Red Chlorophyll Research Articles

Chlorophyll Degradation and its Physiological Function.

Research on chlorophyll degradation has progressed significantly in recent decades. In the 1990s, the structure of linear tetrapyrrole, which is unambiguously a chlorophyll degradation product, was determined. From the 2000s until the 2010s, the major enzymes involved in chlorophyll degradation were identified, and the pheophorbide a oxygenase/phyllobilin pathway was established. This degradation pathway encompasses several steps: (1) initial conversion of chlorophyll b to 7-hydroxymethyl chlorophyll a; (2) conversion of 7-hydroxymethyl chlorophyll a to chlorophyll a; (3) dechelation of chlorophyll a to pheophytin a; (4) dephytylation of pheophytin a to pheophorbide a; (5) opening of the macrocycle to yield a red chlorophyll catabolite; and (6) conversion of red chlorophyll catabolite to phyllobilins. This pathway converts potentially harmful chlorophyll into safe molecules of phyllobilins, which are stored in the central vacuole of terrestrial plants. The expression of chlorophyll-degrading enzymes is mediated by various transcription factors and influenced by light conditions, stress, and plant hormones. Chlorophyll degradation is differently regulated in different organs and developmental stages of plants. The initiation of chlorophyll degradation induces the further expression of chlorophyll-degrading enzymes, resulting in the acceleration of chlorophyll degradation. Chlorophyll degradation was initially considered the last reaction in senescence; however, chlorophyll degradation plays crucial roles in enhancing senescence, degrading chlorophyll-protein complexes, forming photosystem II, and maintaining seed quality. Therefore, controlling chlorophyll degradation has important agricultural applications.

RsOBP2a, a member of OBF BINDING PROTEIN transcription factors, inhibits two chlorophyll degradation genes in green radish

The green radish (Raphanus sativus L.) contains abundant chlorophyll (Chl). DOF-type transcription factor OBF BINDING PROTEIN (OBP) plays crucial functions in plant growth, development, maturation and responses to various abiotic stresses. However, the metabolism by which OBP transcription factors regulate light-induced Chl metabolism in green radish is not well understood. In this study, six OBP genes were identified from the radish genome, distributed unevenly across five chromosomes. Among these genes, RsOBP2a showed significantly higher expression in the green flesh compared to the white flesh of green radish. Analysis of promoter elements suggested that RsOBPs might be involved in stress responses, particularly in light-related processes. Overexpression of RsOBP2a led to increase Chl levels in cotyledons and adventitious roots of radish, while silencing RsOBP2a expression through TYMV-induced gene silencing accelerated leaf senescence. Further investigations revealed that RsOBP2a was localized in the nucleus and served as a transcriptional repressor. RsOBP2a was induced by light and directly suppressed the expression of STAYGREEN (SGR) and RED CHLOROPHYLL CATABOLITE REDUCTASE (RCCR), thereby delaying senescence in radish. Overall, a novel regulatory model involving RsOBP2a, RsSGR, and RsRCCR was proposed to govern Chl metabolism in response to light, offering insights for the enhancement of green radish germplasm.

UV-NDVI for real-time crop health monitoring in vertical farms

This study aims to develop a plant health monitoring system suitable for vertical farms. It began by creating a multispectral LED with UVA and NIR light sources and an IoT device capable of controlling the LED spectrum. The IoT device integrates a camera with switchable filters and RGB CMOS sensor to simultaneously calculate SI-NDVI (single-image normalized difference vegetation index) and UV-NDVI (UV induce red chlorophyll fluorescence normalized difference vegetation index).A lettuce cultivation experiment was conducted on a planting shelf in a controlled environmental room, with two cultivation planters containing the same nutrient solution and red lettuce variety. SI-NDVI and UV-NDVI were calculated every three hours to compare differences. Results showed that both UV-NDVI and SI-NDVI trends were similar in monitoring lettuce growth. However, UV-NDVI was more sensitive to plant health than SI-NDVI. By inducing drought stress, the UV-NDVI indicator was able to detect water deficiency anomalies earlier than visual observation.

Energy Transfer and Radical-Pair Dynamics in Photosystem I with Different Red Chlorophyll a Pigments.

We establish a general kinetic scheme for the energy transfer and radical-pair dynamics in photosystem I (PSI) of Chlamydomonas reinhardtii, Synechocystis PCC6803, Thermosynechococcus elongatus and Spirulina platensis grown under white-light conditions. With the help of simultaneous target analysis of transient-absorption data sets measured with two selective excitations, we resolved the spectral and kinetic properties of the different species present in PSI. WL-PSI can be described as a Bulk Chl a in equilibrium with a higher-energy Chl a, one or two Red Chl a and a reaction-center compartment (WL-RC). Three radical pairs (RPs) have been resolved with very similar properties in the four model organisms. The charge separation is virtually irreversible with a rate of ≈900 ns-1. The second rate, of RP1 → RP2, ranges from 70-90 ns-1 and the third rate, of RP2 → RP3, is ≈30 ns-1. Since RP1 and the Red Chl a are simultaneously present, resolving the RP1 properties is challenging. In Chlamydomonas reinhardtii, the excited WL-RC and Bulk Chl a compartments equilibrate with a lifetime of ≈0.28 ps, whereas the Red and the Bulk Chl a compartments equilibrate with a lifetime of ≈2.65 ps. We present a description of the thermodynamic properties of the model organisms at room temperature.

Physiological and transcriptomic strategies related to photosynthesis of rice in response to vanadium stress

Vanadium (V) is recognized as an environmental contaminant that affects plant growth and development. However, the photosynthetic response of plants to V stress at molecular level remains unclear. In this study, we examined the photosynthetic responses of two rice varieties (Oryza sativa L.) with diverse V tolerance under various V levels (0, 10, 20, 30, and 40 mg L−1) using physiological, cytological, and transcriptomic approaches. Our findings indicated that V stress significantly reduced the photosynthetic rate and chlorophyll content in both genotypes, with the V-sensitive rice displaying a more pronounced reduction. Furthermore, we observed numerous plastoglobuli, deformed stroma, and ruptured thylakoid membranes in the chloroplasts of the V-sensitive rice. In transcriptome analysis, we found 34 differentially expressed genes of 21 enzymes potentially affect chlorophyll metabolism. Among them, most of the genes involved in chlorophyll biosynthesis and phototoxic intermediate degradation were down-regulated under V stress, while chlorophyll degradation-related genes exhibited up-regulation. In addition, the different expression levels of genes encoding uroporphyrinogen III synthase (UROS), protoporphyrinogen oxidase (PPO), chlorophyllase (CLH), pheophorbide a oxygenase (PAO), and red chlorophyll catabolite reductase (RCCR) between the two rice varieties may indicate a potential tolerance mechanism in rice plants in response to V stress. Overall, this study contributes to our understanding of the molecular mechanisms underlying plant tolerance to V toxicity, while also highlighting potential targets for improving plant tolerance in V-contaminated soils.

Ensemble machine learning as a mathematical approach to predict high-voltage electric field-assisted removal of color and oxidative indices from soybean oil using the activated porous electrodes

Porous electrode materials provide a large surface area for electrochemical reactions and facilitate ion transport efficiently. By optimizing the design and properties of porous electrodes, it is possible to enhance the bleaching performance. In this research, three machine learning (ML) algorithms, namely Support Vector Regression, Ridge Regression, and Decision Tree Regression were utilized for predicting the bleaching efficiency under different conditions of HVEF-assisted bleaching (HVEFAB). Subsequently, the outcomes of these models were compared to ensemble models created by combining the algorithms as mentioned earlier using three techniques including bagging, boosting, and stacking. The levels of chlorophyll and carotenoids were reduced as a result of increased voltage, modification in the electrode surface, number of cathode-anode, and the presence of an inert electrolyte. Meanwhile, the reduction of red color and chlorophyll pigments was more significant than yellow color and carotenoid pigments. Moreover, the application of HVEFAB can potentially decrease the levels of polar compounds, such as oxidative compounds on the electrode surface. Based on the MSE, MAE, and R2 metrics, the algorithms' efficacy in forecasting outputs 1 (color index) and 2 (oxidative index) can be ordered as follows: Stacking > Boosting > Bagging > Decision Tree (DT) > Ridge > Support Vector Regression (SVR). The findings demonstrate that integrating these three models, mainly through the stacking technique, substantially improves the accuracy of the predicting model. Particle swarm optimization (PSO) and polynomial regression rather than RSM represented a superior model to predict red color removal.

Improving potato AGB estimation to mitigate phenological stage impacts through depth features from hyperspectral data

The accurate estimation of above ground biomass (AGB) is valuable in grasping the status of potato growth and assessing yield. Spectral analysis techniques play an important role in AGB estimation by virtue of its non-destructive and rapid advantages. However, the models constructed based on spectral features at the entire growth stages due to differences in phenological stages, such as vegetation indices (VIs) and locations at fixed wavelengths, can be not applicable to single growth stages. Therefore, the main objective of this study is to improve the applicability of the AGB estimation model to mitigate the impacts of phenological stages by mining depth features from hyperspectral data. The canopy hyperspectral and AGB data of potato tuber formation, tuber growth and starch accumulation stages were measured in 2017–2019. The acquired canopy hyperspectral reflectance was smoothed by Savitzky-Golay (SG) filter, and the green edge (GE), the red edge (RE), red edge chlorophyll index (CIred-edge), optimized soil adjusted vegetation index (OSAVI), green normalized difference vegetation index (GNDVI), and normalized difference vegetation index (NDVI) were extracted. The spectral changes were analyzed at different growth stages of each year. To obtain features in response to AGB changes, a new approach is proposed to analyze the hidden depth features between sensitive wavelengths by combining a successive projections algorithm (SPA) and a long-short-term memory network (LSTM). The AGB estimation models were constructed by partial least squares regression (PLSR) based on traditional VIs + GE + RE, full spectrum, sensitive wavelengths obtained by SPA, and depth features obtained by SPA-LSTM. The results showed that GE, RE and VIs alone explained only 24 %-36 % of the variation in AGB. The accuracy of estimating AGB at sensitive wavelengths obtained after SPA was higher (R2 = 0.70, RMSE = 406 kg/hm2, and NRMSE = 27.14 %) than that of conventional VIs + GE + RE (R2 = 0.51, RMSE = 536 kg/hm2, and NRMSE = 35.81 %) and full spectrum (R2 = 0.63, RMSE = 452 kg/hm2, and NRMSE = 30.19 %) in the three-year dataset. The depth features extracted by the SPA-LSTM method proposed in this study achieved the best AGB estimation (R2 = 0.82, RMSE = 318 kg/hm2, and NRMSE = 21.25 %). The applicability of the model was validated in different years and growth stages. The R2 of each year was in the range of 0.48–0.77, the RMSE was in the range of 290–367 kg/hm2, and the NRMSE was in the range of 16.69 %-25.81 %. The R2 of each growth stage within three year was in the range of 0.48–0.78, the RMSE was in the range of 184–386 kg/hm2, and the NRMSE was in the range of 14.91 %-29.45 %. The method proposed in this study mitigates the impacts of phenological stage and improves the robustness and accuracy of the AGB estimation model, which provides remote sensing technical support for realizing potato growth monitoring and yield assessment in the field.

Relating soil moisture and Sentinel-2 vegetation index patterns to spruce bark beetle infestations prior to outbreak

Abstract Central Europe experienced severe droughts from 2018 to 2020, which led to bark beetle infestations in Norway spruce (Picea abies L.). While recent studies have identified these droughts as a major factor in the dieback of spruces, the exact mechanism through which drought stress influences spruce vitality and increases vulnerability to infestations is not well understood. To gain insights into the spatial and temporal relations between infestations and drought stress, this study compared soil moisture index (SMI) data for the entire soil layer from the German Drought Monitor and Sentinel-2 satellite time series for healthy and bark beetle infested spruce stands preceding the infestation. The study used t-tests to compare pre-infestation monthly sums of SMI and vegetation indices (VIs) for healthy and infested stands. Yearly trajectories of six VIs were used to compare the separability between infested and healthy plots. The results showed that the monthly sum values of SMI for healthy spruce plots were statistically significantly higher than the infested plots from May 2017 to January 2018, while the monthly sum values of Inverted Red Edge Chlorophyll Index of healthy spruce plots were significantly higher than infested plots during growing seasons. With these findings, the study provides useful information for the understanding of the relation of soil moisture and remote sensing VIs patterns in relation to bark beetle infestations.

Deriving photosystem-level red chlorophyll fluorescence emission by combining leaf chlorophyll content and canopy far-red solar-induced fluorescence: Possibilities and challenges

Solar-induced chlorophyll fluorescence (SIF) emitted from photosystem I (PSI) and photosystem II (PSII) is characterized by two peaks centered in the red and far-red spectral regions. SIF provides a unique remotely sensible signal to track plant photosynthetic dynamics. Compared with far-red SIF, red SIF (RSIF) is more strongly linked to PSII and thus with plant photosynthetic activity, but is subject to stronger reabsorption within leaves and canopies. This hinders the understanding and use of canopy RSIF observations (RSIFobs), which is only a small fraction of the total RSIF emitted by the photosystems (RSIFtotal). Deriving RSIFtotal from RSIFobs is still challenging due to retrieval uncertainty, limited availability of RSIFobs and spectral overlap with chlorophyll absorption. To address the challenges associated with deriving RSIFtotal, we propose an exploratory method framework that combines canopy far-red SIF observations (FRSIFobs) and leaf chlorophyll content (LCC) to derive RSIFtotal. We first downscale FRSIFobs from canopy to leaf, and then leverage LCC information to estimate RSIF at the leaf level. Finally, we incorporate LCC information in the subsequent downscaling of RSIF from leaf to photosystem. To evaluate our approach, we use ground-based observation data in three crop types (rice, wheat, and maize) and SCOPE model simulations. Our results demonstrate that the seasonal patterns of RSIFtotal show a close agreement with the seasonal patterns of gross primary production (GPP) and absorbed photosynthetic active radiation (APAR). More importantly, RSIFtotal slightly outperforms FRSIFobs in estimating GPP for the three crop types. Our study has also revealed a strong linear relationship between the escape probability of RSIFtotal (fesc_R) and the RSIFobs/FRSIFobs ratio affected by LCC. The simplicity and robustness of our approach, along with its potential application in satellite remote sensing, will contribute to the improvement of large-scale GPP estimation and photosynthetic phenology detection. Moreover, our investigation of fesc_R will contribute to a better understanding the physiological and non-physiological dynamics of RSIFobs.

Identification of the Initial Anthesis of Soybean Varieties Based on UAV Multispectral Time-Series Images

Accurate and high-throughput identification of the initial anthesis of soybean varieties is important for the breeding and screening of high-quality soybean cultivars in field trials. The objectives of this study were to identify the initial day of anthesis (IADAS) of soybean varieties based on remote sensing multispectral time-series images acquired by unmanned aerial vehicles (UAVs), and analyze the differences in the initial anthesis of the same soybean varieties between two different climatic regions, Shijiazhuang (SJZ) and Xuzhou (XZ). First, the temporal dynamics of several key crop growth indicators and spectral indices were analyzed to find an effective indicator that favors the identification of IADAS, including leaf area index (LAI), above-ground biomass (AGB), canopy height (CH), normalized-difference vegetation index (NDVI), red edge chlorophyll index (CIred edge), green normalized-difference vegetation index (GNDVI), enhanced vegetation index (EVI), two-band enhanced vegetation index (EVI2) and normalized-difference red-edge index (NDRE). Next, this study compared several functions, like the symmetric gauss function (SGF), asymmetric gauss function (AGF), double logistic function (DLF), and fourier function (FF), for time-series curve fitting, and then estimated the IADAS of soybean varieties with the first-order derivative maximal feature (FDmax) of the CIred edge phenology curves. The relative thresholds of the CIred edge curves were also used to estimate IADAS, in two ways: a single threshold for all of the soybean varieties, and three different relative thresholds for early, middle, and late anthesis varieties, respectively. Finally, this study presented the variations in the IADAS of the same soybean varieties between two different climatic regions and discussed the probable causal factors. The results showed that CIred edge was more suitable for soybean IADAS identification compared with the other investigated indicators because it had no saturation during the whole crop lifespan. Compared with DLF, AGF and FF, SGF provided a better fitting of the CIred edge time-series curves without overfitting problems, although the coefficient of determination (R2) and root mean square error (RMSE) were not the best. The FDmax of the SGF-fitted CIred edge curve (SGF_CIred edge) provided good estimates of the IADAS, with an RMSE and mean average error (MAE) of 3.79 days and 3.00 days, respectively. The SGF-fitted_CIred edge curve can be used to group the soybean varieties into early, middle and late groups. Additionally, the accuracy of the IADAS was improved (RMSE = 3.69 days and MAE = 3.09 days) by using three different relative thresholds (i.e., RT50, RT55, RT60) for the three flowering groups compared to when using a single threshold (RT50). In addition, it was found that the IADAS of the same soybean varieties varied greatly when planted in two different climatic regions due to the genotype–environment interactions. Overall, this study demonstrated that the IADAS of soybean varieties can be identified efficiently and accurately based on UAV remote sensing multispectral time-series data.

A new four-stage approach based on normalized vegetation indices for detecting and mapping sugarcane hail damage using multispectral remotely sensed data

Hailstorms have increased in frequency and intensity over the past decade causing substantial losses in agriculture. Since hailstorms often hit a wide area with no detectable pattern, relying on traditional field-based methods to assess crop damage is difficult. The aim of this study was to develop and test a new four-stage normalized vegetation index approach for detecting the severity of hailstorm damage on sugarcane plants in a large estate in south eastern Zimbabwe. The following six spectral indices were computed for the period before and after a hailstorm event to assess the extent of hailstorm damage on sugarcane: Green Chlorophyll Index (GCI); Normalized Difference Vegetation Index (NDVI); Normalized Difference Senescent Vegetation Index (NDSVI); Red Edge Chlorophyll Index (RECI); Normalized Difference Tillage Index (NDTI); and Modified Soil Adjusted Vegetation Index (MSAVI2). Then, the spectral differences were computed for each index separately and the difference maps are reported as delta (Δ) indices. The results of this study show that within one week and even two weeks after a hailstorm, ΔNDTI, ΔNDVI and ΔRECI were consistently able to detect and characterise the severity of sugarcane damage. When used in partial least squares-discriminant analysis (PLS-DA), ΔNDTI performed best in mapping the severity of crop damage throughout the large estate. ΔNDTI was able to discriminate three different levels of sugarcane damage with an overall accuracy of 90% and a Kappa value of 0.85. Combined these results imply that ΔNDTI computed using multi-spectral datasets within a fortnight after a hailstorm is a promising tool for generating reliable information about the severity of sugarcane damage by hailstorms. Such spatially explicit information is useful for creating customised crop insurance packages sensitive to damage incurred by the farmer.

Field identification of drought tolerant wheat genotypes using canopy vegetation indices instead of plant physiological and biochemical traits

Seasonal drought is a major limiting factor for wheat production in the North China Plain. The use of non-destructive screening tools based on vegetation indices associated with physiological and biochemical traits will help in the identification of drought-tolerant wheat genotypes to counter drought stress. The objectives of this study were to characterize the performance of 10 wheat genotypes with different genetic backgrounds in terms of physical and biochemical traits under two contrasting moisture regimes, and to evaluate the effectiveness of vegetation indices/spectral measurement techniques for assessing drought tolerance in wheat. Traits associated with drought stress were assessed in the 10 wheat genotypes, including plant biophysical parameters, drought tolerance parameters, as well as various vegetation indices. Significant variability was found wheat genotypes with different genetic backgrounds to water stress. The clustering results indicated that Luohan-19, Luohan-22, and Jinmai-47 were drought-tolerant genotypes characterized by high stress tolerance index (STI) values (0.548, 0.535 and 0.523, respectively), drought index (DI) values (0.517, 0.524 and 0.504, respectively), and drought tolerance coefficient (DC) values (1.065, 1.074 and 1.000, respectively). Based on principal component analysis and membership function, genotypes were classified as tolerant (Luohan-19, Luohan-22, and Jinmai-47), moderately tolerant (Luomai-26, Zhongmai-175, and Bainong-207) and sensitive (Yunong-516, Zhongmai-895, Annong-0711 and Huacheng-3366) to water stress, with comprehensive evaluation values (D) of >0.761, 0.422–0.761, and <0.422, respectively. Heatmap analysis of various vegetation indices for screening wheat genotypes showed similar results to the multivariate data analysis; that is, genotypes Luohan-19, Luohan-22, and Jinmai-47 were identified as drought-tolerant genotypes. Among these vegetation indices, the normalized difference water index (NDWI) and the red edge chlorophyll index (CIred edge) were highly correlated with the drought tolerance indices, such as STI and DI (r2 >0.717), and are therefore recommended as rapid indicators for the identification of tolerance genotypes in the field. These vegetation indices, particularly NDWI and CIred edge, exhibited similar performance to the drought tolerance indices and multivariate data analysis results in identifying drought-tolerant genotypes, and thus they can be used as alternative and inexpensive measures for identifying drought tolerance in wheat in the future.

Multi-Stage Corn Yield Prediction Using High-Resolution UAV Multispectral Data and Machine Learning Models

Timely and cost-effective crop yield prediction is vital in crop management decision-making. This study evaluates the efficacy of Unmanned Aerial Vehicle (UAV)-based Vegetation Indices (VIs) coupled with Machine Learning (ML) models for corn (Zea mays) yield prediction at vegetative (V6) and reproductive (R5) growth stages using a limited number of training samples at the farm scale. Four agronomic treatments, namely Austrian Winter Peas (AWP) (Pisum sativum L.) cover crop, biochar, gypsum, and fallow with sixteen replications were applied during the non-growing corn season to assess their impact on the following corn yield. Thirty different variables (i.e., four spectral bands: green, red, red edge, and near-infrared and twenty-six VIs) were derived from UAV multispectral data collected at the V6 and R5 stages to assess their utility in yield prediction. Five different ML algorithms including Linear Regression (LR), k-Nearest Neighbor (KNN), Random Forest (RF), Support Vector Regression (SVR), and Deep Neural Network (DNN) were evaluated in yield prediction. One-year experimental results of different treatments indicated a negligible impact on overall corn yield. Red edge, canopy chlorophyll content index, red edge chlorophyll index, chlorophyll absorption ratio index, green normalized difference vegetation index, green spectral band, and chlorophyll vegetation index were among the most suitable variables in predicting corn yield. The SVR predicted yield for the fallow with a Coefficient of Determination (R2) and Root Mean Square Error (RMSE) of 0.84 and 0.69 Mg/ha at V6 and 0.83 and 1.05 Mg/ha at the R5 stage, respectively. The KNN achieved a higher prediction accuracy for AWP (R2 = 0.69 and RMSE = 1.05 Mg/ha at V6 and 0.64 and 1.13 Mg/ha at R5) and gypsum treatment (R2 = 0.61 and RMSE = 1.49 Mg/ha at V6 and 0.80 and 1.35 Mg/ha at R5). The DNN achieved a higher prediction accuracy for biochar treatment (R2 = 0.71 and RMSE = 1.08 Mg/ha at V6 and 0.74 and 1.27 Mg/ha at R5). For the combined (AWP, biochar, gypsum, and fallow) treatment, the SVR produced the most accurate yield prediction with an R2 and RMSE of 0.36 and 1.48 Mg/ha at V6 and 0.41 and 1.43 Mg/ha at the R5. Overall, the treatment-specific yield prediction was more accurate than the combined treatment. Yield was most accurately predicted for fallow than other treatments regardless of the ML model used. SVR and KNN outperformed other ML models in yield prediction. Yields were predicted with similar accuracy at both growth stages. Thus, this study demonstrated that VIs coupled with ML models can be used in multi-stage corn yield prediction at the farm scale, even with a limited number of training data.

Red solar-induced chlorophyll fluorescence as a robust proxy for ecosystem-level photosynthesis in a rice field

Terrestrial ecosystem photosynthesis plays a crucial role in global carbon cycling. Solar-induced chlorophyll fluorescence (SIF) is a state-of-the-art proxy for ecosystem photosynthesis. Although satellite-derived SIF correlates with gross primary productivity (GPP), it needs to be complemented with ground-based measurements to better understand the dynamics of carbon uptake by plants, because of the coarse spatiotemporal resolution of satellite-based data. We used an ultrafine-resolution spectrometer to detect both red SIF (SIFred, 687 nm) and far-red SIF (SIFfar-red, 760 nm) at ground level. Then, we investigated how well they represented eddy-tower-derived GPP in a rice field of central Japan during the mid to late growing season in 2019. Half-hourly and daily averaged SIFred showed stronger correlation with GPP (R2 = 0.61 and 0.83, respectively) than SIFfar-red (R2 = 0.42 and 0.57, respectively). Stronger seasonal correlation with GPP was observed for canopy-escaping SIF than for total SIF. The SIFred/SIFfar-red ratio was not significantly correlated with seasonal changes in the normalized difference vegetation index or in the pigment-specific simple ratios for chlorophyll-a and -b. There were no clear differences in the SIFred–GPP relationships between sunny and cloudy conditions. We concluded that SIFred could be an equivalent or better proxy than SIFfar-red for ecosystem-level photosynthesis. We further postulated that SIF emission is mainly controlled by plant physiology during the mid to late growing season, when temporal variations of the canopy structure in rice ecosystems are small.

Investigating the response of green and red spinach microgreen yield and chlorophyll content in varied light-emitting diode and plant nutrient

Light-Emitting Diode (LED) improves photosynthesis, vegetative development, seed germination, and chlorophyll content in a controlled-environment plant cultivation system. This study used LEDs as artificial grow illumination with 4 four distinct ratios, 100% blue, 100% red, 100% white, 67%:20%:13% red:blue:white, for 12 hours. Microgreens were watered twice daily with 250 ppm of plant nutrients, using a commercial AB mix and liquid organic fertilizer. Different LED ratios have varied lux and Photosynthetic Photon Flux Density (PPFD), i.e. 100 lux and 44.26 mol·m−2·s−1 PPFD for 100% blue; 600 lux and 20.32 mol·m−2·s−1 PPFD for 100% red; 1000 lux and 95.92 mol·m−2·s−1 PPFD for 100% white; 350 lux, and 37.33 mol·m−2·s−1 for 67%:20%:13% red:blue:white; with maintained 25-27°C of temperature and 70-80% of relative humidity. The 100% white LED lamp with AB Mix as plant nutrient resulted in the best growth of red and green spinach microgreens, with height, leaf area, fresh weight, chlorophyll a, chlorophyll b, and total chlorophyll content of 2.98 cm, 1.32 cm2, 0.14 gr, 19.05 mg/ L, 11.38 mg/L, 19.76 mg/L for green spinach and 3.01 cm, 0.91 cm2, 0.08 g, 19.12 mg/L, 9.93 mg/L, 18.50 mg/L for red spinach.

Effects of Atmospheric Cold Plasma Treatment on the Storage Quality and Chlorophyll Metabolism of Postharvest Tomato.

Atmospheric cold plasma (ACP) is a potential green preservation technology, but its preservation mechanism is still unclear, and the effects of different plasma intensities on postharvest tomatoes are little studied. In this study, the effects of different ACP treatments (0 kV, 40 kV, 60 kV, and 80 kV) on the sensory quality, physiological indexes, key enzyme activities, and gene expression related to the chlorophyll metabolism of postharvest tomatoes were investigated during the storage time. The results showed that compared with the control group, the tomatoes in the plasma treatment group had a higher hardness and total soluble solid (TSS) and titratable acid (TA) contents, a lower respiratory intensity and weight loss rate, a higher brightness, and a lower red transformation rate, especially in the 60 kV treatment group. In addition, chlorophyll degradation, carotenoid accumulation, and chlorophyllase and pheophorbide a mono-oxygenase (PAO) enzyme activities in the postharvest tomatoes were inhibited in the 60 kV treatment group, and the expressions of three key genes related to chlorophyll metabolism, chlorophyll (CLH1), pheophytinase (PPH), and red chlorophyll catabolic reductase (RCCR) were down-regulated. The results of the correlation analysis also confirmed that the enzyme activity and gene expression of the chlorophyll metabolism were regulated by the ACP treatment, aiming to maintain the greenness of postharvest tomatoes.

Concurrent bioimaging of microalgal photophysiology and oxidative stress.

The production of reactive oxygen species (ROS) is an unavoidable consequence of oxygenic photosynthesis and represents a major cause of oxidative stress in phototrophs, having detrimental effects on the photosynthetic apparatus, limiting cell growth, and productivity. Several methods have been developed for the quantification of cellular ROS, however, most are invasive, requiring the destruction of the sample. Here, we present a new methodology that allows the concurrent quantification of ROS and photosynthetic activity, using the fluorochrome dichlorofluorescein (DCF) and in vivo chlorophyll a fluorescence, respectively. Both types of fluorescence were measured using an imaging Pulse Amplitude Modulation (PAM) fluorometer, modified by adding a UVA-excitation light source (385nm) and a green bandpass emission filter (530nm) to enable the sequential capture of red chlorophyll fluorescence and green DCF fluorescence in the same sample. The method was established on Phaeodactylum tricornutum Bohlin, an important marine model diatom species, by determining protocol conditions that permitted the detection of ROS without impacting photosynthetic activity. The utility of the method was validated by quantifying the effects of two herbicides (DCMU and methyl viologen) on the photosynthetic activity and ROS production in P. tricornutum and of light acclimation state in Navicula cf. recens Lange-Bertalot, a common benthic diatom. The developed method is rapid and non-destructive, allowing for the high-throughput screening of multiple samples over time.

HyScreen: A Ground-Based Imaging System for High-Resolution Red and Far-Red Solar-Induced Chlorophyll Fluorescence

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 (F687) and far-red (F760) 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 F687 of 1.13 and F760 of 1.96 mW m-2 sr-1 nm-1 from the SFM method. HyScreen showed good performance for SIF retrievals at both F687 and F760; 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.

Estimating canopy-scale chlorophyll content in apple orchards using a 3D radiative transfer model and UAV multispectral imagery

Chlorophyll content is a key trait for understanding the functioning of agroforestry ecosystems and has important implications for leaf and canopy photosynthesis. However, fine-scale monitoring of canopy chlorophyll content (CCC) of individual fruit trees is rather challenging. This study aims to use a 3D radiative transfer model (RTM) and proposes a joint inversion model based on prior knowledge to estimate the CCC of individual tree crowns (ITCs) in apple orchards. The widely recognized 3D RTM LESS (large-scale remote sensing data and image simulation framework over heterogeneous 3D scenes) was adopted for large-scale apple orchard 3D scenes radiative transfer computing and image simulation. LESS was first evaluated with unmanned aerial vehicle (UAV) multispectral imagery and the results showed that it reasonably characterized the reflectance of apple tree canopies (RMSE = 0.02). An original look-up table (LUT) with reflectance was then produced using LESS, and the final vegetation indices LUT (VI LUT) including Normalized Difference Vegetation Index (NDVI), Green Chlorophyll Index (CIgreen), Red edge Chlorophyll Index (CIred edge) and Green NDVI (GNDVI) was generated from the original LUT form VI interpolation. A physically-based joint inversion model coupling prior knowledge of leaf pigments and leaf area index (LAI) was developed to estimate the CCC of ITCs from high-resolution UAV images. The solution first used linear interpolation to produce a weighted VI LUT corresponding to the sample based on estimated LAI. Linear interpolation was then adopted to screen multiple combinations of leaf chlorophylla+b (Cab) and leaf carotenoids (Cxc) contents from the VI LUT. A prior relationship between Cab and Cxc was finally used to regularize the constraints on multiple VI combinations and determine the estimation of Cab and CCC. The joint inversion model demonstrated an accurate estimation of CCC of ITCs. The model driven by GNDVI yielded the highest result for CCC estimation (R2 = 0.84, RMSE = 24.12 μg/cm2). In addition, CIgreen (R2 = 0.82, RMSE = 32.22 μg/cm2) and CIred edge (R2 = 0.81, RMSE = 34.05 μg/cm2) also achieved satisfactory results. The proposed model facilitates CCC estimation of ITCs from high-resolution imagery in heterogeneous orchard canopies, which is important for advancing the precise nutrition management of fruit trees.

A precise method unaffected by atmospheric reabsorption for ground-based retrieval of red and far-red sun-induced chlorophyll fluorescence

Remote sensing employs solar-induced chlorophyll fluorescence (SIF) as a proxy for photosynthesis from field to airborne and satellite sensors. The investigation of SIF offers a unique way of studying vegetation functioning from the local to the global scale. However, the passive, optical retrieval of the SIF signal is still challenging. Common retrieval approaches extract the SIF infilling directly from atmospheric oxygen bands in down-welling and up-welling radiance. They often involve a complex signal correction to compensate for atmospheric reabsorption and require long computing time. In contrast, the exploitation of solar Fraunhofer lines is devoid of atmospheric disturbances. We propose a new retrieval method for red and far-red SIF directly from up-welling radiance spectra in the spectral range between 650 nm and 810 nm by applying Partial Least Squares (PLS) regression machine learning. Solar Fraunhofer lines are exploited for SIF retrieval with the PLS approach by excluding telluric absorption features. The PLS models are trained and tested on synthetic reflectance and SIF data modeled with SCOPE. We identified a logarithmic relationship of the retrieval error with respect to signal-to-noise ratio of the instrument. The approach has been tested with real-world data measured by the Fluorescence Box (FloX), and evaluated against two well-established retrieval methods: the spectral fitting method (SFM) and the singular value decomposition (SVD). PLS models exploiting solar Fraunhofer lines retrieved meaningful SIF values with high precision and demonstrated robustness against atmospheric reabsorption, including from a 100m tall tower. In addition, PLS retrieval requires no complex correction for atmospheric reabsorption and computes 37 times faster than SFM. Hence, PLS retrieval allows fast and robust exploitation of SIF from solar Fraunhofer lines with high precision under conditions in which other retrieval approaches require complex atmospheric correction.