Search Results (107)

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

This research presents a classification methodology for the detection of new or emerging stress in trees using indices derived from hyperspectral data and tests whether existing hyperspectral indices are effective when used as the classification features for this problem. We tested six existing indices—Water Band Index (WBI), Gitelson–Merzlyak B Index (GMb), Normalized Phaeophytization Index (NPQI), Combined Carotenoid/Chlorophyll Ratio Index (CCRI), Photochemical Reflectance Index (PRI), and Red-Edge Chlorophyll Index (CIre)—along with a seventh Test Index—generated as a composite of PRI and Cire—as classification features. Analysis was conducted using data collected from trees with and without emerald ash borer (EAB) infestation to develop a methodology that could be adapted to measure emerging stress from other pathogens or invasive pests in other tree species. Previous work has focused specifically on the identification of damage or stress symptoms caused by a specific pathogen. In this study, we adapted that work to develop a system of classification that can be applied to the identification of stress symptoms from a range of sources, measurable in trees based on spectral response and, in some cases, detectable prior to the onset of visible symptoms that can be measured through human observation. Our data indicate that existing indices derived from hyperspectral data are effective as classification features when measuring spectral responses indicative of emerging stress in trees. Full article

While crushed pinecone mulch holds promise as a beneficial material for blueberry cultivation, research on its effectiveness remains limited. Crop leaf characteristics can serve as parameters for assessing mulching effects, although there are several limitations, including the need to analyze various distinct characteristics separately. The combination of hyperspectral data and machine learning techniques is expected to enable the selection of only the most important features among these characteristics. In this study, we investigated the impact of various mulching treatments utilizing pine tree byproducts, including crushed pinecones. Mulching variations included non-mulching (NM), crushed pinecones (PCs), a mixture of crushed pinecones and sulfur (PCS), pine needles (PNs), and sulfur treatment (S). Conventional methods were employed to measure leaf growth (length and width) and physiological characteristics (chlorophyll content, chlorophyll fluorescence, and stomatal conductance). Hyperspectral reflectance was also measured, and classification models using Partial Least Squares Discriminant Analysis (PLS-DA) and eXtreme Gradient Boosting (XGBoost) were developed for crop characteristics, vegetation indices (VIs), visible and near-infrared (VNIR), and short-wave infrared (SWIR). The results showed that using crushed pinecones as the sole mulching material for blueberries, without sulfur treatment, had a positive impact on blueberry growth. The PC treatment exhibited a dual effect on plant growth by lowering the soil pH to 5.89 and maintaining soil moisture within the range of 26.33–35.20%. We observed distinct differences in soil inorganic nutrient content, with higher concentrations of organic matter, total nitrogen, and available P₂O₅ and K⁺, which positively influenced blueberry growth. Mulching treatments demonstrated superior physiological characteristics, with two classification models identifying stomatal conductance (gs) as a key parameter influencing treatment classification (VIP scores > 1 rank: 3, variable score rank: 1). The photochemical reflectance index (PRI) emerged as a major parameter among VIs, showing potential for measuring water stress (VIP scores > 1 rank: 2, variable score rank: 1). In the SWIR PLS-DA model, wavelength peaks were mainly observed in the O-H overtone (1410 nm, 1450 nm, 1930 nm, 1940 nm, and 2100 nm). Overall, crushed pinecones were found to positively impact the initial growth of blueberries by enhancing water status (plant respiration). Full article

Photosynthesis is influenced by dynamic energy allocation under various environmental conditions. Solar-induced chlorophyll fluorescence (SIF), an important pathway for dissipating absorbed energy, has been extensively used to evaluate gross primary productivity (GPP). However, the potential for photochemical reflectance index (PRI), as an indicator of non-photochemical quenching (NPQ), to improve the SIF-based GPP estimation, has not been thoroughly investigated. In this study, using continually tower-based observations, we examined how PRI affected the link between SIF and GPP for corn and soybean at half-hourly and daily timescales. The relationship of GPP to SIF and PRI is impacted by stress indicated by vapor pressure deficit (VPD) and crop water stress index (CWSI). Moreover, the ratio of GPP to SIF of corn was more sensitive to PRI compared to soybean. Whether in Pearson or Partial correlation analysis, the relationships of PRI to the ratio of GPP to SIF were almost all significant, regardless of controlling structural-physiological (stomatal conductance, vegetation indices) and environmental variables (light intensity, etc.). Therefore, PRI significantly affects the SIF–GPP relationship for corn (r > 0.31, p < 0.01) and soybean (r > 0.22, p < 0.05). After combining SIF and PRI using the multi-variable linear model, the GPP estimation has been largely improved (the coefficient of determination, abbreviated as R², increased from 0.48 to 0.49 to 0.78 to 0.84 and the Root Mean Square Error, abbreviated as RMSE, decreased from 6.38 to 10.22 to 3.56 to 6.60 $\mathsf{\mu }\mathrm{m}\mathrm{o}\mathrm{l} {\mathrm{C}\mathrm{O}}_2·{\mathrm{m}}^{-2}·{\mathrm{s}}^{-1}$ for corn, R² increased from 0.54 to 0.62 to 0.78 to 0.82 and RMSE decreased from 6.25 to 9.59 to 4.34 to 6.60 $\mathsf{\mu }\mathrm{m}\mathrm{o}\mathrm{l} {\mathrm{C}\mathrm{O}}_2·{\mathrm{m}}^{-2}·{\mathrm{s}}^{-1}$ for soybean). It suggests that better GPP estimations for corn and soybean can be obtained when SIF is combined with PRI. Full article

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