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Solar-induced chlorophyll fluorescence (SIF) has been widely utilized to track the dynamics of gross primary productivity (GPP). It has been shown that the photochemical reflectance index (PRI), which may be utilized as an indicator of non-photochemical quenching (NPQ), improves SIF-based GPP estimation. However, the influence of weather conditions on GPP estimation using SIF and PRI has not been well explored. In this study, using an open-access dataset, we examined the impact of the clearness index (CI), which is associated with the proportional intensity of solar incident radiation and can represent weather conditions, on soybean GPP estimation using SIF and PRI. The midday PRI (xanthophyll de-epoxidation state) minus the early morning PRI (xanthophyll epoxidation state) yielded the corrected PRI (ΔPRI), which described the amplitude of xanthophyll pigment interconversion during the day. The observed canopy SIF at 760 nm ( S I F T O C _ 760 ) was downscaled to the broadband photosystem-level SIF for photosystem II ( S I F T O T _ F U L L _ P S I I ). Our results show that GPP can be accurately estimated using a multi-linear model with S I F T O T _ F U L L _ P S I I and ΔPRI. The ratio of GPP measured using the eddy covariance (EC) method ( G P P E C ) to GPP estimated using S I F T O T _ F U L L _ P S I I and ΔPRI exhibited a non-linear correlation with the CI along both the half-hourly (R2 = 0.21) and daily scales (R2 = 0.25). The GPP estimates using S I F T O T _ F U L L _ P S I I and ΔPRI were significantly improved by the addition of the CI (for the half-hourly data, R2 improved from 0.64 to 0.71 and the RMSE decreased from 8.28 to 7.42 μ mol•m−2•s−1; for the daily data, R2 improved from 0.71 to 0.81 and the RMSE decreased from 6.69 to 5.34 μ mol•m−2•s−1). This was confirmed by the validation results. In addition, the GPP estimated using the Random Forest method was also largely improved by considering the influences of the CI. Therefore, our findings demonstrate that GPP can be well estimated using S I F T O T _ F U L L _ P S I I and ΔPRI, and it can be significantly enhanced by accounting for the CI. These results will be beneficial to vegetation GPP estimation using different remote sensing platforms, especially under various weather conditions. [ABSTRACT FROM AUTHOR]

Plant stress detection is crucial for enhancing crop yield, particularly in the face of climate change. Cold stress significantly affects photosynthetic activity and pigment composition in soybean (a C3 plant) and maize (a C4 plant), underscoring the need to identify stress signals before irreversible damage occurs. This study used portable spectrometers to non-destructively detect the early signs of cold stress-related processes in these crops. The spectral reflectance indices were measured to identify crop-specific stress responses. According to the results, pigment-based indices have better potential for crop-specific stress detection than general stress response indices. The normalized phaeophytinization index (NPQI) and normalized pigment chlorophyll index (NPCI) exhibited diverse responses in C4 maize compared to C3 soybean, indicating the potential for discerning stress-induced changes in chlorophyll and other pigment contents. The photochemical reflectance index (PRI), modified to specific wavelengths, emerged as a crucial tool for stress detection in both crops, showing strong correlations with photosynthetic parameters and biomass traits. Specifically, the strongest correlation was found between PRI(525, 570) and fresh mass in maize and PRI(555, 570) and fresh mass in soybean. This approach underscores the importance of advanced spectral techniques in understanding and improving crop responses to cold stress, highlighting their potential for precision agriculture.

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Tracking plant water status is a critical step towards the adaptive precision irrigation management of processing tomatoes, one of the most important specialty crops in California. The photochemical reflectance index (PRI) from proximal sensors and the high-resolution unmanned aerial vehicle (UAV) imagery provide an opportunity to monitor the crop water status efficiently. Based on data from an experimental tomato field with intensive aerial and plant-based measurements, we developed random forest machine learning regression models to estimate tomato stem water potential (ψ stem), (using observations from proximal sensors and 12-band UAV imagery, respectively, along with weather data. The proximal sensor-based model estimation agreed well with the plant ψ stem with R 2 of 0.74 and mean absolute error (MAE) of 0.63 bars. The model included PRI, normalized difference vegetation index, vapor pressure deficit, and air temperature and tracked well with the seasonal dynamics of ψ stem across different plots. A separate model, built with multiple vegetation indices (VIs) from UAV imagery and weather variables, had an R 2 of 0.81 and MAE of 0.67 bars. The plant-level ψ stem maps generated from UAV imagery closely represented the water status differences of plots under different irrigation treatments and also tracked well the temporal change among flights. PRI was found to be the most important VI in both the proximal sensor- and the UAV-based models, providing critical information on tomato plant water status. This study demonstrated that machine learning models can accurately estimate the water status by integrating PRI, other VIs, and weather data, and thus facilitate data-driven irrigation management for processing tomatoes.

Photosynthesis is influenced by dynamic energy allocation under various environmental conditions. Solar-induced chlorophyll fluorescence (SIF), an important pathway for dissipating absorbed energy, has been extensively used to evaluate gross primary productivity (GPP). However, the potential for photochemical reflectance index (PRI), as an indicator of non-photochemical quenching (NPQ), to improve the SIF-based GPP estimation, has not been thoroughly investigated. In this study, using continually tower-based observations, we examined how PRI affected the link between SIF and GPP for corn and soybean at half-hourly and daily timescales. The relationship of GPP to SIF and PRI is impacted by stress indicated by vapor pressure deficit (VPD) and crop water stress index (CWSI). Moreover, the ratio of GPP to SIF of corn was more sensitive to PRI compared to soybean. Whether in Pearson or Partial correlation analysis, the relationships of PRI to the ratio of GPP to SIF were almost all significant, regardless of controlling structural-physiological (stomatal conductance, vegetation indices) and environmental variables (light intensity, etc.). Therefore, PRI significantly affects the SIF–GPP relationship for corn (r > 0.31, p < 0.01) and soybean (r > 0.22, p < 0.05). After combining SIF and PRI using the multi-variable linear model, the GPP estimation has been largely improved (the coefficient of determination, abbreviated as R2, increased from 0.48 to 0.49 to 0.78 to 0.84 and the Root Mean Square Error, abbreviated as RMSE, decreased from 6.38 to 10.22 to 3.56 to 6.60 μ m o l   C O 2 · m − 2 · s − 1 for corn, R2 increased from 0.54 to 0.62 to 0.78 to 0.82 and RMSE decreased from 6.25 to 9.59 to 4.34 to 6.60 μ m o l   C O 2 · m − 2 · s − 1 for soybean). It suggests that better GPP estimations for corn and soybean can be obtained when SIF is combined with PRI. [ABSTRACT FROM AUTHOR]

Conifers are a common type of plant used in ornamental horticulture. The prompt diagnosis of the phenological state of coniferous plants using remote sensing is crucial for forecasting the consequences of extreme weather events. This is the first study to identify the "Vegetation" and "Dormancy" states in coniferous plants by analyzing their annual time series of spectral characteristics. The study analyzed Platycladus orientalis, Thuja occidentalis and T. plicata using time series values of 81 vegetation indices and 125 spectral bands. Linear discriminant analysis (LDA) was used to identify "Vegetation" and "Dormancy" states. The model contained three to four independent variables and achieved a high level of correctness (92.3 to 96.1%) and test accuracy (92.1 to 96.0%). The LDA model assigns the highest weight to vegetation indices that are sensitive to photosynthetic pigments, such as the photochemical reflectance index (PRI), normalized PRI (PRI_norm), the ratio of PRI to coloration index 2 (PRI/CI2), and derivative index 2 (D2). The random forest method also diagnoses the "Vegetation" and "Dormancy" states with high accuracy (97.3%). The vegetation indices chlorophyll/carotenoid index (CCI), PRI, PRI_norm and PRI/CI2 contribute the most to the mean decrease accuracy and mean decrease Gini. Diagnosing the phenological state of conifers throughout the annual cycle will allow for the effective planning of management measures in conifer plantations. [ABSTRACT FROM AUTHOR]

Background: Canopy phenology, foliar biochemistry, and leaf lifespan vary according to season within and across species. Intrinsically, this variation should also determine changes in the spectral properties of foliar tissue. However, whether canopy phenology and leaf spectroscopy co-vary across species has seldom been tested in seasonal drylands. Questions and / or Hypotheses: We tested the covariation between growth rate, foliage phenology, and leaf spectral indices, expecting that species with high growth rates should have large variability in relative leaf number and foliar spectroscopy. Studied species / data description / Mathematical model: We partition the intra and interspecific variation in relative leaf number and spectroscopy within nine perennial chaparral shrub species. Study site and dates: Rancho El Mogor, Valle de Guadalupe, Baja California, during 2017 and 2018. Methods: Foliage phenology was quantified with an index of relative leaf number, foliar spectroscopy with three standard vegetation indices, and resource-use with stem growth and leaf functional traits. Results: Species with higher variability in foliage phenology (more deciduous species) had higher stem growth rates. The three vegetation indices varied significantly across species (ca. 20 % of explained variance), and across seasons within species (ca. 55 % of explained variance). The values of the three vegetation indices were correlated, and only NDVI was correlated with foliage phenology and stem growth. Conclusions: Our results show that variations in relative leaf number and spectroscopy in chaparral species are coupled with functional traits of species involved in resource exploitation strategies in a seasonally arid environment.

The quantification of phytochemicals with nutritional benefits relies on accuracy yet time-consuming, expensive and destructive methodologies. In contrast, optical indices widely employed in disciplines such as ecology can serve as fast, low-cost, and non-destructive tools for tracking pigment changes. However, their potential application in postharvest phases and commercialization (i.e., supermarkets) remains underexplored. In this context, this work aimed to investigate the feasibility of using the normalized difference vegetation index (NDVI), the chlorophyll content index (CCI) and the photochemical reflectance index (PRI) to estimate phytochemicals of nutraceutical interest (chlorophylls, carotenoids and tocopherols). A fully factorial and randomized experimental design (combining different illumination, packaging, and collecting time) was conducted in two green leafy packaged vegetables (rocket and spinach). With this design that induced differences in physiological parameters a database with variable content of isoprenoids (carotenoids, chlorophylls, and tocopherols) based on treatments was created. By establishing correlations and models between isoprenoids and optical indices, the NDVI was revealed as a valuable tool to estimate chlorophyll content in both species, while the PRI tracked both tocopherol and carotenoid (except for β-carotene) content. Taking this approach, estimation of the sum of total carotenoids and tocopherols was achieved using the PRI. This study confirms the possibility of extending the use of the NDVI and PRI to real-time nutritional monitoring in different stages of production and commercialization. A future application envisions integrating this technology into refrigerators for enhanced consumer access to nutritional information.

Solar-induced chlorophyll fluorescence (SIF) has been widely utilized to track the dynamics of gross primary productivity (GPP). It has been shown that the photochemical reflectance index (PRI), which may be utilized as an indicator of non-photochemical quenching (NPQ), improves SIF-based GPP estimation. However, the influence of weather conditions on GPP estimation using SIF and PRI has not been well explored. In this study, using an open-access dataset, we examined the impact of the clearness index (CI), which is associated with the proportional intensity of solar incident radiation and can represent weather conditions, on soybean GPP estimation using SIF and PRI. The midday PRI (xanthophyll de-epoxidation state) minus the early morning PRI (xanthophyll epoxidation state) yielded the corrected PRI (ΔPRI), which described the amplitude of xanthophyll pigment interconversion during the day. The observed canopy SIF at 760 nm (SIFTOC_760) was downscaled to the broadband photosystem-level SIF for photosystem II (SIFTOT_FULL_PSII). Our results show that GPP can be accurately estimated using a multi-linear model with SIFTOT_FULL_PSII and ΔPRI. The ratio of GPP measured using the eddy covariance (EC) method (GPPEC) to GPP estimated using SIFTOT_FULL_PSII and ΔPRI exhibited a non-linear correlation with the CI along both the half-hourly (R2 = 0.21) and daily scales (R2 = 0.25). The GPP estimates using SIFTOT_FULL_PSII and ΔPRI were significantly improved by the addition of the CI (for the half-hourly data, R2 improved from 0.64 to 0.71 and the RMSE decreased from 8.28 to 7.42 μmol•m−2•s−1; for the daily data, R2 improved from 0.71 to 0.81 and the RMSE decreased from 6.69 to 5.34 μmol•m−2•s−1). This was confirmed by the validation results. In addition, the GPP estimated using the Random Forest method was also largely improved by considering the influences of the CI. Therefore, our findings demonstrate that GPP can be well estimated using SIFTOT_FULL_PSII and ΔPRI, and it can be significantly enhanced by accounting for the CI. These results will be beneficial to vegetation GPP estimation using different remote sensing platforms, especially under various weather conditions.

Light use efficiency (LUE) plays an important role in gross primary production (GPP), so accurate estimation of LUE is crucial. The estimation of LUE using the photochemical reflectance index (PRI) is considerably promising, but on the other hand there is always uncertainty in obtaining the PRI due to the changing viewing angle and sun position in remote sensing observations. The objective of this study was to distinguish between sunlit and shaded leaves to improve the performance of PRI estimating LUE, and we used rice canopy reflectance data collected by the automatic multi-angle remote sensing platform and flux data for each half hour from 9:00 to 15:00 in Shouxian, Anhui Province, China. We modified the two-leaf model based on the characteristics of the rice canopy, P R I t was thought of by distinguishing between sunlit and shaded leaves PRI by two-leaf model on the rice canopy. The results showed that the P R I t -LUE correlation improved relative to PRI-LUE on clear days and was essentially negligible on cloudy days. We found that the photosynthetically active radiation (PAR), temperature, and humidity had some influence on P R I t , the P R I t -LUE correlation was strongest at higher temperature and vapour pressure deficit (VPD) and was linear with the change of temperature. We conclude that the modified two-leaf model improves the integrity of the PRI remote sensing observation and increases the accuracy of its LUE estimation by adequately separating sunlit and shaded leaves in the rice canopy. [ABSTRACT FROM AUTHOR]

Salinization is a significant adverse factor that suppresses productivity of agricultural plants. Multispectral imaging and calculation of reflectance indices, including the typical photochemical reflectance index (PRI), can be used for early revealing of changes in plants under salinization. However, the direction of change in typical PRI is dependent on the type of stressor and the experimental conditions. Earlier, we proposed modified PRIs with shifted measuring wavelengths and analyzed their changes under excess light, water deficit, and heating. In the current work, we investigated the sensitivity of these indices to changes in pea plants under salinization (100, 200, and 400 mM NaCl treatment). It was shown that short-wavelength PRIs were increased under this treatment; in contrast, long-wavelength PRIs were decreased. Most of the modified PRIs were strongly related to the maximal yield of photosystem II and the normalized difference vegetation index. Long-wavelength PRIs were more sensitive to plant changes than short-wavelength PRIs, typical PRI, and the normalized difference vegetation index because their stable decrease under moderate salinization (the 200 mM NaCl treatment) was initiated earlier. Our results show that long-wavelength PRIs, which also decreased under the action of the excess light, water deficit, and heating, can be potentially used as a universal tool for early revealing of stress changes in plants. [ABSTRACT FROM AUTHOR]

Anthropogenic influences on a global scale have caused negative impacts on the environment, among the most prominent being the increase in the concentration of carbon dioxide (CO2). In this study, the objective was then to estimate the potential of carbon flux (CO2 flux) in the riparian vegetation of the Jacareí–Jaguari reservoir, using the digital processing of orbital images of the CBERS 4A system. To determine the CO2 flux, vegetative indices were used: Normalized Difference Vegetation Index (NDVI); Photochemical Reflectance Index (PRI) and the scaled Photochemical Reflectance Index (sPRI), associating them with land use and occupation classifications from the MapBiomas collection, determining the histogram of each class for CO2 flux analysis, revealing CO2 flux between −0.136 and 0.4049. The lower values of CO2 flux in the reservoir are due to the decrease in vegetative classes, indicating the need for (re)planting and plant conservation, confirming the importance of areas with an ecosystem function, of carbon sink. HIGHLIGHTS The vegetation indices show the need for plantation.; The indices were established to have no relationship with aquatic vegetation.; The sPRI presents good correlations with the efficiency of solar energy for photosynthesis.; The NDVI can be used to detect seasonal and temporal variations along the various vegetative stages.; Many factors, diurnal and seasonal, can affect PRI patterns at foliar, canopy and ecosystem levels.;

Photosynthesis is influenced by dynamic energy allocation under various environmental conditions. Solar-induced chlorophyll fluorescence (SIF), an important pathway for dissipating absorbed energy, has been extensively used to evaluate gross primary productivity (GPP). However, the potential for photochemical reflectance index (PRI), as an indicator of non-photochemical quenching (NPQ), to improve the SIF-based GPP estimation, has not been thoroughly investigated. In this study, using continually tower-based observations, we examined how PRI affected the link between SIF and GPP for corn and soybean at half-hourly and daily timescales. The relationship of GPP to SIF and PRI is impacted by stress indicated by vapor pressure deficit (VPD) and crop water stress index (CWSI). Moreover, the ratio of GPP to SIF of corn was more sensitive to PRI compared to soybean. Whether in Pearson or Partial correlation analysis, the relationships of PRI to the ratio of GPP to SIF were almost all significant, regardless of controlling structural-physiological (stomatal conductance, vegetation indices) and environmental variables (light intensity, etc.). Therefore, PRI significantly affects the SIF–GPP relationship for corn (r > 0.31, p < 0.01) and soybean (r > 0.22, p < 0.05). After combining SIF and PRI using the multi-variable linear model, the GPP estimation has been largely improved (the coefficient of determination, abbreviated as R2, increased from 0.48 to 0.49 to 0.78 to 0.84 and the Root Mean Square Error, abbreviated as RMSE, decreased from 6.38 to 10.22 to 3.56 to 6.60 μmol CO2·m−2·s−1 for corn, R2 increased from 0.54 to 0.62 to 0.78 to 0.82 and RMSE decreased from 6.25 to 9.59 to 4.34 to 6.60 μmol CO2·m−2·s−1 for soybean). It suggests that better GPP estimations for corn and soybean can be obtained when SIF is combined with PRI.

Conifers are a common type of plant used in ornamental horticulture. The prompt diagnosis of the phenological state of coniferous plants using remote sensing is crucial for forecasting the consequences of extreme weather events. This is the first study to identify the “Vegetation” and “Dormancy” states in coniferous plants by analyzing their annual time series of spectral characteristics. The study analyzed Platycladus orientalis, Thuja occidentalis and T. plicata using time series values of 81 vegetation indices and 125 spectral bands. Linear discriminant analysis (LDA) was used to identify “Vegetation” and “Dormancy” states. The model contained three to four independent variables and achieved a high level of correctness (92.3 to 96.1%) and test accuracy (92.1 to 96.0%). The LDA model assigns the highest weight to vegetation indices that are sensitive to photosynthetic pigments, such as the photochemical reflectance index (PRI), normalized PRI (PRI_norm), the ratio of PRI to coloration index 2 (PRI/CI2), and derivative index 2 (D2). The random forest method also diagnoses the “Vegetation” and “Dormancy” states with high accuracy (97.3%). The vegetation indices chlorophyll/carotenoid index (CCI), PRI, PRI_norm and PRI/CI2 contribute the most to the mean decrease accuracy and mean decrease Gini. Diagnosing the phenological state of conifers throughout the annual cycle will allow for the effective planning of management measures in conifer plantations.

Tracking plant water status is a critical step towards the adaptive precision irrigation management of processing tomatoes, one of the most important specialty crops in California. The photochemical reflectance index (PRI) from proximal sensors and the high-resolution unmanned aerial vehicle (UAV) imagery provide an opportunity to monitor the crop water status efficiently. Based on data from an experimental tomato field with intensive aerial and plant-based measurements, we developed random forest machine learning regression models to estimate tomato stem water potential (ψstem), (using observations from proximal sensors and 12-band UAV imagery, respectively, along with weather data. The proximal sensor-based model estimation agreed well with the plant ψstem with R² of 0.74 and mean absolute error (MAE) of 0.63 bars. The model included PRI, normalized difference vegetation index, vapor pressure deficit, and air temperature and tracked well with the seasonal dynamics of ψstem across different plots. A separate model, built with multiple vegetation indices (VIs) from UAV imagery and weather variables, had an R² of 0.81 and MAE of 0.67 bars. The plant-level ψstem maps generated from UAV imagery closely represented the water status differences of plots under different irrigation treatments and also tracked well the temporal change among flights. PRI was found to be the most important VI in both the proximal sensor- and the UAV-based models, providing critical information on tomato plant water status. This study demonstrated that machine learning models can accurately estimate the water status by integrating PRI, other VIs, and weather data, and thus facilitate data-driven irrigation management for processing tomatoes. [ABSTRACT FROM AUTHOR]

Light availability drives vertical canopy gradients in photosynthetic functioning and carbon (C) balance, yet patterns of variability in these gradients remain unclear. We measured light availability, photosynthetic CO2 and light response curves, foliar C, nitrogen (N) and pigment concentrations, and the photochemical reflectance index (PRI) on upper and lower canopy needles of white spruce trees (Picea glauca) at the species' northern and southern range extremes. We combined our photosynthetic data with previously published respiratory data to compare and contrast canopy C balance between latitudinal extremes. We found steep canopy gradients in irradiance, photosynthesis and leaf traits at the southern range limit, but a lack of variation across canopy positions at the northern range limit. Thus, unlike many tree species from tropical to mid‐latitude forests, high latitude trees may not require vertical gradients of metabolic activity to optimize photosynthetic C gain. Consequently, accounting for self‐shading is less critical for predicting gross primary productivity at northern relative to southern latitudes. Northern trees also had a significantly smaller net positive leaf C balance than southern trees suggesting that, regardless of canopy position, low photosynthetic rates coupled with high respiratory costs may ultimately constrain the northern range limit of this widely distributed boreal species. Summary statement: Canopy gradients in photosynthetic capacity of white spruce diminish at high compared to low latitudes. Low carbon balance in high latitude trees may determine the extent of northern treeline. [ABSTRACT FROM AUTHOR]

Salinization is a significant adverse factor that suppresses productivity of agricultural plants. Multispectral imaging and calculation of reflectance indices, including the typical photochemical reflectance index (PRI), can be used for early revealing of changes in plants under salinization. However, the direction of change in typical PRI is dependent on the type of stressor and the experimental conditions. Earlier, we proposed modified PRIs with shifted measuring wavelengths and analyzed their changes under excess light, water deficit, and heating. In the current work, we investigated the sensitivity of these indices to changes in pea plants under salinization (100, 200, and 400 mM NaCl treatment). It was shown that short-wavelength PRIs were increased under this treatment; in contrast, long-wavelength PRIs were decreased. Most of the modified PRIs were strongly related to the maximal yield of photosystem II and the normalized difference vegetation index. Long-wavelength PRIs were more sensitive to plant changes than short-wavelength PRIs, typical PRI, and the normalized difference vegetation index because their stable decrease under moderate salinization (the 200 mM NaCl treatment) was initiated earlier. Our results show that long-wavelength PRIs, which also decreased under the action of the excess light, water deficit, and heating, can be potentially used as a universal tool for early revealing of stress changes in plants.

Climate change amplifies the intensity and occurrence of dry periods leading to drought stress in vegetation. For monitoring vegetation stresses, sun-induced chlorophyll fluorescence (SIF) observations are a potential game-changer, as the SIF emission is mechanistically coupled to photosynthetic activity. Yet, the benefit of SIF for drought stress monitoring is not yet understood. This paper analyses the impact of drought stress on canopy-scale SIF emission and surface reflectance over a lettuce and mustard stand with continuous field spectrometer measurements. Here, the SIF measurements are linked to the plant's photosynthetic efficiency, whereas the surface reflectance can be used to monitor the canopy structure. The mustard canopy showed a reduction in the biochemical component of its SIF emission (the fluorescence emission efficiency at 760 nm—ϵ760) as a reaction to drought stress, whereas its structural component (the Fluorescence Correction Vegetation Index—FCVI) barely showed a reaction. The lettuce canopy showed both an increase in the variability of its surface reflectance at a sub-daily scale and a decrease in ϵ760 during a drought stress event. These reactions occurred simultaneously, suggesting that sun-induced chlorophyll fluorescence and reflectance-based indices sensitive to the canopy structure provide complementary information. The intensity of these reactions depend on both the soil water availability and the atmospheric water demand. This paper highlights the potential for SIF from the upcoming FLuorescence EXplorer (FLEX) satellite to provide a unique insight on the plant's water status. At the same time, data on the canopy reflectance with a sub-daily temporal resolution are a promising additional stress indicator for certain species. [ABSTRACT FROM AUTHOR]

A spectral reflectance sensor (SRS) fixed on the near-surface ground was developed to support the continuous monitoring of vegetation indices such as the normalized difference vegetation index (NDVI) and photochemical reflectance index (PRI). NDVI is useful for indicating crop growth/phenology, whereas PRI was developed for observing physiological conditions. Thus, the seasonal change patterns of NDVI and PRI are two valuable pieces of information in a crop-monitoring system. However, capturing the seasonal patterns is considered challenging because the vegetation index values estimated by the reflection from vegetation are often governed by meteorological conditions, such as solar irradiance and precipitation. Further, unlike growth/phenology, the physiological condition has diurnal changes as well as seasonal characteristics. This study proposed a novel filtering method for extracting the seasonal signals of SRS-based NDVI and PRI in paddy rice, barley, and garlic. First, the measurement accuracy of SRSs was compared with handheld spectrometers, and the R2 values between the two devices were 0.96 and 0.81 for NDVI and PRI, respectively. Second, the experimental study of threshold criteria with respect to meteorological variables (i.e., insolation, cloudiness, sunshine duration, and precipitation) was conducted, and sunshine duration was the most useful one for excluding distorted values of the vegetation indices. After data processing based on sunshine duration, the R2 values between the measured vegetation indices and the extracted seasonal signals of vegetation indices increased by approximately 0.002–0.004 (NDVI) and 0.065–0.298 (PRI) on the three crops, and the seasonal signals of vegetation indices became noticeably improved. This method will contribute to an agricultural monitoring system by identifying the seasonal changes in crop growth and physiological conditions.

The sun-induced chlorophyll fluorescence (SIF) is directly related to the photosynthesis of winter wheat and has a good linear relationship with the gross primary production (GPP). However, the relationship of SIF and GPP is still uncertain under different soil water content conditions. The sun-induced chlorophyll fluorescence, GPP and non-photochemical quenching (NPQ) are the three ways of photosynthetically active radiation in plants. The photochemical reflectance index (PRI) can be used as a proxy indicator of NPQ to characterize the change in heat dissipation during photosynthesis process of winter wheat. To detect the impact of soil water content on winter wheat photosynthesis and SIF and GPP relationship, based on the ground measurement data at Zhaoxian Experimental Station in northern China during the growing season in 2018 and the simulated data by the SCOPE model, we use PRI to improve the GPP model estimated by SIF. The results indicate that: (1) PRI is sensitive to soil water content (R2 = 0.64), so it can be used to improve the estimation of GPP based on SIF under different soil water conditions; (2) For the ground measurement data, PRIobv can improve the estimation accuracy of GPPobv by SIFobv. Under different soil water content (SWC) conditions, the relationship between GPPobv and SIFobv is significantly correlated (R2 = 0.33–0.45, p

Rainfall is among the climatic factors that most affect production, as in the Brazilian Cerrado. Non-destructive and automated phenotyping methods are fast and efficient for genotype selection. The objective of this work was to evaluate, under field conditions, the morphophysiological changes, yield, and grain quality of soybean (Glycine max L. Merrill) under water stress in the Brazilian Cerrado. The plots comprised six soybean cultivars and the subplots of four water regimes, corresponding to 31, 44, 64 and 100% of crop evapotranspiration replacement. The experiments were conducted from May to September 2018 and 2019. An irrigation system with a bar of sprinklers with different flow rates was used. Gas exchange, vegetation indices (measured using a hyperspectral sensor embedded in a drone), yield and grain quality were evaluated. Water stress had different effects on gas exchange, vegetation indices, grain yield and chemical composition among the cultivars. Embrapa cultivar BRS 7280 Roundup ready (RR) and Nidera cultivar NA 5909 RG (glyphosate resistant) are yield stable and have a greater tolerance to drought. BRS 7280RR showed a higher tolerance to drought and higher water use efficiency (WUE) than all other tested cultivars. Vegetation indices, such as the NDVI (Normalized Difference Vegetation Index), correlated with the morphophysiological traits, such as plant height, were the most responsive variables to water stress. The NDVI can be used to predict soybean yield as a tool in a selection program under drought. [ABSTRACT FROM AUTHOR]

We investigated the relationship between simulated shipping (SS) without watering or light and post-shipping growth and flowering of Phalaenopsis Sogo Yukidian ‘V3’. Two experimental environments were created: a low-temperature chamber for simulated shipping and a growth chamber for simulated finishing at the destination. Plants from both the control and treatment groups were moved from the low-temperature chamber to the growth chamber after the end of the simulated shipping. Control plants received continuous light and regular irrigation; plants in the treatment group were placed in the low-temperature chamber under light (LSS) or dark (DSS) conditions for 10, 20, 30, 40, or 50 days, without irrigation. Once DSS duration exceeded 40 days, the leaf-yellowing rate increased rapidly. Chlorophyll content decreased from day 10 to 30 of DSS and slightly increased in LSS and DSS over 40 days. The photochemical reflectance index decreased with the SS duration. The maximum quantum yield PSII photochemistry (Fv/Fm) values sharply decreased after the end of SS; after 40 days, neither LSS nor DSS plants recovered to the normal range. In the same SS duration, the number of days to spiking was delayed in the DSS. In addition, the number of days to spiking was delayed, owing to the longer SS duration. LSS for 50 days induced early flowering, as in the control group, but lowered flower quality. The results demonstrate that drought stress from long-term shipping (>40 days) delayed flowering. In particular, DSS delayed flowering more than LSS due to the decrease in chlorophyll content and the reduction in carbohydrates through respiration. [ABSTRACT FROM AUTHOR]

High temporal resolution measurements of solar‐induced chlorophyll fluorescence (F) and the Photochemical Reflectance Index (PRI) encode vegetation functioning. However, these signals are modulated by time‐dependent processes. We tested the applicability of the Singular Spectrum Analysis (SSA) for disentangling fast components (physiology‐driven) and slow components (controlled by structural and biochemical properties) from PRI, far‐red F (F760), and far‐red apparent fluorescence yield (Fy∗760). The proof of concept was developed on spectral and flux time series simulated with the Soil Canopy Observation of Photochemistry and Energy fluxes (SCOPE) model. This allowed the evaluation of SSA decomposition against variables that are independent of physiology or are modified by it. Slow SSA‐decomposed components of PRI and Fy∗760 showed high correlations with the reference variables (R2 = 0.97 and 0.96, respectively). Fast SSA‐decomposed components of PRI and Fy∗760 were better related to the physiological reference variables than the original signals during periods when leaf area index (LAI) was above 1 m2 m−2. The method was also successfully applied to predict light‐use efficiency (LUE) from the fast SSA‐decomposed components of PRI (R2 = 0.70) and Fy∗760 (R2 = 0.68) when discarding data modeled with LAI < 1 m2 m−2 and short‐wave radiation Rin < 250 W m−2. The method was then tested on data acquired in a Mediterranean grassland. In this case, the fast SSA‐decomposed component of apparent LUE∗ showed a stronger correlation with the fast SSA‐decomposed component of Fy∗760 (R2 = 0.42) than with original Fy∗760 (R2 = 0.01). SSA‐based approach is a promising tool for decoupling physiological information from measurements acquired with automated proximal sensing systems. Plain Language Summary: A fraction of the solar light, which is absorbed by leaves but is not used during photosynthesis, is released through heat or as chlorophyll fluorescence (F), a small emission of energy. Recently, it became possible to indirectly estimate the heat and F by measuring the solar light incoming and reflected from leaves using high‐resolution optical instruments. Heat release can be monitored with the Photochemical Reflectance Index (PRI). While both PRI and F are theoretically linked to the processes associated with photosynthesis, there is a need to remove the disturbing effects from these signals. We tested whether the Singular Spectrum Analysis (SSA) method can identify at which temporal scale (e.g., seasonal, diurnal) physiological processes (i.e., photosynthesis) and vegetation biophysical changes (e.g., phenology) drive variability in PRI and F. We applied the method on artificial time series of PRI and F simulated with a model (Soil Canopy Observation of Photochemistry and Energy fluxes [SCOPE]) and found that SSA can successfully split these signals into several components recognized as slow (seasonally changing structure and pigments) and fast (physiological response to stress) processes. The method was also tested on time series collected in a Mediterranean grassland, yielding promising results in detecting physiologically driven changes in apparent fluorescence yield (F normalized by photosynthetically active radiation). Key Points: Singular Spectrum Analysis (SSA) allowed to separate slow and fast temporal dynamics in time series of the Photochemical Reflectance Index and apparent fluorescence yieldSSA successfully extracted the effect of de‐epoxidation state of the xanthophyll cycle on leaf absorptance in time series simulated with Soil Canopy Observation of Photochemistry and Energy fluxes modelSSA allows decoupling long‐term biophysical and rapid physiological changes in high temporal resolution spectral measurements [ABSTRACT FROM AUTHOR]

High-throughput detection of plant environmental stresses is required for minimizing the reduction in crop yield. Environmental stresses in plants have primarily been validated by the measurements of photosynthesis with gas exchange and chlorophyll fluorescence, which involve complicated procedures. Remote sensing technologies that monitor leaf reflectance in intact plants enable real-time visualization of plant responses to environmental fluctuations. The photochemical reflectance index (PRI), one of the vegetation indices of spectral leaf reflectance, is related to changes in xanthophyll pigment composition. Xanthophyll dynamics are strongly correlated with plant stress because they contribute to the thermal dissipation of excess energy. However, an accurate assessment of plant stress based on PRI requires correction by baseline PRI (PRIo) in the dark, which is difficult to obtain in the field. In this study, we propose a method to correct the PRI using NPQT, which can be measured under light. By this method, we evaluated responses of excess light energy stress under drought in wild watermelon (Citrullus lanatus L.), a xerophyte. Demonstration on the farm, the stress behaviors were observed in maize (Zea mays L.). Furthermore, the stress status of plants and their recovery following re-watering were captured as visual information. These results suggest that the PRI is an excellent indicator of environmental stress and recovery in plants and could be used as a high-throughput stress detection tool in agriculture. [ABSTRACT FROM AUTHOR]

The joint use of solar-induced chlorophyll fluorescence (SIF) and the photochemical reflectance index (PRI) has been shown to improve gross primary productivity (GPP) estimation across various plant functional types. However, the utility of PRI in combination with SIF for transpiration (T) estimation has not yet been explored. Additionally, current SIF-driven transpiration models including linear models, semi-mechanical models (combination of canopy conductance, g C , derived from a SIF and vapor pressure deficit, VPD, driven linear model with the Penman-Monteith model), and hybrid models (combination of g C derived from a SIF and VPD driven machine learning model with the Penman-Monteith model) have rarely been mutually assessed. Based on concurrent remotely sensed SIF and PRI, and eddy covariance flux measurements during one growing season for a winter wheat ecosystem in northern China, we investigated the mediating effect of PRI on SIF-driven T estimation under different VPD conditions and compared the performance of linear, semi-mechanical, and hybrid models in estimating T. Our results showed that the mediating effect of PRI on T described in the SIF-driven linear, semi-mechanistic, and hybrid models was significant under high VPD conditions rather than under low VPD conditions. Specifically, based on T partitioned using an underlying water use efficiency method as a benchmark, the root mean square error (RMSE) value of the PRI-mediated linear, semi-mechanistic, and hybrid models was 28.01 W/m2, 22.25 W/m2, and 28.71 W/m2 lower, respectively, than those of the corresponding models without PRI when VPD was >1.5 kPa. Based on T partitioned using a transpiration estimation algorithm as a benchmark, these three models also exhibited a significant reduction in RMSE under high VPD conditions after considering PRI. The main rationale behind the PRI improvement is that PRI can track photosynthetic dynamics under high VPD conditions. Based on the simulation results of the Soil-Canopy-Observation of Photosynthesis and Energy fluxes (SCOPE) model, PRI can serve as an indicator for non-photochemical quenching (NPQ) within this ecosystem. Consequently, PRI can enhance the capability of SIF to characterize the energy dissipation of photosynthetically active radiation and help SIF to yield more accurate information on GPP and g c under high VPD conditions. Finally, the order of model performance in estimating T was generally hybrid model > semi-mechanistic model > linear model. Our findings show the effectiveness of PRI for improving SIF-driven transpiration estimation under high VPD conditions and provide a new hybrid model for estimating T from SIF. • PRI can improve the ability of SIF to estimate T under high VPD conditions. • A hybrid model applies g c from a SIF driven machine learning model to P-M model. • Hybrid model can better estimate T than semi-mechanistic and empirical models. [ABSTRACT FROM AUTHOR]

Climate change amplifies the intensity and occurrence of dry periods leading to drought stress in vegetation. For monitoring vegetation stresses, sun-induced chlorophyll fluorescence (SIF) observations are a potential game-changer, as the SIF emission is mechanistically coupled to photosynthetic activity. Yet, the benefit of SIF for drought stress monitoring is not yet understood. This paper analyses the impact of drought stress on canopy-scale SIF emission and surface reflectance over a lettuce and mustard stand with continuous field spectrometer measurements. Here, the SIF measurements are linked to the plant’s photosynthetic efficiency, whereas the surface reflectance can be used to monitor the canopy structure. The mustard canopy showed a reduction in the biochemical component of its SIF emission (the fluorescence emission efficiency at 760 nm—ϵ760) as a reaction to drought stress, whereas its structural component (the Fluorescence Correction Vegetation Index—FCVI) barely showed a reaction. The lettuce canopy showed both an increase in the variability of its surface reflectance at a sub-daily scale and a decrease in ϵ760 during a drought stress event. These reactions occurred simultaneously, suggesting that sun-induced chlorophyll fluorescence and reflectance-based indices sensitive to the canopy structure provide complementary information. The intensity of these reactions depend on both the soil water availability and the atmospheric water demand. This paper highlights the potential for SIF from the upcoming FLuorescence EXplorer (FLEX) satellite to provide a unique insight on the plant’s water status. At the same time, data on the canopy reflectance with a sub-daily temporal resolution are a promising additional stress indicator for certain species.

Rainfall is among the climatic factors that most affect production, as in the Brazilian Cerrado. Non-destructive and automated phenotyping methods are fast and efficient for genotype selection. The objective of this work was to evaluate, under field conditions, the morphophysiological changes, yield, and grain quality of soybean (Glycine max L. Merrill) under water stress in the Brazilian Cerrado. The plots comprised six soybean cultivars and the subplots of four water regimes, corresponding to 31, 44, 64 and 100% of crop evapotranspiration replacement. The experiments were conducted from May to September 2018 and 2019. An irrigation system with a bar of sprinklers with different flow rates was used. Gas exchange, vegetation indices (measured using a hyperspectral sensor embedded in a drone), yield and grain quality were evaluated. Water stress had different effects on gas exchange, vegetation indices, grain yield and chemical composition among the cultivars. Embrapa cultivar BRS 7280 Roundup ready (RR) and Nidera cultivar NA 5909 RG (glyphosate resistant) are yield stable and have a greater tolerance to drought. BRS 7280RR showed a higher tolerance to drought and higher water use efficiency (WUE) than all other tested cultivars. Vegetation indices, such as the NDVI (Normalized Difference Vegetation Index), correlated with the morphophysiological traits, such as plant height, were the most responsive variables to water stress. The NDVI can be used to predict soybean yield as a tool in a selection program under drought.

The maximum rate of carboxylation in leaves (Vcmax) is an important parameter used to determine the photosynthetic rate. An accurate estimation of Vcmax is important for evaluating the photosynthetic capacity of vegetation productivity and the carbon budget of forest ecosystems. In this study, we measured the light use efficiency (LUE) and Vcmax of leaves and their corresponding spectral characteristics. Spectral analysis was used to find a modified photochemical reflectance index (PRI) of leaves to estimate LUE and thereby estimate leaf Vcmax. The results showed that the precision with which the modified ratio PRI index estimated LUE was significantly increased. The R2 of the estimation model was 0.69, and the root mean square error (RMSE) was 0.0024. The R2 of the model for estimating leaf Vcmax based on the LUE was 0.72, and the RMSE was 8.439 μmol/m2/s. The Vcmax values of conifer trees (Scots pine, Korean pine, and larch) were higher than those of broadleaf trees (white birch, Manchurian walnut, Siberian elm, Manchurian ash, Mongolian oak, Korean aspen, and Amur linden). This method avoids problem that conventional PRI is not sensitive to high leaf LUE values and provides important parameter data for the quantitative estimation of the gross primary productivity of forest ecosystems. [ABSTRACT FROM AUTHOR]

Crop pests and diseases are key factors that damage crop production and threaten food security. Remote sensing techniques may provide an objective and effective alternative for automatic detection of crop pests and diseases. However, ground-based spectroscopic or imaging sensors may be limited in practically guiding the precision application and reduction of pesticide. Therefore, this study developed an unmanned aerial vehicle (UAV)-based remote sensing system to detect leaf folder (Cnaphalocrocis medinalis). Rice canopy reflectance spectra were obtained in the booting growth stage by using the UAV-based hyperspectral remote sensor. Newly developed and published multivariate spectral indices were initially calculated to estimate leaf-roll rates. The newly developed two-band spectral index (R490−R470), three-band spectral index (R400−R470)/(R400−R490), and published spectral index photochemical reflectance index (R550−R531)/(R550+R531) showed good applicability for estimating leaf-roll rates. The newly developed UAV-based micro hyperspectral system had potential in detecting rice stress induced by leaf folder. The newly developed spectral index (R490−R470) and (R400−R470)/(R400−R490) might be recommended as an indicator for estimating leaf-roll rates in the study area, and (R550−R531)/(R550+R531) might serve as a universal spectral index for monitoring leaf folder. [ABSTRACT FROM AUTHOR]

Abstract Karst hills, that is, jagged topography created by dissolution of limestone and other soluble rocks, are distributed extensively in tropical forest regions, including southern parts of China. They are characterized by a sharp mosaic of water and nutrient availability, from exposed hilltops with poor soil development to valleys with occasional flooding, to which trees show species‐specific distributions. Here we report the relationship of leaf functional traits to habitat preference of tropical karst trees. We described leaf traits of 19 tropical tree species in a seasonal karst rainforest in Guangxi Province, China, 12 species in situ and 13 ex situ in a non‐karst arboretum, which served as a common garden, with six species sampled in both. We examined how the measured leaf traits differed in relation to species’ habitat affinity and evaluated trait consistency between natural habitats vs. the arboretum. Leaf mass per area (LMA) and optical traits (light absorption and reflectance characteristics between 400 and 1,050 nm) showed significant associations with each other and habitats, with hilltop species showing high values of LMA and low values of photochemical reflectance index (PRI). For the six species sampled in both the karst forest and the arboretum, LMA, leaf dry matter content, stomatal density, and vein length per area showed inconsistent within‐species variations, whereas some traits (stomatal pore index and lamina thickness) were similar between the two sites. In conclusion, trees specialized in exposed karst hilltops with little soils are characterized by thick leaves with high tissue density indicative of conservative resources use, and this trait syndrome could potentially be sensed remotely with PRI.

Remote sensing is a key method for advancing our understanding of global photosynthesis and is thus critical to understanding terrestrial carbon uptake and climate change. Increasingly sophisticated spectral indices including solar‐induced florescence (SIF) and the photochemical reflectance index (PRI) are considered good proxies of canopy structure, biochemistry, and physiology. However, the relative influences of illumination intensity and angle on these measures are difficult to unravel, particularly at the scale of whole forest canopies. We exploit the solar dimming during the 2017 North American solar eclipse as well as a clear day before and cloudy day after the day of the eclipse. This novel approach allows us to assess changes in spectral vegetation indices due to illumination intensity independent of changes in illumination angle. Physiologically relevant spectral indices were most affected by dimming, with illumination level explaining 97% of variability in SIF and 99% of variability in PRI during the eclipse. The spectral change in reflectance through the eclipse period revealed changes in PRI were driven by reflectance differences at the 570 nm reference band rather than at the 531 nm signal band associated with xanthophyll pigment interconversions. This study refines our interpretation of vegetation properties from space with implications for our interpretation of signals related to terrestrial photosynthesis derived from sensors spanning a range of illumination conditions and angles. Key Points: We investigate how illumination affects spectral indices using a solar eclipse as a shading experiment at the scale of a forest canopyPRI changes resulted from the 570 nm reference band rather than from the 531 nm band associated with xanthophyll pigment interconversionsSensitivity to multiple scattering may confound interpretation of spectral indices in assessing vegetation [ABSTRACT FROM AUTHOR]

In higher‐latitude trees, temperature and photoperiod control the beginning and end of the photosynthetically active season. Elevated temperature (ET) has advanced spring warming and delayed autumn cooling while photoperiod remains unchanged. We assessed the effects of warming on the length of the photosynthetically active season of three provenances of Pinus strobus L. seedlings from different latitudes, and evaluated the accuracy of the photochemical reflectance index (PRI) and the chlorophyll/carotenoid index (CCI) for tracking the predicted variation in spring and autumn phenology of photosynthesis among provenances. Seedlings from northern, local and southern P. strobus provenances were planted in a temperature‐free‐air‐controlled enhancement (T‐FACE) experiment and exposed to ET (+1.5/3°C; day/night). Over 18 months, we assessed photosynthetic phenology by measuring chlorophyll fluorescence, gas exchange, leaf spectral reflectance and pigment content. During autumn, all seedlings regardless of provenance followed the same sequence of phenological events with the initial downregulation of photosynthesis, followed by the modulation of non‐photochemical quenching and associated adjustments of zeaxanthin pool sizes. However, the timing of autumn downregulation differed between provenances, with delayed onset in the southern provenance (SP) and earlier onset in the northern relative to the local provenance, indicating that photoperiod at the provenance origin is a dominant factor controlling autumn phenology. Experimental warming further delayed the downregulation of photosynthesis during autumn in the SP. A provenance effect during spring was also observed but was generally not significant. The vegetation indices PRI and CCI were both effective at tracking the seasonal variations of energy partitioning in needles and the differences of carotenoid pigments indicative of the stress status of needles. These results demonstrate that PRI and CCI can be useful tools for monitoring conifer phenology and for the remote monitoring of the length of the photosynthetically active season of conifers in a changing climate. [ABSTRACT FROM AUTHOR]

Relationships between gross primary productivity (GPP) and the remotely sensed photochemical reflectance index (PRI) suggest that time series of foliar PRI may provide insight into climate change effects on carbon cycling. However, because a large fraction of carbon assimilated via GPP is quickly returned to the atmosphere via respiration, we ask a critical question—can PRI time series provide information about longer term gains in aboveground carbon stocks? Here we study the suitability of PRI time series to understand intra‐annual stem‐growth dynamics at one of the world's largest terrestrial carbon pools—the boreal forest. We hypothesized that PRI time series can be used to determine the onset (hypothesis 1) and cessation (hypothesis 2) of radial growth and enable tracking of intra‐annual tree growth dynamics (hypothesis 3). Tree‐level measurements were collected in 2018 and 2019 to link highly temporally resolved PRI observations unambiguously with information on daily radial tree growth collected via point dendrometers. We show that the seasonal onset of photosynthetic activity as determined by PRI time series was significantly earlier (p <.05) than the onset of radial tree growth determined from the point dendrometer time series which does not support our first hypothesis. In contrast, seasonal decline of photosynthetic activity and cessation of radial tree growth was not significantly different (p >.05) when derived from PRI and dendrometer time series, respectively, supporting our second hypothesis. Mixed‐effects modeling results supported our third hypothesis by showing that the PRI was a statistically significant (p <.0001) predictor of intra‐annual radial tree growth dynamics, and tracked these daily radial tree‐growth dynamics in remarkable detail with conditional and marginal coefficients of determination of 0.48 and 0.96 (for 2018) and 0.43 and 0.98 (for 2019), respectively. Our findings suggest that PRI could provide novel insights into nuances of carbon cycling dynamics by alleviating important uncertainties associated with intra‐annual vegetation response to climate change. [ABSTRACT FROM AUTHOR]

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

• NDVI represents photosynthetic phenology only in deciduous trees. • Gcc tracks photosynthetic phenology more accurately than GRVI at the canopy scale. • ɸ NPQ and ɸ f,D have great influences on the spectral signal changes of NIRv and PRI. • Good consistency was found between NIRv and PRI for predicting A max. Vegetation photosynthetic phenology is an important indicator to characterize the biological responses of terrestrial carbon cycle to climate change. Remote sensing-derived spectral indices have been used to estimate photosynthetic phenology of terrestrial ecosystems, yet there are large uncertainties due to differences in leaf structure and canopy function between evergreen and deciduous trees. In this study, we used in-situ measured meteorology, the maximum rate of leaf-level CO 2 assimilation (A max), chlorophyll fluorescence and canopy color changes in evergreen and deciduous species to evaluate and compare the abilities of spectral indices for photosynthetic phenology modeling. Seasonal variation in A max is used as a proxy for photosynthetic phenology. Several indices were used, including the normalized difference vegetation index (NDVI), near-infrared reflectance vegetation index (NIRv), photochemical reflectance index (PRI), chlorophyll/carotenoid index (CCI), green-red vegetation index (GRVI), and green chromaticity coordinates (Gcc). We found that NIRv and PRI estimated phenological periods for Cedrus deodara and three deciduous trees all differed from A max by less than 5 days, and all were linearly related to A max (R2 > 0.6). CCI had less potential for A max modeling, while NDVI had the largest error in predicting A max. Gcc was a more accurate proxy for photosynthetic phenology than GRVI at the canopy scale. In addition, we found that the variability of different spectral indices in tracking photosynthetic phenology of evergreen and deciduous species was related to non-photochemical quenching processes. Especially for the fraction of absorbed light quenched by dynamic NPQ (ɸ NPQ) and the fraction of absorbed light quenched by sustained NPQ (ɸ f,D), they contribute more than 40 % to the variations of the NIRv and PRI spectral signals. Therefore, NIRv and PRI provide powerful tools for monitoring photosynthetic phenology, and future exploration of NIR v and PRI at larger spatial scales will favor the global plant phenology and carbon uptake modeling. [ABSTRACT FROM AUTHOR]

The photochemical reflectance index (PRI) has been often used as a physiology-based remote sensing indicator of ecosystem carbon fluxes. However, the assessments of PRI in tracking long-term carbon fluxes with climatic anomalies in mangroves are very limited. In this study, four-year (2017–2020) continuous time series measurements from tower-based eddy covariance and spectral systems in a subtropical mangrove were used to explore the ability of PRI in tracking the response of mangrove carbon fluxes to climate fluctuations and drought stress. The results showed that the temporal dynamics of daily PRI and carbon fluxes shared similar variation patterns over the study period, experiencing simultaneously decreasing trends under drought stress. Compared with the first three years, annual mean values of NEE in 2020 decreased by 10.7% and PRI decreased by 29.0%, correspondingly. PRI and carbon fluxes were significantly correlated across diurnal, seasonal, and annual time scales with better fitness under drought stress. Dark-state PRI (PRI0), the constitutive component of PRI variation due to seasonally changing pigment pool size, showed similar temporal variation as PRI in response to drought stress, while delta PRI (ΔPRI), the facultative component of PRI variation due to diurnal xanthophyll cycle, showed no response to drought stress. This study confirms the ability of PRI to track temporal dynamics of mangrove carbon fluxes on both short-term and long-term scales, with the temporal variation of PRI largely affected by the long-term constitutive pigment pool size. This study highlights the potential of PRI to serve as an early and readily detectable indicator to track the response of the mangrove carbon cycle to climatic anomalies such as drought stress.

Plants are affected by numerous environmental factors that influence their physiological processes and productivity. Early revealing of their action based on measuring spectra of reflected light and calculating reflectance indices is an important stage in the protection of agricultural plants. Photochemical reflectance index (PRI) is a widely used parameter related to photosynthetic changes in plants under action of stressors. We developed a new system for proximal imaging of PRI based on using short pulses of measuring light detected simultaneously in green (530 nm) and yellow (570 nm) spectral bands. The system has several advances compared to those reported in literature. Active light illumination and subtraction of the ambient light allow for PRI measurements without periodic calibrations. Short duration of measuring pulses (18 ms) minimizes their influence on plants. Measurements in two spectral bands operated by separate cameras with aligned fields of visualization allow one to exclude mechanically switchable parts like filter wheels thus minimizing acquisition time and increasing durability of the setup. Absolute values of PRI and light-induced changes in PRI (ΔPRI) in pea leaves and changes of these parameters under action of light with different intensities, water shortage, and heating have been investigated using the developed setup. Changes in ΔPRI are shown to be more robust than the changes in the absolute value of PRI which is in a good agreement with our previous studies. Values of PRI and, especially, ΔPRI are strongly linearly related to the energy-dependent component of the non-photochemical quenching and can be potentially used for estimation of this component. Additionally, we demonstrate that the developed system can also measure fast changes in PRI (hundreds of milliseconds and seconds) under leaf illumination by the pulsed green-yellow measuring light. Thus, the developed system of proximal PRI imaging can be used for PRI measurements (including fast changes in PRI) and estimation of stressors-induced photosynthetic changes.

The 2018–2019 Central European drought had a grave impact on natural and managed ecosystems, affecting their health and productivity. We examined patterns in hyperspectral VNIR imagery using an unsupervised learning approach to improve ecosystem monitoring and the understanding of grassland drought responses. The main objectives of this study were (1) to evaluate the application of simplex volume maximisation (SiVM), an unsupervised learning method, for the detection of grassland drought stress in high-dimensional remote sensing data at the ecosystem scale and (2) to analyse the contributions of different spectral plant and soil traits to the computed stress signal. The drought status of the research site was assessed with a non-parametric standardised precipitation–evapotranspiration index (SPEI) and soil moisture measurements. We used airborne HySpex VNIR-1800 data from spring 2018 and 2019 to compare vegetation condition at the onset of the drought with the state after one year. SiVM, an interpretable matrix factorisation technique, was used to derive typical extreme spectra (archetypes) from the hyperspectral data. The classification of archetypes allowed for the inference of qualitative drought stress levels. The results were evaluated using a set of geophysical measurements and vegetation indices as proxy variables for drought-inhibited vegetation growth. The successful application of SiVM for grassland stress detection at the ecosystem canopy scale was verified in a correlation analysis. The predictor importance was assessed with boosted beta regression. In the resulting interannual stress model, carotenoid-related variables had among the highest coefficient values. The significance of the photochemical reflectance index that uses 512 nm as reference wavelength (PRI512) demonstrates the value of combining imaging spectrometry and unsupervised learning for the monitoring of vegetation stress. It also shows the potential of archetypical reflectance spectra to be used for the remote estimation of photosynthetic efficiency. More conclusive results could be achieved by using vegetation measurements instead of proxy variables for evaluation. It must also be investigated how the method can be generalised across ecosystems.

Abstract Background The Photochemical Reflectance Index (PRI) calculated from narrow-band spectral reflectance data is a vegetation index which is increasingly used as an indicator of photosynthetic activity. The leaf-level link between the status of photosynthetic apparatus and PRI has been robustly established under controlled light conditions. However, when a whole canopy is measured instantaneously, the PRI signal is heavily modified by vegetation structure and local variations in incident light conditions. To apply PRI for monitoring the photosynthesis of whole canopies under natural conditions, these large-scale measurements need to be validated against simultaneous leaf PRI. Unfortunately, PRI changes dynamically with incident light and has a large natural variation. No generally accepted procedure exists today for determining the PRI of canopy elements in situ. Results We present a successful procedure for in situ measurements of needle PRI. We describe, characterize and test an optical measurement protocol and demonstrate its applicability in field conditions. The measurement apparatus consisted of a light source, needle clip, spectroradiometer and a controlling computer. The light level inside the clip was approximately two-thirds of that on sunlit needle surfaces at midday. During each measurement the needle was inserted into the clip for approximately 5 s. We found no near-instantaneous changes (sub-second scale jumps) in PRI during the measurements. The time constants for PRI variation in light to full shade acclimations were approximately 10 s. The procedure was successfully applied to monitor the greening-up of Scots pine trees. We detected both facultative (diurnal) PRI changes of 0.02 (unitless) and constitutive (seasonal) variations of 0.1. In order to reliably detect the facultative PRI change of 0.02, 20 needles need to be sampled from both sunlit and shaded locations. Conclusions We established a robust procedure for irradiance-dependent leaf (needle) PRI measurements, facilitating empirical scaling of PRI from leaf (needle) to full canopy level and the application of PRI to monitoring the changes in highly structured vegetation. The measured time constants, and facultative and constitutive PRI variations support the use of an artificial light for in situ PRI measurements at leaf (needle) level.

• The new tri-PRI tracks leaf-level xanthophyll epoxidation state dynamics in a mature eucalypt forest. • Indices using two-bands do not perform well. • The new formulation is robust to constitutive pigment pool size effects across the canopy profile. • tri-PRI achieved results comparable to PLSR and continuum removal using full spectrum data. Plants actively regulate excess absorbed energy to protect photosynthetic machinery through heat dissipation in a process known as non-photochemical quenching (NPQ), a process useful for quantifying plant health and productivity. NPQ can be indirectly measured in the visible wavelengths between 500 nm and 560 nm, most commonly through the Photochemical Reflectance Index (PRI). However, there remains a lack of consensus regarding the optimal functional form and band selection to calculate PRI for the purpose of measuring NPQ mechanisms. Here, we quantitatively evaluate the effectiveness of leaf-level parametric and non-parametric spectral formulations, band locations, and number of bands to track the xanthophyll pigment cycle in a tall mature Eucalypt forest. Subsequently, our recommended approach is the new 'tri-PRI' index robust to constitutive pigment pool sizes across the canopy profile. tri-PRI is a Triangular Vegetation Index (TVI) (tri-PRI = 0.5[(520 - 490)(R 545nm - R 490nm) - (545 – 490)(R 520nm - R 490nm)]) using three reflectance bands around 490 nm, 520 nm and 545 nm, and has a physiological photosynthetic basis. We found that tri-PRI significantly outperformed PRI and other two band combinations for quantifying the xanthophyll EPoxidation State 'EPS' (tri-PRI R2 = 0.75 versus PRI R2 = 0.23), as well as the Φ NPQ and Φ PSII active chlorophyll fluorescence quenching yields. The new band placement enhanced the dynamic EPS absorption peak, while the third band provided an additional normalisation to minimise the confounding effects of pigments with overlapping spectral features. tri-PRI also performed comparably to parametric and non-parametric hyperspectral techniques and formulations using continuous spectral regions, highlighting the utility of targeted multispectral indices over hyperspectral approaches. This leaf-level study represents a foundational step toward indirectly measuring dynamic photosynthetic activity across the canopy profile in a tall mature Eucalypt forest to inform upscaling efforts from above-canopy remote sensing platforms. The application of tri-PRI and other top-performing multi-band TVI formulations for predicting EPS presented here should be explored across different canopy types, temporal-, and spatial scales. [ABSTRACT FROM AUTHOR]

Measurement of the photochemical reflectance index (PRI) is a simple and non-invasive method for the evaluation of photosynthetic processes in higher plants. At the same time, the relationship between photosynthetic parameters and PRI can be significantly modified at the initial stages of illumination; thus, the study of the mechanisms of development of PRI after the beginning of illumination is an important task. The aim of this work was to analyze the relationship between the acidification of chloroplast lumen, which was assessed as an increase in the absorption of light by the leaf at a wavelength of 535 nm (light scattering, LS), and changes in PRI under short-term illumination in the leaves of pea and geranium. It was shown that the illumination caused an increase in LS and a decrease in PRI in both studied objects. Significant differences were found for the amplitude of the photochemical reflectance index decrease, while the differences in the absolute values of PRI at different stages of illumination were not reliable. Correlation analysis showed that the increase in LS during the first two minutes of illumination, reflecting light-induced acidification of chloroplast lumen, strongly correlated with the amplitude of the decrease in PRI; at the same time, at later stages of illumination, such a relationship was absent. Additional analysis carried out on geranium showed that the cessation of illumination caused the opposite dynamics: the decrease in LS was accompanied by an increase in PRI. The results show that the changes in PRI at the first minutes after the beginning of illumination, apparently, were due to the acidification of the chloroplast lumen. [ABSTRACT FROM AUTHOR]

Photochemical reflectance index (PRI) has been found to be closely related with vegetation photosynthetic phenology in many ecosystems including boreal evergreen, but its ability to track carbon dynamics in low‐latitude evergreen mangrove ecosystems has rarely been assessed. To fill up this gap, this paper explored potential mangrove PRI‐carbon links and their environmental controls across temporal scales (diurnal and seasonal), based on 1‐year continuous high‐resolution (half‐hourly) time series measurements of spectral signals, eddy covariance carbon fluxes, and multiple environmental factors at a subtropical mangrove wetland of southeastern China. The diurnal variation of half‐hourly mangrove PRI, a U‐shaped changing pattern, was consistent with previous PRI studies on boreal evergreen, while the seasonal variation of daily mangrove PRI did not experience a hump‐shaped changing pattern as in boreal evergreen. Diurnal and seasonal mangrove PRI variations were significantly correlated with meteorological (radiation and vapor pressure deficit) and carbon‐related parameters (gross primary productivity, net ecosystem exchange, and light use efficiency), and the strength of diurnal PRI‐carbon correlations varied with meteorological factors with stronger correlations at enhanced radiation and vapor pressure deficit. Our results further indicated that physiology‐related PRI performed better than conventional structure‐related normalized difference vegetation index in tracking mangrove photosynthetic parameters. To the best of our knowledge, this is the first study to explore the link between high‐frequency PRI and carbon dynamics across temporal scales in natural mangrove ecosystems. The PRI‐carbon linkage confirmed by this study will improve our understanding of temporal variations in photosynthetic carbon dynamics in mangrove ecosystems. Key Points: Correlation between photochemical reflectance index (PRI) and carbon fluxes were assessed for a subtropical mangrove wetlandDiurnal and seasonal mangrove PRI variations were significantly correlated with meteorological and photosynthetic variablesPRI performed better than normalized difference vegetation index (NDVI) in tracking mangrove photosynthetic phenology [ABSTRACT FROM AUTHOR]

The photochemical reflectance index (PRI) is related to the conversion of violoxanthin into zeaxanthin in the xanthophyll cycle and thereby reflects the activity of electron-transport chain in chloroplasts. The PRI is recorded by a relatively simple and non-invasive technique; therefore it can be used in a perspective approach for the remote monitoring of photosynthetic processes and detection of photosynthetic stress. However, correlation coefficients between PRI and photosynthetic parameters vary widely in different studies; one of the possible causes of such variability under natural conditions may be the changes in illumination and the development of transient processes in the photosynthetic apparatus. In the present study, the influence of the duration of illumination (a minute interval) on the relationship between parameters of light-dependent reactions of photosynthesis and the PRI has been investigated. The photosynthetic parameters and PRI were determined in pea leaves. A high negative correlation has been found between changes of PRI induced by illumination and changes in the quantum yields of the photochemical reactions of photosystems I and II; the PRI changes did not correlate with changes of non-photochemical quenching. There was a positive correlation between the quantum yields of photochemical reactions and PRI averaged over the entire time range and a negative correlation in the case of non-photochemical quenching and PRI. The absolute value of the correlation coefficient of PRI with the parameters of the light-dependent reactions of photosynthesis increased with increasing duration of illumination. Thus, the correlation of PRI with the parameters of the light-dependent reactions of photosynthesis in a pea leaf may depend on the duration of illumination, at least in a minute interval. [ABSTRACT FROM AUTHOR]

Abstract Due to its close link to the photosynthetic process, sun-induced chlorophyll fluorescence (F) opens new possibilities to study dynamics of photosynthetic light reactions and to quantify CO 2 assimilation rates. Although recent studies show that F is linearly related to gross primary production (GPP) on coarse spatial and temporal scales, it is argued that this relationship may be mainly driven by seasonal changes in absorbed photochemical active radiation (APAR) and less by the plant light use efficiency (LUE). In this work a high-resolution spectrometer was used to continuously measure red and far-red fluorescence and different reflectance indices within a sugar beet field during the growing season in 2015. Diurnal and seasonal developments were compared to eddy covariance derived GPP. Additionally, part of the time series coincided with a heatwave. The induced drought stress allowed us to observe F and its relationship to GPP under changing environmental conditions during the seasonal cycle. Across the season a strong linear relationship between GPP and F 760 was found. This relationship however, was mainly driven by changes in APAR and was strongly reduced under drought conditions. We could show that far-red fluorescence yield can explain 59% of the diurnal and 79% of the seasonal variance in the light use efficiency. However, an even stronger relationship between FY 760 and the structural vegetation index MTVI2 was found, implying that FY 760 is affected by seasonal structural changes of the canopy. Nevertheless, the seasonally de-trended FY 760 and PRI show that they share strong interdependencies with seasonal and diurnal LUE, in particular under drought stress conditions. Highlights • An extensive dataset was measured during the 2015 heat wave. • Under stress condition the relationship between F and GPP degreases significantly. • A revised PRI is proposed as indicator for diurnal changes in NPQ. • Seasonally de-trended rFY 760 and rPRI showed strong diurnal link to LUE. [ABSTRACT FROM AUTHOR]

Light use efficiency (LUE) is a key indicator of vegetation photosynthesis, which provides important insights into how vegetation productivity responds to environmental conditions. The photochemical reflectance index (PRI) is based on reflectance at 531 and 570 nm, which reflects the xanthophyll cycle process of plants under different radiation conditions, and makes LUE related to plant optical characteristics. In this study, tower-based PRI and eddy covariance (EC) based LUEs were used to explore the ability of PRI to track LUE variations in a subtropical, evergreen mixed forest in South China. The results indicate that there is a stronger relationship between PRI and LUE, corrected by the bidirectional reflectance distribution function (BRDF), where R2 = 0.46 before correction and R2 = 0.60 after correction. Generally, PRI is able to capture diurnal and seasonal changes in LUE. Simultaneously, this study highlights a significant correlation between LUE and PRI, but there is also a large seasonal difference in its correlation. The correlation in winter was significantly stronger than summer. The strongest correlation is found in November (R2 = 0.91) and the weakest is in July (R2 = 0.34). Photosynthetically active radiation (PAR) had a strong influence on the LUE-PRI relationship, while vapor pressure deficit (VPD) and air temperature (Ta) had negative influences on the relationship between LUE and PRI. Terrestrial laser scanning is used to retrieve the vertical structure of forest crown. Our results show that the vegetation canopy structure (i.e., effective leaf area index, LAIe), extracted from terrestrial laser scanning (TLS) point data in subtropical mixed forests, had a weak influence on LUE. Our research suggests that environmental factors and vegetation canopy structures should be considered when using PRI to accurately estimate LUE.