Scientific and technical challenges in remote sensing of plant canopy reflectance and fluorescence

Hyperspectral and Fluorescence Imaging Approaches for Nondestructive Detection of Rice Chlorophyll

Estimating and monitoring chlorophyll content is a critical step in crop spectral image analysis. The quick, non-destructive assessment of chlorophyll content in rice leaves can optimize nitrogen fertilization, benefit the environment and economy, and improve rice production management and quality. In this research, spectral analysis of rice leaves is performed using hyperspectral and fluorescence spectroscopy for the detection of chlorophyll content in rice leaves. This study generated ninety experimental spectral datasets by collecting rice leaf samples from a farm in Sichuan Province, China. By implementing a feature extraction algorithm, this study compresses redundant spectral bands and subsequently constructs machine learning models to reveal latent correlations among the extracted features. The prediction capabilities of six feature extraction methods and four machine learning algorithms in two types of spectral data are examined, and an accurate method of predicting chlorophyll concentration in rice leaves was devised. The IVSO-IVISSA (Iteratively Variable Subset Optimization-Interval Variable Iterative Space Shrinkage Approach) quadratic feature combination approach, based on fluorescence spectrum data, has the best prediction performance among the CNN+LSTM (Convolutional Neural Network Long Short-Term Memory) algorithms, with corresponding RMSE-Train (Root Mean Squared Error), RMSE-Test, and RPD (Ratio of standard deviation of the validation set to standard error of prediction) indexes of 0.26, 0.29, and 2.64, respectively. We demonstrated in this study that hyperspectral and fluorescence spectroscopy, when analyzed with feature extraction and machine learning methods, provide a new avenue for rapid and non-destructive crop health monitoring, which is critical to the advancement of smart and precision agriculture.

Spectral Reflectance Measurements

This chapter provides a methodology for evaluating plant health and leaf characteristics using spectral reflectance. It provides a step-by-step guide to using spectrometers for high-resolution point measurements of leaf spectral reflectance and multispectral imaging for capturing spatial data, emphasizing the importance of consistent measurement conditions. The chapter further explores the intricacies of multispectral imaging, including calibration, data collection, and image processing. Finally, this chapter delves into the application of various spectral indices for the quantification of key traits such as pigment content, the status of the xanthophyll cycle, water content, and how to identify spectral regions of interest for further research and development. Serving as a guide for researchers and practitioners in plant science, this chapter provides a straightforward framework for plant health assessment using spectral reflectance.

Inhibition of non-photochemical quenching increases functional absorption cross-section of photosystem II as excitation from closed reaction centres is transferred to open centres, facilitating earlier light saturation of photosynthetic electron transport

Induction of non-photochemical quenching (NPQ) of chlorophyll fluorescence in leaves affords photoprotection to the photosynthetic apparatus when, for whatever reason, photon capture in the antennae of photosystems exceeds their capacity to utilise this excitation in photochemistry and ultimately in CO2 assimilation. Here we augment traditional monitoring of NPQ using the fast time resolution, remote and relatively non-intrusive light induced fluorescence transient (LIFT) technique (Kolber et al . 2005 ; Osmond et al . 2017 ) that allows direct measurement of functional (σ'PSII ) and optical cross-sections (a 'PSII ) of PSII in situ , and calculates the half saturation light intensity for ETR (E k ). These parameters are obtained from the saturation and relaxation phases of fluorescence transients elicited by a sequence of 270, high intensity 1 μs flashlets at controlled time intervals over a period of 30 ms in the QA flash at intervals of a few seconds. We report that although σ'PSII undergoes large transient increases after transfer from dark to strong white light (WL) it declines little in steady-state as NPQ is induced in shade- and sun-grown spinach and Arabidopsis genotypes Col , OEpsbs , pgr 5bkg , stn 7 and stn 7/8. In contrast, σ'PSII increases by ~30% when induction of NPQ in spinach is inhibited by dithiothreitol and by inhibition of NPQ in Arabidopsis npq 1, npq 4 and pgr 5. We propose this increase in σ'PSII arises as some excitation from closed PSII reaction centres is transferred to open centres when excitation partitioning to photochemistry (Y II ) and NPQ (Y NP ) declines, and is indicated by an increased excitation dissipation from closed PSII centres (Y NO , including fluorescence emission). Although E k increases following dissipation of excitation as heat when NPQ is engaged, it declines when NPQ is inhibited. Evidently photochemistry becomes more easily light saturated when excitation is transferred from closed RCIIs to open centres with larger σ'PSII . The NPQ mutant pgr 5 is an exception; E k increases markedly in strong light as electron transport QA → PQ and PQ → PSI accelerate and the PQ pool becomes strongly reduced. These novel in situ observations are discussed in the context of contemporary evidence for functional and structural changes in the photosynthetic apparatus during induction of NPQ.

Evaluation of Plant Stress Monitoring Capabilities Using a Portable Spectrometer and Blue-Red Grow Light

Remote sensing offers a non-destructive method to detect plant physiological response to the environment by measuring chlorophyll fluorescence (CF). Most methods to estimate CF require relatively complex retrieval, spectral fitting, or modelling methods. An investigation was undertaken to evaluate measurements of CF using a relatively straightforward technique to detect and monitor plant stress with a spectroradiometer and blue-red light emitting diode (LED). CF spectral response of tomato plants treated with a photosystem inhibitor were assessed and compared to traditional reflectance-based indices: normalized difference vegetation index (NDVI) and photochemical reflectance index (PRI). The blue-red LEDs provided input irradiance and a “window” in the CF emission range of plants (~650 to 850 nm) sufficient to capture distinctive “two-peak” spectra and to distinguish plant health from day to day of the experiment, while within day differences were noisy. CF-based metrics calculated from CF spectra clearly captured signs of vegetation stress earlier than reflectance-based indices and by visual inspection. This CF monitoring technique is a flexible and scalable option for collecting plant function data, especially for indicating early signs of stress. The technique can be applied to a single plant or larger canopies using LED in dark conditions by an individual, or a manned or unmanned vehicle for agricultural or military purposes.

Prediction of Photosynthetic, Biophysical, and Biochemical Traits in Wheat Canopies to Reduce the Phenotyping Bottleneck

To achieve food security, it is necessary to increase crop radiation use efficiency (RUE) and yield through the enhancement of canopy photosynthesis to increase the availability of assimilates for the grain, but its study in the field is constrained by low throughput and the lack of integrative measurements at canopy level. In this study, partial least squares regression (PLSR) was used with high-throughput phenotyping (HTP) data in spring wheat to build predictive models of photosynthetic, biophysical, and biochemical traits for the top, middle, and bottom layers of wheat canopies. The combined layer model predictions performed better than individual layer predictions with a significance as follows for photosynthesis R ² = 0.48, RMSE = 5.24 μmol m^-2 s^-1 and stomatal conductance: R ² = 0.36, RMSE = 0.14 mol m^-2 s^-1. The predictions of these traits from PLSR models upscaled to canopy level compared to field observations were statistically significant at initiation of booting (R ² = 0.3, p < 0.05; R ² = 0.29, p < 0.05) and at 7 days after anthesis (R ² = 0.15, p < 0.05; R ² = 0.65, p < 0.001). Using HTP allowed us to increase phenotyping capacity 30-fold compared to conventional phenotyping methods. This approach can be adapted to screen breeding progeny and genetic resources for RUE and to improve our understanding of wheat physiology by adding different layers of the canopy to physiological modeling.

Application of Reflectance Indices for Remote Sensing of Plants and Revealing Actions of Stressors

Environmental conditions are very changeable; fluctuations in temperature, precipitation, illumination intensity, and other factors can decrease a plant productivity and crop. The remote sensing of plants under these conditions is the basis for the protection of plants and increases their survivability. This problem can be solved through measurements of plant reflectance and calculation of reflectance indices. Reflectance indices are related to the vegetation biomass, specific physiological processes, and biochemical compositions in plants; the indices can be used for both short-term and long-term plant monitoring. In our review, we considered the applications of reflectance indices in plant remote sensing. In Optical Methods and Platforms of Remote Sensing of Plants, we briefly discussed multi- and hyperspectral imaging, including descriptions of multispectral and hyperspectral cameras with different principles and their efficiency for the remote sensing of plants. In Main Reflectance Indices, we described the main reflectance indices, including vegetation, water, and pigment reflectance indices, as well as the photochemical reflectance index and its modifications. We focused on the relationships of leaf reflectance and reflectance indices to plant biomass, development, and physiological and biochemical characteristics. In Problems of Measurement and Analysis of Reflectance Indices, we discussed the methods of the correction of the reflectance indices that can be used for decreasing the influence of environmental conditions (mainly illumination, air, and soil) and plant characteristics (orientation of leaves, their thickness, and others) on their measurements and the analysis of the plant remote sensing. Additionally, the variability of plants was also considered as an important factor that influences the results of measurement and analysis.

Hormesis effects of gamma radiation on growth of quinoa (Chenopodium quinoa)

PURPOSE

Quinoa is an annual plant that grows well in high altitude regions with high radiation and ultraviolet intensity. It has known that high-dose radiation damages living organisms, but low-dose radiation also has a beneficial effect. Therefore, the purpose of this study is to investigate the hormesis effect of gamma-ray on quinoa by growth analysis and hyperspectral imaging.

MATERIALS AND METHODS

Quinoa seeds were irradiated at 50, 100, and 200 Gy emitted by 60CO. Subsequently, the seeds were germinated and transplanted into pots, then conducted growth analysis and physiological evaluation every week, and hyperspectral imaging. Photosynthetic ability was measured at 35 days after transplanting (DAT), and the plants for each dose were divided into aerial and underground parts for biomass evaluation at 91 DAT. Various vegetation indices were estimated from 14 to 35 DAT by hyperspectral analysis, and the specific bands were extracted based on the PLS model using plant height, SPAD value, and chlorophyll fluorescence parameters.

RESULTS

We found that plant height and biomass were increased in quinoa plants treated with a low dose (50 Gy) as compared to control. Chlorophyll content and chlorophyll fluorescence were not different between doses at the early growth stage, but as growth progressed, the plant irradiated at 200 Gy began to be lower. The photosynthetic ability of the quinoa plant treated at 50 Gy was greater than other plants at 35 DAT. The vegetation indices related to the pigment status also were higher in the plants treated by irradiation at 50 Gy than the plants grown in other doses treatment units at the beginning of the growth. Using the PLS model we collected sensitive band wavelengths from hyperspectral image analysis. Among the collected bands, eight bands closely related to plant height, nine bands to chlorophyll content, and ten bands to chlorophyll fluorescence were identified.

CONCLUSION

Our results showed that the growth and physiological parameters of quinoa treated by low dose gamma irradiation to seeds were greater than that of control as well as the plant with higher doses. These findings confirm that the positive changes in the characteristics of quinoa with low dose radiation indicated that hormesis occurs at 50 Gy radiation.

Analyzing Ecological Vulnerability and Vegetation Phenology Response Using NDVI Time Series Data and the BFAST Algorithm

Identifying ecologically vulnerable areas and understanding the responses of phenology to negative changes in vegetation growth are important bases for ecological restoration. However, identifying ecologically vulnerable areas is difficult because it requires high spatial resolution and dense temporal resolution data over a long time period. In this study, a novel method is presented to identify ecologically vulnerable areas based on the normalized difference vegetation index (NDVI) time series from MOD09A1. Here, ecologically vulnerable areas are defined as those that experienced negative changes frequently and greatly in vegetation growth after the disturbances during 2000–2018. The number and magnitude of negative changes detected by the Breaks for Additive Season and Trend (BFAST) algorithm based on the NDVI time-series data were combined to identify ecologically vulnerable areas. TIMESAT was then used to extract the phenology metrics from an NDVI time series dataset to characterize the vegetation responses due to the abrupt negative changes detected by the BFAST algorithm. Focus was given to Jilin Province, a region of China known to be ecologically vulnerable because of frequent drought. The results showed that 13.52% of the study area, mostly in Jilin Province, is ecologically vulnerable. The vulnerability of trees is the lowest, while that of sparse vegetation is the highest. The response of phenology is such that the relative amount of vegetation biomass and length of the growing period were decreased by negative changes in growth for dense vegetation types. The present research results will be useful for the protection of environments being disturbed by regional ecological restoration.

Adjustments to SIF Aid the Interpretation of Drought Responses at the Caatinga of Northeast Brazil

Sun-Induced chlorophyll Fluorescence (SIF) relates directly to photosynthesis yield and stress but there are still uncertainties in its interpretation. Most of these uncertainties concern the influences of the emitting vegetation’s structure (e.g., leaf angles, leaf clumping) and biochemistry (e.g., chlorophyll content, other pigments) on the radiative transfer of fluorescent photons. The Caatinga is a large region in northeast Brazil of semiarid climate and heterogeneous vegetation, where such biochemical and structural characteristics can vary greatly even within a single hectare. With this study we aimed to characterize eleven years of SIF seasonal variation from Caatinga vegetation (2007 to 2017) and to study its responses to a major drought in 2012. Orbital SIF data from the instrument GOME-2 was used along with MODIS MAIAC EVI and NDVI. Environmental data included precipitation rate (TRMM), surface temperature (MODIS) and soil moisture (ESA CCI). To support the interpretation of SIF responses we used red and far-red SIF adjusted by the Sun’s zenith angle (SIF-SZA) and by daily Photosynthetically Active Radiation (dSIF). Furthermore, we also adjusted SIF through two contrasting formulations using NDVI data as proxy for structure and biochemistry, based on previous leaf-level and landscape level studies: SIF-Yield and SIF-Prod. Data was tested with time-series decomposition, rank correlation, spatial correlation and Linear Mixed Models (LMM). Results show that GOME-2 SIF and adjusted SIF formulations responded consistently to the observed environmental variation and showed a marked decrease in SIF emissions in response to a 2012 drought that was generally larger than the corresponding NDVI and EVI decreases. Drought sensitivity of SIF, as inferred from LMM slopes, was correlated to land cover at different regions of the Caatinga. This is the first study to show correlation between landscape-level SIF and an emergent property of ecosystems (i.e., resilience), showcasing the value of remotely sensed fluorescence for ecological studies.

Review of Top-of-Canopy Sun-Induced Fluorescence (SIF) Studies from Ground, UAV, Airborne to Spaceborne Observations

Remote sensing (RS) of sun-induced fluorescence (SIF) has emerged as a promising indicator of photosynthetic activity and related stress from the leaf to the ecosystem level. The implementation of modern RS technology on SIF is highly motivated by the direct link of SIF to the core of photosynthetic machinery. In the last few decades, a lot of studies have been conducted on SIF measurement techniques, retrieval algorithms, modeling, application, validation, and radiative transfer processes, incorporating different RS observations (i.e., ground, unmanned aerial vehicle (UAV), airborne, and spaceborne). These studies have made a significant contribution to the enrichment of SIF science over time. However, to realize the potential of SIF and to explore its full spectrum using different RS observations, a complete document of existing SIF studies is needed. Considering this gap, we have performed a detailed review of current SIF studies from the ground, UAV, airborne, and spaceborne observations. In this review, we have discussed the in-depth interpretation of each SIF study using four RS platforms. The limitations and challenges of SIF studies have also been discussed to motivate future research and subsequently overcome them. This detailed review of SIF studies will help, support, and inspire the researchers and application-based users to consider SIF science with confidence.

Estimating leaf nitrogen concentration based on the combination with fluorescence spectrum and first-derivative

Leaf nitrogen concentration (LNC) is a major indicator in the estimation of the crop growth status which has been diffusely applied in remote sensing. Thus, it is important to accurately obtain LNC by using passive or active technology. Laser-induced fluorescence can be applied to monitor LNC in crops through analysing the changing of fluorescence spectral information. Thus, the performance of fluorescence spectrum (FS) and first-derivative fluorescence spectrum (FDFS) for paddy rice (Yangliangyou 6 and Manly Indica) LNC estimation was discussed, and then the proposed FS + FDFS was used to monitor LNC by multivariate analysis. The results showed that the difference between FS (R ² = 0.781, s.d. = 0.078) and FDFS (R ² = 0.779, s.d. = 0.097) for LNC estimation by using the artificial neural network is not obvious. The proposed FS + FDFS can improved the accuracy of LNC estimation to some extent (R ² = 0.813, s.d. = 0.051). Then, principal component analysis was used in FS and FDFS, and extracted the main fluorescence characteristics. The results indicated that the proposed FS + FDFS exhibited higher robustness and stability for LNC estimation (R ² = 0.851, s.d. = 0.032) than that only using FS (R ² = 0.815, s.d. = 0.059) or FDFS (R ² = 0.801, s.d. = 0.065).

Remote sensing of solar-induced chlorophyll fluorescence (SIF) in vegetation: 50 years of progress

Remote sensing of solar-induced chlorophyll fluorescence (SIF) is a rapidly advancing front in terrestrial vegetation science, with emerging capability in space-based methodologies and diverse application prospects. Although remote sensing of SIF - especially from space - is seen as a contemporary new specialty for terrestrial plants, it is founded upon a multi-decadal history of research, applications, and sensor developments in active and passive sensing of chlorophyll fluorescence. Current technical capabilities allow SIF to be measured across a range of biological, spatial, and temporal scales. As an optical signal, SIF may be assessed remotely using highly-resolved spectral sensors and state-of-the-art algorithms to distinguish the emission from reflected and/or scattered ambient light. Because the red to far-red SIF emission is detectable non-invasively, it may be sampled repeatedly to acquire spatio-temporally explicit information about photosynthetic light responses and steady-state behaviour in vegetation. Progress in this field is accelerating with innovative sensor developments, retrieval methods, and modelling advances. This review distills the historical and current developments spanning the last several decades. It highlights SIF heritage and complementarity within the broader field of fluorescence science, the maturation of physiological and radiative transfer modelling, SIF signal retrieval strategies, techniques for field and airborne sensing, advances in satellite-based systems, and applications of these capabilities in evaluation of photosynthesis and stress effects. Progress, challenges, and future directions are considered for this unique avenue of remote sensing.

Reconstructing Cloud Contaminated Pixels Using Spatiotemporal Covariance Functions and Multitemporal Hyperspectral Imagery

One of the major challenges in optical-based remote sensing is the presence of clouds, which imposes a hard constraint on the use of multispectral or hyperspectral satellite imagery for earth observation. While some studies have used interpolation models to remove cloud affected data, relatively few aim at restoration via the use of multi-temporal reference images. This paper proposes not only the use of image time-series, but also the implementation of a geostatistical model that considers the spatiotemporal correlation between them to fill the cloud-related gaps. Using Hyperion hyperspectral images, we demonstrate a capacity to reconstruct cloud-affected pixels and predict their underlying surface reflectance values. To do this, cloudy pixels were masked and a parametric family of non-separable covariance functions was automated fitted, using a composite likelihood estimator. A subset of cloud-free pixels per scene was used to perform a kriging interpolation and to predict the spectral reflectance per each cloud-affected pixel. The approach was evaluated using a benchmark dataset of cloud-free pixels, with a synthetic cloud superimposed upon these data. An overall root mean square error (RMSE) of between 0.5% and 16% of the reflectance was achieved, representing a relative root mean square error (rRMSE) of between 0.2% and 7.5%. The spectral similarity between the predicted and reference reflectance signatures was described by a mean spectral angle (MSA) of between 1° and 11°, demonstrating the spatial and spectral coherence of predictions. The approach provides an efficient spatiotemporal interpolation framework for cloud removal, gap-filling, and denoising in remotely sensed datasets.

Leaf-Level Spectral Fluorescence Measurements: Comparing Methodologies for Broadleaves and Needles

Successful measurements of chlorophyll fluorescence (ChlF) spectral properties (typically in the wavelength range of 650–850 nm) across plant species, environmental conditions, and stress levels are a first step towards establishing a quantitative link between solar-induced chlorophyll fluorescence (SIF), which can only be measured at discrete ChlF spectral bands, and photosynthetic functionality. Despite its importance and significance, the various methodologies for the estimation of leaf-level ChlF spectral properties have not yet been compared, especially when applied to leaves with complex morphology, such as needles. Here we present, to the best of our knowledge, a first comparison of protocols for measuring leaf-level ChlF spectra: a custom-made system designed to measure ChlF spectra at ambient and 77 K temperatures (optical chamber, OC), the widely used FluoWat leaf clip (FW), and an integrating sphere setup (IS). We test the three methods under low-light conditions, across two broadleaf species and one needle-like species. For the conifer, we characterize the effect of needle arrangements: one needle, three needles, and needle mats with as little gap fraction as technically possible. We also introduce a simple baseline correction method to account for non-fluorescence-related contributions to spectral measurements. Baseline correction was found especially useful in recovering the spectra nearby the filter cut-off. Results show that the shape of the leaf-level ChlF spectra remained largely unaffected by the measurement methodology and geometry in OC and FW methods. Substantially smaller red/far-red ratios were observed in the IS method. The comparison of needle arrangements indicated that needle mats could be a practical solution to investigate temporal changes in ChlF spectra of needle-like leaves as they produced more reproducible results and higher signals.

Potential of Fluorescence Index Derived from the Slope Characteristics of Laser-Induced Chlorophyll Fluorescence Spectrum for Rice Leaf Nitrogen Concentration Estimation

Leaf nitrogen concentration (LNC) is a major biochemical parameter for estimating photosynthetic efficiency and crop yields. Laser-induced fluorescence, which is a promising potential technology, has been widely used to estimate the growth status of crops with the help of multivariate analysis. In this study, a fluorescence index was proposed based on the slope characteristics of fluorescence spectrum and was used to estimate LNC. Then, the performance of different fluorescence characteristics (proposed fluorescence index, fluorescence ratios, and fluorescence characteristics calculated by principal component analysis (PCA)) for LNC estimation was analyzed based on back-propagation neural network (BPNN) model. The proposed fluorescence index exhibited more stability and reliability for LNC estimation than fluorescence ratios and characteristics calculated by PCA. In addition, the effect of different kernel functions and hidden layer sizes of BPNN model on the accuracy of LNC estimation was discussed for different fluorescence characteristics. The optimal train functions “trainrp,” “trainbr,” and “trainlm” were then selected with higher R2 and lower standard deviation (SD) values than those of other train functions. In addition, experimental results demonstrated that the hidden layer size has a smaller impact on the accuracy of LNC estimation than the kernel function of the BPNN model.

Low temperature induced modulation of photosynthetic induction in non-acclimated and cold-acclimated Arabidopsis thaliana: chlorophyll a fluorescence and gas-exchange measurements

Cold acclimation modifies the photosynthetic machinery and enables plants to survive at sub-zero temperatures, whereas in warm habitats, many species suffer even at non-freezing temperatures. We have measured chlorophyll a fluorescence (ChlF) and CO₂ assimilation to investigate the effects of cold acclimation, and of low temperatures, on a cold-sensitive Arabidopsis thaliana accession C24. Upon excitation with low intensity (40 µmol photons m^- 2 s^- 1) ~ 620 nm light, slow (minute range) ChlF transients, at ~ 22 °C, showed two waves in the SMT phase (S, semi steady-state; M, maximum; T, terminal steady-state), whereas CO₂ assimilation showed a linear increase with time. Low-temperature treatment (down to - 1.5 °C) strongly modulated the SMT phase and stimulated a peak in the CO₂ assimilation induction curve. We show that the SMT phase, at ~ 22 °C, was abolished when measured under high actinic irradiance, or when 3-(3, 4-dichlorophenyl)-1, 1- dimethylurea (DCMU, an inhibitor of electron flow) or methyl viologen (MV, a Photosystem I (PSI) electron acceptor) was added to the system. Our data suggest that stimulation of the SMT wave, at low temperatures, has multiple reasons, which may include changes in both photochemical and biochemical reactions leading to modulations in non-photochemical quenching (NPQ) of the excited state of Chl, "state transitions," as well as changes in the rate of cyclic electron flow through PSI. Further, we suggest that cold acclimation, in accession C24, promotes "state transition" and protects photosystems by preventing high excitation pressure during low-temperature exposure.

A correlative approach, combining chlorophyll a fluorescence, reflectance, and Raman spectroscopy, for monitoring hydration induced changes in Antarctic lichen Dermatocarpon polyphyllizum

Lichens are successful colonizers in extreme environments worldwide, and they are considered to have played an important role during the evolution of life. Here, we have used a correlative approach, combining three optical signals (chlorophyll a fluorescence (ChlF), reflectance, and Raman spectra), to monitor hydration induced changes in photosynthetic properties of an Antarctic chlorolichen Dermatocarpon polyphyllizum. We measured these three signals from this lichen at different stages (after 4 h, 24 h, and 48 h) of hydration, and compared the data obtained from this lichen in "dry state" as well as in different "hydrated state". We found that dry state of this lichen has: (1) no variable ChlF, (2) high reflectance, with no red-edge and almost zero photochemical reflectance index (PRI), and (3) low-intensity Raman bands of their carotenoids. Furthermore, 4 h of hydration, increased its relative water content (RWC) by 93%, showed red-edge in reflectance spectra, and changed the maximum quantum yield of PSII photochemistry (F_v/F_m) from 0 to 0.57 ± 0.01. We found that reflectance indices, normalized difference index (NDVI) and PRI, significantly differed between brown and black/green surface areas, at all hydration stages; whereas, a shift in the Raman ν₁(CC) band, between brown and black/green surface areas, occurred in 24 h or 48 h hydrated samples. These data indicate that hydration shortly (within 4 h) activated functions of photosynthetic apparatus, and the de novo synthesis of carotenoids occured in 24 h or 48 h. Furthermore, exposure to high irradiance (2000 μmol photons m^-2 s^-1), in 48 h hydrated lichen, significantly reduced F_v/F_m (signifies photoinhibition) and increased PRI (represents changes in xanthophyll pigments). We conclude that the implication of such a correlative approach is highly useful for understanding survival and protective mechanisms on extremophile photosynthetic organisms.

Analyzing the Effect of Fluorescence Characteristics on Leaf Nitrogen Concentration Estimation

Leaf nitrogen concentration (LNC) is a significant indicator of crops growth status, which is related to crop yield and photosynthetic efficiency. Laser-induced fluorescence is a promising technology for LNC estimation and has been widely used in remote sensing. The accuracy of LNC monitoring relies greatly on the selection of fluorescence characteristics and the number of fluorescence characteristics. It would be useful to analyze the performance of fluorescence intensity and ratio characteristics at different wavelengths for LNC estimation. In this study, the fluorescence spectra of paddy rice excited by different excitation light wavelengths (355 nm, 460 nm, and 556 nm) were acquired. The performance of the fluorescence intensity and fluorescence ratio of each band were analyzed in detail based on back-propagation neural network (BPNN) for LNC estimation. At 355 nm and 460 nm excitation wavelengths, the fluorescence characteristics related to LNC were mainly located in the far-red region, and at 556 nm excitation wavelength, the red region being an optimal band. Additionally, the effect of the number of fluorescence characteristics on the accuracy of LNC estimation was analyzed by using principal component analysis combined with BPNN. Results demonstrate that at least two fluorescence spectral features should be selected in the red and far-red regions to estimate LNC and efficiently improve the accuracy of LNC estimation.

Measuring the dynamic photosynthome

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.

Analysing the effect of paddy rice variety on fluorescence characteristics for nitrogen application monitoring

Paddy rice is one of the most important cereal crops worldwide, so it is very important to accurately monitor its growth status and photosynthetic efficiency. The nitrogen (N) level is a key factor closely related to crop growth. In this study, laser-induced fluorescence (LIF) technology combined with multi-variate analysis was applied to investigate the effect of paddy rice variety on N fertilizer level monitoring. Principal components analysis was conducted to extract the variables of the main fluorescence characteristics to identify N levels. Experimental results demonstrated that no nitrogen fertilizer can be completely identified for each paddy rice variety. In addition, other N levels can also be well classified based on the fluorescence characteristics. The relationship between the fluorescence ratio (F735/F685 : F735, and F685 denote the fluorescence intensity at 735 nm, and 685 nm, respectively) and leaf N content of different paddy rice varieties is also discussed. Experimental results revealed that LIF technology is an effective method of monitoring the N fertilizer and leaf biochemical components of paddy rice.

A robust vegetation index for remotely assessing chlorophyll content of dorsiventral leaves across several species in different seasons

BACKGROUND

Leaf chlorophyll content (LCC) provides valuable information about plant physiology. Most of the published chlorophyll vegetation indices at the leaf level have been based on the spectral characteristics of the adaxial leaf surface, thus, they are not appropriate for estimating LCC when both the adaxial and abaxial leaf surfaces influence the spectral reflectance. We attempted to address this challenge by measuring the spectral reflectance of the adaxial and abaxial leaf surfaces of several plant species at different growth stages using a portable field spectroradiometer. The relationships between more than 30 published reflectance indices with LCC were analyzed to determine which index estimated LCC most effectively. Additionally, since the relationships determined on one set of samples might have poor predictive performances when applied to other samples, a robust wavelength region is required to render the spectral index generally applicable, regardless of the leaf surface or plant species.

RESULTS

The Modified Datt (MDATT) index, which is the ratio of reflectance difference defined as (Rλ3 - Rλ1)/(Rλ3 - Rλ2), exhibited the strongest correlation (R2 = 0.856, RMSE = 6.872 μg/cm2), with LCC of all the indices tested when all the leaf samples from the adaxial and abaxial surfaces were combined. The optimal wavelength regions, which were derived from the contour maps of R2 between the MDATT index and LCC for the datasets of one side or both leaf surfaces of each plant species and their intersection, indicated that the red-edge to near-infrared wavelength (723-885 nm) was optimal for λ1, while the red-edge region (697-771 nm) was optimal for λ2 and λ3. In these optimal wavelength regions, when the MDATT index was used to estimate LCC, an R2 higher than 0.8 could be obtained. The correlation of the MDATT index with LCC was the same when the positions of λ2 and λ3 were exchanged in the index.

CONCLUSIONS

MDATT is proposed as an optimal index for the remote estimation of vegetation chlorophyll content across several plant species in different growth stages when reflectance from both leaf surfaces is considered. The red-edge to near-infrared wavelength (723-885 nm) for λ1, as well as the red-edge region (697-771 nm) for λ2 or λ3, are considered to be the most robust for constructing the MDATT index for estimating LCC, regardless of the leaf surface or plant species.

Specim IQ: Evaluation of a New, Miniaturized Handheld Hyperspectral Camera and Its Application for Plant Phenotyping and Disease Detection

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.

Potential of vegetation indices combined with laser-induced fluorescence parameters for monitoring leaf nitrogen content in paddy rice

Nitrogen (N) is important for the growth of crops. Leaf nitrogen content (LNC) serves as a crucial indicator of the growth status of crops and can help determine the dose of N fertilizer. Laser-induced fluorescence (LIF) technology and the reflectance spectra of crops are widely used to detect the biochemical content of leaves. Many vegetation indices (VIs) and fluorescence parameters have been developed to estimate LNC. However, the comparison among VIs and between fluorescence parameters and VIs has been rarely studied in the estimation of LNC. In this study, the performances of several published empirical VIs and fluorescence parameters for the estimation of paddy rice LNC were analyzed using the support vector machine (SVM) algorithm. Then, the optimal VIs (TVI, MTVI1, MTVI2, and MSAVI) and fluorescence parameters (F735/F460 and F685/F460), which were suitable for LNC monitoring in this study, were chosen. In addition, the combination of the VIs and fluorescence parameters was proposed as the input variables in the SVM model and used to estimate the LNC. Experimental results exhibited the promising potential of the LIF technology combined with reflectance for the accurate estimation of LNC, which provided guidance for monitoring the LNC.

Frequently asked questions about chlorophyll fluorescence, the sequel

Using chlorophyll (Chl) a fluorescence many aspects of the photosynthetic apparatus can be studied, both in vitro and, noninvasively, in vivo. Complementary techniques can help to interpret changes in the Chl a fluorescence kinetics. Kalaji et al. (Photosynth Res 122:121-158, 2014a) addressed several questions about instruments, methods and applications based on Chl a fluorescence. Here, additional Chl a fluorescence-related topics are discussed again in a question and answer format. Examples are the effect of connectivity on photochemical quenching, the correction of F V /F M values for PSI fluorescence, the energy partitioning concept, the interpretation of the complementary area, probing the donor side of PSII, the assignment of bands of 77 K fluorescence emission spectra to fluorescence emitters, the relationship between prompt and delayed fluorescence, potential problems when sampling tree canopies, the use of fluorescence parameters in QTL studies, the use of Chl a fluorescence in biosensor applications and the application of neural network approaches for the analysis of fluorescence measurements. The answers draw on knowledge from different Chl a fluorescence analysis domains, yielding in several cases new insights.

Effect of fluorescence characteristics and different algorithms on the estimation of leaf nitrogen content based on laser-induced fluorescence lidar in paddy rice

Paddy rice is one of the most significant food sources and an important part of the ecosystem. Thus, accurate monitoring of paddy rice growth is highly necessary. Leaf nitrogen content (LNC) serves as a crucial indicator of growth status of paddy rice and determines the dose of nitrogen (N) fertilizer to be used. This study aims to compare the predictive ability of the fluorescence spectra excited by different excitation wavelengths (EWs) combined with traditional multivariate analysis algorithms, such as principal component analysis (PCA), back-propagation neural network (BPNN), and support vector machine (SVM), for estimating paddy rice LNC from the leaf level with three different fluorescence characteristics as input variables. Then, six estimation models were proposed. Compared with the five other models, PCA-BPNN was the most suitable model for the estimation of LNC by improving R² and reducing RMSE and RE. For 355, 460 and 556 nm EWs, R² was 0.89, 0.80 and 0.88, respectively. Experimental results demonstrated that the fluorescence spectra excited by 355 and 556 nm EWs were superior to those excited by 460 nm for the estimation of LNC with different models. BPNN algorithm combined with PCA may provide a helpful exploratory and predictive tool for fluorescence spectra excited by appropriate EW based on practical application requirements for monitoring the N status of crops.

Chlorophyll a fluorescence, under half of the adaptive growth-irradiance, for high-throughput sensing of leaf-water deficit in Arabidopsis thaliana accessions

BACKGROUND

Non-invasive and high-throughput monitoring of drought in plants from its initiation to visible symptoms is essential to quest drought tolerant varieties. Among the existing methods, chlorophyll a fluorescence (ChlF) imaging has the potential to probe systematic changes in photosynthetic reactions; however, prerequisite of dark-adaptation limits its use for high-throughput screening.

RESULTS

To improve the throughput monitoring of plants, we have exploited their light-adaptive strategy, and investigated possibilities of measuring ChlF transients under low ambient irradiance. We found that the ChlF transients and associated parameters of two contrasting Arabidopsis thaliana accessions, Rsch and Co, give almost similar information, when measured either after ~20 min dark-adaptation or in the presence of half of the adaptive growth-irradiance. The fluorescence parameters, effective quantum yield of PSII photochemistry (ΦPSII) and fluorescence decrease ratio (RFD) resulting from this approach enabled us to differentiate accessions that is often not possible by well-established dark-adapted fluorescence parameter maximum quantum efficiency of PSII photochemistry (FV/FM). Further, we screened ChlF transients in rosettes of well-watered and drought-stressed six A. thaliana accessions, under half of the adaptive growth-irradiance, without any prior dark-adaptation. Relative water content (RWC) in leaves was also assayed and compared to the ChlF parameters. As expected, the RWC was significantly different in drought-stressed from that in well-watered plants in all the six investigated accessions on day-10 of induced drought; the maximum reduction in the RWC was obtained for Rsch (16%), whereas the minimum reduction was for Co (~7%). Drought induced changes were reflected in several features of ChlF transients; combinatorial images obtained from pattern recognition algorithms, trained on pixels of image sequence, improved the contrast among drought-stressed accessions, and the derived images were well-correlated with their RWC.

CONCLUSIONS

We demonstrate here that ChlF transients and associated parameters measured even in the presence of low ambient irradiance preserved its features comparable to that of measured after dark-adaptation and discriminated the accessions having differential geographical origin; further, in combination with combinatorial image analysis tools, these data may be readily employed for early sensing and mapping effects of drought on plant's physiology via easy and fully non-invasive means.

Chlorophyll fluorescence, photochemical reflective index and normalized difference vegetative index during plant senescence

The relationship between the Photochemical Reflectance Index (PRI), Normalized Difference Vegetation Index (NDVI) and chlorophyll fluorescence along senescence was investigated in this work. Reflectance and radiance measurements were performed at canopy level in grass species presenting different photosynthetic metabolism: Avena sativa (C3) and Setaria italica (C4), at different stages of the natural senescence process. Sun induced-chlorophyll fluorescence at 760nm (SIF₇₆₀) and the apparent fluorescence yield (SIF₇₆₀/a, with a=irradiance at time of measurement) were extracted from the radiance spectra of canopies using the Fraunhofer Line Discrimination-method. The photosynthetic parameters derived from Kautsky kinetics and pigment content were also calculated at leaf level. Whilst stand level NDVI patterns were related to changes in the structure of canopies and not in pigment content, stand level PRI patterns suggested changes both in terms of canopy and of pigment content in leaves. Both SIF₇₆₀/a and Φ_PSII decreased progressively along senescence in both species. A strong increment in NPQ was evident in A. sativa while in S. italica NPQ values were lower. Our most important finding was that two chlorophyll fluorescence signals, Φ_PSII and SIF₇₆₀/a, correlated with the canopy PRI values in the two grasses assessed, even when tissues at different ontogenic stages were present. Even though significant changes occurred in the Total Chlr/Car ratio along senescence in both studied species, significant correlations between PRI and chlorophyll fluorescence signals might indicate the usefulness of this reflectance index as a proxy of photosynthetic RUE, at least under the conditions of this study. The relationships between stand level PRI and the fluorescence estimators (Φ_PSII and SIF₇₆₀/a) were positive in both cases. Therefore, an increase in PRI values as in the fluorescence parameters would indicate higher RUE.

Sun-induced chlorophyll fluorescence from high-resolution imaging spectroscopy data to quantify spatio-temporal patterns of photosynthetic function in crop canopies

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.

Analyzing the performance of fluorescence parameters in the monitoring of leaf nitrogen content of paddy rice

Leaf nitrogen content (LNC) is a significant factor which can be utilized to monitor the status of paddy rice and it requires a reliable approach for fast and precise quantification. This investigation aims to quantitatively analyze the correlation between fluorescence parameters and LNC based on laser-induced fluorescence (LIF) technology. The fluorescence parameters exhibited a consistent positive linear correlation with LNC in different growing years (2014 and 2015) and different rice cultivars. The R² of the models varied from 0.6978 to 0.9045. Support vector machine (SVM) was then utilized to verify the feasibility of the fluorescence parameters for monitoring LNC. Comparison of the fluorescence parameters indicated that F740 is the most sensitive (the R² of linear regression analysis of the between predicted and measured values changed from 0.8475 to 0.9226, and REs ranged from 3.52% to 4.83%) to the changes in LNC among all fluorescence parameters. Experimental results demonstrated that fluorescence parameters based on LIF technology combined with SVM is a potential method for realizing real-time, non-destructive monitoring of paddy rice LNC, which can provide guidance for the decision-making of farmers in their N fertilization strategies.

Sun-induced fluorescence - a new probe of photosynthesis: First maps from the imaging spectrometer HyPlant

Variations in photosynthesis still cause substantial uncertainties in predicting photosynthetic CO2 uptake rates and monitoring plant stress. Changes in actual photosynthesis that are not related to greenness of vegetation are difficult to measure by reflectance based optical remote sensing techniques. Several activities are underway to evaluate the sun-induced fluorescence signal on the ground and on a coarse spatial scale using space-borne imaging spectrometers. Intermediate-scale observations using airborne-based imaging spectroscopy, which are critical to bridge the existing gap between small-scale field studies and global observations, are still insufficient. Here we present the first validated maps of sun-induced fluorescence in that critical, intermediate spatial resolution, employing the novel airborne imaging spectrometer HyPlant. HyPlant has an unprecedented spectral resolution, which allows for the first time quantifying sun-induced fluorescence fluxes in physical units according to the Fraunhofer Line Depth Principle that exploits solar and atmospheric absorption bands. Maps of sun-induced fluorescence show a large spatial variability between different vegetation types, which complement classical remote sensing approaches. Different crop types largely differ in emitting fluorescence that additionally changes within the seasonal cycle and thus may be related to the seasonal activation and deactivation of the photosynthetic machinery. We argue that sun-induced fluorescence emission is related to two processes: (i) the total absorbed radiation by photosynthetically active chlorophyll; and (ii) the functional status of actual photosynthesis and vegetation stress.

Antarctic moss stress assessment based on chlorophyll content and leaf density retrieved from imaging spectroscopy data

The health of several East Antarctic moss-beds is declining as liquid water availability is reduced due to recent environmental changes. Consequently, a noninvasive and spatially explicit method is needed to assess the vigour of mosses spread throughout rocky Antarctic landscapes. Here, we explore the possibility of using near-distance imaging spectroscopy for spatial assessment of moss-bed health. Turf chlorophyll a and b, water content and leaf density were selected as quantitative stress indicators. Reflectance of three dominant Antarctic mosses Bryum pseudotriquetrum, Ceratodon purpureus and Schistidium antarctici was measured during a drought-stress and recovery laboratory experiment and also with an imaging spectrometer outdoors on water-deficient (stressed) and well-watered (unstressed) moss test sites. The stress-indicating moss traits were derived from visible and near infrared turf reflectance using a nonlinear support vector regression. Laboratory estimates of chlorophyll content and leaf density were achieved with the lowest systematic/unsystematic root mean square errors of 38.0/235.2 nmol g(-1) DW and 0.8/1.6 leaves mm(-1) , respectively. Subsequent combination of these indicators retrieved from field hyperspectral images produced small-scale maps indicating relative moss vigour. Once applied and validated on remotely sensed airborne spectral images, this methodology could provide quantitative maps suitable for long-term monitoring of Antarctic moss-bed health.

Estimating chlorophyll content and photochemical yield of photosystem II (ΦPSII) using solar-induced chlorophyll fluorescence measurements at different growing stages of attached leaves

This paper illustrates the possibility of measuring chlorophyll (Chl) content and Chl fluorescence parameters by the solar-induced Chl fluorescence (SIF) method using the Fraunhofer line depth (FLD) principle, and compares the results with the standard measurement methods. A high-spectral resolution HR2000+ and an ordinary USB4000 spectrometer were used to measure leaf reflectance under solar and artificial light, respectively, to estimate Chl fluorescence. Using leaves of Capsicum annuum cv. 'Sven' (paprika), the relationships between the Chl content and the steady-state Chl fluorescence near oxygen absorption bands of O2B (686nm) and O2A (760nm), measured under artificial and solar light at different growing stages of leaves, were evaluated. The Chl fluorescence yields of ΦF 686nm/ΦF 760nm ratios obtained from both methods correlated well with the Chl content (steady-state solar light: R(2) = 0.73; artificial light: R(2) = 0.94). The SIF method was less accurate for Chl content estimation when Chl content was high. The steady-state solar-induced Chl fluorescence yield ratio correlated very well with the artificial-light-induced one (R(2) = 0.84). A new methodology is then presented to estimate photochemical yield of photosystem II (ΦPSII) from the SIF measurements, which was verified against the standard Chl fluorescence measurement method (pulse-amplitude modulated method). The high coefficient of determination (R(2) = 0.74) between the ΦPSII of the two methods shows that photosynthesis process parameters can be successfully estimated using the presented methodology.

Dynamic response of plant chlorophyll fluorescence to light, water and nutrient availability

Chlorophyll molecules absorb photosynthetic active radiation (PAR). The resulting excitation energy is dissipated by three competing pathways at the level of photosystem: (i) photochemistry (and, by extension, photosynthesis); (ii) regulated and constitutive thermal energy dissipation; and (iii) chlorophyll-a fluorescence (ChlF). Because the dynamics of photosynthesis modulate the regulated component of thermal energy dissipation (widely addressed as non-photochemical quenching (NPQ)), the relationship between photosynthesis, NPQ and ChlF changes with water, nutrient and light availability. In this study we characterised the relationship between photosynthesis, NPQ and ChlF when conducting light-response curves of photosynthesis in plants growing under different water, nutrient and ambient light conditions. Our goals were to test whether ChlF and photosynthesis correlate in response to water and nutrient deficiency, and determine the optimum PAR level at which the correlation is maximal. Concurrent gas exchange and ChlF light-response curves were measured for Camelina sativa (L.) Crantz and Triticum durum (L.) Desf plants grown under (i) intermediate light growth chamber conditions, and (ii) high light environment field conditions respectively. Plant stress was induced by withdrawing water in the chamber experiment, and applying different nitrogen levels in the field experiment. Our study demonstrated that ChlF was able to track the variations in photosynthetic capacity in both experiments, and that the light level at which plants were grown was optimum for detecting both water and nutrient deficiency with ChlF. The decrease in photosynthesis was found to modulate ChlF via different mechanisms depending on the treatment: through the action of NPQ in response to water stress, or through the action of changes in leaf chlorophyll concentration in response to nitrogen deficiency. This study provides support for the use of remotely sensed ChlF as a proxy to monitor plant stress dynamics from space.

Autofluorescence: Biological functions and technical applications

Chlorophylls are the most remarkable examples of fluorophores, and their fluorescence has been intensively studied as a non-invasive tool for assessment of photosynthesis. Many other fluorophores occur in plants, such as alkaloids, phenolic compounds and porphyrins. Fluorescence could be more than just a physicochemical curiosity in the plant kingdom, as several functional roles in biocommunication occur or have been proposed. Besides, fluorescence emitted by secondary metabolites can convert damaging blue and UV into wavelengths potentially useful for photosynthesis. Detection of the fluorescence of some secondary phytochemicals may be a cue for some pollinators and/or seed dispersal organisms. Independently of their functions, plant fluorophores provide researchers with a tool that allows the visualization of some metabolites in plants and cells, complementing and overcoming some of the limitations of the use of fluorescent proteins and dyes to probe plant physiology and biochemistry. Some fluorophores are influenced by environmental interactions, allowing fluorescence to be also used as a specific stress indicator.

Recent developments in fast spectroscopy for plant mineral analysis

Ideal fertilizer management to optimize plant productivity and quality is more relevant than ever, as global food demands increase along with the rapidly growing world population. At the same time, sub-optimal or excessive use of fertilizers leads to severe environmental damage in areas of intensive crop production. The approaches of soil and plant mineral analysis are briefly compared and discussed here, and the new techniques using fast spectroscopy that offer cheap, rapid, and easy-to-use analysis of plant nutritional status are reviewed. The majority of these methods use vibrational spectroscopy, such as visual-near infrared and to a lesser extent ultraviolet and mid-infrared spectroscopy. Advantages of and problems with application of these techniques are thoroughly discussed. Spectroscopic techniques considered having major potential for plant mineral analysis, such as chlorophyll a fluorescence, X-ray fluorescence, and laser-induced breakdown spectroscopy are also described.

Plant leaf chlorophyll content retrieval based on a field imaging spectroscopy system

A field imaging spectrometer system (FISS; 380-870 nm and 344 bands) was designed for agriculture applications. In this study, FISS was used to gather spectral information from soybean leaves. The chlorophyll content was retrieved using a multiple linear regression (MLR), partial least squares (PLS) regression and support vector machine (SVM) regression. Our objective was to verify the performance of FISS in a quantitative spectral analysis through the estimation of chlorophyll content and to determine a proper quantitative spectral analysis method for processing FISS data. The results revealed that the derivative reflectance was a more sensitive indicator of chlorophyll content and could extract content information more efficiently than the spectral reflectance, which is more significant for FISS data compared to ASD (analytical spectral devices) data, reducing the corresponding RMSE (root mean squared error) by 3.3%-35.6%. Compared with the spectral features, the regression methods had smaller effects on the retrieval accuracy. A multivariate linear model could be the ideal model to retrieve chlorophyll information with a small number of significant wavelengths used. The smallest RMSE of the chlorophyll content retrieved using FISS data was 0.201 mg/g, a relative reduction of more than 30% compared with the RMSE based on a non-imaging ASD spectrometer, which represents a high estimation accuracy compared with the mean chlorophyll content of the sampled leaves (4.05 mg/g). Our study indicates that FISS could obtain both spectral and spatial detailed information of high quality. Its image-spectrum-in-one merit promotes the good performance of FISS in quantitative spectral analyses, and it can potentially be widely used in the agricultural sector.

Linking chlorophyll a fluorescence to photosynthesis for remote sensing applications: mechanisms and challenges

Chlorophyll a fluorescence (ChlF) has been used for decades to study the organization, functioning, and physiology of photosynthesis at the leaf and subcellular levels. ChlF is now measurable from remote sensing platforms. This provides a new optical means to track photosynthesis and gross primary productivity of terrestrial ecosystems. Importantly, the spatiotemporal and methodological context of the new applications is dramatically different compared with most of the available ChlF literature, which raises a number of important considerations. Although we have a good mechanistic understanding of the processes that control the ChlF signal over the short term, the seasonal link between ChlF and photosynthesis remains obscure. Additionally, while the current understanding of in vivo ChlF is based on pulse amplitude-modulated (PAM) measurements, remote sensing applications are based on the measurement of the passive solar-induced chlorophyll fluorescence (SIF), which entails important differences and new challenges that remain to be solved. In this review we introduce and revisit the physical, physiological, and methodological factors that control the leaf-level ChlF signal in the context of the new remote sensing applications. Specifically, we present the basis of photosynthetic acclimation and its optical signals, we introduce the physical and physiological basis of ChlF from the molecular to the leaf level and beyond, and we introduce and compare PAM and SIF methodology. Finally, we evaluate and identify the challenges that still remain to be answered in order to consolidate our mechanistic understanding of the remotely sensed SIF signal.

Daily and seasonal dynamics of remotely sensed photosynthetic efficiency in tree canopies

The photosynthesis of various species or even a single plant varies dramatically in time and space, creating great spatial heterogeneity within a plant canopy. Continuous and spatially explicit monitoring is, therefore, required to assess the dynamic response of plant photosynthesis to the changing environment. This is a very challenging task when using the existing portable field instrumentation. This paper reports on the application of a technique, laser-induced fluorescence transient (LIFT), developed for ground remote measurement of photosynthetic efficiency at a distance of up to 50 m. The LIFT technique was used to monitor the seasonal dynamics of selected leaf groups within inaccessible canopies of deciduous and evergreen tree species. Electron transport rates computed from LIFT measurements varied over the growth period between the different species studied. The LIFT canopy data and light-use efficiency measured under field conditions correlated reasonably well with the single-leaf pulse amplitude-modulated measurements of broadleaf species, but differed significantly in the case of conifer tree species. The LIFT method has proven to be applicable for a remote sensing assessment of photosynthetic parameters on a diurnal and seasonal scale; further investigation is, however, needed to evaluate the influence of complex heterogeneous canopy structures on LIFT-measured chlorophyll fluorescence parameters.

Potential use of ground-based sensor technologies for weed detection

Site-specific weed management is the part of precision agriculture (PA) that tries to effectively control weed infestations with the least economical and environmental burdens. This can be achieved with the aid of ground-based or near-range sensors in combination with decision rules and precise application technologies. Near-range sensor technologies, developed for mounting on a vehicle, have been emerging for PA applications during the last three decades. These technologies focus on identifying plants and measuring their physiological status with the aid of their spectral and morphological characteristics. Cameras, spectrometers, fluorometers and distance sensors are the most prominent sensors for PA applications. The objective of this article is to describe-ground based sensors that have the potential to be used for weed detection and measurement of weed infestation level. An overview of current sensor systems is presented, describing their concepts, results that have been achieved, already utilized commercial systems and problems that persist. A perspective for the development of these sensors is given.