Biases and uncertainties in SIF observations can significantly confound interpretation of their relationships and how such relationships respond to environmental variations. Built upon our syntheses, we summarize existing gaps and uncertainties in current SIF observations. Further, we offer our perspectives on innovations needed to help improve informing ecosystem structure, function, and service under climate change, including enhancing in‐situ SIF observing capability especially in "data desert" regions, improving cross‐instrument data standardization and network coordination, and advancing applications by fully harnessing theory and data. [ABSTRACT FROM AUTHOR]

Solar‐induced chlorophyll fluorescence (SIF) is a remotely sensed optical signal emitted during the light reactions of photosynthesis. The past two decades have witnessed an explosion in availability of SIF data at increasingly higher spatial and temporal resolutions, sparking applications in diverse research sectors (e.g., ecology, agriculture, hydrology, climate, and socioeconomics). These applications must deal with complexities caused by tremendous variations in scale and the impacts of interacting and superimposing plant physiology and three‐dimensional vegetation structure on the emission and scattering of SIF. At present, these complexities have not been overcome. To advance future research, the two companion reviews aim to (1) develop an analytical framework for inferring terrestrial vegetation structures and function that are tied to SIF emission, (2) synthesize progress and identify challenges in SIF research via the lens of multi‐sector applications, and (3) map out actionable solutions to tackle these challenges and offer our vision for research priorities over the next 5–10 years based on the proposed analytical framework. This paper is the first of the two companion reviews, and theory oriented. It introduces a theoretically rigorous yet practically applicable analytical framework. Guided by this framework, we offer theoretical perspectives on three overarching questions: (1) The forward (mechanism) question—How are the dynamics of SIF affected by terrestrial ecosystem structure and function? (2) The inference question: What aspects of terrestrial ecosystem structure, function, and service can be reliably inferred from remotely sensed SIF and how? (3) The innovation question: What innovations are needed to realize the full potential of SIF remote sensing for real‐world applications under climate change? The analytical framework elucidates that process complexity must be appreciated in inferring ecosystem structure and function from the observed SIF; this framework can serve as a diagnosis and inference tool for versatile applications across diverse spatial and temporal scales. [ABSTRACT FROM AUTHOR]

In this paper, we present GrowliFlower, a georeferenced, image‐based unmanned aerial vehicle time‐series dataset of two monitored cauliflower fields (0.39 and 0.60 ha) acquired in 2 years, 2020 and 2021. The proposed dataset contains RGB and multispectral orthophotos with coordinates of approximately 14,000 individual cauliflower plants. The coordinates enable the extraction of complete and incomplete time‐series of image patches showing individual plants. The dataset contains the collected phenotypic traits of 740 plants, including the developmental stage and plant and cauliflower size. The harvestable product is completely covered by leaves, thus, plant IDs and coordinates are provided to extract image pairs of plants pre‐ and post‐defoliation. In addition, to facilitate classification, detection, segmentation, instance segmentation, and other similar computer vision tasks, the proposed dataset contains pixel‐accurate leaf and plant instance segmentations, as well as stem annotations. The proposed dataset was created to facilitate the development and evaluation of various machine‐learning approaches. It focuses on the analysis of growth and development of cauliflower and the derivation of phenotypic traits to advance automation in agriculture. Two baseline results of instance segmentation tasks at the plant and leaf level based on labeled instance segmentation data are presented. The complete GrowliFlower dataset is publicly available (http://rs.ipb.uni-bonn.de/data/growliflower/). [ABSTRACT FROM AUTHOR]

Solar-induced chlorophyll fluorescence (SIF) is used as a proxy of photosynthetic efficiency. However, interpreting top-of-canopy (TOC) SIF in relation to photosynthesis remains challenging due to the distortion introduced by the canopy's structural effects (i.e., fluorescence re-absorption, sunlit-shaded leaves, etc.) and sun–canopy–sensor geometry (i.e., direct radiation infilling). Therefore, ground-based, high-spatial-resolution data sets are needed to characterize the described effects and to be able to downscale TOC SIF to the leafs where the photosynthetic processes are taking place. We herein introduce HyScreen, a ground-based push-broom hyperspectral imaging system designed to measure red ( F 687 ) and far-red ( F 760 ) SIF and vegetation indices from TOC with single-leaf spatial resolution. This paper presents measurement protocols, the data processing chain and a case study of SIF retrieval. Raw data from two imaging sensors were processed to top-of-canopy radiance by dark-current correction, radiometric calibration, and empirical line correction. In the next step, the improved Fraunhofer line descrimination (iFLD) and spectral-fitting method (SFM) were used for SIF retrieval, and vegetation indices were calculated. With the developed protocol and data processing chain, we estimated a signal-to-noise ratio (SNR) between 50 and 200 from reference panels with reflectance from 5% to 95% and noise equivalent radiance (NER) of 0.04 (5%) to 0.18 (95%) mW m − 2 sr − 1 nm − 1 . The results from the case study showed that non-vegetation targets had SIF values close to 0 mW m − 2 sr − 1 nm − 1 , whereas vegetation targets had a mean F 687 of 1.13 and F 760 of 1.96 mW m − 2 sr − 1 nm − 1 from the SFM method. HyScreen showed good performance for SIF retrievals at both F 687 and F 760 ; nevertheless, we recommend further adaptations to correct for the effects of noise, varying illumination and sensor optics. In conclusion, due to its high spatial resolution, Hyscreen is a promising tool for investigating the relationship between leafs and TOC SIF as well as their relationship with plants' photosynthetic capacity. [ABSTRACT FROM AUTHOR]

The upcoming Fluorescence Explorer (FLEX) mission will provide sun-induced fluorescence (SIF) products at unprecedented spatial resolution. Thus, accurate calibration and validation (cal/val) of these products are key to guarantee robust SIF estimates for the assessment and quantification of photosynthetic processes. In this study, we address one specific component of the uncertainty budget related to SIF retrieval: the spatial representativeness of in situ SIF observations compared to medium-resolution SIF products (e.g., 300 m pixel size). Here, we propose an approach to evaluate an optimal sampling strategy to characterise the spatial representativeness of in situ SIF observations based on high-spatial-resolution SIF data. This approach was applied for demonstration purposes to two agricultural areas that have been extensively characterized with a HyPlant airborne imaging spectrometer in recent years. First, we determined the spatial representativeness of an increasing number of sampling points with respect to a reference area (either monocultural crop fields or hypothetical FLEX pixels characterised by different land cover types). Then, we compared different sampling approaches to determine which strategy provided the most representative reference data for a given area. Results show that between 3 and 13.5 sampling points are needed to characterise the average SIF value of both monocultural fields and hypothetical FLEX pixels of the agricultural areas considered in this study. The number of sampling points tends to increase with the standard deviation of SIF of the reference area, as well as with the number of land cover classes in a FLEX pixel, even if the increase is not always statistically significant. This study contributes to guiding cal/val activities for the upcoming FLEX mission, providing useful insights for the selection of the validation site network and particularly for the definition of the best sampling scheme for each site. [ABSTRACT FROM AUTHOR]

Water deficit is the most severe stress factor in crop production threatening global food security. In this study, we evaluated the genetic variation in photosynthetic traits among 200 wheat cultivars evaluated under drought and rainfed conditions. Significant genotypic, treatments, and their interaction effects were detected for chlorophyll content and chlorophyll fluorescence parameters. Drought stress reduced the effective quantum yield of photosystem II (YII) from the anthesis growth stage on. Leaf chlorophyll content measured at anthesis growth stages was significantly correlated with YII and non‐photochemical quenching under drought conditions, suggesting that high throughput chlorophyll content screening can serve as a good indicator of plant drought tolerance status in wheat. Breeding significantly increased the photosynthetic efficiency as newer released genotypes had higher YII and chlorophyll content than the older ones. GWAS identified a stable drought‐responsive QTL on chromosome 3A for YII, while under rainfed conditions, it detected another QTL on chromosome 7A for chlorophyll content across both growing seasons. Molecular analysis revealed that the associated alleles of AX‐158576783 (515.889 Mbp) on 3A co‐segregates with the NADH‐ubiquinone oxidoreductase (TraesCS3A02G287600) gene involved in ATP synthesis coupled electron transport and is proximal to WKRY transcription factor locus. This allele on 3A has been positively selected through breeding and has contributed to increasing the grain yield. Highlight: Breeding has aided the selection of multiple pleiotropic and stable drought‐responsive alleles on chromosome 3A associated with improved photosynthetic activity and grain yield under drought stress in winter wheat. [ABSTRACT FROM AUTHOR]

Previous studies investigating the performance of transmission and reflection datasets for disease detection showed inconsistent results. Within the studies, the performance of transmission imaging varied significantly for the detection of biotroph and necrotrophy plant pathogens, while reflection imaging showed excellent results in both studies. The current study explores the hypothesis that the disparity between these results might be correlated with the different interactions of the respective pathogens with the host plants and the way light interacts with the plant tissue. Pyrenophora teres f. teres and Puccinia hordei—the causative agents of net blotch and brown rust in barley—have been investigated with focus on early-stage detection and quantification (disease severity) of symptoms. Datasets of hyperspectral imaging time-series measurements were analysed through application of multiple data analysis methods (support vector machines; principal component analysis with following distance classifier; spectral decomposition) in order to compare the performance of both datasets for the detection of disease symptoms. It could be shown that transmittance-based brown rust detection (e.g. 12% disease severity) is outperformed by reflectance-based detection (e.g. 36% disease severity) regardless of the algorithm. However, both the detection and quantification of brown rust through transmittance were more accurate than those of powdery mildew in earlier studies. Transmittance and reflectance performed similar for the detection of net blotch disease during the experiments (~ 1% disease severity for reflection and transmission). Each data analysis method outperformed manual rating in terms of disease detection (e.g. 15% disease severity according to manual rating and 36% through support vector machines for rust reflection data). Except for the application of a distance classifier on net blotch transmittance data, it could be shown that pixels, which were classified as symptomatic through the data analysis methods while estimated to represent healthy tissue during manual rating, correlate with areas at the edges of manually detected symptoms. The results of this study support the hypothesis that transmission imaging results are highly correlated with the type of plant–pathogen interaction of the respective pathogens, offering new insights into the nature of transmission-based hyperspectral imaging and its application range. [ABSTRACT FROM AUTHOR]

Grapevine is one of the economically most important quality crops. The monitoring of the plant performance during the growth period is, therefore, important to ensure a high quality end-product. This includes the observation, detection, and respective reduction of unhealthy berries (physically damaged, or diseased). At harvest, it is not necessary to know the exact cause of the damage, but rather if the damage is apparent or not. Since a manual screening and selection before harvest is time-consuming and expensive, we propose an automatic, image-based machine learning approach, which can lead observers directly to anomalous areas without the need to monitor every plant manually. Specifically, we train a fully convolutional variational autoencoder with a feature perceptual loss on images with healthy berries only and consider image areas with deviations from this model as damaged berries. We use heatmaps which visualize the results of the trained neural network and, therefore, support the decision making for farmers. We compare our method against a convolutional autoencoder that was successfully applied to a similar task and show that our approach outperforms it. [ABSTRACT FROM AUTHOR]

Summary: Sun‐induced fluorescence in the far‐red region (SIF) is increasingly used as a remote and proximal‐sensing tool capable of tracking vegetation gross primary production (GPP). However, the use of SIF to probe changes in GPP is challenged during extreme climatic events, such as heatwaves.Here, we examined how the 2018 European heatwave (HW) affected the GPP–SIF relationship in evergreen broadleaved trees with a relatively invariant canopy structure. To do so, we combined canopy‐scale SIF measurements, GPP estimated from an eddy covariance tower, and active pulse amplitude modulation fluorescence.The HW caused an inversion of the photosynthesis–fluorescence relationship at both the canopy and leaf scales. The highly nonlinear relationship was strongly shaped by nonphotochemical quenching (NPQ), that is, a dissipation mechanism to protect from the adverse effects of high light intensity. During the extreme heat stress, plants experienced a saturation of NPQ, causing a change in the allocation of energy dissipation pathways towards SIF.Our results show the complex modulation of the NPQ–SIF–GPP relationship at an extreme level of heat stress, which is not completely represented in state‐of‐the‐art coupled radiative transfer and photosynthesis models. [ABSTRACT FROM AUTHOR]

Keywords: remote sensing; high-throughput field phenotyping; unmanned aerial vehicles (UAVs); multi-sensor synergies; open-data standards; vegetation traits; radiative transfer models (RTM); smart farming EN remote sensing high-throughput field phenotyping unmanned aerial vehicles (UAVs) multi-sensor synergies open-data standards vegetation traits radiative transfer models (RTM) smart farming 1 7 7 10/29/21 20211022 NES 211022 Introduction Sustainable and resilient agriculture with a low impact on the environment is pivotal to ensure food security for a growing global population. Better estimating the plant N by combining information on plant physiology from plant phenotyping with new hyperspectral sensors giving a near-real-time estimation can provide input for optimized management strategies to reduce N applications and thus protect the water resources. Bridging the Gap Between Remote Sensing and Plant Phenotyping - Challenges and Opportunities for the Next Generation of Sustainable Agriculture Remote sensing, high-throughput field phenotyping, unmanned aerial vehicles (UAVs), multi-sensor synergies, radiative transfer models (RTM), open-data standards, vegetation traits, smart farming. [Extracted from the article]

Chlorophyll fluorescence (ChlF) is a powerful non‐invasive technique for probing photosynthesis. Although proposed as a method for drought tolerance screening, ChlF has not yet been fully adopted in physiological breeding, mainly due to limitations in high‐throughput field phenotyping capabilities. The light‐induced fluorescence transient (LIFT) sensor has recently been shown to reliably provide active ChlF data for rapid and remote characterisation of plant photosynthetic performance. We used the LIFT sensor to quantify photosynthesis traits across time in a large panel of durum wheat genotypes subjected to a progressive drought in replicated field trials over two growing seasons. The photosynthetic performance was measured at the canopy level by means of the operating efficiency of Photosystem II (Fq′/Fm′) and the kinetics of electron transport measured by reoxidation rates (Fr1′ and Fr2′). Short‐ and long‐term changes in ChlF traits were found in response to soil water availability and due to interactions with weather fluctuations. In mild drought, Fq′/Fm′ and Fr2′ were little affected, while Fr1′ was consistently accelerated in water‐limited compared to well‐watered plants, increasingly so with rising vapour pressure deficit. This high‐throughput approach allowed assessment of the native genetic diversity in ChlF traits while considering the diurnal dynamics of photosynthesis. Photosynthetic performance of a large durum wheat panel under progressive drought in the field was quantified using the light‐induced fluorescence transient (LIFT) sensor for high‐throughput phenotyping. Photosynthesis traits showed high genetic variability, aiding physiological crop breeding. [ABSTRACT FROM AUTHOR]

Background: Obtaining instantaneous gas exchanges data is fundamental to gain information on photosynthesis. Leaf level data are reliable, but their scaling up to canopy scale is difficult as they are acquired in standard and/or controlled conditions, while natural environments are extremely dynamic. Responses to dynamic environmental conditions need to be considered, as measurements at steady state and their related models may overestimate total carbon (C) plant uptake. Results: In this paper, we describe an automatic, low-cost measuring system composed of 12 open chambers (60 × 60 × 150 cm; around 400 euros per chamber) able to measure instantaneous CO2 and H2O gas exchanges, as well as environmental parameters, at canopy level. We tested the system's performance by simulating different CO2 uptake and respiration levels using a tube filled with soda lime or pure CO2, respectively, and quantified its response time and measurement accuracy. We have been also able to evaluate the delayed response due to the dimension of the chambers, proposing a method to correct the data by taking into account the response time ( t 0 ) and the residence time (τ). Finally, we tested the system by growing a commercial soybean variety in fluctuating and non-fluctuating light, showing the system to be fast enough to capture fast dynamic conditions. At the end of the experiment, we compared cumulative fluxes with total plant dry biomass. Conclusions: The system slightly over-estimated (+ 7.6%) the total C uptake, even though not significantly, confirming its ability in measuring the overall CO2 fluxes at canopy scale. Furthermore, the system resulted to be accurate and stable, allowing to estimate the response time and to determine steady state fluxes from unsteady state measured values. Thanks to the flexibility in the software and to the dimensions of the chambers, even if only tested in dynamic light conditions, the system is thought to be used for several applications and with different plant canopies by mimicking different environmental conditions. [ABSTRACT FROM AUTHOR]

Summary: Solar‐induced fluorescence (SIF) is highly relevant in mapping photosynthesis from remote‐sensing platforms. This requires linking SIF to photosynthesis and understanding the role of nonphotochemical quenching (NPQ) mechanisms under field conditions. Hence, active and passive fluorescence were measured in Arabidopsis with altered NPQ in outdoor conditions.Plants with mutations in either violaxanthin de‐epoxidase (npq1) or PsbS protein (npq4) exhibited reduced NPQ capacity. Parallel measurements of NPQ, photosystem II efficiency, SIF and spectral reflectance (ρ) were conducted diurnally on one sunny summer day and two consecutive days during a simulated cold spell.Results showed that both npq mutants exhibited higher levels of SIF compared to wild‐type plants. Changes in reflectance were related to changes in the violaxanthin–antheraxanthin–zeaxanthin cycle and not to PsbS‐mediated conformational changes. When plants were exposed to cold temperatures, rapid onset of photoinhibition strongly quenched SIF in all lines.Using well‐characterized Arabidopsisnpq mutants, we showed for the first time the quantitative link between SIF, photosynthetic efficiency, NPQ components and leaf reflectance. We discuss the functional potential and limitations of SIF and reflectance measurements for estimating photosynthetic efficiency and NPQ in the field. [ABSTRACT FROM AUTHOR]

The CloudRoots field experiment was designed to obtain a comprehensive observational dataset that includes soil, plant, and atmospheric variables to investigate the interaction between a heterogeneous land surface and its overlying atmospheric boundary layer at the sub-hourly and sub-kilometre scale. Our findings demonstrate the need to include measurements at leaf level to better understand the relations between stomatal aperture and evapotranspiration (ET) during the growing season at the diurnal scale. Based on these observations, we obtain accurate parameters for the mechanistic representation of photosynthesis and stomatal aperture. Once the new parameters are implemented, the model reproduces the stomatal leaf conductance and the leaf-level photosynthesis satisfactorily. At the canopy scale, we find a consistent diurnal pattern on the contributions of plant transpiration and soil evaporation using different measurement techniques. From highly resolved vertical profile measurements of carbon dioxide (CO2) and other state variables, we infer a profile of the CO2 assimilation in the canopy with non-linear variations with height. Observations taken with a laser scintillometer allow us to quantify the non-steadiness of the surface turbulent fluxes during the rapid changes driven by perturbation of photosynthetically active radiation by cloud flecks. More specifically, we find 2 min delays between the cloud radiation perturbation and ET. To study the relevance of advection and surface heterogeneity for the land–atmosphere interaction, we employ a coupled surface–atmospheric conceptual model that integrates the surface and upper-air observations made at different scales from leaf to the landscape. At the landscape scale, we calculate a composite sensible heat flux by weighting measured fluxes with two different land use categories, which is consistent with the diurnal evolution of the boundary layer depth. Using sun-induced fluorescence measurements, we also quantify the spatial variability of ET and find large variations at the sub-kilometre scale around the CloudRoots site. Our study shows that throughout the entire growing season, the wide variations in stomatal opening and photosynthesis lead to large diurnal variations of plant transpiration at the leaf, plant, canopy, and landscape scales. Integrating different advanced instrumental techniques with modelling also enables us to determine variations of ET that depend on the scale where the measurement were taken and on the plant growing stage. [ABSTRACT FROM AUTHOR]

Summary: Cassava (Manihot esculenta Crantz) is one of the important staple foods in Sub‐Saharan Africa. It produces starchy storage roots that provide food and income for several hundred million people, mainly in tropical agriculture zones. Increasing cassava storage root and starch yield is one of the major breeding targets with respect to securing the future food supply for the growing population of Sub‐Saharan Africa. The Cassava Source–Sink (CASS) project aims to increase cassava storage root and starch yield by strategically integrating approaches from different disciplines. We present our perspective and progress on cassava as an applied research organism and provide insight into the CASS strategy, which can serve as a blueprint for the improvement of other root and tuber crops. Extensive profiling of different field‐grown cassava genotypes generates information for leaf, phloem, and root metabolic and physiological processes that are relevant for biotechnological improvements. A multi‐national pipeline for genetic engineering of cassava plants covers all steps from gene discovery, cloning, transformation, molecular and biochemical characterization, confined field trials, and phenotyping of the seasonal dynamics of shoot traits under field conditions. Together, the CASS project generates comprehensive data to facilitate conventional breeding strategies for high‐yielding cassava genotypes. It also builds the foundation for genome‐scale metabolic modelling aiming to predict targets and bottlenecks in metabolic pathways. This information is used to engineer cassava genotypes with improved source–sink relations and increased yield potential. Significance Statement: Cassava (Manihot esculenta) is one of the most important staple food crops in Sub‐Sahran Africa. The Cassava Source‐Sink project aims to boost cassava storage root yield by simultaneously improving the plants source, transport and sink capacity. [ABSTRACT FROM AUTHOR]

Passive measurement of sun‐induced chlorophyll fluorescence (F) represents the most promising tool to quantify changes in photosynthetic functioning on a large scale. However, the complex relationship between this signal and other photosynthesis‐related processes restricts its interpretation under stress conditions. To address this issue, we conducted a field campaign by combining daily airborne and ground‐based measurements of F (normalized to photosynthetically active radiation), reflectance and surface temperature and related the observed changes to stress‐induced variations in photosynthesis. A lawn carpet was sprayed with different doses of the herbicide Dicuran. Canopy‐level measurements of gross primary productivity indicated dosage‐dependent inhibition of photosynthesis by the herbicide. Dosage‐dependent changes in normalized F were also detected. After spraying, we first observed a rapid increase in normalized F and in the Photochemical Reflectance Index, possibly due to the blockage of electron transport by Dicuran and the resultant impairment of xanthophyll‐mediated non‐photochemical quenching. This initial increase was followed by a gradual decrease in both signals, which coincided with a decline in pigment‐related reflectance indices. In parallel, we also detected a canopy temperature increase after the treatment. These results demonstrate the potential of using F coupled with relevant reflectance indices to estimate stress‐induced changes in canopy photosynthesis. Passive measurement of sun‐induced chlorophyll fluorescence is a promising tool to quantify stress‐induced changes in photosynthetic functioning on a large scale. In this study, aerial measurements of fluorescence clearly tracked functional impairment of the rate of photosynthetic electron transport and carbon assimilation induced by a treatment with the herbicide Dicuran. Ancillary remote sensing variables such as vegetation indices and canopy temperature were used to quantify the dynamics of non‐photochemical energy dissipation mechanisms, the amount and composition of photosynthetic pigments and the stomatal activity. These results demonstrate the potential of using sun‐induced chlorophyll fluorescence coupled with relevant reflectance indices to estimate stress‐induced changes in canopy photosynthesis. [ABSTRACT FROM AUTHOR]

The CloudRoots field experiment was designed to obtain a comprehensive observational data set that includes soil, plant and atmospheric variables to investigate the interaction between a heterogeneous land surface and its overlying atmospheric boundary layer at the sub- hourly and sub-kilometre scale. Our findings demonstrate the need to include measurements at leaf level in order to obtain accurate parameters for the mechanistic representation of photosynthesis and stomatal aperture. Once the new parameters are implemented, the mechanistic model reproduces satisfactorily the stomatal leaf conductance and the leaf-level photosynthesis. At the canopy scale, we find a consistent diurnal pattern on the contributions of plant transpiration and soil evaporation using different measurement techniques. From the high frequency and vertical resolution state variables and CO2 measurements, we infer a profile of the plant assimilation that shows a strong non- linear behaviour. Observations taken by a laser scintillometer allow us to quantify the non-steadiness of the surface turbulent fluxes during the rapid changes driven by perturbation of the photosynthetically active radiation (PAR) by clouds, the so-called cloud flecks. More specifically, we find two-minute delays between the cloud radiation perturbation and ET. The impact of surface heterogeneity was further studied using ET estimates infer from the sun-induced fluorescence data and show small variation of ET in spite of the plant functional type differences. To study the relevance of advection and surface heterogeneity on the land-atmosphere interaction, we employ a coupled surface-atmospheric conceptual model that integrates the surface and upper-air observations taken at different scales: from the leaf-level to the landscape. At the landscape scale, we obtain the representative sensible heat flux that is consistent with the evolution of the boundary-layer depth evolution. Finally, throughout the entire growing season, the wide variations in stomatal opening and photosynthesis lead to large variations of plant transpiration at the leaf and canopy scales. The use of different instrumental techniques enables us to compare the total ET at various growing stages, from booting to senescence. There is satisfactory agreement between evapotranspiration of total ET, but the values remain sensitive to the scale at which ET is measured or modelled. [ABSTRACT FROM AUTHOR]

Plant phenotyping enables noninvasive quantification of plant structure and function and interactions with environments. High-capacity phenotyping reaches hitherto inaccessible phenotypic characteristics. Diverse, challenging, and valuable applications of phenotyping have originated among scientists, prebreeders, and breeders as they study the phenotypic diversity of genetic resources and apply increasingly complex traits to crop improvement. Noninvasive technologies are used to analyze experimental and breeding populations. We cover the most recent research in controlled-environment and field phenotyping for seed, shoot, and root traits. Select field phenotyping technologies have become state of the art and show promise for speeding up the breeding process in early generations. We highlight the technologies behind the rapid advances in proximal and remote sensing of plants in fields. We conclude by discussing the new disciplines working with the phenotyping community: data science, to address the challenge of generating FAIR (findable, accessible, interoperable, and reusable) data, and robotics, to apply phenotyping directly on farms. [ABSTRACT FROM AUTHOR]

Quantifying heat and mass exchanges processes of plant leaves is crucial for detailed understanding of dynamic plant-environment interactions. The two main components of these processes, convective heat transfer, and transpiration, are inevitably coupled as both processes are restricted by the leaf boundary layer. To measure leaf heat capacity and leaf heat transfer coefficient, we thoroughly tested and applied an active thermography method that uses a transient heat pulse to compute τ, the time constant of leaf cooling after release of the pulse. We validated our approach in the laboratory on intact leaves of spring barley (Hordeum vulgare) and common bean (Phaseolus vulgaris), and measured τ-changes at different boundary layer conditions.By modeling the leaf heat transfer coefficient with dimensionless numbers, we could demonstrate that τ improves our ability to close the energy budget of plant leaves and that modeling of transpiration requires considerations of convection. Applying our approach to thermal images we obtained spatio-temporal maps of τ, providing observations of local differences in thermal responsiveness of leaf surfaces. We propose that active thermography is an informative methodology to measure leaf heat transfer and derive spatial maps of thermal responsiveness of leaves contributing to improve models of leaf heat transfer processes. [ABSTRACT FROM AUTHOR]

Photosynthesis reacts dynamic and in different time scales to changing conditions. Light and temperature acclimation balance photosynthetic processes in a complex interplay with the fluctuating environment. However, due to limitations in the measurements techniques, these acclimations are often described under steady-state conditions leading to inaccurate photosynthesis estimates in the field. Here we analyze the photosynthetic interaction with the fluctuating environment and canopy architecture over two seasons using a fully automated phenotyping system. We acquired over 700,000 chlorophyll fluorescence transients and spectral measurements under semi-field conditions in four crop species including 28 genotypes. As expected, the quantum efficiency of the photosystem II (Fv/Fm in the dark and Fq'/Fm' in the light) was determined by light intensity. It was further significantly affected by spectral indices representing canopy structure effects. In contrast, a newly established parameter, monitoring the efficiency of electron transport (Fr2/Fv in the dark respective Fr2'/Fq' in the light), was highly responsive to temperature (R2 up to 0.75). This parameter decreased with temperature and enabled the detection of cold tolerant species and genotypes. We demonstrated the ability to capture and model the dynamic photosynthesis response to the environment over entire growth seasons. The improved linkage of photosynthetic performance to canopy structure, temperature and cold tolerance offers great potential for plant breeding and crop growth modeling. [ABSTRACT FROM AUTHOR]

Photosynthetic phenotyping requires quick characterization of dynamic traits when measuring large plant numbers in a fluctuating environment. Here, we evaluated the light-induced fluorescence transient (LIFT) method for its capacity to yield rapidly fluorometric parameters from 0.6 m distance. The close approximation of LIFT to conventional chlorophyll fluorescence (ChlF) parameters is shown under controlled conditions in spinach leaves and isolated thylakoids when electron transport was impaired by anoxic conditions or chemical inhibitors. The ChlF rise from minimum fluorescence (Fo) to maximum fluorescence induced by fast repetition rate (Fm−FRR) flashes was dominated by reduction of the primary electron acceptor in photosystem II (QA). The subsequent reoxidation of QA− was quantified using the relaxation of ChlF in 0.65 ms (Fr1) and 120 ms (Fr2) phases. Reoxidation efficiency of QA− (Fr1/Fv, where Fv = Fm−FRR − Fo) decreased when electron transport was impaired, while quantum efficiency of photosystem II (Fv/Fm) showed often no significant effect. ChlF relaxations of the LIFT were similar to an independent other method. Under increasing light intensities, Fr2′/Fq′ (where Fr2′ and Fq′ represent Fr2 and Fv in the light-adapted state, respectively) was hardly affected, whereas the operating efficiency of photosystem II (Fq′/Fm′) decreased due to non-photochemical quenching. Fm−FRR was significantly lower than the ChlF maximum induced by multiple turnover (Fm−MT) flashes. However, the resulting Fv/Fm and Fq′/Fm′ from both flashes were highly correlated. The LIFT method complements Fv/Fm with information about efficiency of electron transport. Measurements in situ and from a distance facilitate application in high-throughput and automated phenotyping. [ABSTRACT FROM AUTHOR]

Abstract: For the first time, we combine depth‐specific soil information obtained from the quantitative inversion of ground‐based multicoil electromagnetic induction data with the airborne hyperspectral vegetation mapping of 1 × 1‐m pixels including Sun‐induced fluorescence (F) to understand how subsurface structures drive above‐surface plant performance. Hyperspectral data were processed to quantitative F and selected biophysical canopy maps, which are proxies for actual photosynthetic rates. These maps showed within‐field spatial patterns, which were attributed to paleo‐river channels buried at around 1‐m depth. The soil structures at specific depths were identified by quantitative electromagnetic induction data inversions and confirmed by soil samples. Whereas the upper plowing layer showed minor correlation to the plant data, the deeper subsoil carrying vital plant resources correlated substantially. Linking depth‐specific soil information with plant performance data may greatly improve our understanding and the modeling of soil‐vegetation‐atmosphere exchange processes. [ABSTRACT FROM AUTHOR]

Background Photosynthesis underpins plant productivity and yet is notoriously sensitive to small changes in environmental conditions, meaning that quantitation in nature across different time scales is not straightforward. The ‘dynamic’ changes in photosynthesis (i.e. the kinetics of the various reactions of photosynthesis in response to environmental shifts) are now known to be important in driving crop yield. Scope It is known that photosynthesis does not respond in a timely manner, and even a small temporal ‘mismatch’ between a change in the environment and the appropriate response of photosynthesis toward optimality can result in a fall in productivity. Yet the most commonly measured parameters are still made at steady state or a temporary steady state (including those for crop breeding purposes), meaning that new photosynthetic traits remain undiscovered. Conclusions There is a great need to understand photosynthesis dynamics from a mechanistic and biological viewpoint especially when applied to the field of ‘phenomics’ which typically uses large genetically diverse populations of plants. Despite huge advances in measurement technology in recent years, it is still unclear whether we possess the capability of capturing and describing the physiologically relevant dynamic features of field photosynthesis in sufficient detail. Such traits are highly complex, hence we dub this the ‘photosynthome’. This review sets out the state of play and describes some approaches that could be made to address this challenge with reference to the relevant biological processes involved. [ABSTRACT FROM AUTHOR]

Abstract: Leaf fluorescence can be used to track plant development and stress, and is considered the most direct measurement of photosynthetic activity available from remote sensing techniques. Red and far‐red sun‐induced chlorophyll fluorescence (SIF) maps were generated from high spatial resolution images collected with the HyPlant airborne spectrometer over even‐aged loblolly pine plantations in North Carolina (United States). Canopy fluorescence yield (i.e., the fluorescence flux normalized by the light absorbed) in the red and far‐red peaks was computed. This quantifies the fluorescence emission efficiencies that are more directly linked to canopy function compared to SIF radiances. Fluorescence fluxes and yields were investigated in relation to tree age to infer new insights on the potential of those measurements in better describing ecosystem processes. The results showed that red fluorescence yield varies with stand age. Young stands exhibited a nearly twofold higher red fluorescence yield than mature forest plantations, while the far‐red fluorescence yield remained constant. We interpreted this finding in a context of photosynthetic stomatal limitation in aging loblolly pine stands. Current and future satellite missions provide global datasets of SIF at coarse spatial resolution, resulting in intrapixel mixture effects, which could be a confounding factor for fluorescence signal interpretation. To mitigate this effect, we propose a surrogate of the fluorescence yield, namely the Canopy Cover Fluorescence Index (CCFI) that accounts for the spatial variability in canopy structure by exploiting the vegetation fractional cover. It was found that spatial aggregation tended to mask the effective relationships, while the CCFI was still able to maintain this link. This study is a first attempt in interpreting the fluorescence variability in aging forest stands and it may open new perspectives in understanding long‐term forest dynamics in response to future climatic conditions from remote sensing of SIF. [ABSTRACT FROM AUTHOR]

The photosynthetic, optical, and morphological characteristics of a chlorophyll-deficient (Chldeficient) "yellow" soybean mutant (MinnGold) were examined in comparison with 2 green varieties (MN0095 and Eiko). Despite the large difference in Chl content, similar leaf photosynthesis rates were maintained in the Chl-deficient mutant by offsetting the reduced absorption of red photons by a small increase in photochemical efficiency and lower non-photochemical quenching. When grown in the field, at full canopy cover, the mutants reflected a significantly larger proportion of incoming shortwave radiation, but the total canopy light absorption was only slightly reduced, most likely due to a deeper penetration of light into the canopy space. As a consequence, canopy-scale gross primary production and ecosystem respiration were comparable between the Chl-deficient mutant and the green variety. However, total biomass production was lower in the mutant, which indicates that processes other than steady state photosynthesis caused a reduction in biomass accumulation over time. Analysis of non-photochemical quenching relaxation and gas exchange in Chl-deficient and green leaves after transitions from high to low light conditions suggested that dynamic photosynthesis might be responsible for the reduced biomass production in the Chl-deficient mutant under field conditions. [ABSTRACT FROM AUTHOR]

Abstract: Plant species that arrive first in the system can affect assembly (priority effects). However, effects of order of arrival of different plant functional groups (PFGs) on root development have not yet been investigated under field conditions. We measured standing and fine root length density in the first and third year of a grassland field experiment. We wanted to know if manipulating PFG order of arrival would affect root development, and if priority effects are modulated by soil type. Sowing legumes first created a priority effect that was found in the first and third year, with a lower standing root length density in this treatment, even though the plant community composition was different in each of the studied years. Fine root length density was not affected by order of arrival, but changed according to the soil type. Synthesis. We found strong evidence that sowing legumes first created a priority effect below‐ground that was found in the first and third year of this field experiment, even though the functional group dominance was different in each of the studied years. [ABSTRACT FROM AUTHOR]

Hyperspectral imaging sensors are promising tools for monitoring crop plants or vegetation in different environments. Information on physiology, architecture or biochemistry of plants can be assessed non-invasively and on different scales. For instance, hyperspectral sensors are implemented for stress detection in plant phenotyping processes or in precision agriculture. Up to date, a variety of non-imaging and imaging hyperspectral sensors is available. The measuring process and the handling of most of these sensors is rather complex. Thus, during the last years the demand for sensors with easy user operability arose. The present study introduces the novel hyperspectral camera Specim IQ from Specim (Oulu, Finland). The Specim IQ is a handheld push broom system with integrated operating system and controls. Basic data handling and data analysis processes, such as pre-processing and classification routines are implemented within the camera software. This study provides an introduction into the measurement pipeline of the Specim IQ as well as a radiometric performance comparison with a well-established hyperspectral imager. Case studies for the detection of powdery mildew on barley at the canopy scale and the spectral characterization of Arabidopsis thaliana mutants grown under stressed and non-stressed conditions are presented. [ABSTRACT FROM AUTHOR]

The first European Space Agency (ESA) and NASA collaboration in an airborne campaign to support ESA's FLuorescence EXplorer (FLEX) mission was conducted in North Carolina, USA during September-October 2013 (FLEX-US 2013) at the Parker Tract Loblolly Pine (LP) Plantation (Plymouth, NC, USA). This campaign combined two unique airborne instrument packages to obtain simultaneous observations of solar-induced fluorescence (SIF), LiDAR-based canopy structural information, visible through shortwave infrared (VSWIR) reflectance spectra, and surface temperature, to advance vegetation studies of carbon cycle dynamics and ecosystem health. We obtained statistically significant results for fluorescence, canopy temperature, and tower fluxes from data collected at four times of day over two consecutive autumn days across an age class chronosequence. Both the red fluorescence (F685) and far-red fluorescence (F740) radiances had highest values at mid-day, but their fluorescence yields exhibited different diurnal responses across LP age classes. The diurnal trends for F685 varied with forest canopy temperature difference (canopy minus air), having a stronger daily amplitude change for young vs. old canopies. The Photochemical Reflectance Index (PRI) was positively correlated with this temperature variable over the diurnal cycle. Tower measurements from mature loblolly stand showed the red/far-red fluorescence ratio was linearly related to canopy light use efficiency (LUE) over the diurnal cycle, but performed even better for the combined morning/afternoon (without midday) observations. This study demonstrates the importance of diurnal observations for interpretation of fluorescence dynamics, the need for red fluorescence to understand canopy physiological processes, and the benefits of combining fluorescence, reflectance, and structure information to clarify canopy function versus structure characteristics for a coniferous forest. [ABSTRACT FROM AUTHOR]

The effect that the canopy structure and the viewing geometry have on the intensity and the spatial distribution of passively measured sun-induced chlorophyll fluorescence at canopy scale is still not well understood. These uncertainties constrain the potential use of fluorescence to quantify photosynthesis at this level. Using a novel technique, we evaluated the diurnal changes in the spatial distribution of sun-induced fluorescence at 760 nm (F760) within the canopy as a consequence of the spatial disposition of the leaves and the viewing angle of the sensor. High resolution spectral and stereo images of a full sugar beet canopy were recorded simultaneously in the field to estimate maps of F760 and the surface angle distribution, respectively. A dedicated algorithm was used to align both maps in the post-processing and its accuracy was evaluated using a sensitivity test. The relative angle between sun and the leaf surfaces primarily determined the amount of incident Photosynthetic Active Radiation (PAR), which in turn was reflected in different values of F760, with the highest values occurring in leaf surfaces that are perpendicularly oriented to the sun. The viewing angle of the sensor also had an impact in the intensity of the recorded F760. Higher viewing angles generally resulted in higher values of F760. We attribute these changes to a direct effect of the vegetation directional reflectance response on fluorescence retrieval. Consequently, at leaf surface level, the spatio-temporal variations of F760 were mainly explained by the sun–leaf–sensor geometry rather than directionality of the fluorescence emission. At canopy scale, the diurnal patterns of F760 observed on the top-of-canopy were attributed to the complex interplay between the light penetration into the canopy as a function of the display of the various leaves and the fluorescence emission of each leaf which is modulated by the exposure of the individual leaf patch to the incoming light and the functional status of photosynthesis. We expect that forward modeling can help derive analytical simplified skeleton assumptions to scale canopy measurements to the leaf functional properties. [ABSTRACT FROM AUTHOR]

Sun-induced fluorescence ( SIF) in the far-red region provides a new noninvasive measurement approach that has the potential to quantify dynamic changes in light-use efficiency and gross primary production ( GPP). However, the mechanistic link between GPP and SIF is not completely understood., We analyzed the structural and functional factors controlling the emission of SIF at 760 nm (F760) in a Mediterranean grassland manipulated with nutrient addition of nitrogen (N), phosphorous (P) or nitrogen-phosphorous ( NP). Using the soil-canopy observation of photosynthesis and energy (SCOPE) model, we investigated how nutrient-induced changes in canopy structure (i.e. changes in plant forms abundance that influence leaf inclination distribution function, LIDF) and functional traits (e.g. N content in dry mass of leaves, N%, Chlorophyll a+b concentration ( Cab) and maximum carboxylation capacity ( Vcmax)) affected the observed linear relationship between F760 and GPP., We conclude that the addition of nutrients imposed a change in the abundance of different plant forms and biochemistry of the canopy that controls F760. Changes in canopy structure mainly control the GPP-F760 relationship, with a secondary effect of Cab and Vcmax., In order to exploit F760 data to model GPP at the global/regional scale, canopy structural variability, biodiversity and functional traits are important factors that have to be considered. [ABSTRACT FROM AUTHOR]

In November 2015, the FLuorescence EXplorer (FLEX) was selected as the eighth Earth Explorer mission of the European Space Agency. The tandem mission concept will provide measurements at a spectral and spatial resolution enabling the retrieval and interpretation of the full chlorophyll fluorescence spectrum emitted by the terrestrial vegetation. This paper provides a mission concept overview of the scientific goals, the key objectives related to fluorescence, and the requirements guaranteeing the fitness for purpose of the resulting scientific data set. We present the mission design at the time of selection, i.e., at the end of project phase Phase A/B1, as developed by two independent industrial consortia. The mission concepts both rely on a single payload Fluorescence Imaging Spectrometer, covering the spectral range from 500 to 780 nm. In the oxygen absorption bands, its spectral resolution will be 0.3 nm with a spectral sampling interval of 0.1 nm. The swath width of the spectrometer is 150 km and the spatial resolution will be 300 \times 300~\textm^2 . The satellite will fly in tandem with Sentinel-3 providing different and complementary measurements with a temporal collocation of 6 to 15 s. The FLEX launch is scheduled for 2022. [ABSTRACT FROM AUTHOR]

Hyperspectral imaging sensors are valuable tools for plant disease detection and plant phenotyping. Reflectance properties are influenced by plant pathogens and resistance responses, but changes of transmission characteristics of plants are less described. In this study we used simultaneously recorded reflectance and transmittance imaging data of resistant and susceptible barley genotypes that were inoculated with Blumeria graminis f. sp. hordei to evaluate the added value of imaging transmission, reflection and absorption for characterisation of disease development. These datasets were statistically analysed using principal component analysis, and compared with visual and molecular disease estimation. Reflection measurement performed significantly better for early detection of powdery mildew infection, colonies could be detected 2 days before symptoms became visible in RGB images. Transmission data could be used to detect powdery mildew 2 days after symptoms becoming visible in reflection based RGB images. Additionally distinct transmission changes occurred at 580-650 nm for pixels containing disease symptoms. It could be shown that the additional information of the transmission data allows for a clearer spatial differentiation and localisation between powdery mildew symptoms and necrotic tissue on the leaf then purely reflectance based data. Thus the information of both measurement approaches are complementary: reflectance based measurements facilitate an early detection, and transmission measurements provide additional information to better understand and quantify the complex spatio-temporal dynamics of plant-pathogen interactions. [ABSTRACT FROM AUTHOR]

Passive detection of sun-induced chlorophyll fluorescence (SIF) using spectroscopy has been proposed as a proxy to quantify changes in photochemical efficiency at canopy level under natural light conditions. In this study, we explored the use of imaging spectroscopy to quantify spatio-temporal dynamics of SIF within crop canopies and its sensitivity to track patterns of photosynthetic activity originating from the interaction between vegetation structure and incoming radiation as well as variations in plant function. SIF was retrieved using the Fraunhofer Line Depth (FLD) principle from imaging spectroscopy data acquired at different time scales a few metres above several crop canopies growing under natural illumination. We report the first maps of canopy SIF in high spatial resolution. Changes of SIF were monitored at different time scales ranging from quick variations under induced stress conditions to seasonal dynamics. Natural changes were primarily determined by varying levels and distribution of photosynthetic active radiation (PAR). However, this relationship changed throughout the day demonstrating an additional physiological component modulating spatio-temporal patterns of SIF emission. We successfully used detailed SIF maps to track changes in the canopy's photochemical activity under field conditions, providing a new tool to evaluate complex patterns of photosynthesis within the canopy. [ABSTRACT FROM AUTHOR]

Sun-induced chlorophyll fluorescence (F) is a novel remote sensing parameter providing an estimate of actual photosynthetic rates. A combination of this new observable and Monteith's light use efficiency (LUE) concept was suggested for an advanced modeling of gross primary productivity (GPP). In this demonstration study, we evaluate the potential of both F and the more commonly used photochemical reflectance index (PRI) to approximate the LUE term in Monteith's equation and eventually improve the forward modeling of GPP diurnals. Both F and the PRI were derived from ground and airborne based spectrometer measurements over two different crops. We demonstrate that approximating dynamic changes of LUE using F and PRI significantly improves the forward modeling of GPP diurnals. Especially in sugar beet, a changing photosynthetic efficiency during the day was traceable with F and incorporating F in the forward modeling significantly improved the estimation of GPP. Airborne data were projected to produce F and PRI maps for winter wheat and sugar beet fields over the course of one day. We detected a significant variability of both, F and the PRI within one field and particularly between fields. The variability of F and PRI was higher in sugar beet, which also showed a physiological down-regulation of leaf photosynthesis. Our results underline the potential of F to serve as a superior indicator for the actual efficiency of the photosynthetic machinery, which is linked to physiological responses of vegetation. [ABSTRACT FROM AUTHOR]

Studies on the function of root traits and the genetic variation in these traits are often conducted under controlled conditions using individual potted plants. Little is known about root growth under field conditions and how root traits are affected by agronomic practices in particular sowing density. We hypothesized that with increasing sowing density, root length density (root length per soil volume, cm cm-3) increases in the topsoil as well as specific root length (root length per root dry weight, cm g-1) due to greater investment in fine roots. Therefore, we studied two spring barley cultivars at ten different sowing densities (24-340 seeds m-2) in 2 consecutive years in a clay loam field in Germany and established sowing density dose-response curves for several root and shoot traits. We took soil cores for measuring roots up to a depth of 60cm in and between plant rows (inter-row distance 21 cm). Root length density increased with increasing sowing density and was greatest in the plant row in the topsoil (0-10 cm). Greater sowing density increased specific root length partly through greater production of fine roots in the topsoil. Rooting depth (D50) of the major root axes (root diameter class 0.4-1.0 mm) was not affected. Root mass fraction decreased, while stem mass fraction increased with sowing density and over time. Leaf mass fraction was constant over sowing density but greater leaf area was realized through increased specific leaf area. Considering fertilization, we assume that light competition caused plants to grow more shoot mass at the cost of investment into roots, which is partly compensated by increased specific root length and shallow rooting. Increased biomass per area with greater densities suggest that density increases the efficiency of the cropping system, however, declines in harvest index at densities over 230 plants m-2 suggest that this efficiency did not translate into greater yield. We conclude that plant density is a modifier of root architecture and that root traits and their utility in breeding for greater productivity have to be understood in the context of high sowing densities. [ABSTRACT FROM AUTHOR]

Remote Sensing of Sun-Induced Chlorophyll Fluorescence (SIF) is a research field of growing interest because it offers the potential to quantify actual photosynthesis and to monitor plant status. New satellite missions from the European Space Agency, such as the Earth Explorer 8 FLuorescence EXplorer (FLEX) mission--scheduled to launch in 2022 and aiming at SIF mapping--and from the National Aeronautics and Space Administration (NASA) such as the Orbiting Carbon Observatory-2 (OCO-2) sampling mission launched in July 2014, provide the capability to estimate SIF from space. The detection of the SIF signal from airborne and satellite platform is difficult and reliable ground level data are needed for calibration/validation. Several commercially available spectroradiometers are currently used to retrieve SIF in the field. This study presents a comparison exercise for evaluating the capability of four spectroradiometers to retrieve SIF. The results show that an accurate far-red SIF estimation can be achieved using spectroradiometers with an ultrafine resolution (less than 1 nm), while the red SIF estimation requires even higher spectral resolution (less than 0.5 nm). Moreover, it is shown that the Signal to Noise Ratio (SNR) plays a significant role in the precision of the far-red SIF measurements. [ABSTRACT FROM AUTHOR]

Cercospora leaf spot (CLS) infection can cause severe yield loss in sugar beet. Introduction of Cercospora-resistant varieties in breeding programmes is important for plant protection to reduce both fungicide applications and the risk of the development of fungal resistance. However, in vivo monitoring of the sugar-containing taproots at early stages of foliar symptoms and the characterization of the temporal development of disease progression has proven difficult. Non-invasive magnetic resonance imaging (MRI) measurements were conducted to quantify taproot development of genotypes with high (HS) and low (LS) levels of susceptibility after foliar Cercospora inoculation. Fourteen days postinoculation (dpi) the ratio of infected leaf area was still low (~7%) in both the HS and LS genotypes. However, during this period, the volumetric growth of the taproot had already started to decrease. Additionally, inoculated plants showed a reduction of the increase in width of inner cambial rings while the width of outer rings increased slightly compared with non-inoculated plants. This response partly compensated for the reduced development of inner rings that had a vascular connection with Cercospora-inoculated leaves. Hence, alterations in taproot anatomical features such as volume and cambial ring development can be non-invasively detected already at 14 dpi, providing information on the early impact of the infection on whole-plant performance. All these findings show that MRI is a suitable tool to identify promising candidate parent lines with improved resistance to Cercospora, for example with comparatively lower taproot growth reduction at early stages of canopy infection, for future introduction into breeing programmes. [ABSTRACT FROM AUTHOR]

Rice farming in Northeast China is crucially important for China's food security and sustainable development. A key challenge is how to optimize nitrogen (N) management to ensure high yield production while improving N use efficiency and protecting the environment. Handheld chlorophyll meter (CM) and active crop canopy sensors have been used to improve rice N management in this region. However, these technologies are still time consuming for large-scale applications. Satellite remote sensing provides a promising technology for large-scale crop growth monitoring and precision management. The objective of this study was to evaluate the potential of using FORMOSAT-2 satellite images to diagnose rice N status for guiding topdressing N application at the stem elongation stage in Northeast China. Five farmers' fields (three in 2011 and two in 2012) were selected from the Qixing Farm in Heilongjiang Province of Northeast China. FORMOSAT-2 satellite images were collected in late June. Simultaneously, 92 field samples were collected and six agronomic variables, including aboveground biomass, leaf area index (LAI), plant N concentration (PNC), plant N uptake (PNU), CM readings and N nutrition index (NNI) defined as the ratio of actual PNC and critical PNC, were determined. Based on the FORMOSAT-2 imagery, a total of 50 vegetation indices (VIs) were computed and correlated with the field-based agronomic variables. Results indicated that 45% of NNI variability could be explained using Ratio Vegetation Index 3 (RVI3) directly across years. A more practical and promising approach was proposed by using satellite remote sensing to estimate aboveground biomass and PNU at the panicle initiation stage and then using these two variables to estimate NNI indirectly (R² = 0.52 across years). Further, the difference between the estimated PNU and the critical PNU can be used to guide the topdressing N application rate adjustments. [ABSTRACT FROM AUTHOR]

Measurements of sun-induced chlorophyll fluorescence (SIF) over plant canopies provide a proxy for plant photosynthetic capacity and are of high interest for plant research. Together with spectral reflectance, SIF has the potential to act as a noninvasive approach to quantify photosynthetic plant traits from field to air and spaceborne scales. However, SIF is a small signal contribution to the reflected sunlight and often not distinguishable from sensor noise. SIF estimation is, therefore, affected by an unquantified uncertainty, making it difficult to estimate accurately how much SIF is truly emitted from the plant. To investigate and overcome this, we designed a device based on a spectrometer covering the visible range and equipped it with an LED emitting at the wavelength of SIF. Using this as a reference and applying thorough calibrations, we present consistent evidence of the instrument’s capability of SIF retrieval and accuracy estimations. The LED’s intensity was measured under sunlight with 1.27 \pm 0.27\textrm mW\times \textrm sr^-1\textm^-2 nm ^-1 stable over the day. The large increase of SIF due to the Kautsky effect was measured spectrally and temporally proving the biophysical origin of the signal. We propose rigorous tests for instruments intended to measure SIF and show ways to further improve the presented methods. [ABSTRACT FROM PUBLISHER]

Background: Three-dimensional canopies form complex architectures with temporally and spatially changing leaf orientations. Variations in canopy structure are linked to canopy function and they occur within the scope of genetic variability as well as a reaction to environmental factors like light, water and nutrient supply, and stress. An important key measure to characterize these structural properties is the leaf angle distribution, which in turn requires knowledge on the 3-dimensional single leaf surface. Despite a large number of 3-d sensors and methods only a few systems are applicable for fast and routine measurements in plants and natural canopies. A suitable approach is stereo imaging, which combines depth and color information that allows for easy segmentation of green leaf material and the extraction of plant traits, such as leaf angle distribution. Results: We developed a software package, which provides tools for the quantification of leaf surface properties within natural canopies via 3-d reconstruction from stereo images. Our approach includes a semi-automatic selection process of single leaves and different modes of surface characterization via polygon smoothing or surface model fitting. Based on the resulting surface meshes leaf angle statistics are computed on the whole-leaf level or from local derivations. We include a case study to demonstrate the functionality of our software. 48 images of small sugar beet populations (4 varieties) have been analyzed on the base of their leaf angle distribution in order to investigate seasonal, genotypic and fertilization effects on leaf angle distributions. We could show that leaf angle distributions change during the course of the season with all varieties having a comparable development. Additionally, different varieties had different leaf angle orientation that could be separated in principle component analysis. In contrast nitrogen treatment had no effect on leaf angles. Conclusions: We show that a stereo imaging setup together with the appropriate image processing tools is capable of retrieving the geometric leaf surface properties of plants and canopies. Our software package provides whole-leaf statistics but also a local estimation of leaf angles, which may have great potential to better understand and quantify structural canopy traits for guided breeding and optimized crop management. [ABSTRACT FROM AUTHOR]