Journal articles on the topic 'Plant reflectance spectra'
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1
Steddom, K., G. Heidel, D. Jones, and C. M. Rush. "Remote Detection of Rhizomania in Sugar Beets." Phytopathology® 93, no. 6 (June 2003): 720–26. http://dx.doi.org/10.1094/phyto.2003.93.6.720.
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As a prelude to remote sensing of rhizomania, hyper-spectral leaf reflectance and multi-spectral canopy reflectance were used to study the physiological differences between healthy sugar beets and beets infested with Beet necrotic yellow vein virus. This study was conducted over time in the presence of declining nitrogen levels. Total leaf nitrogen was significantly lower in symptomatic beets than in healthy beets. Chlorophyll and carotenoid levels were reduced in symptomatic beets. Vegetative indices calculated from leaf spectra showed reductions in chlorophyll and carotenoids in symptomatic beets. Betacyanin levels estimated from leaf spectra were decreased at the end of the 2000 season and not in 2001. The ratio of betacyanins to chlorophyll, estimated from canopy spectra, was increased in symptomatic beets at four of seven sampling dates. Differences in betacyanin and carotenoid levels appeared to be related to disease and not nitrogen content. Vegetative indices calculated from multi-spectral canopy spectra supported results from leaf spectra. Logistic regression models that incorporate vegetative indices and reflectance correctly predicted 88.8% of the observations from leaf spectra and 87.9% of the observations for canopy reflectance into healthy or symptomatic classes. Classification was best in August with a gradual decrease in accuracy until harvest. These results indicate that remote sensing technologies can facilitate detection of rhizomania.
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Schweiger, Anna K., Jeannine Cavender-Bares, Shan Kothari, Philip A. Townsend, Michael D. Madritch, Jake J. Grossman, Hamed Gholizadeh, Ran Wang, and John A. Gamon. "Coupling spectral and resource-use complementarity in experimental grassland and forest communities." Proceedings of the Royal Society B: Biological Sciences 288, no. 1958 (September 2021): 20211290. http://dx.doi.org/10.1098/rspb.2021.1290.
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Reflectance spectra provide integrative measures of plant phenotypes by capturing chemical, morphological, anatomical and architectural trait information. Here, we investigate the linkages between plant spectral variation, and spectral and resource-use complementarity that contribute to ecosystem productivity. In both a forest and prairie grassland diversity experiment, we delineated n -dimensional hypervolumes using wavelength bands of reflectance spectra to test the association between the spectral space occupied by individual plants and their growth, as well as between the spectral space occupied by plant communities and ecosystem productivity. We show that the spectral space occupied by individuals increased with their growth, and the spectral space occupied by plant communities increased with ecosystem productivity. Furthermore, ecosystem productivity was better explained by inter-individual spectral complementarity than by the large spectral space occupied by productive individuals. Our results indicate that spectral hypervolumes of plants can reflect ecological strategies that shape community composition and ecosystem function, and that spectral complementarity can reveal resource-use complementarity.
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Kvíčala, Miroslav, Eva Lacková, and Michaela Štamborská. "Internal Reflectance Modelling ofHordeum vulgareLeaves During Drying." Journal of Chemistry 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/210679.
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Spectral reflectance, or indexes that characterize spectral reflectance at concrete wavelength, is commonly used as an indicator of plant stress, or its photosynthetic apparatus status. In this paper, new leaf optical model is presented. Within this paper, experimental determination of surface and internal reflectance of Spring barley leaves and mathematical-physical modelling of internal reflectance were performed. It was proven that a new proposed theoretical model and the experimental spectra of internal reflectance are strongly correlated. It can be concluded that the total reflectance is not a function of epidermis condition, but it testifies about overall functional condition of Spring barley leaves.
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Merrick, Trina, Ralf Bennartz, Maria Luisa S. P. Jorge, Stephanie Pau, and John Rausch. "Evaluation of Plant Stress Monitoring Capabilities Using a Portable Spectrometer and Blue-Red Grow Light." Sensors 22, no. 9 (April 29, 2022): 3411. http://dx.doi.org/10.3390/s22093411.
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Remote sensing offers a non-destructive method to detect plant physiological response to the environment by measuring chlorophyll fluorescence (CF). Most methods to estimate CF require relatively complex retrieval, spectral fitting, or modelling methods. An investigation was undertaken to evaluate measurements of CF using a relatively straightforward technique to detect and monitor plant stress with a spectroradiometer and blue-red light emitting diode (LED). CF spectral response of tomato plants treated with a photosystem inhibitor were assessed and compared to traditional reflectance-based indices: normalized difference vegetation index (NDVI) and photochemical reflectance index (PRI). The blue-red LEDs provided input irradiance and a “window” in the CF emission range of plants (~650 to 850 nm) sufficient to capture distinctive “two-peak” spectra and to distinguish plant health from day to day of the experiment, while within day differences were noisy. CF-based metrics calculated from CF spectra clearly captured signs of vegetation stress earlier than reflectance-based indices and by visual inspection. This CF monitoring technique is a flexible and scalable option for collecting plant function data, especially for indicating early signs of stress. The technique can be applied to a single plant or larger canopies using LED in dark conditions by an individual, or a manned or unmanned vehicle for agricultural or military purposes.
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Zhu, Yan, Yingxue Li, Wei Feng, Yongchao Tian, Xia Yao, and Weixing Cao. "Monitoring leaf nitrogen in wheat using canopy reflectance spectra." Canadian Journal of Plant Science 86, no. 4 (October 10, 2006): 1037–46. http://dx.doi.org/10.4141/p05-157.
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Non-destructive monitoring of leaf nitrogen (N) status can assist in growth diagnosis, N management and productivity forecast in field crops. The objectives of this study were to determine the relationships of leaf nitrogen concentration on a leaf dry weight basis (LNC) and leaf nitrogen accumulation per unit soil area (LNA) to ground-based canopy reflectance spectra, and to derive regression equations for monitoring N nutrition status in wheat (Triticum aestivum L.). Four field experiments were conducted with different N application rates and wheat cultivars across four growing seasons, and time-course measurements were taken on canopy spectral reflectance, LNC and leaf dry weights under the various treatments. In these studies, LNC and LNA in wheat increased with increasing N fertilization rates. The canopy reflectance differed significantly under varied N rates, and the pattern of response was consistent across the different cultivars and years. Overall, an integrated regression equation of LNC to normalized difference index (NDI) of 1220 and 710 nm of canopy reflectance spectra described the dynamic pattern of change in LNC in wheat. The ratios of several near infrared (NIR) bands to visible light were linearly related to LNA, with the ratio index (RI) of the average reflectance over 760, 810, 870, 950 and 1100 nm to 660 nm having the best index for quantitative estimation of LNA in wheat. When independent data were fit to the derived equations, the average root mean square error (RMSE) values for the predicted LNC and LNA relative to the observed values were no more than 15.1 and 15.2%, respectively, indicating a good fit. Our relationships of leaf N status to spectral indices of canopy reflectance can be potentially used for non-destructive and real-time monitoring of leaf N status in wheat. Key words: Wheat, leaf nitrogen concentration, leaf nitrogen accumulation, canopy reflectance, spectral index, nitrogen monitoring
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Stone, Christine, Laurie A. Chisholm, and Simon McDonald. "Spectral reflectance characteristics of Pinus radiata needles affected by dothistroma needle blight." Canadian Journal of Botany 81, no. 6 (June 1, 2003): 560–69. http://dx.doi.org/10.1139/b03-053.
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Dothistroma needle blight, caused by Dothistroma septosporum (Dorog) Morelet, is an economically significant disease of several Pinus species in New Zealand, Australia, Chile, South Africa, and parts of Asia, North America, and Europe. The spectral reflectance properties of Pinus radiata D. Don needles infected by D. septosporum were examined over the visible and near-infrared wavelength region (4001000 nm). The largest reflectance difference occurred on the shoulder of the near-infrared region at 763 nm. Wavelengths of greatest sensitivity to D. septosporum infection were located in the ranges of 675691 nm, followed by wavelengths near 760 and 550 nm. Sensitivity minima occurred at 434, 493, 506, 709, and 1373 nm. The reflectance ratio best correlated to needle damage was 709/691 nm (r = 0.739, P < 0.001). Among the other reflectance indices tested, an index based on wavelengths of the upper red edge (710740 nm) was also well correlated (r = 0.730, P < 0.001). There was not a strong linear relationship between the point of maximum slope in the red edge region (red edge position) and needle damage category. This may be because D. septosporum is a necrotrophic fungal pathogen producing a complex series of damage symptoms: initial chlorosis, production of red and brown metabolites, rapid loss of cellular integrity, cell necrosis, and eventual desiccation. Diagnostic features identified in the infected needle spectra may contribute to the formulation of remotely sensed spectral indices for detecting and monitoring dothistroma needle blight in plantations.Key words: Dothistroma, Pinus radiata, needle damage, reflectance spectra, remote sensing.
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Basinger, Nicholas T., Katherine M. Jennings, Erin L. Hestir, David W. Monks, David L. Jordan, and Wesley J. Everman. "Phenology affects differentiation of crop and weed species using hyperspectral remote sensing." Weed Technology 34, no. 6 (August 18, 2020): 897–908. http://dx.doi.org/10.1017/wet.2020.92.
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AbstractThe effect of plant phenology and canopy structure of four crops and four weed species on reflectance spectra were evaluated in 2016 and 2017 using in situ spectroscopy. Leaf-level and canopy-level reflectance were collected at multiple phenologic time points in each growing season. Reflectance values at 2 wk after planting (WAP) in both years indicated strong spectral differences between species across the visible (VIS; 350–700 nm), near-infrared (NIR; 701–1,300 nm), shortwave-infrared I (SWIR1; 1,301–1,900 nm), and shortwave-infrared II (SWIR2; 1,901–2,500 nm) regions. Results from this study indicate that plant spectral reflectance changes with plant phenology and is influenced by plant biophysical characteristics. Canopy-level differences were detected in both years across all dates except for 1 WAP in 2017. Species with similar canopy types (e.g., broadleaf prostrate, broadleaf erect, or grass/sedge) were more readily discriminated from species with different canopy types. Asynchronous phenology between species also resulted in spectral differences between species. SWIR1 and SWIR2 wavelengths are often not included in multispectral sensors but should be considered for species differentiation. Results from this research indicate that wavelengths in SWIR1 and SWIR2 in conjunction with VIS and NIR reflectance can provide differentiation across plant phenologies and, therefore should be considered for use in future sensor technologies for species differentiation.
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Solovchenko, Alexei, Alexei Dorokhov, Boris Shurygin, Alexandr Nikolenko, Vitaly Velichko, Igor Smirnov, Dmitriy Khort, Aleksandr Aksenov, and Andrey Kuzin. "Linking Tissue Damage to Hyperspectral Reflectance for Non-Invasive Monitoring of Apple Fruit in Orchards." Plants 10, no. 2 (February 5, 2021): 310. http://dx.doi.org/10.3390/plants10020310.
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Reflected light carries ample information about the biochemical composition, tissue architecture, and physiological condition of plants. Recent technical progress has paved the way for affordable imaging hyperspectrometers (IH) providing spatially resolved spectral information on plants on different levels, from individual plant organs to communities. The extraction of sensible information from hyperspectral images is difficult due to inherent complexity of plant tissue and canopy optics, especially when recorded under ambient sunlight. We report on the changes in hyperspectral reflectance accompanying the accumulation of anthocyanins in healthy apple (cultivars Ligol, Gala, Golden Delicious) fruits as well as in fruits affected by pigment breakdown during sunscald development and phytopathogen attacks. The measurements made outdoors with a snapshot IH were compared with traditional “point-type” reflectance measured with a spectrophotometer under controlled illumination conditions. The spectra captured by the IH were suitable for processing using the approaches previously developed for “point-type” apple fruit and leaf reflectance spectra. The validity of this approach was tested by constructing a novel index mBRI (modified browning reflectance index) for detection of tissue damages on the background of the anthocyanin absorption. The index was suggested in the form of mBRI = (R640−1 + R800−1) − R678−1. Difficulties of the interpretation of fruit hyperspectral reflectance images recorded in situ are discussed with possible implications for plant physiology and precision horticulture practices.
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Li, Ying, Brian K. Via, Yaoxiang Li, and Guozhong Wang. "Determination of Geographical Origin and Tree Species Using Vis-NIR and Chemometric Methods." Forest Products Journal 72, no. 3 (May 1, 2022): 147–54. http://dx.doi.org/10.13073/fpj-d-22-00011.
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Abstract The variation of wood properties between different geographical origin and tree species has an important influence on end use applications. This study aimed to investigate the feasibility of wood origin and species classification based on visible and near infrared spectroscopy and chemometric methods. The influence of geographical origin on tree species identification also was analyzed. A total of 530 samples with 2 origins and 5 tree species were collected for analysis. The raw reflectance spectra were preprocessed by spectral transformation technique, and nonlinear discrimination models were built by support vector machine (SVM) using various spectral forms. Three algorithms—grid search (GS), genetic algorithm (GA), and particle swarm optimization (PSO)—were applied to optimize the parameters of SVM models, respectively. Regardless of spectral forms and optimization techniques, the prediction accuracy was lower than that of the calibration set for wood origin and tree species identification. Except for reflectance spectra, prediction accuracy of 100 percent was obtained based on SVM in combination with three algorithms for origin discrimination. However, SVM in combination with reflectance spectra and GS technique achieved the best prediction accuracy (93.18%) for tree species identification. These results demonstrated that visible and near infrared spectroscopy combined with chemometric techniques can be used for geographical origin and tree species determination.
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Pandey, Jitendra Kumar, and R. Gopal. "Laser-induced chlorophyll fluorescence and reflectance spectroscopy of cadmium treatedTriticum aestivumL. plants." Spectroscopy 26, no. 2 (2011): 129–39. http://dx.doi.org/10.1155/2011/640232.
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The present study deals with laser-induced chlorophyll fluorescence (LICF) spectra, reflectance spectra and fluorescence induction kinetics (FIK) curves ofTriticum aestivumL. plants treated with different concentrations of cadmium (0.01, 0.1 and 1.0 mM). LICF spectra were recorded in the region of 650–780 nm using violet diode laser (405 nm) and FIK curves were recorded at 685 and 730 nm using red diode laser (635 nm) for excitation. Reflectance spectra were recorded in the region of 400–800 nm using spectrophotometer with an integrating sphere. The fluorescence intensity ratios (FIR) were determined from LICF spectra, vitality index (Rfd) from FIK curves and narrow band vegetation index (NBVI) from reflectance spectra. These parameters along with plant growth parameters and photosynthetic pigment contents were used to analyze the effect of cadmium on wheat plants. The results clearly show that lower concentration of Cd (0.01 mM) shows stimulatory response; whereas higher concentrations (0.1 and 1.0 Mm) are hazardous for plant growth, photosynthetic pigments and photosynthetic activity of wheat plants.
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Pinit, Sompop, Natthanan Ruengchaijatuporn, Sira Sriswasdi, Teerapong Buaboocha, Supachitra Chadchawan, and Juthamas Chaiwanon. "Hyperspectral and genome-wide association analyses of leaf phosphorus status in local Thai indica rice." PLOS ONE 17, no. 4 (April 20, 2022): e0267304. http://dx.doi.org/10.1371/journal.pone.0267304.
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Phosphorus (P) is an essential mineral nutrient and one of the key factors determining crop productivity. P-deficient plants exhibit visual leaf symptoms, including chlorosis, and alter spectral reflectance properties. In this study, we evaluated leaf inorganic phosphate (Pi) contents, plant growth and reflectance spectra (420–790 nm) of 172 Thai rice landrace varieties grown hydroponically under three different P supplies (overly sufficient, mildly deficient and severely deficient conditions). We reported correlations between Pi contents and reflectance ratios computed from two wavebands in the range of near infrared (720–790 nm) and visible energy (green-yellow and red edge) (r > 0.69) in Pi-deficient leaves. Artificial neural network models were also developed which could classify P deficiency levels with 85.60% accuracy and predict Pi content with R2 of 0.53, as well as highlight important waveband sections. Using 217 reflectance ratio indices to perform genome-wide association study (GWAS) with 113,114 SNPs, we identified 11 loci associated with the spectral reflectance traits, some of which were also associated with the leaf Pi content trait. Hyperspectral measurement offers a promising non-destructive approach to predict plant P status and screen large germplasm for varieties with high P use efficiency.
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Martinez, Nicole, Julia Sharp, Thomas Johnson, Wendy Kuhne, Clay Stafford, and Martine Duff. "Reflectance-Based Vegetation Index Assessment of Four Plant Species Exposed to Lithium Chloride." Sensors 18, no. 9 (August 21, 2018): 2750. http://dx.doi.org/10.3390/s18092750.
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This study considers whether a relationship exists between response to lithium (Li) exposure and select vegetation indices (VI) determined from reflectance spectra in each of four plant species: Arabidopsis thaliana, Helianthus annuus (sunflower), Brassica napus (rape), and Zea mays (corn). Reflectance spectra were collected every week for three weeks using an ASD FieldSpec Pro spectroradiometer with both a contact probe (CP) and a field of view probe (FOV) for plants treated twice weekly in a laboratory setting with 0 mM (control) or 15 mM of lithium chloride (LiCl) solution. Plants were harvested each week after spectra collection for determination of relevant physical endpoints such as relative water content and chlorophyll content. Mixed effects analyses were conducted on selected endpoints and vegetation indices (VI) to determine the significance of the effects of treatment level and length of treatment as well as to determine which VI would be appropriate predictors of treatment-dependent endpoints. Of the species considered, A. thaliana exhibited the most significant effects and corresponding shifts in reflectance spectra. Depending on the species and endpoint, the most relevant VIs in this study were NDVI, PSND, YI, R1676/R1933, R750/R550, and R950/R750.
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Chowdhury, Milon, Viet-Duc Ngo, Md Nafiul Islam, Mohammod Ali, Sumaiya Islam, Kamal Rasool, Sang-Un Park, and Sun-Ok Chung. "Estimation of Glucosinolates and Anthocyanins in Kale Leaves Grown in a Plant Factory Using Spectral Reflectance." Horticulturae 7, no. 3 (March 21, 2021): 56. http://dx.doi.org/10.3390/horticulturae7030056.
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The spectral reflectance technique for the quantification of the functional components was applied in different studies for different crops, but related research on kale leaves is limited. This study was conducted to estimate the glucosinolate and anthocyanin components of kale leaves cultivated in a plant factory based on diffuse reflectance spectroscopy through regression methods. Kale was grown in a plant factory under different treatments. After specific periods of transplantation, leaf samples were collected, and reflectance spectra were measured immediately from nine different points on each leaf. The same leaf samples were freeze-dried and stored for analysis of the functional components. Regression procedures, such as principal component regression (PCR), partial least squares regression (PLSR), and stepwise multiple linear regression (SMLR), were applied to relate the functional components with the spectral data. In the laboratory analysis, progoitrin and glucobrassicin, as well as cyanidin and malvidin, were found to be dominating components in glucosinolates and anthocyanins, respectively. From the overall analysis, the SMLR model showed better performance, and the identified wavelengths for estimating the glucosinolates and anthocyanins were in the early near-infrared (NIR) region. Specifically, reflectance at 742, 761, 787, 796, 805, 833, 855, 932, 947, and 1000 nm showed a strong correlation.
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Zhao, Yibo, Shaogang Lei, Xingchen Yang, Chuangang Gong, Cangjiao Wang, Wei Cheng, Heng Li, and Changchao She. "Study on Spectral Response and Estimation of Grassland Plants Dust Retention Based on Hyperspectral Data." Remote Sensing 12, no. 12 (June 24, 2020): 2019. http://dx.doi.org/10.3390/rs12122019.
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Accurate monitoring of plant dust retention can provide a basis for dust pollution control and environmental protection. The aims of this study were to analyze the spectral response features of grassland plants to mining dust and to predict the spatial distribution of dust retention using hyperspectral data. The dust retention content was determined by an electronic analytical balance and a leaf area meter. The leaf reflectance spectrum was measured by a handheld hyperspectral camera, and the airborne hyperspectral data were obtained using an imaging spectrometer. We analyzed the difference between the leaf spectral before and after dust removal. The sensitive spectra of dust retention on the leaf- and the canopy-scale were determined through two-dimensional correlation spectroscopy (2DCOS). The competitive adaptive reweighted sampling (CARS) algorithm was applied to select the feature bands of canopy dust retention. The estimation model of canopy dust retention was built through random forest regression (RFR), and the dust distribution map was obtained based on the airborne hyperspectral image. The results showed that dust retention enhanced the spectral reflectance of leaves in the visible wavelength but weakened the reflectance in the near-infrared wavelength. Caused by the canopy structure and multiple scattering, a slight difference in the sensitive spectra on dust retention existed between the canopy and leaves. Similarly, the sensitive spectra of leaves and the canopy were closely related to dust and plant physiological parameters. The estimation model constructed through 2DCOS-CARS-RFR showed higher precision, compared with genetic algorithm-random forest regression (GA-RFR) and simulated annealing algorithm-random forest regression (SAA-RFR). Spatially, the amount of canopy dust increased and then decreased with increasing distance from the mining area, reaching a maximum within 300–500 m. This study not only demonstrated the importance of extracting feature bands based on the response of plant physical and chemical parameters to dust, but also laid a foundation for the rapid and non-destructive monitoring of grassland plant dust retention.
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Eredics, Attila, Zsolt István Németh, Rita Rákosa, Ervin Rasztovits, Norbert Móricz, and Péter Vig. "The Effect of Soil Moisture on the Reflectance Spectra Correlations in Beech and Sessile Oak Foliage/ Talajnedvesség hatása bükk és kocsánytalan tölgy lombozat reflexiós spektrumainak korrelációira." Acta Silvatica et Lignaria Hungarica 11, no. 1 (June 1, 2015): 9–26. http://dx.doi.org/10.1515/aslh-2015-0001.
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Abstract Reflectance intensities of foliage are mostly due to biomaterials synthesised by plants. Adaptation to the continuously changing environment requires the regulated alteration of metabolic processes, which also influences the UV-VIS (Ultraviolet-Visible) and IR (Infra Red) spectra of leaves. For the calculation of various Vegetation Indices (VIs), e.g. NDVI (Normalized Difference Vegetation Index), the common practice is to use the reflectance spectrum of the whole foliage and when individual leaves of the same plant are sampled, an average VI is derived. On the contrary, our method exploits the small differences between individual leaves of the same plant, making use of the similar distributions of measured reflectance values. Using particular wavelength pairs, linear regressions of reflectance intensities have been investigated. The parameters of these regressions (slope and intercept) have been compared to the temporal variations of the environmental factors, such as temperature, vapour pressure deficit and soil moisture. By assessing the sensitivity of the regression coefficient (slope) to the changing environment, wavelength pairs can be selected whose sensitivity change reflects the effect of soil moisture deficit on the plant. Based on the state-dependent correlations of the reflectance spectra of plant foliage, a new concept is presented that is capable of indicating the level of environmental stress, e.g. drought stress.
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Barradas, Ana, Pedro M. P. Correia, Sara Silva, Pedro Mariano, Margarida Calejo Pires, Ana Rita Matos, Anabela Bernardes da Silva, and Jorge Marques da Silva. "Comparing Machine Learning Methods for Classifying Plant Drought Stress from Leaf Reflectance Spectra in Arabidopsis thaliana." Applied Sciences 11, no. 14 (July 11, 2021): 6392. http://dx.doi.org/10.3390/app11146392.
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Plant breeders and plant physiologists are deeply committed to high throughput plant phenotyping for drought tolerance. A combination of artificial intelligence with reflectance spectroscopy was tested, as a non-invasive method, for the automatic classification of plant drought stress. Arabidopsis thaliana plants (ecotype Col-0) were subjected to different levels of slowly imposed dehydration (S0, control; S1, moderate stress; S2, severe stress). The reflectance spectra of fully expanded leaves were recorded with an Ocean Optics USB4000 spectrometer and the soil water content (SWC, %) of each pot was determined. The entire data set of the reflectance spectra (intensity vs. wavelength) was given to different machine learning (ML) algorithms, namely decision trees, random forests and extreme gradient boosting. The performance of different methods in classifying the plants in one of the three drought stress classes (S0, S1 and S2) was measured and compared. All algorithms produced very high evaluation scores (F1 > 90%) and agree on the features with the highest discriminative power (reflectance at ~670 nm). Random forests was the best performing method and the most robust to random sampling of training data, with an average F1-score of 0.96 ± 0.05. This classification method is a promising tool to detect plant physiological responses to drought using high-throughput pipelines.
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Huemmrich, K. Fred, Petya Campbell, Sergio A. Vargas Z, Sarah Sackett, Steven Unger, Jeremy May, Craig Tweedie, and Elizabeth Middleton. "Leaf-level chlorophyll fluorescence and reflectance spectra of high latitude plants." Environmental Research Communications 4, no. 3 (March 1, 2022): 035001. http://dx.doi.org/10.1088/2515-7620/ac5365.
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Abstract Little is known about the chlorophyll fluorescence spectra for high latitude plants. A FluoWat leaf clip was used to measure leaf-level reflectance and chlorophyll fluorescence spectra of leaves of common high latitude plants to examine general spectral characteristics of these species. Fluorescence yield (Fyield) was calculated as the ratio of the emitted fluorescence divided by the absorbed radiation for the wavelengths from 400 nm up to the wavelength of the cut-off for the FluoWat low pass filter (either 650 or 700 nm). The Fyield spectra grouped into distinctly different patterns among different plant functional types. Black spruce (Picea mariana) Fyield spectra had little red fluorescence, which was reabsorbed in the shoot, but displayed a distinct far-red peak. Quaking aspen (Populus tremuloides) had both high red and far-red Fyield peaks, as did sweet coltsfoot (Petasites frigidus). Cotton grass (Eriophorum spp.) had both red and far-red Fyield peaks, but these peaks were much lower than for aspen or coltsfoot. Sphagnum moss (Sphagnum spp.) had a distinct Fyield red peak but low far-red fluorescence. Reindeer moss lichen (Cladonia rangiferina) had very low fluorescence levels, although when damp displayed a small red Fyield peak. These high latitude vegetation samples showed wide variations in Fyield spectral shapes. The Fyield values for the individual red or far-red peaks were poorly correlated to chlorophyll content, however the ratio of far-red to red Fyield showed a strong correlation with chlorophyll content. The spectral variability of these plants may provide information for remote sensing of vegetation type but may also confound attempts to measure high latitude vegetation biophysical characteristics and function using solar induced fluorescence (SIF).
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Lin, Yinghao, Qingjiu Tian, Baojun Qiao, Yu Wu, Xianyu Zuo, Yi Xie, and Yang Lian. "A Synthetic Angle Normalization Model of Vegetation Canopy Reflectance for Geostationary Satellite Remote Sensing Data." Agriculture 12, no. 10 (October 10, 2022): 1658. http://dx.doi.org/10.3390/agriculture12101658.
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High-frequency imaging characteristics allow a geostationary satellite (GSS) to capture the diurnal variation in vegetation canopy reflectance spectra, which is of very important practical significance for monitoring vegetation via remote sensing (RS). However, the observation angle and solar angle of high-frequency GSS RS data usually differ, and the differences in bidirectional reflectance from the reflectance spectra of the vegetation canopy are significant, which makes it necessary to normalize angles for GSS RS data. The BRDF (Bidirectional Reflectance Distribution Function) prototype library is effective for the angle normalization of RS data. However, its spatiotemporal applicability and error propagation are currently unclear. To resolve this problem, we herein propose a synthetic angle normalization model (SANM) for RS vegetation canopy reflectance; this model exploits the GSS imaging characteristics, whereby each pixel has a fixed observation angle. The established model references a topographic correction method for vegetation canopies based on path-length correction, solar zenith angle normalization, and the Minnaert model. It also considers the characteristics of diurnal variations in vegetation canopy reflectance spectra by setting the time window. Experiments were carried out on the eight Geostationary Ocean Color Imager (GOCI) images obtained on 22 April 2015 to validate the performance of the proposed SANM. The results show that SANM significantly improves the phase-to-phase correlation of the GOCI band reflectance in the morning time window and retains the instability of vegetation canopy spectra in the noon time window. The SANM provides a preliminary solution for normalizing the angles for the GSS RS data and makes the quantitative comparison of spatiotemporal RS data possible.
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Graeff, Simone, Johanna Link, and Wilhelm Claupein. "Identification of powdery mildew (Erysiphe graminis sp. tritici) and take-all disease (Gaeumannomyces graminis sp. tritici) in wheat (Triticum aestivum L.) by means of leaf reflectance measurements." Open Life Sciences 1, no. 2 (June 1, 2006): 275–88. http://dx.doi.org/10.2478/s11535-006-0020-8.
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AbstractThe ability to identify diseases in an early infection stage and to accurately quantify the severity of infection is crucial in plant disease assessment and management. A greenhouse study was conducted to assess changes in leaf spectral reflectance of wheat plants during infection by powdery mildew and take-all disease to evaluate leaf reflectance measurements as a tool to identify and quantify disease severity and to discriminate between different diseases. Wheat plants were inoculated under controlled conditions in different intensities either with powdery mildew or take-all. Leaf reflectance was measured with a digital imager (Leica S1 Pro, Leica, Germany) under controlled light conditions in various wavelength ranges covering the visible and the near-infrared spectra (380–1300 nm). Leaf scans were evaluated by means of L*a*b*-color system. Visual estimates of disease severity were made for each of the epidemics daily from the onset of visible symptoms to maximum disease severity. Reflectance within the ranges of 490780 nm (r2 = 0.69), 510780nm (r2 = 0.74), 5161300nm (r2 = 0.62) and 5401300 nm (r2 = 0.60) exhibited the strongest relationship with infection levels of both powdery mildew and take-all disease. Among the evaluated spectra the range of 490780nm showed most sensitive response to damage caused by powdery mildew and take-all infestation. The results of this study indicated that disease detection and discrimination by means of reflectance measurements may be realized by the use of specific wavelength ranges. Further studies have to be carried out, to discriminate powdery mildew and take-all infection from other plant stress factors in order to develop suitable decision support systems for site-specific fungicide application.
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Hlavka, Christine A., David L. Peterson, Lee F. Johnson, and Barry Ganapol. "Analysis of Forest Foliage Spectra Using a Multivariate Mixture Model." Journal of Near Infrared Spectroscopy 5, no. 3 (June 1997): 167–73. http://dx.doi.org/10.1255/jnirs.110.
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Wet chemical measurements and near infrared spectra of dry ground leaf samples were analysed to test a multivariate regression technique for estimating component spectra. The technique is based on a linear mixture model for log(1/ R) pseudoabsorbance derived from diffuse reflectance measurements. The resulting unmixed spectra for carbohydrates, lignin and protein resemble the spectra of extracted plant carbohydrates, lignin and protein. The unmixed protein spectrum has prominent absorption peaks at wavelengths that have been associated with nitrogen bonds. It therefore appears feasible to incorporate the linear mixture model in whole leaf models of photon absorption and scattering so that effects of varying nitrogen and carbon concentration on leaf reflectance may be simulated.
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Li, Fenling, Li Wang, Jing Liu, Yuna Wang, and Qingrui Chang. "Evaluation of Leaf N Concentration in Winter Wheat Based on Discrete Wavelet Transform Analysis." Remote Sensing 11, no. 11 (June 3, 2019): 1331. http://dx.doi.org/10.3390/rs11111331.
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Leaf nitrogen concentration (LNC) is an important indicator for accurate diagnosis and quantitative evaluation of plant growth status. The objective was to apply a discrete wavelet transform (DWT) analysis in winter wheat for the estimation of LNC based on visible and near-infrared (400–1350 nm) canopy reflectance spectra. In this paper, in situ LNC data and ground-based hyperspectral canopy reflectance was measured over three years at different sites during the tillering, jointing, booting and filling stages of winter wheat. The DWT analysis was conducted on canopy original spectrum, log-transformed spectrum, first derivative spectrum and continuum removal spectrum, respectively, to obtain approximation coefficients, detail coefficients and energy values to characterize canopy spectra. The quantitative relationships between LNC and characteristic parameters were investigated and compared with models established by sensitive band reflectance and typical spectral indices. The results showed combining log-transformed spectrum and a sym8 wavelet function with partial least squares regression (PLS) based on the approximation coefficients at decomposition level 4 most accurately predicted LNC. This approach could explain 11% more variability in LNC than the best spectral index mSR705 alone, and was more stable in estimating LNC than models based on random forest regression (RF). The results indicated that narrowband reflectance spectroscopy (450–1350 nm) combined with DWT analysis and PLS regression was a promising method for rapid and nondestructive estimation of LNC for winter wheat across a range in growth stages.
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Maqbool, R., D. C. Percival, M. S. Adl, Q. U. Zaman, and D. Buszard. "In situ estimation of foliar nitrogen in wild blueberry using reflectance spectra." Canadian Journal of Plant Science 92, no. 6 (November 2012): 1155–61. http://dx.doi.org/10.4141/cjps2011-203.
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Maqbool, R., Percival, D. C., Adl, M. S., Zaman, Q. U. and Buszard, D. 2012. In situ estimation of foliar nitrogen in wild blueberry using reflectance spectra. Can. J. Plant Sci. 92: 1155–1161. Remote sensing techniques have the potential to serve as an important nutrient management tool in wild blueberry. The potential of visible (VIS), near infrared (NIR) and shortwave infrared (SWIR) spectroscopy was evaluated during 2006 (sprout/vegetative phase of production) to estimate foliar nitrogen (N). Canopy reflectance measurements were taken from two nutrient management experimental sites located in Nova Scotia (NS) and New Brunswick (NB). Partial least squares regression (PLSR) estimated foliar N, giving the coefficients of determination (R 2) values ranging from 0.69 to 0.85, and root mean square errors of cross validation (RMSECV) from 0.16% (±8.29% of mean) to 0.24% (±12.43% of mean) for different spectral ranges used in this study. The green peak region located in the VIS region best estimated foliar N. The tested spectral ranges differed in their predictive ability, but generally followed the biochemical basis. Variable importance in projection scores (VIP), regression vector coefficients and PLSR loading weights (LWs) plots highlight the importance of wavebands (∼550 nm, ∼610 nm, 1510 nm, ∼1690 nm, ∼1730 nm, ∼1980 nm and ∼2030 nm) for in situ foliar N estimations. Thus, it was concluded that reflectance spectra may be used to estimate and ultimately map foliar N in wild blueberry production. The results illustrated the ability of multivariate techniques, such as PLSR to explore hyperspectral data and estimate leaf tissue nutrient content.
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Weksler, Shahar, Offer Rozenstein, Nadav Haish, Menachem Moshelion, Rony Walach, and Eyal Ben-Dor. "A Hyperspectral-Physiological Phenomics System: Measuring Diurnal Transpiration Rates and Diurnal Reflectance." Remote Sensing 12, no. 9 (May 8, 2020): 1493. http://dx.doi.org/10.3390/rs12091493.
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A novel hyperspectral-physiological system that monitors plants dynamic response to abiotic alterations was developed. The system is a sensor-to-plant platform which can determine the optimal time of day during which physiological traits can be successfully identified via spectral means. The directly measured traits include momentary and daily transpiration rates throughout the daytime and daily and periodical plant weight loss and gain. The system monitored and evaluated pepper plants response to varying levels of potassium fertilization. Significant momentary transpiration rates differences were found between the treatments during 07:00–10:00 and 14:00–17:00. The simultaneous frequently measured high-resolution spectral data provided the means to correlate the two measured data sets. Significant correlation coefficients between the spectra and momentary transpiration rates resulted with a selection of three bands (ρ523, ρ697 and ρ818nm) that were used to capture transpiration rate differences using a normalized difference formula during the morning, noon and the afternoon. These differences also indicated that the best results are not always obtained when spectral (remote or proximal) measurements are typically preformed around noon (when solar illumination is the highest). Valuable information can be obtained when the spectral measurements are timed according to the plants’ dynamic physiological status throughout the day, which may vary among plant species and should be considered when planning remote sensing data acquisition.
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Meireles, José Eduardo, Jeannine Cavender‐Bares, Philip A. Townsend, Susan Ustin, John A. Gamon, Anna K. Schweiger, Michael E. Schaepman, et al. "Leaf reflectance spectra capture the evolutionary history of seed plants." New Phytologist 228, no. 2 (July 24, 2020): 485–93. http://dx.doi.org/10.1111/nph.16771.
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Li, Ying, Brian K. Via, Qingzheng Cheng, Jinghan Zhao, and Yaoxiang Li. "New Pretreatment Methods for Visible–Near-Infrared Calibration Modeling of Air-Dry Density of Ulmus pumila Wood." Forest Products Journal 69, no. 3 (January 1, 2019): 188–94. http://dx.doi.org/10.13073/fpj-d-19-00004.
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Abstract Due to the multidimensional complexity and redundancy between wavelengths in the visible and near infrared (Vis-NIR) region, the speed and accuracy of data analysis can be affected. This study aims to investigate the feasibility of simplifying high dimensional data based on transformation of the spectra and local correlation maximization (LCM). These two methods will be applied to determine the prediction accuracy of air-dry density of Ulmus pumila wood. In this study, the reflectance spectra (Refl.) were subjected to the reciprocal (1/Refl.) and logarithm reflectance to improve the spectra signal for prediction. LCM was developed for selecting spectral sensitive regions that were important in the prediction of density. A local correlation coefficient (r) criterion was developed such that if the r ≥ 0.75 (between wavelength and density), then partial least squares and support vector machine (SVM) were employed as the prediction method. Likewise, 2D correlation spectroscopy plots were used to further reduce the data matrix by removing redundant wavelengths. The results showed that (1) although the sensitive region of density was different, the region of r ≥ 0.80 was mainly in the Vis and NIR spectral region. Additionally, the performance of models developed from the sensitive region was better than that of data used from the less-sensitive region. (2) The SVM model was optimized by a genetic algorithm based on the log (1/Refl.) of the sensitive region. In conclusion, it was found that the spectral transformation presented better density estimation results ( = 0.909, root mean square error of calibration = 0.014) than when less sensitive wavelengths were used in the data matrix.
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Li, Xiao-li, and Yong He. "Chlorophyll Assessment and Sensitive Wavelength Exploration for Tea (Camellia sinensis) Based on Reflectance Spectral Characteristics." HortScience 43, no. 5 (August 2008): 1586–91. http://dx.doi.org/10.21273/hortsci.43.5.1586.
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A nondestructive method for the determination of chlorophyll index for the tea plant based on reflectance spectral characteristics was investigated. Spectral data were collected from 184 samples with a spectroradiometer in a field experiment. Multivariate analysis techniques, including partial least squares (PLS) and multiple linear regression (MLR), were used for developing calibration models for the determination of chlorophyll index of the tea plant. The best calibration model was achieved using the PLS technique with a correlation coefficient (r) of 0.95, a se of prediction of 3.40, and a bias of 1.9e−06. When the model was used for predicting the unknown samples, good performance was also obtained with r of 0.91, se of calibration of 4.77, and bias of 0.02. Sensitive wavelengths were selected through loading analysis of latent variables in the optimal PLS model, and the validity of these wavelengths was proved by MLR and statistical analysis. Three fingerprint wavelengths (488, 695, and 931 nm) were determined and could potentially be used for developing a simple, low-cost, and efficient instrument for the measurement of chlorophyll index. The results proved the feasibility of reflectance spectra for measurement of chlorophyll index of the tea plant.
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LEUNING, R., D. HUGHES, P. DANIEL, N. COOPS, and G. NEWNHAM. "A multi-angle spectrometer for automatic measurement of plant canopy reflectance spectra." Remote Sensing of Environment 103, no. 3 (August 15, 2006): 236–45. http://dx.doi.org/10.1016/j.rse.2005.06.016.
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Rebelo, Alanna J., Ben Somers, Karen J. Esler, and Patrick Meire. "Plant functional trait data and reflectance spectra for 22 palmiet wetland species." Data in Brief 20 (October 2018): 1209–19. http://dx.doi.org/10.1016/j.dib.2018.08.113.
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Kokaly, Raymond F., and Andrew K. Skidmore. "Plant phenolics and absorption features in vegetation reflectance spectra near 1.66 μm." International Journal of Applied Earth Observation and Geoinformation 43 (December 2015): 55–83. http://dx.doi.org/10.1016/j.jag.2015.01.010.
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Blumthal, Meredith R., L. Art Spomer, Daniel F. Warnock, and Raymond A. Cloyd. "Flower Color Preferences of Western Flower Thrips." HortTechnology 15, no. 4 (January 2005): 846–53. http://dx.doi.org/10.21273/horttech.15.4.0846.
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Flower color preference of western flower thrips [WFT (Frankliniella occidentalis) (Thysanoptera: Thripidae)] was assessed by observing insect location after introduction into chambers containing four different colored flowers of each of three plant species: transvaal daisy (Gerbera jamesonii), matsumoto aster (Callistephus chinensis), and chrysanthemum (Dendranthema ×grandiflorum). Preference was based on the number of WFT adults found on each flower 72 hours after infestation. Significantly higher numbers of WFT were found on yellow transvaal daisy and yellow chrysanthemum. When these accessions were compared in a subsequent experiment, WFT displayed a significant greater preference for the yellow transvaal daisy. Visible and near infrared reflectance spectra of the flowers used in the study were measured to determine the presence of distinct spectral features that would account for the relative attractiveness of the flowers. Likewise, the reflectance spectra of three commercially available sticky cards (blue, yellow, and yellow with a grid pattern) that are used to trap or sample for WFT were compared to those of the flowers to determine any shared spectral features that would support observed WFT flower color preference. The observed similarity between the yellow transvaal daisy and yellow sticky card reflectance spectra supports the hypothesis that flower color contributes to attractiveness of WFT. In particular, the wavelengths corresponding to green-yellow (500 to 600 nm) seem to be responsible for attracting WFT. These findings also indicate that yellow sticky cards may be more appropriate in sampling for WFT than blue sticky cards. Although further research is needed, under the conditions of this study, yellow transvaal daisy appears to be a potentially useful trap crop for WFT.
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Grašič, Mateja, Mateja Piberčnik, Igor Zelnik, Dragan Abram, and Alenka Gaberščik. "Invasive Alien Vines Affect Leaf Traits of Riparian Woody Vegetation." Water 11, no. 11 (November 15, 2019): 2395. http://dx.doi.org/10.3390/w11112395.
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The vines Echinocystis lobata and Parthenocissus quinquefolia are spreading over the natural vegetation in riparian zones, which may significantly affect riparian vegetation properties and the quality of litter for aquatic organisms. We examined leaf morphological, biochemical and optical traits of these invasive alien species, each paired with its host, the willows Salix caprea and S. fragilis, respectively. The vines altered the host radiation environment and the amount of photosynthetic pigments. Both vines had significantly higher specific leaf area and lower leaf tissue density compared to the willows, even though the leaves of P. quinquefolia were significantly thicker. Leaf optical properties varied significantly between vines and willows in some spectral regions. Compared to the willows, the vines reflected less light as UV, and more as green, and transmitted more light as green, yellow and red. The overgrowth of the willows with vines affected the reflectance of the willow leaves. Redundancy analysis of the relationships between leaf biochemical traits and reflectance spectra showed that chlorophyll a, anthocyanins, and UVB- and UVA-absorbing substances explained 45% of the reflectance spectra variability, while analysis with morphological traits revealed that specific leaf area, leaf thickness and upper cuticle thickness explained 43%. For leaf transmittance, UVB- and UVA-absorbing substances, carotenoids and anthocyanins explained 53% of the transmittance spectra variability, while analysis with morphological traits revealed that specific leaf area explained 51%. These data show that invasive alien vines can be discerned from each other and their hosts by their spectral signatures. In addition, the differences in the leaf functional traits between the vines and their hosts indicate significant differences in the quality of the plant litter entering the river.
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Qi, Haixia, Bingyu Zhu, Lingxi Kong, Weiguang Yang, Jun Zou, Yubin Lan, and Lei Zhang. "Hyperspectral Inversion Model of Chlorophyll Content in Peanut Leaves." Applied Sciences 10, no. 7 (March 26, 2020): 2259. http://dx.doi.org/10.3390/app10072259.
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The purpose of this study is to determine a method for quickly and accurately estimating the chlorophyll content of peanut plants at different plant densities. This was explored using leaf spectral reflectance to monitor peanut chlorophyll content to detect sensitive spectral bands and the optimum spectral indicators to establish a quantitative model. Peanut plants under different plant density conditions were monitored during three consecutive growth periods; single-photon avalanche diode (SPAD) and hyperspectral data derived from the leaves under the different plant density conditions were recorded. By combining arbitrary bands, indices were constructed across the full spectral range (350–2500 nm) based on blade spectra: the normalized difference spectral index (NDSI), ratio spectral index (RSI), difference spectral index (DSI) and soil-adjusted spectral index (SASI). This enabled the best vegetation index reflecting peanut-leaf SPAD values to be screened out by quantifying correlations with chlorophyll content, and the peanut leaf SPAD estimation models established by regression analysis to be compared and analyzed. The results showed that the chlorophyll content of peanut leaves decreased when plant density was either too high or too low, and that it reached its maximum at the appropriate plant density. In addition, differences in the spectral reflectance of peanut leaves under different chlorophyll content levels were highly obvious. Without considering the influence of cell structure as chlorophyll content increased, leaf spectral reflectance in the visible (350–700 nm): near-infrared (700–1300 nm) ranges also increased. The spectral bands sensitive to chlorophyll content were mainly observed in the visible and near-infrared ranges. The study results showed that the best spectral indicators for determining peanut chlorophyll content were NDSI (R520, R528), RSI (R748, R561), DSI (R758, R602) and SASI (R753, R624). Testing of these regression models showed that coefficient of determination values based on the NDSI, RSI, DSI and SASI estimation models were all greater than 0.65, while root mean square error values were all lower than 2.04. Therefore, the regression model established according to the above spectral indicators was a valid predictor of the chlorophyll content of peanut leaves.
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Sullivan, Franklin B., Scott V. Ollinger, Mary E. Martin, Mark J. Ducey, Lucie C. Lepine, and Haley F. Wicklein. "Foliar nitrogen in relation to plant traits and reflectance properties of New Hampshire forests." Canadian Journal of Forest Research 43, no. 1 (January 2013): 18–27. http://dx.doi.org/10.1139/cjfr-2012-0324.
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Several recent studies have shown that the mass-based concentration of nitrogen in foliage (%N) is positively correlated with canopy near-infrared reflectance (NIRr) and midsummer shortwave albedo across North American forests. Understanding the mechanisms behind this relationship would aid in interpretation of remote sensing imagery and improve our ability to predict changes in reflectance under future environmental conditions. The purpose of this study was to investigate the extent to which foliar nitrogen at leaf and canopy scales covary with leaf- and canopy-scale structural traits that are known to influence NIR scattering and reflectance. To accomplish this, we compared leaf and canopy traits with reflectance spectra at 17 mixed temperate forest stands. We found significant positive associations among %N and NIRr at both the leaf and canopy scale. At the canopy scale, both %N and NIRr were correlated with a number of structural traits as well as with the proportional abundance of deciduous and evergreen foliage. Identifying specific causal factors for observed reflectance patterns was complicated by interrelations among multiple traits across scales. Among simple metrics of canopy structure, we saw no relationship between NIRr and leaf area index, but we observed a strong, inverse relationship with the number of leaves per unit canopy volume.
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Bi, Kaiyi, Zheng Niu, Shunfu Xiao, Jie Bai, Gang Sun, Ji Wang, Zeying Han, and Shuai Gao. "Estimation of Maize Photosynthesis Traits Using Hyperspectral Lidar Backscattered Intensity." Remote Sensing 13, no. 21 (October 20, 2021): 4203. http://dx.doi.org/10.3390/rs13214203.
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High-throughput measurement of plant photosynthesis ability presents a challenge for the breeding process aimed to improve crop yield. As a novel technique, hyperspectral lidar (HSL) has the potential to characterize the spatial distribution of plant photosynthesis traits under less confounding factors. In this paper, HSL reflectance spectra of maize leaves were utilized for estimating the maximal velocity of Rubisco carboxylation (Vcmax) and maximum rate of electron transport at a specific light intensity (J) based on both reflectance-based and trait-based methods, and the results were compared with the commercial Analytical Spectral Devices (ASD) system. A linear combination of the Lambertian model and the Beckmann law was conducted to eliminate the angle effect of the maize point cloud. The results showed that the reflectance-based method (R2 ≥ 0.42, RMSE ≤ 28.1 for J and ≤4.32 for Vcmax) performed better than the trait-based method (R2 ≥ 0.31, RMSE ≤ 33.7 for J and ≤5.17 for Vcmax), where the estimating accuracy of ASD was higher than that of HSL. The Lambertian–Beckmann model performed well (R2 ranging from 0.74 to 0.92) for correcting the incident angle at different wavelength bands, so the spatial distribution of photosynthesis traits of two maize plants was visually displayed. This study provides the basis for the further application of HSL in high-throughput measurements of plant photosynthesis.
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Stone, Christine, Laurie Chisholm, and Simon McDonald. "Effects of leaf age and psyllid damage on the spectral reflectance properties of Eucalyptus saligna foliage." Australian Journal of Botany 53, no. 1 (2005): 45. http://dx.doi.org/10.1071/bt04062.
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Leaf chlorophyll content is influenced directly by many environmental stress factors. Because leaf pigment absorption is wavelength dependent, numerous narrow-band reflectance-based indices have been proposed as a means of assessing foliar health and condition. Chlorophyll content, however, also varies with leaf developmental stage. In this study, a range of morphological and physiological traits including insect damage, relative chlorophyll content (SPAD values), chlorophyll fluorescence (Fv/Fm) and reflectance spectra was measured of leaves sampled from mature Eucalyptus saligna. Relative differences among three leaf-age cohorts were compared with differences obtained from mature leaves that were either healthy or infested with the psyllid Glycaspis baileyi. Differences in relative chlorophyll content were greater between immature and mature foliage than between damaged and healthy mature leaves. These differences were confirmed in the comparisons of reflectance spectra and indices. As many eucalypt species have opportunistic crown phenology and long-lived leaves, leaf-age composition of crowns needs to be taken into account when applying reflectance-based indices to assess foliar condition of eucalypts.
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Lu, Jingshan, Jan U. H. Eitel, Jyoti S. Jennewein, Jie Zhu, Hengbiao Zheng, Xia Yao, Tao Cheng, Yan Zhu, Weixing Cao, and Yongchao Tian. "Combining Remote Sensing and Meteorological Data for Improved Rice Plant Potassium Content Estimation." Remote Sensing 13, no. 17 (September 3, 2021): 3502. http://dx.doi.org/10.3390/rs13173502.
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Potassium (K) plays a significant role in the formation of crop quality and yield. Accurate estimation of plant potassium content using remote sensing (RS) techniques is therefore of great interest to better manage crop K nutrition. To improve RS of crop K, meteorological information might prove useful, as it is well established that weather conditions affect crop K uptake. We aimed to determine whether including meteorological data into RS-based models can improve K estimation accuracy in rice (Oryza sativa L.). We conducted field experiments throughout three growing seasons (2017–2019). During each year, different treatments (i.e., nitrogen, potassium levels and plant varieties) were applied and spectra were taken at different growth stages throughout the growing season. Firstly, we conducted a correlation analysis between rice plant potassium content and transformed spectra (reflectance spectra (R), first derivative spectra (FD) and reciprocal logarithm-transformed spectra (log [1/R])) to select correlation bands. Then, we performed the genetic algorithms partial least-squares and linear mixed effects model to select important bands (IBs) and important meteorological factors (IFs) from correlation bands and meteorological data (daily average temperature, humidity, etc.), respectively. Finally, we used the spectral index and machine learning methods (partial least-squares regression (PLSR) and random forest (RF)) to construct rice plant potassium content estimation models based on transformed spectra, transformed spectra + IFs and IBs, and IBs + IFs, respectively. Results showed that normalized difference spectral index (NDSI (R1210, R1105)) had a moderate estimation accuracy for rice plant potassium content (R2 = 0.51; RMSE = 0.49%) and PLSR (FD-IBs) (R2 = 0.69; RMSE = 0.37%) and RF (FD-IBs) (R2 = 0.71; RMSE = 0.40%) models based on FD could improve the prediction accuracy. Among the meteorological factors, daily average temperature contributed the most to estimating rice plant potassium content, followed by daily average humidity. The estimation accuracy of the optimal rice plant potassium content models was improved by adding meteorological factors into the three RS models, with model R2 increasing to 0.65, 0.74, and 0.76, and RMSEs decreasing to 0.42%, 0.35%, and 0.37%, respectively, suggesting that including meteorological data can improve our ability to remotely sense plant potassium content in rice.
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Mascarenhas, Marcella, John Dighton, and Georgia A. Arbuckle. "Characterization of Plant Carbohydrates and Changes in Leaf Carbohydrate Chemistry Due to Chemical and Enzymatic Degradation Measured by Microscopic ATR FT-IR Spectroscopy." Applied Spectroscopy 54, no. 5 (May 2000): 681–86. http://dx.doi.org/10.1366/0003702001950166.
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Leaf litter decomposition is largely effected by the enzymatic action of fungal colonizers of leaf material. Microscopic attenuated total reflectance (ATR) infrared spectroscopy would be a useful tool to evaluate changes in leaf litter carbohydrate chemistry over time during the colonization process at the scale of resolution of the fungal hyphae. This paper reports the first studies to use microspectroscopy in the mid-infrared (IR) region to perform analyses within an area of 250 × 250 μm to gain spectra of single species of sugars and complex carbohydrates (cellulose, hemicellulose, lignin) to identify characteristic IR reflectance peaks and to be able to separate the species in complex media. Changes in leaf surface carbohydrate chemistry were interpreted from spectra obtained from leaf material that underwent the following: (1) treatment with acetone (to remove surface waxes), (2) treatment with enzymes, and (3) observation after colonization by fungi. Analysis of spectra obtained from random locations or from the same points on the leaf surface over time permitted changes in carbohydrate chemistry to be detected. Comparative analysis of spectra was carried out by using time-series analysis of variance of selected characteristic peak heights and multivariate statistics.
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Siebke, Katharina, and Marilyn C. Ball. "Non-destructive measurement of chlorophyll b:a ratios and identification of photosynthetic pathways in grasses by reflectance spectroscopy." Functional Plant Biology 36, no. 11 (2009): 857. http://dx.doi.org/10.1071/fp09201.
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Equations for non-destructive determination of chlorophyll b : a ratios in grasses were developed from reflectance spectra of intact leaves of barley (Hordeum vulgare L.) and two barley mutants: clorina f2, which lacks chlorophyll b and clorina f104, which has a low chlorophyll b content. These plants enabled separation of effects of chlorophyll composition on reflectance spectra due to differential light absorption by chlorophylls a and b and to measure the effects of chlorophyll b on the contribution of fluorescence emitted by chlorophyll a to the reflectance spectra. Indices developed from these data were then tested on growth chamber-grown leaves from six C3 and 17 C4 grass species (7 NAD-ME and 10 NADP-ME subtypes). We used the chlorophyll b : a ratio because the data were less skewed than the chlorophyll a : b ratio. The best index for determination of the chlorophyll b : a ratio utilised wavelengths affected by chlorophyll absorbance: [R626 – 0.5 (R603 + R647)]/[R552– R626]. The chlorophyll b : a ratio was significantly lower in the C4 than C3 grasses, but was not sufficient in itself to separate these two functional groups. However, because of differences in fluorescence characteristics, C3 and C4 species could be distinguished by an index based on wavelengths affected by chlorophyll fluorescence: [R696 to 709/R545 to 567].
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Coops, Nicholas C., and Christine Stone. "A comparison of field-based and modelled reflectance spectra from damaged Pinus radiata foliage." Australian Journal of Botany 53, no. 5 (2005): 417. http://dx.doi.org/10.1071/bt04129.
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Accurate and cost-effective monitoring of the health and condition of Australian Pinus radiata D.Don plantations is crucial to predicting the impact of damaging agents on wood yield and, where appropriate, targeting timely intervention. Stressful agents can induce changes in the biochemical, physiological and structural integrity of pine needles and subsequently reduce tree growth and ultimately cause plant death. Three important stressful agents occurring within Australian P. radiata plantations are the aphid Essigella californica, soil nitrogen deficiency and Sphaeropsis sapinea, a fungal pathogen. Within a study site in southern New South Wales, needles were sampled from crowns exhibiting key symptoms at three levels of crown severity. Needle level spectra were measured with a field spectroradiometer and foliage samples taken to extract needle chlorophyll a and b and to determine needle moisture content. A radiative transfer model (LIBERTY) was also used to estimate theoretical needle reflectance, given changes in two of its five input parameters (needle chlorophyll and moisture content). Two specific questions were posed. First, given that most spectral indices are based on a reference or stable wavelength as well as sensitive wavelengths, what is the most effective suite of stable wavelengths for predicting of needle chlorophyll and moisture? Second, which published spectral indices best discriminated the three categories of crown-damage severity for each damaging agent? Analysis of needle samples indicated that the needles affected by E. californica were the least chlorotic compared with the other damaging agents. For all damaging agents, needles showed an increase in reflectance with a lowering of chlorophyll content in the visible region (400–700 nm), associated with increasing severity. Changes in the shape of the spectral curve in the red-edge region of the electromagnetic spectrum were minor for E. californica-affected and nitrogen-deficient needles; however, changes were significant when comparing the S. sapinea severity classes. Correlations with published vegetation indices indicated that needle chlorophyll content was most highly correlated with a number of the recently proposed indices, including the structurally insensitive simple ratio. In general, the best results were obtained with 705 nm as the chlorophyll sensitive wavelength and either 750 or 445 nm as the insensitive wavelengths to account for needle reflectance and surface properties. By varying two of the input parameters of the LIBERTY model, the estimated spectra generally matched the trends and magnitude of actual spectra. This suggests that the application of radiative transfer models, correctly parameterised, can provide important information when estimating discrimination categories of needle damage.
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Vitrack-Tamam, Snir, Lilach Holtzman, Reut Dagan, Shai Levi, Yuval Tadmor, Tamir Azizi, Onn Rabinovitz, Amos Naor, and Oded Liran. "Random Forest Algorithm Improves Detection of Physiological Activity Embedded within Reflectance Spectra Using Stomatal Conductance as a Test Case." Remote Sensing 12, no. 14 (July 10, 2020): 2213. http://dx.doi.org/10.3390/rs12142213.
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Plants transpire water through their tissues in order to move nutrients and water to the cells. Transpiration includes various mechanisms, primarily stomata movement, which controls the rate of CO2 and water vapor exchange between the tissues and the atmosphere. Assessment of stomatal conductance is available for gas exchange techniques at leaf level, yet these techniques are not scalable to the whole plant let alone a large vegetation area. Hyperspectral reflectance spectroscopy, which acquires hundreds of bands in a single scan, may capture a glimpse of the crop’s physiological activity and therefore meet the scalability challenge. In this study, classic chemometric analyses are used alongside advanced statistical learning algorithms in order to identify stomatal conductance cues in hyperspectral measurements of cotton plants experiencing a gradient of irrigation. Random forest of regression trees identified 23 wavelengths related to both structural properties of the plant as well as water content. Partial least squares regression succeeded in relating these wavelengths to stomatal conductance, but only partially (R2 < 0.2). An artificial neural network algorithm reported an R2 = 0.54 with an 89% error-free performance on the same data subset. This study discusses implementation of machine learning methodologies as a benchmark for deeper analysis of spectral information, such as required when searching for plant physiology-related attenuations embedded within reflectance spectra.
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Nie, Leichao, Zhiguo Dou, Lijuan Cui, Xiying Tang, Xiajie Zhai, Xinsheng Zhao, Yinru Lei, Jing Li, Jinzhi Wang, and Wei Li. "Hyperspectral Inversion of Soil Carbon and Nutrient Contents in the Yellow River Delta Wetland." Diversity 14, no. 10 (October 11, 2022): 862. http://dx.doi.org/10.3390/d14100862.
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Hyperspectral inversion techniques can facilitate soil quality monitoring and evaluation. In this study, the Yellow River Delta Wetland Nature Reserve was used as the study area. By measuring and analyzing soil samples under different vegetation types and collecting soil reflectance spectra, the relationships between vegetation types, soil depth, and the changes in soil total carbon (TC), total nitrogen (TN), and total phosphorus (TP) contents were assessed. The spectral data set was changed by spectral first derivative processing and division of the sample set according to vegetation type. The correlation between soil carbon, nitrogen, and phosphorus contents, and soil spectra was also analyzed, sensitive bands were selected, and the partial least-squares (PLS) method, support vector machine (SVM) method, and random forest (RF) model were used to establish the inversion model based on the characteristic bands. The optimal combination of spectral transformation, sample set partitioning, and inversion model was explored. The results showed significant differences (p < 0.05) in soil TC, TN, and TP contents under reed and saline alkali poncho vegetation, but not between soil element contents under different stratifications of the same plant species. The first derivative reflectance had higher correlation coefficients with soil TC, TN, and TP contents compared with the original reflectance, while the sensitive bands and quantities of the three elements differed. The division of the sample sets according to vegetation type and the first derivative treatment can improve the prediction accuracy of the model. The best combination of sample set plus FD plus RF for TC, TN, and TP in reed soil and sample set plus FD plus SVM for TC, TN, and TP in saline alkali pine soil provides technical support to further improve the prediction accuracy of TC, TN, and TP in wetland soil.
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Marques, Pedro, Rosa Carvalho, and Anabela Fernandes-Silva. "Preliminary Assessment of the Relationship between Pigments in Olive Leaves and Vegetation Indices." Proceedings of the Latvian Academy of Sciences. Section B. Natural, Exact, and Applied Sciences. 76, no. 4 (August 1, 2022): 517–25. http://dx.doi.org/10.2478/prolas-2022-0080.
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Abstract Content of leaf pigments such as chlorophyll a and b, chlorophyll a+b and carotenoids can provide valuable insight into the physiological performance of plants. These compounds have selective proprieties for light absorption and reflectance in the visible spectra that can be used to evaluate alternative methods to biochemical to estimate their content. Numerous studies in the literature have established correlations between these compounds, spectral reflectance and vegetation indices. Nevertheless, the appropriate use of these indices depends on plant species and cultivars. Thus, the objective of this study was to assess the most common vegetation indices for the estimation of chlorophyll a and b, chlorophyll a+b and carotenoids of three olive tree cultivars (Olea europaea L, cv. Cobrançosa, cv. Verdeal Transmontana and cv. Madural) under six irrigation treatments, using spectroscopy. The results showed that the correlation between leaf pigments and vegetation indices depends not only on the type of pigment but also on the cultivar. Among the studied cultivars, cv. Cobrançosa showed the best correlation between the “M Locherer chlorophyll” index (MLO) and chlorophyll a content (r2 = 0.66) and for the carotenoid reflectance index (CRI) 2 and carotenoids content (r2 = 0.87). Although the results are preliminary, it seems that vegetation indices could be a useful tool for leaf pigment evaluation, and to give information about plant interactions with biotic and abiotic environmental stress conditions.
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Pacheco-Labrador, J., U. Weber, X. Ma, M. D. Mahecha, N. Carvalhais, C. Wirth, A. Huth, et al. "EVALUATING THE POTENTIAL OF DESIS TO INFER PLANT TAXONOMICAL AND FUNCTIONAL DIVERSITIES IN EUROPEAN FORESTS." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVI-1/W1-2021 (February 11, 2022): 49–55. http://dx.doi.org/10.5194/isprs-archives-xlvi-1-w1-2021-49-2022.
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Abstract. Tackling the accelerated human-induced biodiversity loss requires tools able to map biodiversity and its changes globally. Remote sensing (RS) offers unique capabilities of characterizing Earth surfaces; therefore, it could map plant biodiversity continuously and globally. This approach is supported by the Spectral Variation Hypothesis (SVH), which states that spectra and species (taxonomic and trait) diversities are linked through environmental heterogeneity. In this work, we evaluate the capability of the DESIS hyperspectral imager to capture plant diversity patterns as measured in dedicated plots of the network FunDivEUROPE. We computed functional and taxonomical diversity metrics from field taxonomic, structural, and foliar measurements in vegetation plots sampled in Spain and Romania. In addition, we also computed functional diversity metrics both from the DESIS reflectance factors and from vegetation parameters estimated via inversion of a radiative transfer model. Results showed that only metrics computed from spectral reflectance were able to capture taxonomic variability in the area. However, the lack of sensitivity was related to the insufficient plot size and the lack of spatial match between remote sensing and field data, but also the differences between the information contained in the field traits and remote sensing data, and the potential uncertainties in the remote estimates of vegetation parameters. Thus, while DESIS showed some sensitivity to plant diversity, further efforts are needed to deploy suitable biodiversity evaluation and validation plots and networks that support the development of biodiversity remote sensing products.
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Noda, Hibiki M., Takeshi Motohka, Kazutaka Murakami, Hiroyuki Muraoka, and Kenlo Nishida Nasahara. "Reflectance and transmittance spectra of leaves and shoots of 22 vascular plant species and reflectance spectra of trunks and branches of 12 tree species in Japan." Ecological Research 29, no. 2 (December 18, 2013): 111. http://dx.doi.org/10.1007/s11284-013-1096-z.
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Fu, Peng, Katherine Meacham‐Hensold, Kaiyu Guan, Jin Wu, and Carl Bernacchi. "Estimating photosynthetic traits from reflectance spectra: A synthesis of spectral indices, numerical inversion, and partial least square regression." Plant, Cell & Environment 43, no. 5 (February 27, 2020): 1241–58. http://dx.doi.org/10.1111/pce.13718.
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Srivastava, A., S. Roy, M. M. Kimothi, P. Kumar, S. Sehgal, S. Mamatha, and S. S. Ray. "DETECTION OF BACTERIAL WILT DISEASE (<i>PSEUDOMONAS SOLANCEARUM</i>) IN BRINJAL USING HYPERSPECTRAL REMOTE SENSING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W6 (July 26, 2019): 515–20. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w6-515-2019.
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<p><strong>Abstract.</strong> Bacterial wilt disease (pathogen: <i>Pseudomonas solancearum</i>) is a major problem affecting brinjal crop. Infected leaves show yellowing, loss in turgidity, drying and ultimately the entire plant collapses. The study aims to examine the potential of hyperspectral remote sensing for detection of biotic stress caused due to bacterial wilt disease and identify best spectral band widths and hyperspectral indices indicative of disease infestation. This study was conducted in a farmer’s plot at Alampur in Baruipur block, South 24 Pargana district, West Bengal. Canopy spectra (using ASD Fieldspec 2 Spectroradiometer), chlorophyll content (by Chlorophyll meter) and Leaf Area Index (LAI) (by plant canopy imager) were collected. The healthy plants had green and fully turgid leaves whereas diseased plants had lower chlorophyll content and LAI. The reduction in chlorophyll content lowered reflectance in green region and internal leaf damage in near-infrared region. A correlation analysis was carried out between reflectance at specific bandwidths and hyperspectral indices with chlorophyll content and LAI of healthy and stressed plants. Bandwidths of 528&ndash;531&thinsp;nm, 550&ndash;570&thinsp;nm, 710&ndash;760&thinsp;nm, and single bands such as 800&thinsp;nm and 920&thinsp;nm and indices viz. Greenness index, Modified Chlorophyll Absorption in Reflectance Index (MCARI), Transformed Chlorophyll Absorption in Reflectance Index (TCARI), Triangular Vegetation Index (TVI), Simple Ratio Pigment Index (SRPI), Photochemical Reflectance Index (PRI 2), Lichtenthaler Indices (LIC1, LIC2), Structure Intensive Pigment Index (SIPI) etc. were found to have strong positive correlation (R<sup>2</sup>&thinsp;&gt;&thinsp;0.9) with plant parameters. These specific bandwidths and indices can be helpful in biophysical parameter estimation and early detection of crop stress, crop growth and disease monitoring.</p>
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Chen, Zhe, Yang Niu, Chang-Qiu Liu, and Hang Sun. "Red flowers differ in shades between pollination systems and across continents." Annals of Botany 126, no. 5 (June 1, 2020): 837–48. http://dx.doi.org/10.1093/aob/mcaa103.
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Abstract Background and Aims Floral colour is a primary signal in plant–pollinator interactions. The association between red flowers and bird pollination is well known, explained by the ‘bee avoidance’ and ‘bird attraction’ hypotheses. Nevertheless, the relative importance of these two hypotheses has rarely been investigated on a large scale, even in terms of colour perception per se. Methods We collected reflectance spectra for 130 red flower species from different continents and ascertained their pollination systems. The spectra were analysed using colour vision models for bees and (three types of) birds, to estimate colour perception by these pollinators. The differences in colour conspicuousness (chromatic and achromatic contrast, purity) and in spectral properties between pollination systems and across continents were analysed. Key Results Compared with other floral colours, red flowers are very conspicuous to birds and much less conspicuous to bees. The red flowers pollinated by bees and by birds are more conspicuous to their respective pollinators. Compared with the bird flowers in the Old World, the New World ones are less conspicuous to bees and may be more conspicuous not only to violet-sensitive but also to ultraviolet-sensitive birds. These differences can be explained by the different properties of the secondary reflectance peak (SP). SP intensity is higher in red flowers pollinated by bees than those pollinated by birds (especially New World bird flowers). A transition from high SP to low SP in red flowers can induce chromatic contrast changes, with a greater effect on reducing attraction to bees than enhancing attraction to birds. Conclusions Shades of red flowers differ between pollination systems. Moreover, red bird flowers are more specialized in the New World than in the Old World. The evolution towards colour specialization is more likely to result in higher efficiency of bee avoidance than bird attraction
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Chen, Hanyue, Wenjiang Huang, Wang Li, Zheng Niu, Liming Zhang, and Shihe Xing. "Estimation of LAI in Winter Wheat from Multi-Angular Hyperspectral VNIR Data: Effects of View Angles and Plant Architecture." Remote Sensing 10, no. 10 (October 13, 2018): 1630. http://dx.doi.org/10.3390/rs10101630.
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View angle effects present in crop canopy spectra are critical for the retrieval of the crop canopy leaf area index (LAI). In the past, the angular effects on spectral vegetation indices (VIs) for estimating LAI, especially in crops with different plant architectures, have not been carefully assessed. In this study, we assessed the effects of the view zenith angle (VZA) on relationships between the spectral VIs and LAI. We measured the multi-angular hyperspectral reflectance and LAI of two cultivars of winter wheat, erectophile (W411) and planophile (W9507), across different growing seasons. The reflectance of each angle was used to calculate a variety of VIs that have already been published in the literature as well as all possible band combinations of Normalized Difference Spectral Indices (NDSIs). The above indices, along with the raw reflectance of representative bands, were evaluated with measured LAI across the view zenith angle for each cultivar of winter wheat. Data analysis was also supported by the use of the PROSAIL (PROSPECT + SAIL) model to simulate a range of bidirectional reflectance. The study confirmed that the strength of linear relationships between different spectral VIs and LAI did express different angular responses depending on plant type. LAI–VI correlations were generally stronger in erectophile than in planophile wheat types, especially at the zenith angle where the background is expected to be more evident for erectophile type wheat. The band combinations and formulas of the indices also played a role in shaping the angular signatures of the LAI–VI correlations. Overall, off-nadir angles served better than nadir angle and narrow-band indices, especially NDSIs with combinations of a red-edge (700~720 nm) and a green band, were more useful for LAI estimation than broad-band indices for both types of winter wheat. But the optimal angles much differed between two plant types and among various VIs. High significance (R2 > 0.9) could be obtained by selecting appropriate VIs and view angles on both the backward and forward scattering direction. These results from the in-situ measurements were also corroborated by the simulation analysis using the PROSAIL model. For the measured datasets, the highest coefficient was obtained by NDSI(536,720) at −35° in the backward (R2 = 0.971) and NDSI(571,707) at 55° in the forward scattering direction (R2 = 0.984) for the planophile and erectophile varieties, respectively. This work highlights the influence of view geometry and plant architecture. The identification of crop plant type is highly recommended before using remote sensing VIs for the large-scale mapping of vegetation biophysical variables.
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Mevy, Jean-Philippe, Charlotte Biryol, Marine Boiteau-Barral, and Franco Miglietta. "The Optical Response of a Mediterranean Shrubland to Climate Change: Hyperspectral Reflectance Measurements during Spring." Plants 11, no. 4 (February 12, 2022): 505. http://dx.doi.org/10.3390/plants11040505.
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Remote sensing techniques in terms of monitoring plants’ responses to environmental constraints have gained much attention during recent decades. Among these constraints, climate change appears to be one of the major challenges in the Mediterranean region. In this study, the main goal was to determine how field spectrometry could improve remote sensing study of a Mediterranean shrubland submitted to climate aridification. We provided the spectral signature of three common plants of the Mediterranean garrigue: Cistus albidus, Quercus coccifera, and Rosmarinus officinalis. The pattern of these spectra changed depending on the presence of a neighboring plant species and water availability. Indeed, the normalized water absorption reflectance (R975/R900) tended to decrease for each species in trispecific associations (11–26%). This clearly indicates that multispecific plant communities will better resist climate aridification compared to monospecific stands. While Q. coccifera seemed to be more sensible to competition for water resources, C. albidus exhibited a facilitation effect on R. officinalis in trispecific assemblage. Among the 17 vegetation indices tested, we found that the pigment pheophytinization index (NPQI) was a relevant parameter to characterize plant–plant coexistence. This work also showed that some vegetation indices known as indicators of water and pigment contents could also discriminate plant associations, namely RGR (Red Green Ratio), WI (Water Index), Red Edge Model, NDWI1240 (Normalized Difference Water Index), and PRI (Photochemical Reflectance Index). The latter was shown to be linearly and negatively correlated to the ratio of R975/R900, an indicator of water status.
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Navratil, M., and C. Buschmann. "Measurements of reflectance and fluorescence spectra for nondestructive characterizing ripeness of grapevine berries." Photosynthetica 54, no. 1 (March 1, 2016): 101–9. http://dx.doi.org/10.1007/s11099-015-0163-9.
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