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The maximum carboxylation rate (Vcmax) of a leaf is a rate-limiting step in photosynthesis. A quantitative spatial distribution of Vcmax is necessary to predictively understand forest gross primary production (GPP). In this study, we propose a new method that combines hyperspectral data with the Farquhar photosynthetic mechanistic model to estimate Vcmax. First, the photochemical reflectance index (PRI) of a leaf was calculated, and then a model of PRI and sunlit leaf light-use efficiency (LUE) was constructed to estimate sunlit leaf LUE. Next, sunlit leaf LUE and absorbed photosynthetically active radiation (APAR) were used to estimate the GPP of the sunlit leaf. Finally, Vcmax was deduced based on the Farquhar mechanistic model by using the GPP of sunlit leaves, and differences in Vcmax were analyzed across various types of vegetation. The results showed that the coefficient of determination between Vcmax estimated using the mechanistic model and the mean Vcmax measured at the sampling sites was 0.82, with a root mean square error of 4.27μmol·m⁻²·s⁻¹. Using a forest-type map of the Mao'er Mountain region as a reference, statistical analysis showed that coniferous forest showed the highest Vcmax, followed by coniferous-broadleaf mixed forest, while broad-leaved forest showed the lowest Vcmax. The proposed method can generate regional Vcmax distribution maps efficiently.

The purpose of the study was to investigate how the regulatory factors (i.e., temperature, moisture, vegetation indexes, and leaf traits) of soil respiration differed in the different croplands. A field study including 2019 soybean, maize, and sweet potato and 2020 peanut, pea, and sesame growing seasons was performed. The soil respiration, soil temperature, moisture, hyperspectral vegetation indexes, leaf area index (LAI), and chlorophyll content (SPAD value) were measured. The seasonal cumulative soil respiration was 0.523 ± 0.040, 0.383 ± 0.029, 0.358 ± 0.018, 0.561 ± 0.010, 0.570 ± 0.017, and 0.586 ± 0.028 kg C m−2 in the soybean, maize, sweet potato, peanut, pea, and sesame croplands, respectively. Soil respiration was significantly (P 0.05) in 2020. The main regulatory factors of soil respiration were temperature, normalized difference vegetation index (NDVI), difference vegetation index (DVI), and SPAD value in the soybean and maize croplands, while they were temperature, NDVI, photochemical reflectance index (PRI), and LAI in the sweet potato, peanut, pea, and sesame croplands. A model based on soil temperature, hyperspectral vegetation indexes, and LAI (or SPAD value) explained 77.2‒89.1% of the variation in soil respiration in the soybean, maize, peanut, pea, and sesame croplands. This study demonstrated the potential applicability of vegetation indexes, LAI, and SPAD value in estimating soil respiration in different croplands at a large scale.

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.

Radiation use efficiency can be predicted with ~70% accuracy. Canopy water content, greenness, and gas exchange spectral indices are the best predictors for RUE, biomass accumulation, and light interception., Wheat yields are stagnating or declining in many regions, requiring efforts to improve the light conversion efficiency, known as radiation use efficiency (RUE). RUE is a key trait in plant physiology because it links light capture and primary metabolism with biomass accumulation and yield, but its measurement is time consuming and this has limited its use in fundamental research and large-scale physiological breeding. In this study, high-throughput plant phenotyping (HTPP) approaches were used among a population of field-grown wheat with variation in RUE and photosynthetic traits to build predictive models of RUE, biomass, and intercepted photosynthetically active radiation (IPAR). Three approaches were used: best combination of sensors; canopy vegetation indices; and partial least squares regression. The use of remote sensing models predicted RUE with up to 70% accuracy compared with ground truth data. Water indices and canopy greenness indices [normalized difference vegetation index (NDVI), enhanced vegetation index (EVI)] are the better option to predict RUE, biomass, and IPAR, and indices related to gas exchange, non-photochemical quenching [photochemical reflectance index (PRI)] and senescence [structural-insensitive pigment index (SIPI)] are better predictors for these traits at the vegetative and grain-filling stages, respectively. These models will be instrumental to explain canopy processes, improve crop growth and yield modelling, and potentially be used to predict RUE in different crops or ecosystems.

International audience; Evergreen conifer forests are the most prevalent land cover type in North America. Seasonal changes in the color of evergreen forest canopies have been documented with near-surface remote sensing, but the physiological mechanisms underlying these changes, and the implications for photosynthetic uptake, have not been fully elucidated. Here, we integrate on-the-ground phenological observations, leaf-level physiological measurements, near surface hyperspectral remote sensing and digital camera imagery, towerbased CO 2 flux measurements, and a predictive model to simulate seasonal canopy color dynamics. We show that seasonal changes in canopy color occur independently of new leaf production, but track changes in chlorophyll fluorescence, the photochemical reflectance index, and leaf pigmentation. We demonstrate that at winter-dormant sites, seasonal changes in canopy color can be used to predict the onset of canopy-level photosynthesis in spring, and its cessation in autumn. Finally, we parameterize a simple temperature-based model to predict the seasonal cycle of canopy greenness, and we show that the model successfully simulates interannual variation in the timing of changes in canopy color. These results provide mechanistic insight into the factors driving seasonal changes in evergreen canopy color and provide opportunities to monitor and model seasonal variation in photosynthetic activity using color-based vegetation indices.

This study assessed the use of reflectance indices for detecting the combined effects of water and nitrogen stress in tomatoes (Solanum lycopersicum L.). Spectral reflectance data were acquired from tomato plants, subjected to three water and three nitrogen treatments. Irrigation water was applied in amounts of 100, 70, and 30% of full replenishment of root zone soil water to field capacity. Nitrogen application was 100, 70, and 30% of crop nutrient requirement. The treatments were replicated five times in a randomised complete block design. Plant stress indicators, including leaf temperature (Tc), relative water content (RWC), yield, and leaf chlorophyll content (LCC) were measured at the same time of leaf reflectance data, during the growing season. Reflectance indices including Normalised Difference Vegetation Index (NDVI), Renormalised Difference Vegetation Index (RDVI), Optimised Soil Adjusted Vegetation Index (OSAVI), Photochemical Reflectance Index centered at 550 nm (PRI550), normalised PRI (PRInorm), Transformed Chlorophyll Absorption in Reflectance Index (TCARI), Water Index (WI), and WI/NDVI were obtained from the reflectance data. The results showed that the PRI550, PRInorm, and WI were the most sensitive indices for distinguishing crop water stress, while RDVI, PRInorm, and TCARI had the best correlation with nitrogen stress indicators. PRInorm was the most sensitive index for detecting the combined effect of water and nitrogen stress. This study provided more insights into the usefulness of leaf spectral features for assessing crop abiotic stress. Measuring these indices with hyperspectral sensors provides a rapid, non-destructive, and reliable approach for estimating crop stress.

Photosynthetic phenology is an important indicator of annual gross primary productivity (GPP). Assessing photosynthetic phenology remotely is difficult for evergreen conifers as they remain green year-round. Carotenoid-based vegetation indices such as the photochemical reflectance index (PRI) and chlorophyll/carotenoid index (CCI) are promising tools to remotely track the invisible phenology of photosynthesis by assessing carotenoid pigment dynamics. PRI, CCI and the near-infrared reflectance of vegetation (NIRV ) index may act as proxies of photosynthetic efficiency (ɛ), an important parameter in light-use efficiency models, or direct proxies of photosynthesis. To understand the physiological mechanisms reflected by PRI and CCI and the ability of vegetation indices to act as proxies of photosynthetic activity for estimating GPP, we measured leaf pigment composition, PRI, CCI, NIRV and photosynthetic activity at the leaf and canopy scales over 2 years in an evergreen and mixed deciduous forest. PRI and CCI captured the large seasonal carotenoid/chlorophyll ratio changes and good relationships were observed between PRI-ɛ and CCI-photosynthesis and NIRV -photosynthesis. PRI-, CCI- and NIRV -based models effectively tracked observed seasonal GPP. We propose that carotenoid-based and near-infrared reflectance vegetation indices may provide useful proxies of photosynthetic activity and can improve remote sensing-based models of GPP in evergreen and deciduous forests.

Biodiversity loss in tropical forests is rapidly increasing, which directly influence the biomass and productivity of an ecosystem. In situ methods for species diversity assessment and biomass in synergy with hyperspectral data can adeptly serve this purpose and hence adopted in this study. Quadrat sampling was carried out in Shoolpaneshwar Wildlife Sanctuary (SWS), Gujarat, which was used to compute Shannon–Weiner Diversity Index (H′). Above ground biomass (AGB) was calculated measuring the Height and Diameter at Breast Height (DBH) of different trees in the sampling plots. Four spectral indices, namely Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Photochemical Reflectance Index (PRI), and Structure Insensitive Pigment Index (SIPI) were derived from the EO-1 Hyperion Data. Spearman and Pearson’s correlation analysis was performed to examine the relationship between H′, AGB and spectral indices. The best fit model was developed by establishing a relationship between H′ and AGB. Fifteen models were developed by performing multiple linear regression analysis using all possible combinations of spectral indices and H′ and their validation was performed by relating observed H′ with model predicted H′. Pearson’s correlation relation showed that SIPI has the best relationship with the H′. Model 15 with a combination of NDVI, PRI and SIPI was determined as the best model for retrieving H′ based on its statistics performance and hence was used for generating species diversity map of the study area. Power model showed the best relationship between AGB and H′, which was used for the development of AGB map.

Heavy metals contamination is a serious problem of China. It is necessary to estimate bioavailability concentrations of heavy metals in agricultural soil for keeping the food security and human health. This study aimed to use hyperspectral data of rice (Oryza sativa) leaves as an indicator to retrieve the CaCl2-extractable concentrations of heavy metals in agricultural soil. Twenty-one rice samples, soil samples and reflectance spectra of rice leaves were collected, respectively. The potential relations between hyperspectral data and CaCl2-extractable heavy metals (E-HM) were explored. The partial least-squares regression (PLSR) method with leave-one-out cross-validation has been used to predict concentrations of CaCl2-extractable cadmium (E-Cd) and concentrations of CaCl2-extractable lead (E-Pb) in farmland soil. The results showed that the concentrations of E-Cd in soil had significant correlation with concentrations of Cd in rice leaves; the number of bands associated with E-Cd was more than that of E-Pb. Four indices (normalized difference vegetation index (NDVI), carotenoid reflectance index (CRI), photochemical reflectance index 2 (PRI2), normalized pigments chlorophyll ratio index (NPCI)) were significant (P R2 = 0.592 and RMSE = 0.046. We conclude that if the rice was sensitive to E-HM and/or the crop was stressed by the E-HM, the hyperspectral data of field rice leaves hold potentials in estimating concentration of E-HM in farmland soil. Therefore, this method provides a new insight to monitoring the E-HM content in agricultural soil.

Drought and water scarcity due to global warming, climate change, and social development have been the most death-defying threat to global agriculture production for the optimization of water and food security. Reflectance indices obtained by an Analytical Spectral Device (ASD) Spec 4 hyperspectral spectrometer from tomato growth in two soil texture types exposed to four water stress levels (70–100% FC, 60–70% FC, 50–60% FC, and 40–50% FC) was deployed to schedule irrigation and management of crops’ water stress. The treatments were replicated four times in a randomized complete block design (RCBD) in a 2 × 4 factorial experiment. Water stress treatments were monitored with Time Domain Reflectometer (TDR) every 12 h before and after irrigation to maintain soil water content at the desired (FC%). Soil electrical conductivity (Ec) was measured daily throughout the growth cycle of tomatoes in both soil types. Ec was revealing a strong correlation with water stress at R2 above 0.95 p &lt, 0.001. Yield was measured at the end of the end of the growing season. The results revealed that yield had a high correlation with water stress at R2 = 0.9758 and 0.9816 p &lt, 0.01 for sandy loam and silty loam soils, respectively. Leaf temperature (LT °C), relative leaf water content (RLWC), leaf chlorophyll content (LCC), Leaf area index (LAI), were measured at each growth stage at the same time spectral reflectance data were measured throughout the growth period. Spectral reflectance indices used were grouped into three: (1) greenness vegetative indices, (2) water overtone vegetation indices, (3) Photochemical Reflectance Index centered at 570 nm (PRI570), and normalized PRI (PRInorm). These reflectance indices were strongly correlated with all four water stress indicators and yield. The results revealed that NDVI, RDVI, WI, NDWI, NDWI1640, PRI570, and PRInorm were the most sensitive indices for estimating crop water stress at each growth stage in both sandy loam and silty loam soils at R2 above 0.35. This study recounts the depth of 858 to 1640 nm band absorption to water stress estimation, comparing it to other band depths to give an insight into the usefulness of ground-based hyperspectral reflectance indices for assessing crop water stress at different growth stages in different soil types.

The accurate characterisation of the instrumental spectral performance of field spectroradiometers acquires particular importance in view of the instruments' widespread use for calibrating and validating airborne- and satellite-based optical sensor systems. Typically, spectral calibration relies on the basic assumption of a Gaussian instrumental spectral response function (ISRF). We test alternative ISRF parameterisations and examine the resulting changes in spectral performance of four ASD field spectroradiometers. We then use a MODTRAN-simulated spectrum to compare an ideal Bottom Of Atmosphere (BOA) reflectance with the reflectances derived from ASD-convolved radiances using nominal (per ASD specifications), Gaussian and Symmetric Super Gaussian parameterisations of the spectroradiometers. We highlight the impact of the small but significant differences by retrieving the Photochemical Reflectance Index (PRI) values, which change by a few percentage points compared to the reference PRI.

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.

Cities require and use large quantities of energy and materials, metabolizing them and generating large quantities of waste products and pollutants, resulting in unsustainable urban environments. This polluted urban environment adversely affects ecological integrity and develop stress for different planting sites. Research is needed to evaluate the relative tolerance of tree species against changing urban ecological conditions with specific environment pollutants. The current state of art examines effects of air pollution on four major biochemical perimeters of tree health i.e. chlorophyll content, water content, carotenoid content and anthocyanin content. A Spectro-radiometer and remote sensing indices integrated approach is used to evaluate the impact of deteriorating air quality on mango tree planted in and around the Indian Institute of Technology Roorkee, campus. Four different Vegetation Indices content (Normalized Difference Vegetation Indices, modified normalized difference vegetation index, simple ratio and modified simple ratio) are used for estimate chlorophyll. Five different Water indices (Water index, Normalized water indices-1, Normalized water indices-2, Normalized water indices-3 and Normalized water indices-4) are used for estimating water content. Five different photochemical information indices (Carotenoid concentration index, photochemical reflectance index, Plant senescencing reflectance Index, and Carotenoid concentration index) are used for enumerate carotenoid content. Three different Anthocyanin Reflectance Indices (Modified Anthocyanin Content Index, Anthocyanin Reflectance Index and Modified Anthocyanin Reflectance Index) are used for determining anthocyanin content.

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)(R545nm - R490nm) - (545 – 490)(R520nm - R490nm)]) 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.

Traditional methods of carbon monitoring in mountainous regions are challenged by complex terrain. Recently, solar‐induced fluorescence (SIF) has been found to be an indicator of gross primary production (GPP), and the increased availability of remotely sensed SIF provides an opportunity to estimate GPP across the Western United States. Although the empirical linkage between SIF and GPP is strong, the current mechanistic understanding of this linkage is incomplete and depends upon changes in leaf biochemical processes in which absorbed sunlight leads to photochemistry, heat (via nonphotochemical quenching [NPQ]), fluorescence, or tissue damage. An improved mechanistic understanding is necessary to leverage SIF observations to improve representation of ecosystem processes within land surface models. Here we included an improved fluorescence model within the Community Land Model, Version 4.5 (CLM 4.5), to simulate seasonal changes in SIF at a subalpine forest in Colorado. We found that when the model accounted for sustained NPQ, this provided a larger seasonal change in fluorescence yield leading to simulated SIF that more closely resembled the observed seasonal pattern (Global Ozone Monitoring Experiment‐2 [GOME‐2] satellite platform and a tower‐mounted spectrometer system). We found that an acclimation model based on mean air temperature was a useful predictor for sustained NPQ. Although light intensity was not an important factor for this analysis, it should be considered before applying the sustained NPQ and SIF to other cold climate evergreen biomes. More leaf‐level fluorescence measurements are necessary to better understand the seasonal relationship between sustained and reversible components of NPQ and to what extent that influences SIF.

Early detection of plant water status is essential to optimize crop water use, and to implement water savings methods such as precision irrigation. This study investigated the potential of using crop reflectance indices to detect water stress, in order to improve irrigation of bell pepper (Capsicum annuum L.), grown under greenhouse conditions. Spectral data were acquired from bell pepper plants, with five different irrigation regimes namely 100, 80, 60, 40, and 20% of plant available water, in a completely randomized design. Plant stress parameters including stomatal conductance (Gs), canopy temperature (Tc), relative water content (RWC), yield, and volumetric soil moisture content (SMC) were concurrently measured with spectral data acquisition from the plants throughout the growing season. Various reflectance indices including Normalized Difference Vegetation Index (NDVI), Renormalized Difference Vegetation Index (RDVI), Optimized Soil Adjusted Vegetation Index (OSAVI), Photochemical Reflectance Index centered at 570 nm (PRI570), Photochemical Reflectance Index centered at 553 nm (PRI553), normalized PRI (PRInorm), Water Index (WI), and WI/NDVI were obtained from the spectral data. The relationships between these crop reflectance indices and the water stress indicators were statistically examined at the five irrigation levels. The results revealed that PRI553, WI, RDVI, PRInorm, and WI/NDVI were the most useful indices for detecting water stress in bell pepper plants. The findings of this study show promise of using a proximal method for assessing water stress and to improve water management of high value vegetable crops grown under greenhouse conditions.

Global warming is an important issue which is leading to considerable changes in biomes. Environmental biologists are continuously assessing how global warming is going to impact the current population. Peatlands are one of the most important component of the terrestrial ecosystems which makes only 3% of them but contain around 40% of soil organic carbon. The composition of peatland vegetation impacts a range of ecosystem functions, therefore, with a change in composition, the behavior and structure of peatland plant communities are predicted to change. This work is focused on the response of Sphagnum peatland (in Poland), to the manipulated environmental conditions. The experimental design consisted of four treatments (control, warming, reduced precipitation, and a combination of warming & reduced precipitation), three replicates of each. Active warming with infrared heaters (˜1.0–1.4 °C peat temperature increase) and an automatic curtain to reduce precipitation (˜30% reduction in the rain) was applied continuously since 2015. The warming and reduced precipitation caused a significant variation in the vegetation indices such as Normalized Difference Vegetation Index (NDVI), Photochemical Reflectance Index (PRI), Water Band Index (WBI), and Chlorophyll Green Index (CIgreen) between manipulated sites. The abundance of major plant species was recorded between manipulated sites. The prompt chlorophyll fluorescence measurement was performed in the peak of the vegetation season of August 2017 which had shown a significant variation in photosynthetic induction curve and different photosynthetic parameters from the same plant species in between the manipulated sites. The data clearly indicate that a short-term climate manipulation resulted in the change of plant abundance and its physiology, which varied for different plant species. Our study indicates that the different plant species respond differently to climate change. The study also proposes the significance of simple vegetation indices for peatland vegetation observation.

Relatively little is known of how the world's largest vegetation transition zone – the Forest Tundra Ecotone (FTE) – is responding to climate change. Newly available, satellite-derived time-series of the photochemical reflectance index (PRI) across North America and Europe could provide new insights into the physiological response of evergreen trees to climate change by tracking changes in foliar pigment pools that have been linked to photosynthetic phenology. However, before implementing these data for such purpose at these evergreen dominated systems, it is important to increase our understanding of the fine scale mechanisms driving the connection between PRI and environmental conditions. The goal of this study is thus to gain a more mechanistic understanding of which environmental factors drive changes in PRI during late-season phenological transitions at the FTE – including factors that are susceptible to climate change (i.e., air- and soil-temperatures), and those that are not (photoperiod). We hypothesized that late-season phenological changes in foliar pigment pools captured by PRI are largely driven by photoperiod as opposed to less predictable drivers such as air temperature, complicating the utility of PRI time-series for understanding climate change effects on the FTE. Ground-based, time-series of PRI were acquired from individual trees in combination with meteorological variables and photoperiod information at six FTE sites in Alaska. A linear mixed-effects modeling approach was used to determine the significance (α = 0.001) and effect size (i.e., standardized slope b*) of environmental factors on late-seasonal changes in the PRI signal. Our results indicate that photoperiod had the strongest, significant effect on late-season changes in PRI (b* = 0.08, p

Current trends in research for detection of infections in forests almost exclusively involve the use of a single imaging sensor. However, combining information from a range of sensors could potentially enhance the ability to diagnose and quantify the infection. This study investigated the potential of combining hyperspectral and LiDAR data for red band needle blight detection. A comparative study was performed on the spectral signatures retrieved for two plots established in lodgepole pine stands and on a range of LiDAR metrics retrieved at individual tree-level. Leaf spectroscopy of green and partially chlorotic needles affected by red band needle blight highlighted the green, red and short near-infrared parts of the electromagnetic spectrum as the most promising. A good separation was found between the two pine stands using a number of spectral indices utilising those spectral regions. Similarly, a distinction was found when intra-canopy distribution of LiDAR returns was analysed. The percentage of ground returns within canopy extents and the height-normalised 50th percentile (height normalisation was performed to each tree’s canopy extents) were identified as the most useful features among LiDAR metrics for separation of trees between the plots. Analysis based on those metrics yielded an accuracy of 80.9%, indicating a potential for using LiDAR metrics to detect disease-induced defoliation. Stepwise discriminant function analysis identified Enhanced Vegetation Index, Normalised Green Red Difference Index, percentage of ground returns, and the height-normalised 50th percentile to be the best predictors for detection of changes in the canopy resulting from red band needle blight. Using a combination of these variables led to a substantial decrease of unexplained variance within the data and an improvement in discrimination accuracy (96.7%). The results suggest combining information from different sensors can improve the ability to detect red band needle blight.

Mangrove forests, with high productivity, strong carbon fixation ability, and high ecological value, are a critical component of wetland ecosystems. However, in recent years, species invasion is threatening the health of mangrove forests. In order to trace the change and protect them, Light Use Efficiency (LUE) has been used as a significant parameter to estimate the vegetation productivity of mangrove forests. Recent studies have shown that the Photochemical Reflectance Index (PRI) has a strong relationship with LUE. Nevertheless, their relationships undergone significant changes under the influence of vegetation types and external environment. In this paper, we evaluated the relationship between PRI and LUE for different mangrove species (Avicennia marina and Aegiceras corniculatum) and the effects of Spartina alterniflora invasion on the relationship. The results showed that LUE had a strong correlation with PRI. The correlation of Avicennia marina was slightly higher than that of Aegiceras corniculatum. In addition, LUE values of mangrove forests reduced as a result of Spartina alterniflora invasion. The mean LUE value of Aegiceras corniculatum decreased by 27.91% which was bigger than the decrease in Avicennia marina (23.19%). Furthermore, Spartina alterniflora invasion weakened the LUE-PRI relationship of mangrove forests. The coefficient of determination (R2) of the LUE-PRI relationship of Avicennia marina and Aegiceras corniculatum dropped by 19.48% in total, and 17% and 25.17% respectively. This research provides an effective approach to estimate the LUE of mangrove forests, which is significant for the evaluation of photosynthetic capacity and productivity of mangrove ecosystems in the future.

Photosynthesis is a critical component to understanding how CO2 uptake and vegetation productivity drives the terrestrial carbon cycle. Light use efficiency (LUE) is a key indicator of photosynthesis, which is determined by the most limiting/co-limiting environmental stresses such as nutrition, water, and temperature, restraining the photochemical reaction process across both space and time due to remote sensing offers a capability to relate changes in LUE with changes in the spectral characteristics of leaves through the photochemical reflectance index (PRI). The PRI is a narrow waveband index that responds with changes in xanthophyll-induced absorption feature at 531 nm. It remains challenging, however, to upscale PRI from leaf to canopy and ultimately to the landscape level due to physiological and structural factors such as leaf angle distribution, leaf area, canopy structure and pigment pool size as well as observational considerations such as sun-view geometry and background reflectance. As a result, understanding patterns of PRI and LUE across different climate-induced stress conditions and within age-induced vertical stand structures is helpful for improving our understanding how these relationships change over forested landscapes in anticipation of future airborne and ultimately satellite-based observations. In this study, we report on the use of the automated multi-angular spectro-radiometer (AMSPEC) and eddy-covariance techniques to examine how PRI and LUE varies at young (HDF88) and older (DF49) Douglas-fir sites. Results indicate that a strong relationship between bidirectional reflectance distribution function (BRDF) corrected PRI and LUE exists (DF49: R2 = 0.73; HDF88: R2 = 0.79). The overall LUE at the older, more structurally complex site was higher than at the younger, structurally simpler stand under a range of air temperature and light conditions. We used the standard deviation (SD) of light detection and ranging (LiDAR) return pulses to characterize canopy vertical structure of the site, and found that complex vertical structures (i.e., high SD) may contribute to a higher long-term averaged LUE (R2 = 0.87). While interesting we conclude the mechanism of the relationship between LUE and vertical forest structures needs to be further explored because of the key role internal and external shadowing has on light regimes with stands and thus vertical patterns of LUE varying across forest stands of different age.

Early detection of nitrogen deficit stress is essential to effectively and precisely manage crop production under greenhouse conditions. This article demonstrates the use of hyperspectral machine vision as a non-contact technique for detecting crop nitrogen deficit in a soilless tomato crop. Three different levels of nitrogen concentration were applied in tomato plants grown in a growth chamber under controlled environment conditions. The results demonstrate that crop reflectance increased due to nitrogen deficiency, mostly in the wavelength bands between 775 nm–850 nm and 910 nm–960 nm. Based on the reflectance measurements several reflectance indices were calculated and correlated with the tomato leaf chlorophyll or nitrogen content and with the leaf photosynthesis rate (As). The results showed that when the As and the chlorophyll content values changed more than 0.5 μmol m−2 s−1 and 2.8 μg cm−2, respectively, the photochemical reflectance index (PRI) and the transformed chlorophyll absorption in reflectance index (TCARI) values varied more than 0.05 and 2.8, respectively. In addition, the results showed that for N changes higher than 0.20%, the optimised soil adjusted vegetation index (OSAVI) and the modified soil adjusted vegetation index (MSAVI) values varied more than 0.05 and 0.25, respectively. A new spectral index (background adjustment nitrogen index – BANI) was developed and validated under experimental conditions for the estimation of tomato plant nitrogen concentration.

Significant advances toward the remote sensing of photosynthetic activity have been achieved in the last decades, including sensor design and radiative transfer model (RTM) development. Nevertheless, finding methods to accurately quantify carbon assimilation across species and spatial scales remains a challenge. Most methods are either empirical and not transferable across scales or can only be applied if highly complex input data are available. Under stress, the photosynthetic rate is limited by the maximum carboxylation rate (Vcmax), which is determined by the leaf biochemistry and the environmental conditions. Vcmax has been connected to plant photoprotective mechanisms, photosynthetic activity and chlorophyll fluorescence emission. Recent RTM developments such as the Soil-Canopy Observation of Photosynthesis and Energy fluxes (SCOPE) model allow the simulation of the sun-induced chlorophyll fluorescence (SIF) and Vcmax effects on the canopy spectrum. This development provides an approach to retrieve Vcmax through RTM model inversion and track assimilation rate. In this study we explore SIF, narrow-band indices and RTM inversion to track changes in photosynthetic efficiency as a function of vegetation stress. We use hyperspectral imagery acquired over an almond orchard under different management strategies which affected the assimilation rates measured in the field. Vcmax used as an indicator of assimilation was retrieved through SCOPE model inversion from pure-tree crown hyperspectral data. The relationships between field-measured assimilation rates and Vcmax retrieved from model inversion were higher (r2 = 0.7–0.8) than when SIF was used alone (r2 = 0.5–0.6) or when traditional vegetation indices were used (r2 = 0.3–0.5). The method was proved successful when applied to two independent datasets acquired at two different dates throughout the season, ensuring its robustness and transferability. When applied to both dates simultaneously, the results showed a unique significant trend between the assimilation measured in the field and Vcmax derived using SCOPE (r2 = 0.56, p < 0.001). This work demonstrates that tracking assimilation in almond trees is feasible using hyperspectral imagery linked to radiative transfer-photosynthesis models., The authors gratefully acknowledge the financial support of the Spanish Ministry of Science and Education for projects AGL2012-40053-C03-01 and AGL2012-35196 and of the Regional Government of Andalusia, Spain, for project P12-AGR-2521.

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 1m2m−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&nbsp

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.

At the seasonal timescale, daily photochemical reflectance index (PRI) measurements track changes in photoprotective pigment pools as plants respond to seasonally variable environmental conditions. As such, remotely‐sensed PRI products present opportunities to study seasonal processes in evergreen conifer forests, where complex vegetation dynamics are difficult to capture due to small annual changes in chlorophyll content or leaf structure. Because PRI is tied explicitly to short and long term changes in photoprotective pigments that are responsible for regulating stress, we hypothesize that PRI by extension could serve as a proxy for stomatal response to seasonally changing hydroclimate, assuming plant functional responses to stress covary in space and time. To test this, we characterized PRI in a semi‐arid, montane mixed conifer forest in the Madrean sky islands of Arizona, USA during the monsoon growing season subject to precipitation pulse dynamics. To determine the sensitivity of PRI to ecohydrologic variability and associated changes in gross primary productivity (GPP), canopy spectral measurements were coupled with eddy covariance CO₂ flux and sap flow measurements. Seasonally, there was a significant relationship between PRI and sap flow velocity (R²=0.56), and multiple linear regression analysis demonstrated a PRI response to dynamic water and energy limitations in this system. We conclude that PRI has potential to serve as a proxy for forest functional response to seasonal ecohydrologic forcing. The coordination between photoprotective pigments and seasonal stomatal regulation demonstrated here could aid characterization of vegetation response to future changes in hydroclimate at increasing spatial scales.

Efficient and accurate methods to monitor crop physiological responses help growers better understand crop physiology and improve crop productivity. In recent years, developments in unmanned aerial vehicles (UAV) and sensor technology have enabled image acquisition at very-high spectral, spatial, and temporal resolutions. However, potential applications and limitations of very-high-resolution (VHR) hyperspectral and thermal UAV imaging for characterization of plant diurnal physiology remain largely unknown, due to issues related to shadow and canopy heterogeneity. In this study, we propose a canopy zone-weighting (CZW) method to leverage the potential of VHR (≤9 cm) hyperspectral and thermal UAV imageries in estimating physiological indicators, such as stomatal conductance (Gs) and steady-state fluorescence (Fs). Diurnal flights and concurrent in-situ measurements were conducted during grapevine growing seasons in 2017 and 2018 in a vineyard in Missouri, USA. We used neural net classifier and the Canny edge detection method to extract pure vine canopy from the hyperspectral and thermal images, respectively. Then, the vine canopy was segmented into three canopy zones (sunlit, nadir, and shaded) using K-means clustering based on the canopy shadow fraction and canopy temperature. Common reflectance-based spectral indices, sun-induced chlorophyll fluorescence (SIF), and simplified canopy water stress index (siCWSI) were computed as image retrievals. Using the coefficient of determination (R2) established between the image retrievals from three canopy zones and the in-situ measurements as a weight factor, weighted image retrievals were calculated and their correlation with in-situ measurements was explored. The results showed that the most frequent and the highest correlations were found for Gs and Fs, with CZW-based Photochemical reflectance index (PRI), SIF, and siCWSI (PRICZW, SIFCZW, and siCWSICZW), respectively. When all flights combined for the given field campaign date, PRICZW, SIFCZW, and siCWSICZW significantly improved the relationship with Gs and Fs. The proposed approach takes full advantage of VHR hyperspectral and thermal UAV imageries, and suggests that the CZW method is simple yet effective in estimating Gs and Fs.

Despite an advanced ability to forecast ecosystem functions and climate at regional and global scales, little is known about relationships between local variations in water and carbon fluxes and large-scale phenomena. To enable data collection of local-scale ecosystem functions to support such investigations, we developed the EcoSpec system, a highly equipped remote sensing system that houses a hyperspectral radiometer (350&ndash, 2500 nm) and five optical and infrared sensors in a compact tower. Its custom software controls the sequence and timing of movement of the sensors and system components and collects measurements at 12 locations around the tower. The data collected using the system was processed to remove sun-angle effects, and spectral vegetation indices computed from the data (i.e., the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Photochemical Reflectance Index (PRI), and Moisture Stress Index (MSI)) were compared with the fraction of photochemically active radiation (fPAR) and canopy temperature. The results showed that the NDVI, NDWI, and PRI were strongly correlated with fPAR, the MSI was correlated with canopy temperature at the diurnal scale. These correlations suggest that this type of near-surface remote sensing system would complement existing observatories to validate satellite remote sensing observations and link local and large-scale phenomena to improve our ability to forecast ecosystem functions and climate. The system is also relevant for precision agriculture to study crop growth, detect disease and pests, and compare traits of cultivars.

Glyphosate is one of the most widely used non-selective systemic herbicides, but nowadays its application is controversially discussed. Optical remote sensing techniques might provide a sufficient tool for monitoring glyphosate use. In order to investigate the potential of this technology, a laboratory experiment was set-up using pots with rolled grass sods. Glyphosate-treated plants were compared to drought-stressed and control plants. All pots were frequently measured using a field spectrometer and a hyperspectral-imaging camera. Plant samples were analysed for photosynthetic pigments, polyphenols and dry matter content. Eight selected vegetation indices were calculated from the spectral measurements. The results show that photosynthetic pigments were sensitive to differentiate between control and glyphosate treated plants already 2 days after application. From the vegetation indices, the normalized difference lignin index (NDLI) responded most sensitively followed by indices referring to photosynthetic pigments, namely, the carotenoid reflectance index (CRI-1) and the photochemical reflectance index (PRI). It can be concluded that spectral vegetation indices are, in principal, a suitable proxy to non-destructively monitor glyphosate application on agricultural fields. Further research is needed to verify its applicability under field conditions. An operational monitoring is, however, currently limited by the requirements for temporal and spectral resolution of the satellite sensors.

Photosynthesis by terrestrial plants represents the majority of CO2 uptake on Earth, yet it is difficult to measure directly from space. Estimation of gross primary production (GPP) from remote sensing indices represents a primary source of uncertainty, in particular for observing seasonal variations in evergreen forests. Recent vegetation remote sensing techniques have highlighted spectral regions sensitive to dynamic changes in leaf/needle carotenoid composition, showing promise for tracking seasonal changes in photosynthesis of evergreen forests. However, these have mostly been investigated with intermittent field campaigns or with narrow-band spectrometers in these ecosystems. To investigate this potential, we continuously measured vegetation reflectance (400–900 nm) using a canopy spectrometer system, PhotoSpec, mounted on top of an eddy-covariance flux tower in a subalpine evergreen forest at Niwot Ridge, Colorado, USA. We analyzed driving spectral components in the measured canopy reflectance using both statistical and process-based approaches. The decomposed spectral components co-varied with carotenoid content and GPP, supporting the interpretation of the photochemical reflectance index (PRI) and the chlorophyll/carotenoid index (CCI). Although the entire 400–900 nm range showed additional spectral changes near the red edge, it did not provide significant improvements in GPP predictions. We found little seasonal variation in both normalized difference vegetation index (NDVI) and the near-infrared vegetation index (NIRv) in this ecosystem. In addition, we quantitatively determined needle-scale chlorophyll-to-carotenoid ratios as well as anthocyanin contents using full-spectrum inversions, both of which were tightly correlated with seasonal GPP changes. Reconstructing GPP from vegetation reflectance using partial least-squares regression (PLSR) explained approximately 87 % of the variability in observed GPP. Our results linked the seasonal variation in reflectance to the pool size of photoprotective pigments, highlighting all spectral locations within 400–900 nm associated with GPP seasonality in evergreen forests.

The photochemical reflectance index (PRI) has been suggested as an indicator of light use efficiency (LUE), and for use in the improvement of estimating gross primary production (GPP) in LUE models. Over the last two decades, solar-induced fluorescence (SIF) observations from remote sensing have been used to evaluate the distribution of GPP over a range of spatial and temporal scales. However, both PRI and SIF observations have been decoupled from photosynthesis under a variety of non-physiological factors, i.e., sun-view geometry and environmental variables. These observations are important for estimating GPP but rarely reported in the literature. In our study, multi-angle PRI and SIF observations were obtained during the 2018 growing season in a maize field. We evaluated a PRI-based LUE model for estimating GPP, and compared it with the direct estimation of GPP using concurrent SIF measurements. Our results showed that the observed PRI varied with view angles and that the averaged PRI from the multi-angle observations exhibited better performance than the single-angle observed PRI for estimating LUE. The PRI-based LUE model when compared to SIF, demonstrated a higher ability to capture the diurnal dynamics of GPP (the coefficient of determination (R2) = 0.71) than the seasonal changes (R2 = 0.44), while the seasonal GPP variations were better estimated by SIF (R2 = 0.50). Based on random forest analyses, relative humidity (RH) was the most important driver affecting diurnal GPP estimation using the PRI-based LUE model. The SIF-based linear model was most influenced by photosynthetically active radiation (PAR). The SIF-based linear model did not perform as well as the PRI-based LUE model under most environmental conditions, the exception being clear days (the ratio of direct and diffuse sky radiance &gt, 2). Our study confirms the utility of multi-angle PRI observations in the estimation of GPP in LUE models and suggests that the effects of changing environmental conditions should be taken into account for accurately estimating GPP with PRI and SIF observations.

Spectral reflectance-based vegetation indices have sensitive characteristics to crop growth and health conditions. The performance of each vegetation index to a certain condition is different and needs to be interpreted, correspondingly. This study aimed to assess the most suitable vegetation index to identify the crop response against elevated air temperatures, heat stress, and herbicide damage. The spectral reflectance, yield components, and growth parameters such as plant height, leaf area index (LAI), and above-ground dry matter of paddy rice, which was cultivated in a temperature gradient field chamber to simulate global warming conditions, were observed from 2016 to 2018. The relationships between the vegetation indices and the crop parameters were assessed considering stress conditions. The normalized difference vegetation index (NDVI) represented the changes in plant height (R-square = 0.93) and the LAI (R-square = 0.901) before the heading stage. Furthermore, the NDVI and the cumulative growing degree days had a Sigmoid curve and an R-square value of 0.937 under the normal growth case, but it decreased significantly in the herbicide damage case. This characteristic was useful for detecting the damaged crop growth condition. Additionally, to estimate the grain yield of paddy rice, the medium resolution imaging spectrometer (MERIS) terrestrial chlorophyll index was better: R-square = 0.912; root mean square error = 95.69 g/m2. Photochemical reflectance index was sensitive to physiological stress caused by the heatwave, and it decreased in response to extremely high air temperatures. These results will contribute towards determining vegetation indices under stress conditions and how to effectively utilize them.

Photochemical reflectance index (PRI) as a proxy for light use efficiency (LUE) has the potential to improve the estimates of vegetation gross primary productivity (GPP) using LUE model. Solar-induced fluorescence (SIF) has increasingly been shown to be a promising approach for directly estimating GPP. However, a number of factors including the view-geometry and environmental variables, which may disassociate PRI and SIF products from photosynthesis, are important for the estimation of GPP, but rarely investigated. In this study, we observed multi-angle SIF and PRI in a maize field during the 2018 growing season, and compared the PRI-based LUE model and SIF-based linear model in estimating GPP. Our results showed that the averaged PRI and SIF using the multi-angle observations performed better than the single angle observed PRI and SIF in estimating LUE and, GPP respectively. We also found that the seasonal GPP dynamics were better captured by the SIF-based linear model (R2=0.50) than the PRI-based LUE model (R2=0.45), while the PRI-based LUE model performed better in estimating the diurnal variations of GPP (R2=0.71). Random forest analysis demonstrated that PAR and RH were of the most importance in the estimation of diurnal GPP variations using the SIF-based and the PRI-based models, respectively. The PRI-based LUE model performed better than the SIF-based model under most environmental conditions, while SIF should be a preference under clear days (Q>2). Thus, our study confirmed the importance of multi-angle observation of SIF and PRI in estimating GPP and LUE, and suggested that the environmental effects should be considered for accurately estimating GPP using SIF and PRI.

Agriculture is considered to be the human activity that consumes the most mobilized water on a global scale. However, crops planted in semi-arid areas regularly face periods of moderate to extreme water stress. Such water stress periods have a considerable impact on the seasonal yield of these crops. In order to participate in a more rational irrigation water management, monitoring of the rapid changes in plant water status is necessary. For this purpose, the combination of two different wavelength ranges will be explored : an index based on Xanthophyll cycle (Photochemical Reflectance Index, PRI) and a commonly-used index from thermal infrared spectral range (LST). An experiment on winter wheat was carried out over two agricultural campaigns (2016 to 2018) in the Haouz basin, which is located in the Marrakech region, to better assimilate the temporal dynamics of PRI and surface temperature. In this study, four different approaches are proposed to study the functioning of wheat : 1- an approach based on solar angle to remove the structure effect (PRI0) from the PRI signal and to derive a water stress index PRIj, 2- an approach based on global radiation (Rg) to extrapolate a theoretical PRI (PRIth) for Rg equal to zero and to calculate a water stress index PRIlin, 3- an approach that determines an optimal PRI (PRIpot) on the basis of the available water content (AWC) criterion in order to derive a stress index I-PRI and 4- an energy balance approach to extract dry and wet surface temperatures in order to establish a normalized surface temperature index (Tnorm). The results of this work show a strong correlation between the PRI0 and the Leaf Area Index with a coefficient of determination equal to 0.92, indicating that it is possible to isolate the structural effects of wheat on the PRI signal. In addition, over the range of variation in AWC, a significant correlation with PRIj, PRIjlin and I-PRI was observed with coefficients of determination of 0.71, 0.42 and 0.24, respectively. In contrast to the Tnorm, which varies only for values of AWC below 30%, a coefficient of determination of 0.22 is obtained. Finally, the PRI allows us to acquire early and complete information on the response of wheat to change in AWC as opposed to the surface temperature index, revealing the potential of the PRI to monitor the water status of plants and their responses to changing environmental conditions.

To increase the olive tree water use efficiency, an early tracking of water stress is crucial. Several indices based on the near-infrared (the vegetation temperature) and the visible (The photochemical reflectance index (PRI)) spectral bands have shown to be useful to remotely assess the tree eco-physiological functioning status. First, at plant scale, we analyzed the relations between close-range remote sensing measurements (PRI, vegetation temperature) and the plant water stress measurements (plant available water (PAW), the transpiration, the water stress index…). The vegetation temperature was closely associated with the transpiration and the plant water stress indicator. However, the PRI, which was promising as an indicator of the water functioning of the plant, was not able to detect water stress over complex crown of isolated tree due to its high sensitivity to the structure.

Peatlands provide important ecosystem services including carbon storage and biodiversity conservation. Remote sensing shows potential for monitoring peatlands, but most off-the-shelf data products are developed for unsaturated environments and it is unclear how well they can perform in peatland ecosystems. Sphagnum moss is an important peatland genus with specific characteristics which can affect spectral reflectance, and we hypothesized that the prevalence of Sphagnum in a peatland could affect the spectral signature of the area. This article combines results from both laboratory and field experiments to assess the relationship between spectral indices and the moisture content and gross primary productivity (GPP) of peatland (blanket bog) vegetation species. The aim was to consider how well the selected indices perform under a range of conditions, and whether Sphagnum has a significant impact on the relationships tested. We found that both water indices tested [normalized difference water index (NDWI) and floating water band index (fWBI)] were sensitive to the water content changes in Sphagnum moss in the laboratory, and there was little difference between them. Most of the vegetation indices tested [the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), structure insensitive pigment index (SIPI), and chlorophyll index (CIm)] were found to have a strong relationship with GPP both in the laboratory and in the field. The NDVI and EVI are useful for large-scale estimation of GPP, but are sensitive to the proportion of Sphagnum present. The CIm is less affected by different species proportions and might therefore be the best to use in areas where vegetation species cover is unknown. The photochemical reflectance index (PRI) is shown to be best suited to small-scale studies of single species.

We studied the influence of changing carotenoid pigments on the sensitivity of the photochemical reflectance index (PRI) to photosynthesis dynamics. The goal of the measurements was to examine how the introduction of ΔPRI into the working dataset can improve the estimation of photosynthesis. Spectral and photosynthetic characteristics of European beech and Norway spruce saplings were periodically measured in growth chambers with an adjustable irradiance and temperature. Patterns of environmental changes inside the growth chambers were created by periodic changes in irradiance and temperature. Four general irradiance periods lasting 10–12 days each were established. Introduced irradiance regimes varied in the sum of daily irradiance and amplitude of irradiance changes. Temperature was changed with more complex patterns to induce changes in xanthophyll cycle pigments at various time scales within these regimes. Our measurements confirmed the PRI linkage to photosynthetic light use efficiency (LUE). However, the strength of this connection was found to be dependent on changing pigment concentrations, specifically on the change in the ratio of chlorophylls to carotenoids. Furthermore, a negative interference in photosynthesis estimation from PRI was recorded if the temperature was lowered overnight to 12 °C. The differential PRI (ΔPRI), calculated as the simple difference between the PRI value measured during the daytime period and in early morning (PRI0), revealed a decreased effect from pigments and cold temperature on LUE estimation. The regression analysis among all measured data identified an increased association between PRI and LUE following the introduction of ΔPRI from R2 = 0.26 to 0.69 in beech and from R2 = 0.61 to 0.77 in spruce data. The analyses showed that both leaf carotenoid concentrations and the conversion state of xanthophyll cycle pigments played a significant role in determining PRI and PRI0 values and that the accurate assessment of these pigments in PRI across multiple levels of stress from irradiance and temperature might improve estimations of LUE through ΔPRI. In our data, ΔPRI appeared to be a good measure of photosynthesis, the dynamics of which differed between beech and spruce saplings upon switching temperatures.

The increasing amount of continuous time series of solar-induced fluorescence (SIF) and vegetation indices (e.g. Photochemical Reflectance Index, PRI) acquired with high temporal (sub-minute) frequencies is foreseen to allow tracking of the structural and physiological changes of vegetation in a variety of ecosystems. Coupled with observations of CO2, water, and energy fluxes from eddy covariance flux towers, these measurements can bring new insights into the remote monitoring of ecosystem functioning. However, continuously changing solar-view geometry imposes directional effects on diurnal cycles of the fluorescence radiance in the observation direction (F) and PRI, controlled by structural and biochemical vegetation properties. An improved understanding of these variations can potentially help to disentangle directional responses of vegetation from physiological ones in the continuous long-term optical measurements and, therefore, allow to deconvolve the physiological information relevant to ecosystem functioning. Moreover, this will also be useful for better interpreting and validating F and PRI satellite products (e.g., from the upcoming ESA FLEX mission). Many previous studies focused on the characterization of reflectance directionality, but only a handful of studies investigated directional effects on F and vegetation indices related to plant physiology. The aim of this study is to contribute to the understanding of red (F687) and far-red (F760) fluorescence and PRI anisotropy based on field spectroscopy data and simulations with the Soil-Canopy Observation of Photochemistry and Energy fluxes (SCOPE) model. We present an extensive dataset of multi-angular measurements of F and PRI collected at canopy level with a high-resolution instrument (FloX, JB Hyperspectral Devices UG, Germany) over different ecosystems: Mediterranean grassland, alfalfa, chickpea and rice. We found, that F760 and F687 directional responses of horizontally homogeneous canopies are characterized by higher values in the backscatter direction with a maximum in the hotspot and lower values in the forward scatter direction. The PRI exhibited similar response due to its sensitivity to sunlit-shaded canopy fractions. As confirmed by radiative transfer forward simulations, we show that in the field measurements leaf inclination distribution function controls the shape of F and PRI anisotropic response (bowl-like/dome-like shapes), while leaf area index and the ratio of leaf width to canopy height affect the magnitude and the width of the hotspot. Finally, we discuss the implications of off-nadir viewing geometry for continuous ground measurements. F observations under oblique viewing angles showed up to 67 % difference compared to nadir observations, therefore, we suggest maintaining nadir viewing geometry for continuous measurements of F and vegetation indices. Alternatively, a correction scheme should be developed and tested against multi-angular measurements to properly account for anisotropy of canopy F and PRI observations. The quantitative characterization of these effects in varying illumination geometries for different canopies that was performed in this study will also be useful for the validation of remote sensing F and PRI products at different spatial and temporal scales.

This study aimed to investigate whether the optimal vegetation indices (VIs) derived from the in situ hyperspectral data to estimate the nitrogen nutrition index (NNI) can also be used at the local scale using unmanned aerial vehicle (UAV) multispectral images, and whether texture metrics derived from UAV images could improve the remote estimation of the NNI in winter oilseed rape. Three field experiments with different N fertilization levels were conducted in two sites in Hubei Province, China. The mechanistic and empirical methods were both employed to estimate NNI. With the in situ hyperspectral data, the empirical method based on structural VIs (R2 is about 0.70) or the photochemical reflectance index (PRI) (R2 = 0.73) provided more accurate estimations of NNI than the mechanistic method did (R2 = 0.62). Although most of the studied VIs were strongly correlated with the NNI, they had different responses to the NNI at the low N fertilization and the optimal to excessive N fertilization rates. For the UAV multispectral images, the mean VI of all pixels within the region of interest (ROI) (referred to VI_mixed) outperformed the mean VI of vegetation pixels within the ROI (referred to VI_pure). The mean normalized difference vegetation index (NDVI_mixed), the modified soil adjusted vegetation index 2 (MSAVI2_mixed), and the red edge chlorophyll index (CIred edge_mixed) of all pixels within the ROI yielded more accurate NNI estimates than the other VIs. Furthermore, the stepwise multiple linear regression models with VIs and texture metrics of VIs provided more accurate NNI estimations than the models based solely on VIs. Results of this study suggested the great potential of UAV multispectral images in monitoring the crop N status at local scales.

Farming in developing countries is majorly dependent on the traditional knowledge of farmers, with unscientific agricultural practices commonly implemented, leading to low productivity and degradation of resources. Moreover, mechanization has not been integral to farming, and thus managing a farm is a time-consuming and labor-intensive process. Consequently, precision agriculture (PA) offers great opportunities for improvement. Using geographic information and communication technology (Geo-ICTs) principles, PA offers the opportunity for a farmer to apply the right amount of treatment at the right time and at the right location in the farm. However, in order to collect timely high-resolution data, drone-based sensing and image interpretation is required. These high-resolution images can give detailed information about the soil and crop condition, which can be used for farm management purposes. Leaf area index, normalized difference vegetation index, photochemical reflectance index, crop water stress index, and other such vegetation indices can provide important information on crop health. Temporal changes in these indices can give vital information about changes in health and canopy structure of the crop over time, which can be related to its biophysical and biochemical stress. These stresses may have occurred due to insufficient soil nutrient, inappropriate soil moisture, or pest attack. Through UAV-based PA, stressed areas can be identified in real time, and some corrective measures can also be carried out (e.g., fertilizer and pesticide spraying). Moreover, the advantages and different approaches to integrate the UAV data in the crop models are also described.

The relationship between the Photochemical Reflectance Index (PRI) and Light Use Efficiency (LUE) is well established at leaf and small canopy scales, but upscaling to ecosystem level is still a challenge. Only few studies have applied satellite-derived PRI to estimate LUE, mostly using MODIS, and although the results are promising, many external factors have been identified affecting PRI performance. The present study investigates determinants and restrictions of MODIS-derived PRI potential to follow the LUE variability of a Mediterranean coniferous forest. Daily and half-hour LUE values were calculated from eddy covariance measurements, dividing GPP by either Photosynthetically Active Radiation (PAR) or the absorbed fraction of PAR (APAR). Also, various PRI datasets were created based on different sensor (Terra, Aqua, Both), reference band (1, 12, 13) and observation/illumination angles. Overall, PRI correlated better with LUE calculated using PAR instead of APAR and Aqua PRI yielded better results than Terra. Restricting acquisitions according to observation/illumination angles improves the PRI:LUE relationship (maximum R2 = 0.512), with backscatter observations yielding the best correlations. Our findings suggest that MODIS-derived PRI is more sensitive to relatively large seasonal LUE changes, but is unable to closely follow severe drought events. Among the tested reference bands, the best results were derived using band 12 (546 - 556 nm), although the optimum reference band seems to depend on viewing conditions. The PRI:LUE relationship was further improved when half-hour LUE of the satellite overpass was used instead of daily LUE. However, it was found that the PRI:LUE relationships for the different datasets were strongly affected by the range of LUE values corresponding to each PRI group, with lower LUE variability resulting to weaker PRI:LUE correlations. LUE range effect should be accounted for in future studies, when different PRI datasets are compared and might explain the contradicting findings in the existing literature.

The purpose of this research is deploying a proximal sensing solution using non-invasive and cost-effective sensors onboard an Autonomous Ground Vehicle (AGV) as a feasible way for building high-resolution maps of water potential in vineyards. The final objective is offering growers a practical system to make decisions about water management, especially for arid climatic conditions. The monitoring AGV was entirely developed within this research context, and as a result, it is a machine specifically designed to endure off-road conditions and harsh environments. The autonomous vehicle served as a massive, non-invasive, and on-the-go data collector robotic platform. The sensors used for measuring the relevant field variables were two spectral reflectance sensors (SRS), an infrared radiometer, and an on-board weather sensor. The collected data were displayed on comprehensible grid maps using the Local Tangent Plane (LTP) coordinate system. The proposed model has a coefficient of determination R2 of 0.69, and results from combining six parameters: the canopy and air temperatures (as the temperature difference), the relative humidity, the altitude difference, the Normalized Difference Vegetation Index (NDVI), and the Photochemical Reflectance Index (PRI). The strongest relationships found in this study were between the temperature difference and PRI, with an R2 of 0.75, and the temperature difference with the leaf water potential with an R2 of 0.61. The practical use of these high-resolution maps includes irrigation scheduling and harvest zoning for sorting grape quality, with a further use as inputs to complex artificial intelligence algorithms considering larger areas or complementing airborne data. Future improvements to make the models more robust and versatile will entail considering additional variables, locations, or grapevine cultivars, and even other crops grown in vertical trellis systems.

Photosynthetic light use efficiency (LUE) determines the ability of a plant to assimilate atmospheric carbon dioxide to biomass and is known to be controlled by environmental conditions, light regimes and forest age. The photochemical reflectance index (PRI), derived from leaf or canopy remotely sensed spectra, has been shown to be an effective and accurate estimator of LUE. In this study, we propose a new LUE estimation method that separates the PRI into daily maximal PRI (PRI0) for indicating daily maximal light use efficiency (LUEmax) and ΔPRI, defined as the difference between PRI0 and instantaneous PRI, for estimating the diurnal physiological stress (fstress). We develop and apply the method across three temperate pine stands and a deciduous stand of different ages, in Southern Ontario, Canada. Half hourly canopy level spectra were acquired from a tower-based spectro-radiometer system (AMSPEC-III) over the growing season at the four stands. Results show that the PRI0 predicted well LUEmax (R2 > 0.6, p

Mapping tree species within urban areas is essential for sustainable urban planning as well as to improve our understanding of the role of urban vegetation as an ecological service. Urban trees contribute significantly in mitigating the urban heat island effect and supporting biodiversity. However, accurate and up-to-date mapping of urban tree species is difficult because of the time-consuming nature of field sampling, fine-scale spatial variation, and potentially high species diversity. Advanced remote sensing data such as airborne Light Detection and Ranging (LiDAR) with high pulse density (25 point/m 2 ) and hyperspectral imagery offer two different yet complementary approaches to estimating crown structure and canopy physiological information at the individual crown scale, which can be useful for mapping tree species. In this paper, we evaluate the potential of these technologies to map 15 common urban tree species using a Random Forest (RF) classifier in the City of Surrey, British Columbia, Canada. LiDAR-derived crown structural information was combined with hyperspectral-derived spectral vegetation indices for species classification. Results indicate an overall accuracy of 51.1%, 61.0%, and 70.0% using hyperspectral, LiDAR and the combined data respectively. The overall accuracy for the two most important and iconic native coniferous species improved markedly from 78.3% up to 91% using the combined data. The results of this research highlight that (1) the combination of structural and spectral information provided an improved classification accuracy than when used separately, and variables derived from LiDAR data contributed more to the accurate prediction of species than hyperspectral features; (2) higher classification accuracies were observed for evergreen species, species with distinguishable crown structure, and species undergoing flowering; (3) and finally the anthocyanin content index and photochemical reflectance index were the most important hyperspectral features for the discrimination of tree species in the spring budburst stage.

Remote sensing of plant carbon uptake, or gross primary production (GPP), in a repeatable and consistent manner remains a key element of a comprehensive understanding of the role of vegetation within the global carbon cycle. To further this understanding at a landscape level or global scale, accurate remote sensing of photosynthetic light-use efficiency (LUE) is required to understand photosynthetic down-regulation and environmental constraints to plant photosynthesis. The past decade has seen advances in detecting both leaf- and canopy-level physiological stress behaviours using the photochemical reflectance index (PRI), a narrow-waveband normalized difference index that relates LUE to a xanthophyll-induced absorption feature at 531 nm. To date, however, much of this research has occurred using top of canopy measurements, while our understanding of the vertical distribution of LUE within the crown is limited. In this study, we demonstrate an approach which could be used to scale photosynthetic behaviour of vegetation vertically and horizontally using estimates of vertical canopy structure obtained from terrestrial Light Detection and Ranging (LiDAR) data to predict proportions of shaded and sunlit canopy which are then linked to predictions of LUE. We apply the approach over a mature Aspen study site located in central Saskatchewan, Canada utilising full-waveform LiDAR data provided by the ground-based laser scanner system and canopy spectra obtained by the AMSPEC II spectro-radiometer. Agreement between predictions of Gross Primary Productivity (GPP) using the developed approach compared to independent observations was highly significant at hourly intervals (R 2 = 0.80, p

Accurate estimation of light use efficiency (LUE) of plant canopies is essential for calculating gross primary productivity (GPP) using LUE models and is also useful for calibrating process-based models for regional and global applications. A promising method for estimating LUE is through remote sensing of the photochemical reflectance index (PRI). However, there are internal (e.g. pigment concentrations) and external factors (e.g. environmental conditions and sun-target-view geometry) that affect PRI signals. Considering the reflectance difference between sunlit and shaded leaves, the ratio of observed canopy reflectance to leaf reflectance is used to represent the observed fraction of sunlit leaves, and the observed fraction of shaded leaves is calculated with a geometrical optical model. Thus, a canopy-level PRI observation is separated into sunlit and shaded PRI values, and a two-leaf canopy PRI (PRI t ) is calculated as sum of these two values weighted by their respective sunlit and shaded leaf area indices. The usefulness of PRI t in assessing the canopy-level LUE is evaluated with automated multi-angle PRI observations acquired on a flux tower from April to September 2013 over a sub-tropical coniferous forest in southern China. In each 15-minute observation cycle, PRI is observed at four view zenith angles fixed at (37°, 47°, 57°) or (42°, 52°, 62°) and the instantaneous solar zenith angle in the azimuth angle range from 45° to 325° (from the geodetic north). In both the half-hourly and daily time steps, PRI t can effectively improve (> 50% and > 35% increases in R 2 , respectively) the ability as a proxy of LUE derived from the tower flux measurements over the big-leaf PRI taken as the arithmetic average of the multi-angle measurements in a given time interval. In the dry season from July to September, correlations of PRI with LUE at daily time steps are much stronger in the two-leaf case than in the big-leaf case. The correlation between PRI t and LUE is the strongest ( R 2 = 0.785, p t is very effective in detecting the light and low-moderate drought stress on LUE at half-hourly time steps, while ineffective in detecting severe atmospheric water and heat stress, which is probably due to alternative radiative energy sink, i.e. photorespiration. Overall, the two-leaf approach well overcomes some external effects (e.g. sun-target-view geometry) that interfere with PRI signals.

Light use efficiency (LUE) models offer an effective way for regional gross primary productivity (GPP) estimation. However, LUE is not easily determined at the landscape level due to its complexity and dependence on various environmental factors. One possible strategy to avoid the requirement for assessing environmental stressors is using the photochemical reflectance index (PRI) to determine LUE via the epoxidation state of the xanthophyll cycle. Integration of such measurements into GPP models could lead to more realistic GPP estimates of landscape level. Conventional, “one-leaf” LUE models, however, seem less suitable for integration of such remote sensing observations, as optically derived estimates are dependent on the shadow fraction viewed at a given time. Here, we utilize the two-leaf LUE (TL-LUE) model to parameterize LUE from multiangle PRI observations and compare it with MOD17 approach. Significant relationships were found between LUE (LUE, LUEsun, and LUEshaded) and PRI (PRI, PRIsun, and PRIshaded) over 8- and 16-day time steps. Similarly, $R^{2}$ values for the relationships between modeled GPP and observed GPP (EC derived measurements of GPP) were 0.87 (TL-LUE) and 0.81 (MOD17) at deciduous forest and 0.54 (TL-LUE) and 0.46 (MOD17) at evergreen forest for eight-day periods, as well as 0.84 (TL-LUE) and 0.74 (MOD17) at deciduous forest and 0.49 (TL-LUE) and 0.46 (MOD17) at evergreen forest for 16-day periods. Our results are relevant when planning potential future satellite missions to help constrain existing GPP models using remotely sensed data, as such observations will likely be affected by canopy shading effects at the time of observation.

This study assessed the capability of several xanthophyll, chlorophyll and structure-sensitive spectral indices to detect water stress in a commercial farm consisting of five fruit tree crop species with contrasting phenology and canopy architecture. Plots irrigated and non-irrigated for eight days of each species were used to promote a range of plant water status. Multi-spectral and thermal images were acquired from an unmanned aerial system while concomitant measurements of stomatal conductance (gs), stem water potential (Ψs) and photosynthesis were taken. The Normalized Difference Vegetation Index (NDVI), red-edge ratio (R700/R670), Transformed Chlorophyll Absorption in Reflectance Index normalized by the Optimized Soil Adjusted Vegetation Index (TCARI/OSAVI), the Photochemical Reflectance Index using reflectance at 530 (PRI) and 515 nm [PRI(570–515)] and the normalized PRI (PRInorm) were obtained from the narrow-band multi-spectral images and the relationship with the in-field measurements explored. Results showed that within the Prunus species, Ψs yielded the best correlations with PRI and PRI(570–515) (r2 = 0.53) in almond trees, with TCARI/OSAVI (r2 = 0.88) in apricot trees and with PRInorm, R700/R670 and NDVI (r2 from 0.72 to 0.88) in peach trees. Weak or no correlations were found for the Citrus species due to the low level of water stress reached by the trees. Results from the sensitivity analysis pointed out the canopy temperature (Tc) and PRI(570–515) as the first and second most sensitive indicators to the imposed water conditions in all the crops with the exception of apricot trees, in which Ψs was the most sensitive indicator at midday. PRInorm was the least sensitive index among all the water stress indicators studied. When all the crops were analyzed together, PRI(570–515) and NDVI were the indices that better correlations yielded with Crop Water Stress Index, gs and, particularly, Ψs (r2 = 0.61 and 0.65, respectively). This work demonstrated the feasibility of using narrow-band multispectral-derived indices to retrieve water status for a variety of crop species with contrasting phenology and canopy architecture.