Photochemical Reflectance Index Research Articles

Accuracy Assessment of Photochemical Reflectance Index (PRI) and Chlorophyll Carotenoid Index (CCI) Derived from GCOM-C/SGLI with In Situ Data

The photochemical reflectance index (PRI) and the chlorophyll carotenoid index (CCI) are carotenoid-sensitive vegetation indices, which can monitor vegetation’s photosynthetic activities. One unique satellite named “Global Change Observation Mission-Climate (GCOM-C)” is equipped with a sensor, “Second Generation Global Imager (SGLI)”, which has the potential to frequently and simultaneously observe PRI and CCI over a wide swath. However, the observation accuracy of PRI and CCI derived from GCOM-C/SGLI remains unclear in forests. Thus, we demonstrated their accuracy assessment by comparing them with in situ data. We collected in situ spectral irradiance data at four forest sites in Japan for three years. We statistically compared satellite PRI with in situ PRI, and satellite CCI with in situ CCI. From the obtained results, the satellite PRI showed poor agreement (the best: r=0.294 (p<0.05)) and the satellite CCI showed good agreement (the best: r=0.911 (p<0.001)). The greater agreement of satellite CCI is possibly because satellite CCI contained fewer outliers and satellite CCI was more resistant to small noise, compared to satellite PRI. Our results suggest that the satellite CCI is more suitable for practical use than the satellite PRI with the latest version (version 3) of GCOM-C/SGLI’s products.

Vertical gradients in photosynthetic physiology diverge at the latitudinal range extremes of white spruce.

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, photosynthesisand 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.

Assessment of crop traits retrieved from airborne hyperspectral and thermal remote sensing imagery to predict wheat grain protein content

Wheat (Triticum spp.) is among the world’s most widely grown crops and receives large quantities of nitrogen (N) fertiliser. Grain protein content (GPC) is influenced by genetic, agronomic and weather conditions affecting crops’ physiological status and stress levels; accurate GPC prediction has potential to reduce N losses and improve profit. Success in GPC estimation from remotely sensed plant traits has been limited. For progress to be made, it is necessary to robustly identify imaging spectroscopy-based physiological traits most closely associated with GPC in both experimental and commercial contexts. We present results from piloted hyperspectral flights and ground campaigns at two dryland field experiments with divergent water supply and wide-ranging N treatments, and from two years’ flights over 17 commercial fields planted to either bread (T. aestivum) or durum (T. durum) wheat, in the southern Australian wheat belt. Imagery was acquired with airborne hyperspectral and thermal sensors, with spatial resolutions of approx. 0.3 m and 0.5 m for experimental plots and 1 m/1.7 m in commercial fields. Leaf clip measurements, leaf and grain samples were collected and, in commercial fields, ∼40,000 records obtained from harvester-mounted protein monitors. Crop Water Stress Index (CWSI), solar-induced fluorescence (SIF), reflectance indices and PRO4SAIL radiative transfer model inverted parameters were retrieved for each plot and GPC record location. The photochemical reflectance index (PRI) related to xanthophyll pigments was consistently associated with GPC at both leaf and canopy scale in the plots and transect. In the commercial crops, a gradient boosted machine learning algorithm (GBM) ranked CWSI as the strongest indicator of GPC under severe water stress, while SIF, PRI and inverted biochemical constituents anthocyanins and carotenoids were consistently important under more benign conditions. Structural parameters inverted from the hyperspectral reflectance imagery were not prominent except under severe drought. We attained statistically significant results estimating GPC in unseen samples, with best relationships between predicted and observed GPC of r2 = 0.80 in a model built with thermal and physiological traits obtained from the hyperspectral and thermal imagery.

Importance of AVIRIS-NG data in assessing the assemblage of guilds of tropical trees over a contrasting climate

Generating large-scale field-based guild data for assessing tree species sorting and distribution over tropical covers is difficult. In this study, we combined field, and the Airborne Visible-Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) datasets of tropical covers across a climate gradient to develop precision maps of abundant tree species. This assisted in the extraction of a large dataset of guilds with tree species assortment and occupancy. We observed parity in the distribution of local and regional species with an increase in the proportionate presence of local species over the climate gradient. We saw greater distribution of Fabaceae members in sites with relatively lesser rainfall. To address the impact of species sorting on the functional aspects of guilds, available R codes (Ackerly and Cornwell, 2007), and three vegetation indices (VIs) as functional traits (Photochemical Reflectance Index, PRI, Chlorophyll/Carotenoid Index, CCI, and Normalized Difference Water Index, NDWI) were tested. Species assortment and spread affected the guild's functional diversity, as noticed in the abundance weighted guild means of VIs. We also observed a consistent increase in the mean CCI of all species across the climate gradient correlating positively with rainfall and negatively with vapour pressure deficit (VPD). This shows that CCI could be an adequate measure for large-scale ecological observations of these tropical regions. Wider amplitudes in the measured VIs of deciduous species demonstrated their adaptability to the climate gradient. Gradient-driven species assembly dynamics and functional traits variability indicates of tropical tree biodiversity feedback to the climate dynamics.

Snapshot-Based Visible-Near Infrared Multispectral Imaging for Early Screening of Heat Injury during Growth of Chinese Cabbage

Heat stress in particular can damage physiological processes, adaptation, cellular homeostasis, and yield of higher plants. Early detection of heat stress in leafy crops is critical for preventing extensive loss of crop productivity for global food security. Thus, this study aimed to evaluate the potential of a snapshot-based visible-near infrared multispectral imaging system for detecting the early stage of heat injury during the growth of Chinese cabbage. Two classification models based on partial least squares-discriminant analysis (PLS-DA) and least-squares support vector machine (LS-SVM) were developed to identify heat stress. Various vegetation indices (VIs), including the normalized difference vegetation index (NDVI), red-edge ratio (RE/R), and photochemical reflectance index (PRI), which are closely related to plant heat stress, were acquired from sample images, and their values were compared with the developed models for the evaluation of their discriminant performance of developed models. The highest classification accuracies for LS-SVM, PLS-DA, NDVI, RE/R, and PRI were 93.6%, 92.4%, 72.5%, 69.6%, and 58.1%, respectively, without false-positive errors. Among these methods for identifying plant heat stress, the developed LS-SVM and PLS-DA models showed more reliable discriminant performance than the traditional VIs. This clearly demonstrates that the developed models are much more effective and efficient predictive tools for detecting heat stress in Chinese cabbage in the early stages compared to conventional methods. The developed technique shows promise as an accurate and cost-effective screening tool for rapid identification of heat stress in Chinese cabbage.

Phenological, morphological and physiological drivers of cereal grain yield in Mediterranean agroforestry systems

Climate change will pose a major threat to food supply worldwide. Agroforestry has been proposed as an effective approach to minimize its effects on crops. However, to design sustainable and productive agroforestry systems, net responses of crops to trees need to be clarified, particularly in regions where competitive interactions will outweigh facilitative ones. The study of plant traits has become a useful approach to select best-adapted plants, yet the sensitivity of yield components to agroforestry and their relationship with plant traits needs investigation. In this experiment, we assess the phenological, morphological and physiological responses of shade-adapted cultivars of winter wheat (Triticum aestivum L.) and barley (Hordeum vulgare L.), growing in an agroforestry system (AF) with walnut trees (Juglans x intermedia Mj209xRa), as compared to cereal monocrops (MC). Agroforestry reduced the light intercepted by crops by 58 %, buffered extreme temperatures at the crop canopy level and increased air relative humidity, advancing plant development for wheat and barley. Major tree effects on crops were observed after walnut budburst. Under trees, crops increased the relative water content (RWC), Photochemical Reflectance Index (PRI) and P content of both wheat and barley leaves, increased Normalized Difference Vegetation Index (NDVI) of barley leaves, and reduced leaf mass area (LMA) in wheat and cell membrane damage in both species. In agroforestry, crops also reduced plant growth, leaf area (LA), leaf area index (LAI) and grain yield in both wheat and barley. Drivers of grain yield differed among agroforestry and open conditions in both species, showing for the first time that the selection of cereal cultivars for specific conditions could be based on plant traits, along with the shade tolerance, to establish successful agroforestry systems.

Water Availability Affects the Capability of Reflectance Indices to Estimate Berry Yield and Quality Attributes in Rain-Fed Vineyards

Remote sensing methods are known to provide estimates of berry quality. However, previous studies have shown that the Normalized Difference Vegetation Index (NDVI) failed to predict berry quality attributes in rain-fed vineyards. This study explores the association of several reflectance indices with vine biophysical characteristics and berry yield and quality attributes and their temporal stability. The study was conducted in rain-fed Chardonnay vineyards located around Masquefa (Penedès region, Catalonia, Spain) over four years. Canopy reflectance, fractional Intercepted Photosynthetic Active Radiation, predawn water potential and canopy temperature at midday were measured at veraison whereas berry yield and quality attributes were determined at harvest. Water availability and vine biophysical attributes showed large temporal stability whereas berry quality attributes were not temporally stable. The capability of reflectance indices to estimate berry quality attributes was subject to the timing and extent of water deficits. The Photochemical Reflectance Index (PRI), the NDVI and the Water Index (WI) provided estimates of berry quality attributes under mild, moderate and severe water deficits, respectively. These results might have potential applications in precision viticulture activities such as selective harvesting according to grape quality attributes and the assessment of ripening.

Remote Sensing-Based Evaluation of Heat Stress Damage on Paddy Rice Using NDVI and PRI Measured at Leaf and Canopy Scales

Extremely high air temperature at the heading stage of paddy rice causes a yield reduction due to the increasing spikelet sterility. Quantifying the damage to crops caused by high temperatures can lead to more accurate estimates of crop yields. The remote sensing technique evaluates crop conditions indirectly but provides information related to crop physiology, growth, and yield. In this study, we aim to assess the crop damage caused by heat stress in paddy rice examined under elevated air temperatures in a temperature gradient field chamber from 2016 to 2019, using remote-sensed vegetation indices. A leaf-spectrometer, field-spectrometers, and a multi-spectral camera were used to monitor the conditions of paddy rice. Although, in the leaf- and canopy-scales, the values of normalized difference vegetation index (NDVI) and photochemical reflectance index (PRI) decreased after the heading of rice under normal conditions, the decreasing sensitivity of NDVI and PRI was different depending on the degree of physiological heat stress by high temperature conditions. The NDVI after the heading under extremely high air temperature was not dropped and remained the value before heading. The PRI decreased at all air temperature conditions after the heading; the PRI of the plot exposed to the elevated air temperature was higher than that under ambient air temperature. Further, the relative change in NDVI and PRI after the heading exhibited a strong relationship with the ripening ratio of paddy rice, which is the variable related to crop yield. These remote-sensing results aid in evaluating the crop damage caused by heat stress using vegetation indices.

Monitoring the photosynthetic performance of grape leaves using a hyperspectral-based machine learning model

Photosynthesis is a direct expression of the crop growth status and an important indicator predicting yield and quality. Rapid and accurate monitoring of the dynamics of photosynthetic is key to field management. In this study, we obtained photosynthetic pigments and water status parameters at the leaf scale during different growth periods of grape. The potential maximum quantum yield (Fv/Fm) of photosystem II (PSII) under dark adaptation and the light response curve (LRC) of the PSII electron transfer rate (ETR) under light adaptation were measured using a pulse amplitude modulated (PAM) chlorophyll fluorometer, while leaf spectral information was recorded using a hyperspectral imager. The maximum ETR (ETRmax) and initial quantum efficiency (kα) were calculated using the LRC model. A Bayesian neural network (BNN) model (implemented in Tensorfolw2.8) was developed to predict Fv/Fm, ETRmax and kα by quantifying the spectral response indices of photosynthetic pigments and water status parameters. A comparison was made with the partial least squares (PLS) and photochemical reflectance index (PRI). The results show that BNN, PLS model and PRI have better predictive performance for Fv/Fm than ETRmax and kα. Compared with the PLS and PRI, the BNN model was able to significantly improve the prediction accuracy, where the validation results for Fv/Fm were R2 of 0.78, ETRmax of 0.57 and kα of 0.53. In addition, the importance of the BNN model input features varied with Fv/Fm, ETRmax and kα, with the vegetation index describing the photosynthetic pigments having the highest importance. The PRI had the worst predictive performance probably because the de-epoxidation state of the xanthophyll cycle pigments is strongly influenced by temporal changes. The model developed in this paper for monitoring photosynthetic performance parameters in grape leaves can simplify the complex photosynthetic reaction process, expand the application of PAM technology and provide a method for rapid and accurate monitoring of photosynthetic performance.

Modeling plant phenology by MODIS derived photochemical reflectance index (PRI)

Vegetation phenology is a sensitive indicator of ecosystem responses to climate change, and thus the accurate estimation of vegetation phenology is critical to evaluate the impact of climate change on terrestrial ecosystems. Common structural vegetation indices (VIs) such as the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Near-infrared Reflectance of Vegetation (NIRv) and Plant Phenology Index (PPI), are the most widely used indicators of phenology, but they have limited potential in tracking autumn phenology, especially for evergreen species with low seasonal variability of canopy greenness. Given the important role of carotenoid pigments in regulating photosynthetic activity and plant phenology, we hypothesize that satellite-based indicators of leaf pigments derived from MODIS ocean bands could be useful for phenology modeling. Using 624 site-years of flux data at 84 FLUXNET sites and 9979 ground observations at 138 PEP725 sites, we first explored the potential of different forms of scaled photochemical reflectance index (sPRIref) in monitoring photosynthetic activity, and found that band 10 and band 13 were more suitable for tracking gross primary productivity (GPP) than other reference bands. By comparing with canopy photosynthetic phenology, sPRI10 and sPRI13 showed improved representation of phenological transitions (the start and end of growing season, SOS and EOS, respectively) than structural VIs. In spring, all VIs exhibited comparable performances for estimating SOS at deciduous broadleaf forests (DBF) and grasslands (GRA) sites; however, sPRI10 and sPRI13 were better predictors of SOS than structural VIs at evergreen needleleaf forests (ENF) and mixed forests (MF) sites. In autumn, sPRI10 and sPRI13 showed improved predictive strength of EOS than structural VIs for ENF, MF and GRA sites. Further investigations using the ground observed phenological records also confirmed the improved performances of sPRI10 and sPRI13 for both SOS and EOS estimation. We also investigated the spatial patterns of sPRI10-derived SOS and EOS over the Northern Hemisphere with respect to different plant functional types. We showed that sPRI10 reliably tracked plant phenology with 83.0% and 78.8% success in detecting SOS and EOS, respectively. Spatial patterns of SOS exhibited obvious latitudinal gradients, while EOS showed a strong regional heterogeneity. In addition, sPRI10 predicted an overall earlier SOS (61.8%) and later EOS (51.2%) than the MODIS phenology product (VNP22Q2 v001) estimated from structural VI, suggesting the latter underestimated the greening potential of the Northern Hemisphere. Our results suggest that MODIS PRI could be useful to monitor vegetation phenology, and further reveal the importance of underappreciated carotenoid pigments in tracking plant seasonal changes, particularly in autumn months.

Water Stress Impacts on Grapevines (Vitis vinifera L.) in Hot Environments: Physiological and Spectral Responses

The projected increase in temperature and water scarcity represents a challenge for winegrowers due to changing climatic conditions. Although heat and drought often occur concurrently in nature, there is still little known about the effects of water stress (WS) on grapevines in hot environments. This study aimed to assess whether the grapevine’s physiological and spectral responses to WS in hot environments differ from those expected under lower temperatures. Therefore, we propose an integrated approach to assess the physiological, thermal, and spectral response of two grapevine varieties (Vitis vinifera L.), Grenache and Shiraz, to WS in a hot environment. In a controlled environment room (CER), we imposed high-temperature conditions (TMIN 30 °C–TMAX 40 °C) and compared the performance of well-watered (WW) and WS-ed potted own-rooted Shiraz and Grenache grapevines (SH_WW, SH_WS, GR_WW, and GR_WS, respectively). We monitored the vines’ physiological, spectral, and thermal trends from the stress imposition to the recovery after re-watering. Then, we performed a correlation analysis between the physiological parameters and the spectral and thermal vegetation indices (VIs). Finally, we looked for the best-fitting models to predict the physiological parameters based on the spectral VIs. The results showed that GR_WS was more negatively impacted than SH_WS in terms of net photosynthesis (Pn, GR-WS = 1.14 μmol·CO2 m−2·s−1; SH-WS = 3.64 μmol·CO2 m−2·s−1), leaf transpiration rate (E, GR-WS = 1.02 mmol·H2O m−2·s−1; SH-WS = 1.75 mmol·H2O m−2·s−1), and stomatal conductance (gs, GR-WS = 0.04 mol·H2O m−2·s−1; SH-WS = 0.11 mol·H2O m−2·s−1). The intrinsic water-use efficiency (WUEi = Pn/gs) of GR_WS (26.04 μmol·CO2 mol−1 H2O) was lower than SH_WS (34.23 μmol·CO2 mol−1 H2O) and comparable to that of SH_WW (26.31 μmol·CO2 mol−1 H2O). SH_WS was not unaffected by water stress except for E. After stress, Pn, gs, and E of GR_WS did not recover, as they were significantly lower than the other treatments. The correlation analysis showed that the anthocyanin Gitelson (AntGitelson) and the green normalised difference vegetation index (GNDVI) had significant negative correlations with stem water potential (Ψstem), Pn, gs, and E and positive correlation with WUEi. In contrast, the photochemical reflectance index (PRI), the water index (WI), and the normalised difference infrared index (NDII) showed an opposite trend. Finally, the crop water stress (CWSI) had significant negative correlations with the Ψstem in both varieties. Our findings help unravel the behaviour of vines under WS in hot environments and suggest instrumental approaches to help the winegrowers managing abiotic stress.

Early validation study of the photochemical reflectance index (PRI) and the normalized difference vegetation index (NDVI) derived from the GCOM-C satellite in Mongolian grasslands

ABSTRACT The Global Change Observation Mission-Climate (GCOM-C), launched in 2017, has suitable bands matching the photochemical reflectance index (PRI) definition. It also has the bands for the normalized difference vegetation index (NDVI). The PRI has a unique capability to detect plant stress caused by excessive light and drought. However, no moderate-resolution satellites had suitable bands for the PRI, requiring two narrow bands in green light in the definition. In this study, we conducted the early validation study of PRI and NDVI derived from the GCOM-C satellite and demonstrated those accuracies and characteristics in Mongolian grassland. The Mongolian Steppes (dry grasslands) are widely distributed on the plateau and therefore suitable for satellite validation. It is particularly suitable for the PRI validation because Mongolian grasslands have water stress due to the small amount of precipitation in summer. Therefore, we carried out field campaigns at three study sites in Mongolia. In this study, we found the seasonal pattern of PRI suggesting the potential to detect the water stress of vegetation, which is essential information for informed management of the grasslands. However, the correlation between the satellite-derived PRI and the in-situ PRI was negative because of the dependence of GCOM-C PRI on the soil moisture at sparse vegetation. For the accuracy assessment of PRI, which depends on rapidly changing light and soil moisture in a day, more exact synchronization of in-situ and satellite observation is required. On the other hand, we found that the NDVI derived from GCOM-C was highly accurate: The correlation coefficient (R) between the satellite-derived NDVI and the in-situ NDVI was 0.988 (RMSE=0.052). GCOM-C NDVI has enough similarities with MODIS NDVI in terms of accuracy, spatial resolution, and frequency. For example, we demonstrated that GCOM-C NDVI could detect the phenology with the same or better accuracy than MODIS NDVI. We also demonstrated their difference: the soil moisture dependence in sparse vegetation. The less dependency of GCOM-C NDVI on the soil moisture leads to a better classification of vegetation and non-vegetation in the sparse grassland than MODIS NDVI.

Research of Light Use Efficiency for Paddy Rice Using Multi-Angle Hyperspectral Observations

Light use efficiency (LUE) is a critical variable for modeling gross primary production (GPP) of vegetation. The photochemical reflectance index (PRI) is based on reflectance at 531 and 570 nm, which has great potential in predicting the light use efficiency (LUE) and makes LUE related to plant optical characteristics. Despite the great achievement in understanding the correlation of PRI and LUE on forests, the application of PRI in estimating LUE on crops has almost been ignored. The present study reported the ability of multi-angle PRI to track eddy covariance (EC)-based LUE in a rice field in East China. We found that PRI can estimate LUE in sunny days (R2 = 0.4; p < 0.05). The correlation of PRI and LUE was greater than that at OZA of 0° (R2 = 0.4; p < 0.05) after distinguishing different OZAs and scatter directions and was the greatest in the backscatter direction with an OZA of 60° (R2 = 0.7; p < 0.01). This correlation was also corrected by the bidirectional reflectance distribution function (BRDF), where R2 = 0.72 after correction. We noted that the relationship between PRI and LUE on cloudy days was poor, where R2 = 0.26. Photosynthetically active radiation (PAR) had an influence on the LUE and PRI, while vapor pressure deficit (VPD) and air temperature (Ta) had negative influences on the correlation of PRI and LUE. Our research suggests that PRI can efficiently track the LUE of the rice growth period when considering environmental factors and rice canopy structures.

Exploring the interrelated effects of soil background, canopy structure and sun-observer geometry on canopy photochemical reflectance index

Photochemical reflectance index (PRI) is an intriguing avenue for monitoring photosynthetic light use efficiency (LUE), which is a crucial physiological variable and the primary source of uncertainty in gross primary production (GPP) prediction. The correlations between PRI and LUE induced by the energy-dependent xanthophyll cycle are overall convincing at the leaf scale. However, their relationship becomes complicated and less reliable at the canopy scale due to several confounding factors, such as soil background, canopy structure and sun-observer geometry. A fundamental understanding of the interrelated effects of these factors is still missing. In this study, the roles of soil reflectance, canopy structure and sun-observer geometry were examined by investigating radiative transfer processes that determine canopy PRI. Theoretical analysis shows that the deviation between canopy- and leaf-level PRI is mainly attributed to soil background, namely the fraction of sunlit soil in the scene and the discrepancy between leaf and soil reflectance. Canopy structure and sun-observer geometry are not the direct drivers of variations in canopy PRI. They affect canopy PRI by altering the contribution of soil to canopy reflectance. Furthermore, several numerical experiments based on 1D and 3D radiative transfer models, which compare PRI of canopies with black soil (i.e., soil reflectance is zero) and non-black background, were conducted to confirm and validate the decisive role of soil background in explaining the deviation between canopy and leaf PRI. Multi-angular canopy PRI from PROBA-CHRIS data was employed to examine the angular dependency of canopy PRI changing with vegetation coverage. The numerical experiments demonstrate that canpy PRI differs from leaf PRI as large as 150% when canopy leaf area index (LAI) or the fraction of vegetation coverage is small. Canopy PRI is almost identical to soil PRI (i.e., PRI computed from soil reflectance) when the contribution of soil background to canopy reflectance is large. In contrast, when the soil reflectance is zero or LAI is greater than four, canopy PRI is almost identical to leaf PRI and independent of canopy structure and sun-observer geometry. Nonetheless, when leaf and soil reflectance are similar, canopy PRI is always close to leaf PRI regardless canopy structure and sun-observer geometry. The analysis of multi-angular PRI reveals that the angular dependency decreases with vegetation coverage due to weaker soil background effect. This study narrows down the causes of variation in canopy PRI to the fraction of observed soil background and soil reflectance. It suggests that estimating and accounting for the soil contribution to canopy PRI are necessary to mitigate angular and canopy structural effects on canopy PRI and eventually extract leaf physiological information from canopy PRI.

Hyperspectral differences between sunlit and shaded leaves in a Manchurian ash canopy in Northeast China

ABSTRACT The spectral characteristics of sunlit and shaded leaves are critical to improving the utilization of remote sensing methodology to quantify forest physiology. However, spectral characteristics within the tree canopies, especially normalized difference vegetation index (NDVI) and photochemical reflectance index (PRI), are poorly understood. Our study used an imaging observation platform to obtain hyperspectral imagery of a Manchurian Ash canopy on Changbai Mountain. A non-imaging spectrometer was employed for an assisted analysis. The study results of the corresponding spectrum obtained at two observation spatial scales were significantly different between sunlit and shaded leaves. For imaging spectral observations, there were significant differences in NDVI and PRI between sunlit and shaded leaves (P < 0.001). PRI near the petiole was significantly lower than in other parts of leaves (P = 0.049). Non-imaging spectral observations of the reflectance of sunlit and shaded leaves were different only in the visible light region. The PRI of the shaded leaves were higher than that of sunlit leaves, which was consistent with the imaging spectral observations. The complexity of light environment within the canopy, especially the differences in incident irradiance, contributed to the range of leaf attribute measurements, which resulted in the variability of spectral characteristics.

Investigating Plant Response to Soil Characteristics and Slope Positions in a Small Catchment

Methods enabling stakeholders to receive information on plant stress in agricultural settings in a timely manner can help mitigate a possible decrease in plant productivity. The present work aims to study the soil–plant interaction using field measurements of plant reflectance, soil water content, and selected soil physical and chemical parameters. Particular emphasis was placed on sloping transects. We further compared ground- and Sentinel-2 satellite-based Normalized Vegetation Index (NDVI) time series data in different land use types. The Photochemical Reflectance Index (PRI) and NDVI were measured concurrently with calculating the fraction of absorbed photochemically active radiation (fAPAR) and leaf area index (LAI) values of three vegetation types (a grassland, three vineyard sites, and a cropland with maize). Each land use site had an upper and a lower study point of a given slope. The NDVI, fAPAR, and LAI averaged values were the lowest for the grassland (0.293, 0.197, and 0.51, respectively), which showed the highest signs of water stress. Maize had the highest NDVI values (0.653) among vegetation types. Slope position affected NDVI, PRI, and fAPAR values significantly for the grassland and cropland (p &lt; 0.05), while the soil water content (SWC) was different for all three vineyard sites (p &lt; 0.05). The strongest connections were observed between soil physical and chemical parameters and NDVI values for the vineyard samples and the selected soil parameters and PRI for the grassland. Measured and satellite-retrieved NDVI values of the different land use types were compared, and strong correlations (r = 0.761) between the methods were found. For the maize, the satellite-based NDVI values were higher, while for the grassland they were slightly lower compared to the field-based measurements. Our study indicated that incorporating Sentinel-derived NDVI can greatly improve the value of field monitoring and provides an opportunity to extend field research in more depth. The present study further highlights the close relations in the soil–plant–water system, and continuous monitoring can greatly help in developing site-specific climate change mitigating methods.

Fluorescence ratio and photochemical reflectance index as a proxy for photosynthetic quantum efficiency of photosystem II along a phosphorus gradient

Sun-induced chlorophyll fluorescence (SIF) is one of the most promising remote-sensing signals to assess spatio-temporal variation in photosynthesis. Yet, it has been shown that the positive linear relationship of SIF and photosynthesis, often reported from satellite and proximal remote sensing, is mainly driven by the amount of absorbed photosynthetic active radiation (APAR). By normalizing SIF by APAR these structural first-order effects can accounted for and SIF is then reflecting physiological regulation of photosynthetic efficiency. However, because of the confounding contribution of non-photochemical energy dissipation, the relationship between SIF and photosynthetic efficiency is non-linear, and therefore additional measurements have to be included to constrain the predictions of photosynthetic efficiency and photosynthetic electron transport.We grew Zea mays at different phosphorus (P) levels to assess if P-induced reduction in quantum efficiency of PSII (ΦPSII), can be estimated by the fluorescence efficiency parameters, APAR normalized fluorescence (Fyield) and the ratio of the two emitted fluorescence peaks (F↑ratio), at leaf level. Results were compared to the photochemical reflectance index (PRI), a well-established index related to the activity of the xanthophyll cycle, a protection mechanism which activates under light-stress conditions. We demonstrate that the relationship between ΦPSII and Fyield is non-monotonic across a P limitation gradient, rendering the prediction of ΦPSII by Fyield alone unfeasible. We show, however, that the pigment corrected PRI (cPRI) and F↑ratio (cF↑ratio) share a strong linear relationship with ΦPSII, thereby enabling the estimation of ΦPSII. We demonstrate that a compensation for reabsorption effects improved the estimation of ΦPSII by Fratio at foliar level. This may allow improved predictions of photosynthetic light use efficiency parameters without the need of measuring green APAR.

Identification and Classification of Downy Mildew Severity Stages in Watermelon Utilizing Aerial and Ground Remote Sensing and Machine Learning.

Remote sensing and machine learning (ML) could assist and support growers, stakeholders, and plant pathologists determine plant diseases resulting from viral, bacterial, and fungal infections. Spectral vegetation indices (VIs) have shown to be helpful for the indirect detection of plant diseases. The purpose of this study was to utilize ML models and identify VIs for the detection of downy mildew (DM) disease in watermelon in several disease severity (DS) stages, including low, medium (levels 1 and 2), high, and very high. Hyperspectral images of leaves were collected in the laboratory by a benchtop system (380–1,000 nm) and in the field by a UAV-based imaging system (380–1,000 nm). Two classification methods, multilayer perceptron (MLP) and decision tree (DT), were implemented to distinguish between healthy and DM-affected plants. The best classification rates were recorded by the MLP method; however, only 62.3% accuracy was observed at low disease severity. The classification accuracy increased when the disease severity increased (e.g., 86–90% for the laboratory analysis and 69–91% for the field analysis). The best wavelengths to differentiate between the DS stages were selected in the band of 531 nm, and 700–900 nm. The most significant VIs for DS detection were the chlorophyll green (Cl green), photochemical reflectance index (PRI), normalized phaeophytinization index (NPQI) for laboratory analysis, and the ratio analysis of reflectance spectral chlorophyll-a, b, and c (RARSa, RASRb, and RARSc) and the Cl green in the field analysis. Spectral VIs and ML could enhance disease detection and monitoring for precision agriculture applications.

Modified Photochemical Reflectance Indices as New Tool for Revealing Influence of Drought and Heat on Pea and Wheat Plants.

In environmental conditions, plants can be affected by the action of numerous abiotic stressors. These stressors can induce both damage of physiological processes and adaptive changes including signaling-based changes. Development of optical methods of revealing influence of stressors on plants is an important task for plant investigations. The photochemical reflectance index (PRI) based on plant reflectance at 531 nm (measuring wavelength) and 570 nm (reference wavelength) can be effective tool of revealing plant stress changes (mainly, photosynthetic changes); however, its efficiency is strongly varied at different conditions. Earlier, we proposed series of modified PRIs with moderate shifts of the measuring wavelength and showed that these indices can be effective for revealing photosynthetic changes under fluctuations in light intensity. The current work was devoted to the analysis of sensitivity of these modified PRIs to action of drought and short-term heat stress. Investigation of spatially-fixed leaves of pea plants showed that the modified PRI with the shorter measuring wavelength (515 nm) was increased under response of drought and heat; by contrast, the modified PRI with the longer wavelength (555 nm) was decreased under response to these stressors. Changes of investigated indices could be related to parameters of photosynthetic light reactions; however, these relations were stronger for the modified PRI with the 555 nm measuring wavelength. Investigation of canopy of pea (vegetation room) and wheat (vegetation room and open-ground) supported these results. Thus, moderate changes in the measuring wavelengths of PRI can strongly modify the efficiency of their use for the estimation of plant physiological changes (mainly photosynthetic changes) under action of stressors. It is probable that the modified PRI with the 555 nm measuring wavelength (or similar indices) can be an effective tool for revealing photosynthetic changes induced by stressors.

Non-invasive Estimation of Foliar Nitrogen Concentration Using Spectral Characteristics of Menthol Mint (Mentha arvensis L.).

Menthol mint (Mentha arvensis L., Family: Lamiaceae), popularly known as corn mint or Japanese mint, is an important industrial crop that is widely grown for its valued essential oil. Nitrogen (N) is an important macro-nutrient and an essential factor for optimizing the yield and quality of crops. Hence, rapid and accurate estimation of the N content is crucial for nutrient diagnosis in plants and to make precise N fertilizer recommendations. Generally, N concentration is estimated by destructive sampling methods; however, an indirect assessment may be possible based on spectral characteristics. This study aimed to compare the foliar N concentration based on non-destructive (reflectance) and destructive (laboratory analyses) methods in menthol mint. Foliar N concentration was measured through the Kjeldahl method and reflectance by Miniature Leaf Spectrometer C-710 (CID Bio-Science). Using reflectance data, several vegetation indices (VIs), that is, normalized difference red edge (NDRE), red edge normalized difference vegetation index (reNDVI), simple ratio (SR), green–red vegetation index (GRVI), canopy chlorophyll content index (CCCI), photochemical reflectance index (PRI), green chlorophyll index (CIGreen), red edge chlorophyll index (CIRed Edge), canopy chlorophyll index (CCI), normalized pigment chlorophyll ratio index (NPCI), and structure insensitive pigment index (SIPI), were developed to determine the foliar N concentration. The highest correlation (r) between VIs and foliar N concentrations was achieved by NDRE (0.89), followed by reNDVI (0.84), SR (0.83), GRVI (0.78), and CCCI (0.76). Among the VIs, the NDRE index has been found to be the most accurate index that can precisely predict the foliar N concentration (R2 = 0.79, RMSE = 0.18). In summary, the N deficiencies faced by the crop during its growth period can be detected effectively by calculating NDRE and reNDVI, which can be used as indicators for recommending precise management strategies for the application of nitrogenous fertilizers.