Search Results (111)

The presence of soil hydrocarbon parameters (SHPs), including total petroleum hydrocarbons (TPHs), total organic carbon (TOC; %), and soil toxicity (EC50; mg L⁻¹), can affect vegetation in several ways. This study assessed the impact of SHPs on vegetation in the Niger Delta using field-measured, leaf-scale hyperspectral data acquired across the region. Red-edge position (REP) and four hyperspectral vegetation indices (HVIs)—mND705, photochemical reflectance index (PRI), Normalised Difference Vegetation Vigour Index (NDVVI₈₄₄,₄₄₇; a vegetation vigour index), and modified DATT (MDATT; a chlorophyll-sensitive red-edge index)—were used to quantify chlorophyll content in the vegetation types of Awolowo grass, elephant grass, mango trees, oil palm trees, and mangrove vegetation and to explore their variation with SHPs. The results show that mangrove vegetation was the most impacted by TPHs (R = −0.683), while mango vegetation was the most impacted by TOC (R = −0.725), based on Pearson correlation coefficients derived from the mND705 index. Similarly, mango and mangrove vegetation showed the strongest responses to soil toxicity (EC50; mg L⁻¹), based on Spearman correlation coefficients (r_s = 0.657 and r_s = 0.870, respectively) using the MDATT index. These findings highlight species-specific physiological responses to soil hydrocarbon contamination and demonstrate the applicability of red-edge-based hyperspectral techniques for assessing vegetation stress in complex coastal ecosystems such as the Niger Delta. Full article

The GCOM-C satellite possesses optimal wavelength bands around 530 nm and 570 nm for monitoring seasonal variations in the photochemical reflectance index (PRI) and chlorophyll–carotenoid index (CCI), which are sensitive to carotenoid contents and its ratio to chlorophyll contents, respectively. As well as NDVI, these indices are excellent indicators for monitoring pigment contents of evergreen trees in winter, which are considered susceptible to climate change impacts. In this study, to investigate the characteristics and usefulness of the GCOM-C-derived indices, the seasonal variations in these indices were analyzed between 2018 and 2024 at two evergreen forest sites in Japan, and compared to CCI and NDVI derived from MODIS, which also has a band near 530 nm. The satellite observation results show that the decreases in all indices for both satellites in winter were observed in the order of PRI, CCI, NDVI. This is thought to indicate that carotenoid contents increased in response to the decrease in land surface temperature to mitigate low-temperature stress, followed by a delayed decrease in chlorophyll contents. GCOM-C showed 0.1 larger NDVI values and 0.2 larger CCI values than MODIS, and the difference was estimated to be largely influenced by the disparity in sensor sensitivity in the red bands. The dispersion of each index was reduced by using data with small sensor zenith angles (below 20 degrees for GCOM-C and 0 to 30 degrees for MODIS); however, MODIS showed a decline in observation accuracy due to satellite drifting in 2024. Spectral measurements of leaves collected at the site also showed similar VI decreases; however, the satellite-derived CCI were 0.12 lower, suggesting that reflection from dead leaves influences the satellite data. This study confirmed that GCOM-C, which can measure both PRI and CCI with high spatial resolution, is suitable for observing seasonal variations in carotenoid and chlorophyll contents in evergreen forests. Full article

Strawberry (Fragaria × ananassa (Weston) Rozier) is a high-value crop whose market success depends on fruit quality traits such as sweetness, firmness, and pigmentation. In sustainable agriculture, wood distillates are gaining interest as natural biostimulants. This study evaluated the effects of foliar application of two commercial wood distillates (WD1 and WD2) and one produced in a pilot plant at the Institute for Bioeconomy of the National Research Council of Italy (IBE-CNR) on strawberry physiology, fruit yield, and fruit quality under greenhouse conditions. Non-destructive ecophysiological measurements were integrated using optical sensors for proximal phenotyping, enabling continuous monitoring of plant physiology and fruit ripening. Leaf gas exchange and chlorophyll fluorescence were measured with a portable photosynthesis system, while vegetation indices and pigment-related parameters were obtained using spectroradiometric sensors and fluorescence devices. To assess the functional relevance of vegetation indices, a linear regression analysis was performed between net photosynthetic rate (A) and the Photochemical Reflectance Index (PRI), confirming a significant positive correlation and supporting PRI as a proxy for photosynthetic efficiency. All treatments improved photosynthetic efficiency during fruiting, with significant increases in net photosynthetic rate, quantum yield of photosystem II, and electron transport rate compared to control plants. IBE-CNR and WD2 enhanced fruit yield, while all treatments increased fruit soluble solids content. Non-invasive monitoring enabled real-time assessment of physiological responses and pigment accumulation, confirming the potential of wood distillates as biostimulants and the value of advanced sensing technologies for sustainable, data-driven crop management. Full article

The photochemical reflectance index (PRI) based on reflectance at 531 and 570 nm and its modifications with shifted measuring wavelengths are well-known indicators of stress changes in photosynthetic processes which can be induced in plants under the action of numerous adverse factors (e.g., drought). However, the relationships between photosynthetic characteristics and the PRI are varied in different works; this means that photosynthetic responses are not the only reason for PRI changes. In the current work, we analyzed the influence of the light scattering of leaf surfaces and mesophyll layers and concentrations of leaf pigments on typical and modified PRIs. The analytical model of light reflectance and transmittance in the leaves of dicot plants, which had been previously developed in our work, and experimental measurements were used to analyze this influence. It was shown that increasing the light scattering of the leaf surface and the anthocyanin concentration and decreasing the light scattering of the spongy mesophyll increased PRIs with short measuring wavelengths and decreased PRIs with long measuring wavelengths. The action of drought induced similar changes in typical and modified PRIs, which were accompanied by an increased light scattering of the leaf surface and anthocyanin concentration and a decreased light scattering of the spongy mesophyll. The results show that changes in the light scattering of the leaf surface and spongy mesophyll and in the anthocyanin concentration can be the important mechanisms of slow changes in typical and modified PRIs, including drought-induced ones. Full article

The valorization of genetic intravarietal variability through the identification of the most suitable genotypes for yield and must quality is an adequate strategy for grapevine selection. Currently, climate change affects vine yield and wine quality in numerous ways, but little information is available on intravarietal variability regarding responses to abiotic stresses. In the current work, the intravarietal genetic variability of the Portuguese white variety Arinto was studied for yield, must quality, and for tolerance to abiotic stress, through indirect, rapid, and nondestructive measurements in the field. An innovative approach in selection for abiotic stress tolerance is described. The surface leaf temperature (SLT) of clones under environmental conditions of drought and extreme heat was measured, as were the NDVI (Normalized Difference Vegetation Index); PRI (Photochemical Reflectance Index); and chlorophyll content through the SPAD index, yield, and the characteristics of the must (pH, acidity, and °Brix). The application of this methodology was carried out in an experimental population of 165 Arinto clones for three years. Linear mixed models were fitted to the data from evaluated traits, and the empirical best linear unbiased predictors (EBLUPs) of genotypic effects were obtained, as well as the coefficient of genotypic variation (CVG) and broad-sense heritability. The genotypes were ranked according to their level of tolerance to abiotic stress without loss of yield/quality. SLT enabled the identification of clones that regulate stomata opening during stress, thus correlating positively with yield. SLT appears, thus, to be the most robust and reliable indicator to assess tolerance to stress in large field trials for grapevine selection. The results enabled the selection of a group of ten clones with increased tolerance to stress, compared to the average of the variety which maintained the typical must quality of Arinto. Full article

Plant genders could express different functional strategies to compensate for different reproductive costs, as females have an additional role in fruit and seed production. Secondary sexual dimorphism (SSD) expression is frequently greater under stress than under optimal growth conditions. The early gender identification in papaya may help to reduce orchard costs because the most desirable fruit shape is formed by hermaphrodite plants. We hypothesized that (a) gender ecophysiological phenotyping can be an alternative to make gender segregations in papaya seedlings, and (b) such gender segregation will be more efficient after a short drought exposure than under adequate water conditions. To test such hypotheses, seedlings of two papaya varieties (‘Candy’ and ‘THB’) were exposed to two kind of treatments: (1) water shortage (WS) for 45 h, after which they were well watered, and (2) continuously well-watered (WW). Study assessed the ecophysiological responses, such as stomatal conductance (g_s), SPAD index, optical reflectance indices, morphological traits, and biomass accumulation in females (F) and hermaphrodites (H). In WS treatment, the SSD was expressed in 14 of 18 traits investigated, while in WW treatment, the SSD was expressed only in 7 of 18 traits. As tools for SSD expression, g_s and simple ratio pigment index (SRPI) must be measured on the first or second day after the imposed WS was interrupted, respectively, while the other parameters must be measured after a period of four days. In some traits, the SSD was expressed in only one variety, or the response of H and F plants were of opposite values for two varieties. The choice of the clearest responses of gender segregation in WS treatment will be greenness index, combination of normalized difference vegetation index (CNDVI), photochemical reflectance index (PRI), water band index (WBI), SRPI, leaf number, leaf dry mass, and leaf mass ratio. If the WW conditions are maintained for papaya seedling production, the recommendation in gender segregation will be the analysis of CNDVI, carotenoid reflectance index 2 (CRI2), WBI, and SRPI. The non-destructive optical leaf indices segregated papaya hermaphrodites from females under both water conditions and eventually could be adjusted for wide-scale platform evaluations, with planned space arrangements of seedlings, and sensor’s set. Full article

Early detection of plant water status is crucial for efficient crop management. In this research, proximal sensing tools (i.e., hyperspectral imaging HSI and thermal IR camera) were used to monitor changes in spectral and thermal profiles of a citrus orchard in Sicily (Italy), managed under five irrigation systems. The irrigation systems differ in the amount of water distribution and allow four different strategies of deficit irrigation to be obtained. The physiological traits, stem water potential, net photosynthetic rate, stomatal conductance and the amount of leaf chlorophyll were measured over the crop’s growing season for each treatment. The proximal sensing data consisted of thermal and hyperspectral imagery acquired in June–September during the irrigation seasons 2023–2024 and 2024–2025. Significant variation in physiological traits was observed in relation to the different irrigation strategies, highlighting the highest plant water stress in July, in particular for the partial root-zone drying irrigation system. The water-use efficiency (WUE) values in subsurface drip irrigation were similar to the moderate deficit irrigation treatment and more efficient (up to 50%) as compared to control. Proximal sensing measures confirmed a different plant water status in relation to the five different irrigations strategies. Moreover, four spectral indices (Normalized Difference Vegetation Index NDVI; Water Index WI; Photochemical Reflectance Index PRI; Transformed Chlorophyll Absorption Ratio Index TCARI), calculated from HSI spectra, highlighted strong correlations with physiological traits, especially with stem water potential and the amount of leaf chlorophyll (coefficient of correlation ranged between −0.4 and −0.5). This study demonstrated the effectiveness of using proximal sensing tools in precision agriculture and ecosystem monitoring, helping to ensure optimal plant health and water use efficiency. Full article

Cowpea (Vigna unguiculata (L.) Walp.) is a vital legume crop recognized for its nutritional value and adaptability to various growing conditions. However, exposure of cowpea to drought stress during the early growth stages can significantly restrict growth and yield potential. Therefore, identifying cowpea genotypes tolerant to drought during early growth and development is essential for maintaining yield potential. This study characterized 15 diverse cowpea genotypes for various physiological, pigment, and morphological traits that may contribute to drought tolerance. At the V2 stage, the cowpea genotypes were subjected to two moisture regimes: control (100% irrigation) and drought (50% irrigation) for 22 days to assess trait responses and their relationship to drought tolerance. Drought-stressed plants decreased stomatal conductance by 79%, negatively correlating with a 2.9 °C increase in canopy temperature. Under drought, the photochemical reflectance index (PRI) was strongly associated with the quantum yield of PSII and electron transport rate. Shoot biomass decreased by 51% and root biomass by 32% under drought. Leaf area and shoot weight were correlated with root traits such as total length, surface area, and weight. Among all genotypes, 280785-11 and UCR 1004 demonstrated superior rooting vigor under drought, emphasizing their efficiency in resource utilization. These findings highlight the relevance of utilizing drought-adaptive traits to improve early-season drought tolerance. Full article

Despite the abundance of available studies on optical and microwave methods devoted to investigating agricultural crop conditions, there is a lack of research that explores the integration between microwave and optical data and the link between photosynthetic activity, measured by PRI (photochemical reflectance index), and vegetation water content, detected by radar sensors. In particular, there is a lack of vision that links these measures to better understand how plants react and adapt to possible water stress conditions. Most of the existing research tends to treat optical and microwave information separately, without investigating how the integration of these techniques can provide a more complete and accurate understanding of the research topic, corroborated by ground data. In this paper, an integrated approach using microwave and optical satellite data, respectively acquired by Sentinel-1 (S-1) and Sentinel-2 (S-2), was presented for monitoring vegetation status. Experimental data and electromagnetic models have been combined to relate backscattering from S-1 and optical indices from S-2 to plant conditions, which were evaluated by measuring PRI, plant water content (PWC), and soil water content. Field data were collected in two sorghum fields close to Florence in Tuscany (Central Italy) during the summers of 2022 and 2023. The results show significant correlations between microwave and optical data with respect to field measurements, highlighting the potential of remote sensing techniques for agricultural monitoring and management, also in response to climate change. Determination coefficients of R² = 0.51 between PRI and PWC, where PWC is retrieved by S-1, and R² = 0.73 between PSRI (plant senescence reflectance index) and PRI were obtained. Full article

Soil drought and salinization are key abiotic stressors for agricultural plants; the development of methods of their early detection is an important applied task. Measurement of red-green-blue (RGB) indices, which are calculated on basis of color images, is a simple method of proximal and remote sensing of plant health under the action of stressors. Potentially, RGB indices can be used to estimate narrow-band reflectance indices and/or photosynthetic parameters in plants. Analysis of this problem was the main task of the current work. We investigated relationships of six RGB indices (r, g, b, ExG, VEG, and VARI) to widely used narrow-band reflectance indices (the normalized difference vegetation index, NDVI, and photochemical reflectance index, PRI) and the potential quantum yield of photosystem II (Fv/Fm) in wheat and pea plants under soil drought and salinization. It was shown that investigated RGB indices, NDVI, PRI, and Fv/Fm were significantly changed under the action of both stressors; changes in some RGB indices (e.g., ExG) were initiated on the early stage of action of drought or salinization. Correlation analysis showed that RGB indices (especially, ExG, VARY, and g) were strongly related to the NDVI, PRI, and Fv/Fm; linear regressions between these values were calculated. It means that RGB indices measured by simple and low-cost color cameras can be used to estimate plant parameters (NDVI, PRI, and Fv/Fm) requiring sophisticated equipment to measure. Full article

In the context of global warming, which is mainly due to the increasing atmospheric concentration of carbon dioxide, the prediction of climate change requires a good assessment of the involvement of vegetation in the global carbon cycle. In particular, determining when vegetative activity ceases in deciduous forests remains a great challenge. Remote sensing of solar-induced fluorescence (SIF) has been considered as a potential proxy for ecosystem photosynthesis and, therefore, a relevant indicator of the end of the vegetation period as compared to other vegetation indices, such as EVI (Enhanced Vegetation Index) and NDVI (Normalized Difference Vegetation Index). However, many challenges remain to be addressed due to the lack of knowledge of the response of SIF at different time scales, different species and different environmental conditions. The aim of this study was to explore the diurnal and seasonal variations in the SIFA and SIFB signals in a pubescent oak forest undergoing senescence. We show that apparent SIFA yield may be considered an earlier indicator of the end of the vegetation period compared to NDVI, which primarily reflects the ratio of SIFB/SIFA. The apparent SIFA yield signal was positively and highly correlated with PRI (Photochemical Reflectance Index), EVI and NDVI. Air contents in CO₂ and O₃ were similarly significantly correlated to SIFs emission but only during the growth phase of the phenology of Q. pubescens. At the seasonal scale, the results show that SIF variations were mainly driven by variations in PAR, air VPD and temperature. A higher dependence of the SIF signal on these last three variables was observed at the diurnal scale through Pearson correlation coefficients, which were greater than seasonal ones. Full article

Tea (Camellia sinensis L.) holds agricultural economic value and forestry carbon sequestration potential, with Taiwan’s annual tea production exceeding TWD 7 billion. However, climate change-induced stressors threaten tea plant growth, photosynthesis, yield, and quality, necessitating an accurate real-time monitoring system to enhance plantation management and production stability. This study surveys tea plantations at low, mid-, and high elevations in Nantou County, central Taiwan, collecting data from 21 fields using conventional farming methods (CFMs), which emphasize intensive management, and agroecological farming methods (AFMs), which prioritize environmental sustainability. This study integrates leaf area index (LAI), photochemical reflectance index (PRI), and quantum yield of photosystem II (ΦPSII) data with unmanned aerial vehicles (UAV)-derived visible-light and multispectral imagery to compute color indices (CIs) and multispectral indices (MIs). Using feature ranking methods, an optimized dataset was developed, and the predictive performance of eight regression algorithms was assessed for estimating tea plant physiological parameters. The results indicate that LAI was generally lower in AFMs, suggesting reduced leaf growth density and potential yield differences. However, PRI and ΦPSII values revealed greater environmental adaptability and potential long-term ecological benefits in AFMs compared to CFMs. Among regression models, MIs provided greater stability for tea plant physiological parameters, whereas feature ranking methods had minimal impact on accuracy. XGBoost outperformed all models in predicting parameters, achieving optimal results for (1) LAI: R² = 0.716, RMSE = 1.01, MAE = 0.683, (2) PRI: R² = 0.643, RMSE = 0.013, MAE = 0.009, and (3) ΦPSII: R² = 0.920, RMSE = 0.048, MAE = 0.013. Overall, we highlight the effectiveness of integrating gradient boosting models with multispectral data to capture tea plant physiological characteristics. This study develops generalizable predictive models for tea plant physiological parameter estimation and advances non-contact crop physiological monitoring for tea plantation management, providing a scientific foundation for precision agriculture applications. Full article

This study investigates the influence of different light qualities, including red, green, blue, purple, and white lights, on the growth, physiological activity, and ornamental characteristics of two Coleus cultivars. Emphasizing the importance of leveraging phenotypic plasticity in plants within controlled environments, using light quality is a practice prevalent in the ornamental industry. The research explores the varied responses of two Coleus cultivars to distinct light spectra. The key findings reveal the efficacy of red light in enhancing shoot and leaf parameters in C. ‘Highway Ruby’, while red and green light exhibit comparable effects on shoot width and leaf dimensions in C. ‘Wizard Jade’. White light-emitting diodes (LEDs), particularly with color temperatures of 4100 K and 6500 K, promote root length growth in the respective cultivars. Moreover, chlorophyll content and remote sensing vegetation indices, including chlorophyll content (SPAD units), the normalized difference vegetation index (NDVI), the modified chlorophyll absorption ratio index (MCARI), and the photochemical reflectance index (PRI), along with the chlorophyll fluorescence, were significantly affected by light qualities, with distinct responses observed between the cultivars. In summary, this study highlights the transformative potential of LED technology in optimizing the growth and ornamental quality of foliage plants like Coleus, setting a benchmark for light quality conditions. By leveraging LED technology, producers and nursery growers access enhanced energy efficiency and unparalleled versatility, paving the way for significant advancements in plant growth, color intensity, and two-tone variations. This presents a distinct advantage over conventional production methods, offering a more sustainable and economically viable approach for increased plant reproduction and growth development. Likewise, the specific benefits derived from this study provide invaluable insights, enabling growers to strategically develop ornamental varieties that thrive under optimized light conditions and exhibit heightened visual appeal and market desirability. Full article

Solar-induced chlorophyll fluorescence (SIF) has been widely utilized to track the dynamics of gross primary productivity (GPP). It has been shown that the photochemical reflectance index (PRI), which may be utilized as an indicator of non-photochemical quenching (NPQ), improves SIF-based GPP estimation. However, the influence of weather conditions on GPP estimation using SIF and PRI has not been well explored. In this study, using an open-access dataset, we examined the impact of the clearness index (CI), which is associated with the proportional intensity of solar incident radiation and can represent weather conditions, on soybean GPP estimation using SIF and PRI. The midday PRI (xanthophyll de-epoxidation state) minus the early morning PRI (xanthophyll epoxidation state) yielded the corrected PRI (ΔPRI), which described the amplitude of xanthophyll pigment interconversion during the day. The observed canopy SIF at 760 nm (${\mathrm{S}\mathrm{I}\mathrm{F}}_{\mathrm{T}\mathrm{O}\mathrm{C}\_760}$) was downscaled to the broadband photosystem-level SIF for photosystem II (${\mathrm{S}\mathrm{I}\mathrm{F}}_{\mathrm{T}\mathrm{O}\mathrm{T}\_\mathrm{F}\mathrm{U}\mathrm{L}\mathrm{L}\_\mathrm{P}\mathrm{S}\mathrm{I}\mathrm{I}}$). Our results show that GPP can be accurately estimated using a multi-linear model with ${\mathrm{S}\mathrm{I}\mathrm{F}}_{\mathrm{T}\mathrm{O}\mathrm{T}\_\mathrm{F}\mathrm{U}\mathrm{L}\mathrm{L}\_\mathrm{P}\mathrm{S}\mathrm{I}\mathrm{I}}$ and ΔPRI. The ratio of GPP measured using the eddy covariance (EC) method (${\mathrm{G}\mathrm{P}\mathrm{P}}_{\mathrm{E}\mathrm{C}}$) to GPP estimated using ${\mathrm{S}\mathrm{I}\mathrm{F}}_{\mathrm{T}\mathrm{O}\mathrm{T}\_\mathrm{F}\mathrm{U}\mathrm{L}\mathrm{L}\_\mathrm{P}\mathrm{S}\mathrm{I}\mathrm{I}}$ and ΔPRI exhibited a non-linear correlation with the CI along both the half-hourly (R² = 0.21) and daily scales (R² = 0.25). The GPP estimates using ${\mathrm{S}\mathrm{I}\mathrm{F}}_{\mathrm{T}\mathrm{O}\mathrm{T}\_\mathrm{F}\mathrm{U}\mathrm{L}\mathrm{L}\_\mathrm{P}\mathrm{S}\mathrm{I}\mathrm{I}}$ and ΔPRI were significantly improved by the addition of the CI (for the half-hourly data, R² improved from 0.64 to 0.71 and the RMSE decreased from 8.28 to 7.42 $\mathsf{\mu }$mol•m⁻²•s⁻¹; for the daily data, R² improved from 0.71 to 0.81 and the RMSE decreased from 6.69 to 5.34 $\mathsf{\mu }$mol•m⁻²•s⁻¹). This was confirmed by the validation results. In addition, the GPP estimated using the Random Forest method was also largely improved by considering the influences of the CI. Therefore, our findings demonstrate that GPP can be well estimated using ${\mathrm{S}\mathrm{I}\mathrm{F}}_{\mathrm{T}\mathrm{O}\mathrm{T}\_\mathrm{F}\mathrm{U}\mathrm{L}\mathrm{L}\_\mathrm{P}\mathrm{S}\mathrm{I}\mathrm{I}}$ and ΔPRI, and it can be significantly enhanced by accounting for the CI. These results will be beneficial to vegetation GPP estimation using different remote sensing platforms, especially under various weather conditions. Full article

Larix kaempferi is in high demand in Korea due to its value in timber and afforestation. However, it faces challenges in terms of propagation and the collection of physiological information for seedling production. In particular, moisture supply is crucial in seedling production. Therefore, establishing efficient irrigation regimes based on optical measurements is essential. Optical measurement methods are expected to be non-destructive, rapid, and reduce labor consumption in nursery systems. This study applied optical measurements using vegetation indices (VIs), chlorophyll fluorescence (FL) imaging, and thermal (TH) imaging to explore the efficient irrigation period for one-year-old Larix kaempferi seedlings in greenhouse conditions under drought stress and perform rehydration experiments. It was observed that all the seedlings survived without irrigation until day 4 (D4) (soil moisture content: 5.3%). Upon rehydration on D6, 83.33% of the seedlings survived until D14. According to the optical measurement results, the TH parameters, PRI (photochemical reflectance index), and Fm (maximum fluorescence in a dark-adapted state) showed sensitive stress responses in all drought treatment pots on D6. Among them, thermal imaging was found to have the highest potential for addressing limitations and being utilized in the greenhouse. The results of this study are expected to provide foundational data for the development of smart nursery systems for efficient irrigation in the future. Full article