Journal articles on the topic 'Leaf spectral reflectance'
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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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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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Yang, Chwen-Ming. "Estimation of Leaf Nitrogen Content from Spectral Characteristics of Rice Canopy." Scientific World JOURNAL 1 (2001): 81–89. http://dx.doi.org/10.1100/tsw.2001.387.
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Ground-based remotely sensed reflectance spectra of hyperspectral resolution were monitored during the growing period of rice under various nitrogen application rates. It was found that reflectance spectrum of rice canopy changed in both wavelength and reflectance as the plants developed. Fifteen characteristic wavebands were identified from the apparent peaks and valleys of spectral reflectance curves, in accordance with the results of the first-order differentiation, measured over the growing season of rice. The bandwidths and center wavelengths of these characteristic wavebands were different among nitrogen treatments. The simplified features by connecting these 15 characteristic wavelengths may be considered as spectral signatures of rice canopy, but spectral signatures varied with developmental age and nitrogen application rates. Among these characteristic wavebands, the changes of the wavelength in band 11 showed a positive linear relationship with application rates of nitrogen fertilizer, while it was a negative linear relationship in band 5. Mean reflectance of wavelengths in bands 1, 2, 3, 5, 11, and 15 was significantly correlated with application rates. Reflectance of these six wavelengths changed nonlinearly after transplanting and could be used in combination to distinguish rice plants subjected to different nitrogen application rates. From the correlation analyses, there are a variety of correlation coefficients for spectral reflectance to leaf nitrogen content in the range of 350-2400 nm. Reflectance of most wavelengths exhibited an inverse correlation with leaf nitrogen content, with the largest negative value (r = �0.581) located at about 1376 nm. Changes in reflectance at 1376 nm to leaf nitrogen content during the growing period were closely related and were best fitted to a nonlinear function. This relationship may be used to estimate and to monitor nitrogen content of rice leaves during rice growth. Reflectance of red light minimum and near-infrared peak and leaf nitrogen content were correlated nonlinearly.
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Yang, Hongye, Bo Ming, Chenwei Nie, Beibei Xue, Jiangfeng Xin, Xingli Lu, Jun Xue, et al. "Maize Canopy and Leaf Chlorophyll Content Assessment from Leaf Spectral Reflectance: Estimation and Uncertainty Analysis across Growth Stages and Vertical Distribution." Remote Sensing 14, no. 9 (April 28, 2022): 2115. http://dx.doi.org/10.3390/rs14092115.
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Accurate estimation of the canopy chlorophyll content (CCC) plays a key role in quantitative remote sensing. Maize (Zea mays L.) is a high-stalk crop with a large leaf area and deep canopy. It has a non-uniform vertical distribution of the leaf chlorophyll content (LCC), which limits remote sensing of CCC. Therefore, it is crucial to understand the vertical heterogeneity of LCC and leaf reflectance spectra to improve the accuracy of CCC monitoring. In this study, CCC, LCC, and leaf spectral reflectance were measured during two consecutive field growing seasons under five nitrogen treatments. The vertical LCC profile showed an asymmetric ‘bell-shaped’ curve structure and was affected by nitrogen application. The leaf reflectance also varied greatly between spatio–temporal conditions, which could indicate the influence of vertical heterogeneity. In the early growth stage, the spectral differences between leaf positions were mainly concentrated in the red-edge (RE) and near-infrared (NIR) regions, whereas differences were concentrated in the visible region during the mid-late filling stage. LCC had a strong linear correlation with vegetation indices (VIs), such as the modified red-edge ratio (mRER, R2 = 0.87), but the VI–chlorophyll models showed significant inversion errors throughout the growth season, especially at the early vegetative growth stage and the late filling stage (rRMSE values ranged from 36% to 87.4%). The vertical distribution of LCC had a strong correlation with the total chlorophyll in canopy, and sensitive leaf positions were identified with a multiple stepwise regression (MSR) model. The LCC of leaf positions L6 in the vegetative stage (R2-adj = 0.9) and L11 + L14 in the reproductive stage (R2-adj = 0.93) could be used to evaluate the canopy chlorophyll status (L12 represents the ear leaf). With a strong relationship between leaf spectral reflectance and LCC, CCC can be estimated directly by leaf spectral reflectance (mRER, rRMSE = 8.97%). Therefore, the spatio–temporal variations of LCC and leaf spectral reflectance were analyzed, and a higher accuracy CCC estimation approach that can avoid the effects of the leaf area was proposed.
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Xu, Jin Hong, and Jin Ting Yu. "Air Dustfall Impact on Spectrum of Ficus Microcarpa’s Leaf." Advanced Materials Research 655-657 (January 2013): 813–15. http://dx.doi.org/10.4028/www.scientific.net/amr.655-657.813.
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This article has studied the correlation relationship between the spectral features of polluted leaf surface of Ficus microcarpa and air dustfall in Guangzhou City. The results show that the spectral reflectance of leaves in the industrial area and large traffic area is 3-5.5% higher than that of leaves in cleaning area in the visible band, but is 10-15% lower in the near infrared band. Compared to the spectral reflectance of the cleaned leaf, the spectral reflectance of leaf on nature dirty is 6.6％ higher in the visible band and 25.6% lower in the infrared band. The spectral reflectance difference between dirty leaf and cleaned leaf in the infrared band has a strong correlation with air dustfall in Guangzhou city. The correlation coefficient is 0.821. It is simple and convenient, fast, economic method to monitor the air dustfall using the spectral characteristic of Ficus microcarpa’s leaf.
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Hunt, E. Raymond. "Spectral discrimination using infinite leaf reflectance and simulated canopy reflectance." International Journal of Remote Sensing 42, no. 8 (January 20, 2021): 3039–55. http://dx.doi.org/10.1080/01431161.2020.1864061.
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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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Zhu, Yan, Dongqin Zhou, Xia Yao, Yongchao Tian, and Weixing Cao. "Quantitative relationships of leaf nitrogen status to canopy spectral reflectance in rice." Australian Journal of Agricultural Research 58, no. 11 (2007): 1077. http://dx.doi.org/10.1071/ar06413.
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Non-destructive and quick methods for assessing leaf nitrogen (N) status are helpful for precision N management in field crops. The present study was conducted to determine the quantitative relationships of leaf N concentration on a leaf dry weight basis (LNC) and leaf N accumulation per unit soil area (LNA) to ground-based canopy spectral reflectance in rice (Oryza sativa L.). Time-course measurements were taken on canopy spectral reflectance, LNC, and leaf dry weights, with 4 field experiments under different N application rates and rice cultivars across 4 growing seasons. All possible ratio vegetation indices (RVI), difference vegetation indices (DVI), and normalised difference vegetation indices (NDVI) of key wavebands from the MSR16 radiometer were calculated. The results showed that LNC, LNA, and canopy reflectance spectra all markedly varied with N rates, with consistent change patterns among different rice cultivars and experiment years. There were highly significant linear correlations between LNC and canopy reflectance in the visible region from 560 to 710 nm (|r| > 0.85), between LNA and canopy reflectance from 760 to 1100 nm (|r| > 0.79), and from 460 to 710 nm wavelengths (|r| > 0.70). Among all possible RVI, DVI, and NDVI of key wavebands from the MSR16 radiometer, NDVI of 1220 and 710 nm was most highly correlated to LNC, and RVI of 950 and 660 nm and RVI of 950 and 680 nm were the best spectral indices for quantitative monitoring of LNA in rice. The average relative root mean square errors (RRMSE) between the predicted LNC and LNA and the observed values with independent data were no more than 11% and 25%, respectively. These results indicated that the canopy spectral reflectance can be potentially used for non-destructive and real-time monitoring of leaf N status in rice.
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Crusiol, Luis Guilherme Teixeira, Marcos Rafael Nanni, Renato Herrig Furlanetto, Rubson Natal Ribeiro Sibaldelli, Everson Cezar, Liang Sun, José Salvador Simonetto Foloni, et al. "Classification of Soybean Genotypes Assessed Under Different Water Availability and at Different Phenological Stages Using Leaf-Based Hyperspectral Reflectance." Remote Sensing 13, no. 2 (January 6, 2021): 172. http://dx.doi.org/10.3390/rs13020172.
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Monitoring of soybean genotypes is important because of intellectual property over seed technology, better management over seed genetics, and more efficient strategies for its agricultural production process. This paper aims at spectrally classifying soybean genotypes submitted to diverse water availability levels at different phenological stages using leaf-based hyperspectral reflectance. Leaf reflectance spectra were collected using a hyperspectral proximal sensor. Two experiments were conducted as field trials: one experiment was at Embrapa Soja in the 2016/2017, 2017/2018, and 2018/2019 cropping seasons, where ten soybean genotypes were grown under four water conditions; and another experiment was in the experimental farm of Unoeste University in the 2018/2019 cropping season, where nine soybean genotypes were evaluated. The spectral data collected was divided into nine spectral datasets, comprising single and multiple cropping seasons (from 2016 to 2019), and two contrasting crop-growing environments. Principal component analysis, applied as an indicator of the explained variance of the reflectance spectra among genotypes within each spectral dataset, explained over 94% of the spectral variance in the first three principal components. Linear discriminant analysis, used to obtain a model of classification of each reflectance spectra of soybean leaves into each soybean genotype, achieved accuracy between 61% and 100% in the calibration procedure and between 50% and 100% in the validation procedure. Misclassification was observed only between genotypes from the same genetic background. The results demonstrated the great potential of the spectral classification of soybean genotypes at leaf-scale, regardless of the phenological stages or water status to which plants were submitted.
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Deepak, Maya, Sarita Keski-Saari, Laure Fauch, Lars Granlund, Elina Oksanen, and Markku Keinänen. "Leaf Canopy Layers Affect Spectral Reflectance in Silver Birch." Remote Sensing 11, no. 24 (December 4, 2019): 2884. http://dx.doi.org/10.3390/rs11242884.
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The availability of light within the tree canopy affects various leaf traits and leaf reflectance. We determined the leaf reflectance variation from 400 nm to 2500 nm among three canopy layers and cardinal directions of three genetically identical cloned silver birches growing at the same common garden site. The variation in the canopy layer was evident in the principal component analysis (PCA), and the influential wavelengths responsible for variation were identified using the variable importance in projection (VIP) based on partial least squares discriminant analysis (PLS-DA). Leaf traits, such as chlorophyll, nitrogen, dry weight, and specific leaf area (SLA), also showed significant variation among the canopy layers. We found a shift in the red edge inflection point (REIP) for the canopy layers. The canopy layers contribute to the variability in the reflectance indices. We conclude that the largest variation was among the canopy layers, whereas the differences among individual trees to the leaf reflectance were relatively small. This implies that within-tree variation due to the canopy layer should be taken into account in the estimation of intraspecific variation in the canopy reflectance.
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Chi, Guang Yu, Yi Shi, Xin Chen, Jian Ma, and Tai Hui Zheng. "Effects of Metal Stress on Visible/Near-Infrared Reflectance Spectra of Vegetation." Advanced Materials Research 347-353 (October 2011): 2735–38. http://dx.doi.org/10.4028/www.scientific.net/amr.347-353.2735.
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Vegetation which suffers from heavy metal stresses can cause changes of leaf color, shape and structural changes. The spectral characteristics of vegetation leaves is related to leaf thickness, leaf surface characteristics, the content of water, chlorophyll and other pigments. So the eco-physiology changes of plants can be reflected by spectral reflectance. Studies on the spectral response of vegetation to heavy metal stress can provide a theoretical basis for remote sensing monitoring of metal pollution in soils. In recent decades, there are substantial amounts of literature exploring the effects of heavy metals on vegetation spectra.
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Zhu, Jiyou, Qing Xu, Jiangming Yao, Xinna Zhang, and Chengyang Xu. "The Changes of Leaf Reflectance Spectrum and Leaf Functional Traits of Osmanthus fragrans Are Related to the Parasitism of Cuscuta japonica." Applied Sciences 11, no. 4 (February 23, 2021): 1937. http://dx.doi.org/10.3390/app11041937.
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Studies on the influence of parasitism on plants based on hyperspectral analysis have not been reported so far. To fully understand the variation characteristics and laws of leaf reflectance spectrum and functional traits after the urban plant parasitized by Cuscuta japonica Choisy. Osmanthus fragrans (Thunb.) Lour. was taken as the research object to analyze the spectral reflectance and functional traits characteristics at different parasitical stages. Results showed that the spectral reflectance was higher than those being parasitized in the visible and near-infrared range. The spectral reflectance in 750~1400 nm was the sensitive range of spectral response of host plant to parasitic infection, which is universal at different parasitic stages. We established a chlorophyll inversion model (y = −65913.323x + 9.783, R2 = 0.6888) based on the reflectance of red valley, which can be used for chlorophyll content of the parasitic Osmanthus fragrans. There was a significant correlation between spectral parameters and chlorophyll content index. Through the change of spectral parameters, we can predict the chlorophyll content of Osmanthus fragrans under different parasitic degrees. After being parasitized, the leaf functional traits of host plant were generally characterized by large leaf thickness, small leaf area, small specific leaf area, low relative chlorophyll content, high leaf dry matter content and high leaf tissue density. These findings indicate that the host plant have adopted a certain trade-off strategy to maintain their growth in the invasion environment of parasitic plants. Therefore, we suspect that the leaf economics spectrum may also exist in the parasitic environment, and there was a general trend toward the “slow investment-return” type in the global leaf economics spectrum.
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Stone, C., L. Chisholm, and N. Coops. "Spectral reflectance characteristics of eucalypt foliage damaged by insects." Australian Journal of Botany 49, no. 6 (2001): 687. http://dx.doi.org/10.1071/bt00091.
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Variables related to foliar damage, leaf morphology, spectral reflectance, chlorophyll fluorescence and chlorophyll content were measured from leaves sampled from mature eucalypts exhibiting symptoms of crown dieback associated with bell miner colonisation located in Olney State Forest, near Wyong, New South Wales. Insect-damaged mature leaves and healthy young expanding leaves of some species exhibited a conspicuous red coloration caused by the presence of anthocyanin pigmentation. For the mature leaves, the level of red coloration was significantly correlated with insect herbivory and leaf necrosis. Significant correlations were also found between the level of red pigmentation and the following four spectral features: maximum reflectance at the green peak (550 nm); the wavelength position and maximum slope of the red edge (690–740 nm) and the maximum reflectance at 750 nm in the near-infrared portion of the electromagnetic spectrum. While it has been shown that anthocyanin pigments are synthesised in some eucalypt species in response to certain abiotic stresses causing photoinhibition and activation of photoprotective mechanisms, this work proposes that biotic agents such as leaf damaging insects and fungal pathogens may induce a similar response in eucalypt foliage resulting in increased levels of anthocyanins. The potential of anthocyanin levels to be related to leaf ontogeny for some eucalypt species was also illustrated in the reflectance spectra. Thus, it is essential that leaf age be considered. This work demonstrates that the identification of a number of key features of leaf spectra can provide a basis for the development of a robust forest health indicator that may be obtained from airborne or spaceborne hyperspectral sensors.
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Davenport, J. R., R. G. Stevens, E. M. Perry, and N. S. Lang. "Leaf Spectral Reflectance for Nondestructive Measurement of Plant Nutrient Status." HortTechnology 15, no. 1 (January 2005): 31–35. http://dx.doi.org/10.21273/horttech.15.1.0031.
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The ability to monitor plant nutrient status of high value horticultural crops and to adjust seasonal nutrient supply via fertilizer application has economic and environmental benefits. Recent technological advances may enable growers and field consultants to conduct this type of monitoring nondestructively in the future. Using the perennial crop apple (Malus domestica) and the annual crop potato (Solanum tuberosum), a hand-held leaf reflectance meter was used to evaluate leaf nitrogen (N) status throughout the growing season. In potato, this meter showed good correlation with leaf blade N content. Both time of day and time of season influenced leaf meter measurement, but leaf position did not. In apple, three different leaf meters were compared: the leaf spectral reflectance meter and two leaf greenness meters. Correlation with both N rate and leaf N content were strongest for the leaf reflectance meter early in the season but nonsignificant late in the season, whereas the leaf greenness meters gave weak but significant correlations throughout the growing season. The tapering off of leaf reflectance values found with the hand-held meter is consistent with normalized difference vegetation index (NDVI) values calculated from satellite images from the same plots. Overall, the use of leaf spectral reflectance shows promise as a tool for nondestructive monitoring of plant leaf status and would enable multiple georeferenced measurements throughout a field for differential N management.
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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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Liu, Tao, Tiezhu Shi, Huan Zhang, and Chao Wu. "Detection of Rise Damage by Leaf Folder (Cnaphalocrocis medinalis) Using Unmanned Aerial Vehicle Based Hyperspectral Data." Sustainability 12, no. 22 (November 10, 2020): 9343. http://dx.doi.org/10.3390/su12229343.
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Crop pests and diseases are key factors that damage crop production and threaten food security. Remote sensing techniques may provide an objective and effective alternative for automatic detection of crop pests and diseases. However, ground-based spectroscopic or imaging sensors may be limited in practically guiding the precision application and reduction of pesticide. Therefore, this study developed an unmanned aerial vehicle (UAV)-based remote sensing system to detect leaf folder (Cnaphalocrocis medinalis). Rice canopy reflectance spectra were obtained in the booting growth stage by using the UAV-based hyperspectral remote sensor. Newly developed and published multivariate spectral indices were initially calculated to estimate leaf-roll rates. The newly developed two-band spectral index (R490−R470), three-band spectral index (R400−R470)/(R400−R490), and published spectral index photochemical reflectance index (R550−R531)/(R550+R531) showed good applicability for estimating leaf-roll rates. The newly developed UAV-based micro hyperspectral system had potential in detecting rice stress induced by leaf folder. The newly developed spectral index (R490−R470) and (R400−R470)/(R400−R490) might be recommended as an indicator for estimating leaf-roll rates in the study area, and (R550−R531)/(R550+R531) might serve as a universal spectral index for monitoring leaf folder.
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Carter, Gregory A. "RESPONSES OF LEAF SPECTRAL REFLECTANCE TO PLANT STRESS." American Journal of Botany 80, no. 3 (March 1993): 239–43. http://dx.doi.org/10.1002/j.1537-2197.1993.tb13796.x.
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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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RAMA RAO, N., P. K. GARG, S. K. GHOSH, and V. K. DADHWAL. "Estimation of leaf total chlorophyll and nitrogen concentrations using hyperspectral satellite imagery." Journal of Agricultural Science 146, no. 1 (September 26, 2007): 65–75. http://dx.doi.org/10.1017/s0021859607007514.
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SUMMARYRemotely sensed estimates of biochemical parameters of agricultural crops are central to the precision management of agricultural crops (precision farming). Past research using in situ and airborne spectral reflectance measurements of various vegetation species has proved the usefulness of hyperspectral data for the estimation of various biochemical parameters of vegetation. In order to exploit the vast spectral and radiometric resources offered by space-borne hyperspectral remote sensing for the improved estimation of plant biochemical parameters, the relationships observed between spectral reflectance and various biochemical parameters at in situ and airborne levels needed to be evaluated in order to establish the existence of a reliable and stable relationship between spectral reflectance and plant biochemical parameters at the pixel scale. The potential of the EO-1 Hyperion hyperspectral sensor was investigated for the estimation of total chlorophyll and nitrogen concentrations of cotton crops in India by developing regression models between hyperspectral reflectance and laboratory measurements of leaf total chlorophyll and nitrogen concentrations. A comprehensive and rigorous analysis was carried out to identify the spectral bands and spectral indices for accurate retrieval of leaf total chlorophyll and nitrogen concentrations of cotton crop. The performance of these critical spectral reflectance indices was validated using independent samples. A new vegetation index, named the plant biochemical index (PBI), is proposed for improved estimation of the plant biochemicals from space-borne hyperspectral data; it is simply the ratio of reflectance at 810 and 560 nm. Further, the applicability of PBI to a different crop and at a different geographical location was also assessed. The present results suggest the use of space-borne hyperspectral data for accurate retrieval of leaf total chlorophyll and nitrogen concentrations and the proposed PBI has the potential to retrieve leaf total chlorophyll and nitrogen concentrations of various crops and at different geographical locations.
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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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CASA, R., F. CASTALDI, S. PASCUCCI, and S. PIGNATTI. "Chlorophyll estimation in field crops: an assessment of handheld leaf meters and spectral reflectance measurements." Journal of Agricultural Science 153, no. 5 (July 18, 2014): 876–90. http://dx.doi.org/10.1017/s0021859614000483.
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SUMMARYThe widespread adoption by agronomists and researchers of handheld leaf chlorophyll meters stimulates enquiries on instrumental calibration issues, given the necessity, for some applications, of inferring actual chlorophyll concentrations from the readings provided. This is especially required for recently developed and more innovative devices such as the Dualex (Force-A, France), which unlike the more common SPAD-502 (Minolta, Japan) has not undergone extensive (published) calibration tests. Additionally, devices for spectral reflectance measurements are also becoming increasingly available. In the present paper, the calibration of SPAD on maize (Zea mays L.) and of Dualex on winter wheat (Triticum aestivum L.), durum wheat (Triticum durum Desf.), horse bean (Vicia faba L.) and maize, was compared to spectral reflectance indices and full spectral information (400–2500 nm) acquired by a spectroradiometer (ASD FieldSpec) equipped with a contact probe and leaf clip. Full spectral data were exploited using partial least squares regression (PLSR). The measurements were performed in the field at Maccarese (Central Italy) in 2012, gathering a specific experimental dataset. The calibration models obtained on experimental data for SPAD (on maize) and Dualex (on four crops) showed intermediate or high estimation accuracy with root-mean-square error (RMSE) values ranging between 7 and 11 μg/cm2 depending on the species. These results were slightly better than those achieved using spectral reflectance indices, which were inferior though to those provided by PLSR using full spectral resolution. A synthetic database, generated by the physically based PROSPECT model, simulating hemispherical leaf reflectance and transmittance, was used to compare the performances of the reflectance indices and the chlorophyll meters for a wider range of leaf properties. The results confirmed the substantial equivalence of reflectance-based and transmittance-based (i.e. simulated SPAD and Dualex) indices and the advantage of exploiting the full spectral information, e.g. through PLSR, if available.
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Lu, Bu, and Lu. "Estimating Chlorophyll Content of Leafy Green Vegetables from Adaxial and Abaxial Reflectance." Sensors 19, no. 19 (September 20, 2019): 4059. http://dx.doi.org/10.3390/s19194059.
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As a primary pigment of leafy green vegetables, chlorophyll plays a major role in indicating vegetable growth status. The application of hyperspectral remote sensing reflectance offers a quick and nondestructive method to estimate the chlorophyll content of vegetables. Reflectance of adaxial and abaxial leaf surfaces from three common leafy green vegetables: Pakchoi var. Shanghai Qing (Brassica chinensis L. var. Shanghai Qing), Chinese white cabbage (Brassica campestris L. ssp. Chinensis Makino var. communis Tsen et Lee), and Romaine lettuce (Lactuca sativa var longifoliaf. Lam) were measured to estimate the leaf chlorophyll content. Modeling based on spectral indices and the partial least squares regression (PLS) was tested using the reflectance data from the two surfaces (adaxial and abaxial) of leaves in the datasets of each individual vegetable and the three vegetables combined. The PLS regression model showed the highest accuracy in estimating leaf chlorophyll content of pakchoi var. Shanghai Qing (R2 = 0.809, RMSE = 62.44 mg m−2), Chinese white cabbage (R2 = 0.891, RMSE = 45.18 mg m−2) and Romaine lettuce (R2 = 0.834, RMSE = 38.58 mg m−2) individually as well as of the three vegetables combined (R2 = 0.811, RMSE = 55.59 mg m−2). The good predictability of the PLS regression model is considered to be due to the contribution of more spectral bands applied in it than that in the spectral indices. In addition, both the uninformative variable elimination PLS (UVE-PLS) technique and the best performed spectral index: MDATT, showed that the red-edge region (680–750 nm) was effective in estimating the chlorophyll content of vegetables with reflectance from two leaf surfaces. The combination of the PLS regression model and the red-edge region are insensitive to the difference between the adaxial and abaxial leaf structure and can be used for estimating the chlorophyll content of leafy green vegetables accurately.
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Qiu, Chen, Croft, Li, Zhang, Zhang, and Ju. "Retrieving Leaf Chlorophyll Content by Incorporating Variable Leaf Surface Reflectance in the PROSPECT Model." Remote Sensing 11, no. 13 (July 2, 2019): 1572. http://dx.doi.org/10.3390/rs11131572.
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Leaf chlorophyll content plays a vital role in plant photosynthesis. The PROSPECT model has been widely used for retrieving leaf chlorophyll content from remote sensing data over various plant species. However, despite wide variations in leaf surface reflectance across different plant species and environmental conditions, leaf surface reflectance is assumed to be the same for different leaves in the PROSPECT model. This work extends the PROSPECT model by taking into account the variation of leaf surface reflection. In the modified model named PROSPECT-Rsurf, an additional surface layer with a variable refractive index is bounded on the N elementary layers. Leaf surface reflectance (Rs) is characterized by the difference between the refractive indices of leaf surface and interior layers. The specific absorption coefficients of the leaf total chlorophyll and carotenoids were recalibrated using a cross-calibration method and the refractive indices of leaf surface and interior layers were obtained during model inversion. Chlorophyll content (Cab) retrieval and spectral reconstruction in the visible spectral region (VIS, 400–750 nm) were greatly improved using PROSPECT-Rsurf, especially for leaves covered by heavy wax or hard cuticles that lead to high surface reflectance. The root mean square error (RMSE) of chlorophyll estimates decreased from 11.1 µg/cm2 to 8.9 µg/cm2 and the Pearson’s correlation coefficient (r) increased from 0.81 to 0.88 (p < 0.01) for broadleaf samples in validation, compared to PROSPECT-5. For needle leaves, r increased from 0.71 to 0.89 (p < 0.01), but systematic overestimation of Cab was found due to the edge effects of needles. After incorporating the edge effects in PROSPECT-Rsurf, the overestimation of Cab was alleviated and its estimation was improved for needle leaves. This study explores the influence of leaf surface reflectance on Cab estimation at the leaf level. By coupling PROSPECT-Rsurf with canopy models, the influence of leaf surface reflectance on canopy reflectance and therefore canopy chlorophyll content retrieval can be investigated across different spatial and temporal scales.
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Osco, Lucas Prado, Ana Paula Marques Ramos, Mayara Maezano Faita Pinheiro, Érika Akemi Saito Moriya, Nilton Nobuhiro Imai, Nayara Estrabis, Felipe Ianczyk, et al. "A Machine Learning Framework to Predict Nutrient Content in Valencia-Orange Leaf Hyperspectral Measurements." Remote Sensing 12, no. 6 (March 12, 2020): 906. http://dx.doi.org/10.3390/rs12060906.
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This paper presents a framework based on machine learning algorithms to predict nutrient content in leaf hyperspectral measurements. This is the first approach to evaluate macro- and micronutrient content with both machine learning and reflectance/first-derivative data. For this, citrus-leaves collected at a Valencia-orange orchard were used. Their spectral data was measured with a Fieldspec ASD FieldSpec® HandHeld 2 spectroradiometer and the surface reflectance and first-derivative spectra from the spectral range of 380 to 1020 nm (640 spectral bands) was evaluated. A total of 320 spectral signatures were collected, and the leaf-nutrient content (N, P, K, Mg, S, Cu, Fe, Mn, and Zn) was associated with them. For this, 204,800 (320 × 640) combinations were used. The following machine learning algorithms were used in this framework: k-Nearest Neighbor (kNN), Lasso Regression, Ridge Regression, Support Vector Machine (SVM), Artificial Neural Network (ANN), Decision Tree (DT), and Random Forest (RF). The training methods were assessed based on Cross-Validation and Leave-One-Out. The Relief-F metric of the algorithms’ prediction was used to determine the most contributive wavelength or spectral region associated with each nutrient. This approach was able to return, with high predictions (R2), nutrients like N (0.912), Mg (0.832), Cu (0.861), Mn (0.898), and Zn (0.855), and, to a lesser extent, P (0.771), K (0.763), and S (0.727). These accuracies were obtained with different algorithms, but RF was the most suitable to model most of them. The results indicate that, for the Valencia-orange leaves, surface reflectance data is more suitable to predict macronutrients, while first-derivative spectra is better linked to micronutrients. A final contribution of this study is the identification of the wavelengths responsible for contributing to these predictions.
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Gao, Bo-Cai, and Alexander F. H. Goetz. "Extraction of dry leaf spectral features from reflectance spectra of green vegetation." Remote Sensing of Environment 47, no. 3 (March 1994): 369–74. http://dx.doi.org/10.1016/0034-4257(94)90104-x.
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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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Suplick-Pl, M. R., S. F. Alshammary, and Y. L. Qian. "Spectral Reflectance Response of Three Turfgrasses to Leaf Dehydration." Asian Journal of Plant Sciences 10, no. 1 (December 15, 2010): 67–73. http://dx.doi.org/10.3923/ajps.2011.67.73.
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Tian, Y., Y. Zhu, and W. Cao. "Monitoring leaf photosynthesis with canopy spectral reflectance in rice." Photosynthetica 43, no. 4 (December 1, 2005): 481–89. http://dx.doi.org/10.1007/s11099-005-0078-y.
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Hunt, E. Raymond, James E. McMurtrey, Amy E. Parker Williams, and Lawrence A. Corp. "Spectral characteristics of leafy spurge (Euphorbia esula) leaves and flower bracts." Weed Science 52, no. 4 (August 2004): 492–97. http://dx.doi.org/10.1614/ws-03-132r.
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Leafy spurge can be detected during flowering with either aerial photography or hyperspectral remote sensing because of the distinctive yellow-green color of the flower bracts. The spectral characteristics of flower bracts and leaves were compared with pigment concentrations to determine the physiological basis of the remote sensing signature. Compared with leaves of leafy spurge, flower bracts had lower reflectance at blue wavelengths (400 to 500 nm), greater reflectance at green, yellow, and orange wavelengths (525 to 650 nm), and approximately equal reflectances at 680 nm (red) and at near-infrared wavelengths (725 to 850 nm). Pigments from leaves and flower bracts were extracted in dimethyl sulfoxide, and the pigment concentrations were determined spectrophotometrically. Carotenoid pigments were identified using high-performance liquid chromatography. Flower bracts had 84% less chlorophylla, 82% less chlorophyllb, and 44% less total carotenoids than leaves, thus absorptance by the flower bracts should be less and the reflectance should be greater at blue and red wavelengths. The carotenoid to chlorophyll ratio of the flower bracts was approximately 1:1, explaining the hue of the flower bracts but not the value of reflectance. The primary carotenoids were lutein, β-carotene, and β-cryptoxanthin in a 3.7:1.5:1 ratio for flower bracts and in a 4.8:1.3:1 ratio for leaves, respectively. There was 10.2 μg g−1fresh weight of colorless phytofluene present in the flower bracts and none in the leaves. The fluorescence spectrum indicated high blue, red, and far-red emission for leaves compared with flower bracts. Fluorescent emissions from leaves may contribute to the higher apparent leaf reflectance in the blue and red wavelength regions. The spectral characteristics of leafy spurge are important for constructing a well-documented spectral library that could be used with hyperspectral remote sensing.
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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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Tiruneh, Gizachew Ayalew, Derege Tsegaye Meshesha, Enyew Adgo, Atsushi Tsunekawa, Nigussie Haregeweyn, Ayele Almaw Fenta, and José Miguel Reichert. "A leaf reflectance-based crop yield modeling in Northwest Ethiopia." PLOS ONE 17, no. 6 (June 16, 2022): e0269791. http://dx.doi.org/10.1371/journal.pone.0269791.
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Crop yield prediction provides information to policymakers in the agricultural production system. This study used leaf reflectance from a spectroradiometer to model grain yield (GY) and aboveground biomass yield (ABY) of maize (Zea mays L.) at Aba Gerima catchment, Ethiopia. A FieldSpec IV (350–2,500 nm wavelengths) spectroradiometer was used to estimate the spectral reflectance of crop leaves during the grain-filling phase. The spectral vegetation indices, such as enhanced vegetation index (EVI), normalized difference VI (NDVI), green NDVI (GNDVI), soil adjusted VI, red NDVI, and simple ratio were deduced from the spectral reflectance. We used regression analyses to identify and predict GY and ABY at the catchment level. The coefficient of determination (R2), the root mean square error (RMSE), and relative importance (RI) were used for evaluating model performance. The findings revealed that the best-fitting curve was obtained between GY and NDVI (R2 = 0.70; RMSE = 0.065; P < 0.0001; RI = 0.19), followed by EVI (R2 = 0.65; RMSE = 0.024; RI = 0.61; P < 0.0001). While the best-fitting curve was obtained between ABY and GNDVI (R2 = 0.71; RI = 0.24; P < 0.0001), followed by NDVI (R2 = 0.77; RI = 0.17; P < 0.0001). The highest GY (7.18 ton/ha) and ABY (18.71 ton/ha) of maize were recorded at a soil bunded plot on a gentle slope. Combined spectral indices were also employed to predict GY with R2 (0.83) and RMSE (0.24) and ABY with R2 (0.78) and RMSE (0.12). Thus, the maize’s GY and ABY can be predicted with acceptable accuracy using spectral reflectance indices derived from spectroradiometer in an area like the Aba Gerima catchment. An estimation model of crop yields could help policy-makers in identifying yield-limiting factors and achieve decisive actions to get better crop yields and food security for Ethiopia.
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Zhou, Jing-Jing, Ya-Hao Zhang, Ze-Min Han, Xiao-Yang Liu, Yong-Feng Jian, Chun-Gen Hu, and Yuan-Yong Dian. "Evaluating the Performance of Hyperspectral Leaf Reflectance to Detect Water Stress and Estimation of Photosynthetic Capacities." Remote Sensing 13, no. 11 (May 31, 2021): 2160. http://dx.doi.org/10.3390/rs13112160.
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Advanced techniques capable of early, rapid, and nondestructive detection of the impacts of drought on fruit tree and the measurement of the underlying photosynthetic traits on a large scale are necessary to meet the challenges of precision farming and full prediction of yield increases. We tested the application of hyperspectral reflectance as a high-throughput phenotyping approach for early identification of water stress and rapid assessment of leaf photosynthetic traits in citrus trees by conducting a greenhouse experiment. To this end, photosynthetic CO2 assimilation rate (Pn), stomatal conductance (Cond) and transpiration rate (Trmmol) were measured with gas-exchange approaches alongside measurements of leaf hyperspectral reflectance from citrus grown across a gradient of soil drought levels six times, during 20 days of stress induction and 13 days of rewatering. Water stress caused Pn, Cond, and Trmmol rapid and continuous decline throughout the entire drought period. The upper layer was more sensitive to drought than middle and lower layers. Water stress could also bring continuous and dynamic changes of the mean spectral reflectance and absorptance over time. After trees were rewatered, these differences were not obvious. The original reflectance spectra of the four water stresses were surprisingly of low diversity and could not track drought responses, whereas specific hyperspectral spectral vegetation indices (SVIs) and absorption features or wavelength position variables presented great potential. The following machine-learning algorithms: random forest (RF), support vector machine (SVM), gradient boost (GDboost), and adaptive boosting (Adaboost) were used to develop a measure of photosynthesis from leaf reflectance spectra. The performance of four machine-learning algorithms were assessed, and RF algorithm yielded the highest predictive power for predicting photosynthetic parameters (R2 was 0.92, 0.89, and 0.88 for Pn, Cond, and Trmmol, respectively). Our results indicated that leaf hyperspectral reflectance is a reliable and stable method for monitoring water stress and yield increase, with great potential to be applied in large-scale orchards.
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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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Li, Meng, Ronghao Chu, Xiuzhu Sha, Feng Ni, Pengfei Xie, Shuanghe Shen, and Abu Reza Md Towfiqul Islam. "Hyperspectral Characteristics and Scale Effects of Leaf and Canopy of Summer Maize under Continuous Water Stresses." Agriculture 11, no. 12 (November 23, 2021): 1180. http://dx.doi.org/10.3390/agriculture11121180.
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The scale effect problem is one of the most challenging issues in remote sensing studies. However, the research on the methodology and theory of the scale effect is scarcely applied in practice. To this end, in this study, 3 years of field experimental data of continuous water stresses on summer maize were used for this purpose. Furthermore, the Prospect and Sail models were employed to investigate the scale effects of reflectance characteristics and vegetation indexes. The results indicated that the spectral characteristics of canopy and leaf of summer maize were similar under continuous water stresses at various stages. The reflectance at the canopy level was distinct from that at the leaf level, considering the soil background differences. From leaf to canopy scales, with the increase in the leaf area index (LAI), the spectral reflectance of all treatments in the visible band decreased, but increased in the near-infrared band, and the reflectance was saturated when LAI increased to 5. The reflectance difference caused by LAI variation was enlarged as the drought stress intensified in the short-wave infrared band. The spectral reflectance in the near-infrared band was susceptible to leaf inclination angle (LIA) variation and changed significantly, especially in the closed canopy. With the increase in LAI, the difference vegetation index (DVI) and normalized difference vegetation index (NDVI) values under each treatment showed a gradually increasing trend. With the increase in LIA, the DVI value decreased gradually, and the DVI value under the saturated canopy was significantly higher than that under the unclosed canopy. However, the NDVI values of all treatments did not change with LIA, mostly under the closed canopy. Overall, the results demonstrated that LAI had a more significant influence on canopy reflectance than LIA. In addition, NDVI was not able to capture the LAI and LIA information when the canopy was closed, but DVI performed better.
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Yang, Lechan, Song Deng, Shouming Ma, and Fangxiong Xiao. "Estimation model of leaf nitrogen content based on GEP and leaf spectral reflectance." Computers & Electrical Engineering 98 (March 2022): 107648. http://dx.doi.org/10.1016/j.compeleceng.2021.107648.
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Röll, Georg, Jens Hartung, and Simone Graeff-Hönninger. "Determination of Plant Nitrogen Content in Wheat Plants via Spectral Reflectance Measurements: Impact of Leaf Number and Leaf Position." Remote Sensing 11, no. 23 (November 26, 2019): 2794. http://dx.doi.org/10.3390/rs11232794.
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The determination of plant nitrogen (N) content (%) in wheat via destructive lab analysis is expensive and inadequate for precision farming applications. Vegetation indices (VI) based on spectral reflectance can be used to predict plant N content indirectly. For these VI, reflectance from space-borne, airborne, or ground-borne sensors is captured. Measurements are often taken at the canopy level for practical reasons. Hence, translocation processes of nutrients that take place within the plant might be ignored or measurements might be less accurate if nutrient deficiency symptoms occur on the older leaves. This study investigated the impact of leaf number and measurement position on the leaf itself on the determination of plant N content (%) via reflectance measurements. Two hydroponic experiments were carried out. In the first experiment, the N fertilizer amount and growth stage for the determination of N content was varied, while the second experiment focused on a secondary induction of N deficiency due to drought stress. For each plant, reflectance measurements were taken from three leaves (L1, L2, L3) and at three positions on the leaf (P1, P2, P3). In addition, the N content (%) of the whole plant was determined by chemical lab analysis. Reflectance spectrometer measurements (400–1650 nm) were used to calculate 16 VI for each combination of leaf and position. N content (%) was predicted using each VI for each leaf and each position. Significant lower mean residual error variance (MREV) was found for leaves L1 and L3 and for measurement position on P3 in the N trial, but the difference of MREV between the leaves was very low and therefore considered as not relevant. The drought stress trial also led to no significant differences in MREV between leaves and positions. Neither the position on the leaf nor the leaf number had an impact on the accuracy of plant nitrogen determination via spectral reflectance measurements, wherefore measurements taken at the canopy level seem to be a valid approach.
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Ding, Pinghai, Leslie H. Fuchigami, and Carolyn F. Scagel. "Simple Linear Regression and Reflectance Sensitivity Analysis Used to Determine the Optimum Wavelengths for the Nondestructive Assessment of Chlorophyll in Fresh Leaves Using Spectral Reflectance." Journal of the American Society for Horticultural Science 134, no. 1 (January 2009): 48–57. http://dx.doi.org/10.21273/jashs.134.1.48.
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The accuracy of nondestructive optical methods for chlorophyll (Chl) assessment based on leaf spectral characteristics depends on the wavelengths used for Chl assessment. Using spectroscopy, the optimum wavelengths (OW) for Chl assessment were determined by using 1-year-old almond (Prunus dulcis), poplar (Populus trichocarpa × P. deltoides), and apple (Malus ×domestica) trees grown at different rates of nitrogen fertilization to produce leaves with different Chl concentrations. Spectral reflectance of leaf discs was measured using a spectroradiometer (300 to 1100 nm at 1-nm intervals), and total Chl concentration in leaf discs was extracted and determined in 80% acetone. The OW for nondestructive Chl assessment by reflectance spectroscopy was estimated using 1) the coefficient of determination (r 2) from simple linear regression; 2) reflectance sensitivity analysis (a measure for changes of spectral reflectance on unit change in leaf Chl concentration); and 3) the first spectral derivative method. Our results indicated that the first derivative method can be used only to identify OW in the red edge region of the spectrum, whereas r 2 and reflectance sensitivity analysis can be used to identify the OW in both the red edge and green regions. Our results indicate that using simple linear r 2 in combination with reflectance sensitivity and/or the first derivative analyses is a reliable method for determining OW in plant leaves tested. Two optimum wavebands with larger r 2, smaller root mean square error, and higher reflectance sensitivity were found in red edge (700 to 730 nm) and green (550 to 580 nm) regions, respectively, which can be used as common OW for Chl reflectance assessment in poplar, apple, and almond leaves tested. Single-wavelength indices if developed with OW were even more accurate than those more wavelength indices that developed without using OW. The accuracy of indices can be further improved if indices developed by using one OW and one Chl-insensitive wavelength from near infrared (NIR) (750 to 1100 nm) in the form of RNIR/ROW or (RNIR – ROW)/(RNIR + ROW).
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Tosin, Renan, Isabel Pôças, and Mário Cunha. "Spectral and thermal data as a proxy for leaf protective energy dissipation under kaolin application in grapevine cultivars." Open Agriculture 4, no. 1 (July 19, 2019): 294–304. http://dx.doi.org/10.1515/opag-2019-0028.
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AbstractThe dynamic effects of kaolin clay particle film application on the temperature and spectral reflectance of leaves of two autochthonous cultivars (Touriga Nacional (TN, n=32) and Touriga Franca (TF, n=24)) were studied in the Douro wine region. The study was implemented in 2017, in conditions prone to multiple environmental stresses that include excessive light and temperature as well as water shortage. Light reflectance from kaolin-sprayed leaves was higher than the control (leaves without kaolin) on all dates. Kaolin’s protective effect over leaves’ temperatures was low on the 20 days after application and ceased about 60 days after its application. Differences between leaves with and without kaolin were explained by the normalized maximum leaf temperature (T_max_f_N), reflectance at 400 nm, 532 nm, and 737 nm, as assessed through TN data. The wavelengths of 532 nm and 737 nm are associated with plant physiological processes, which support the selection of these variables for assessing kaolin’s effects on leaves. The application of principal component analysis to the TF data, based on these four variables (T_max_f_N and reflectances: 400, 532, 737 nm) selected for TN, explained 83.56% of data variability (considering two principal components), obtaining a clear differentiation between leaves with and without kaolin. The T_max_f_N and the reflectance at 532 nm were the variables with a greater contribution for explaining data variability. The results improve the understanding of the vines’ response to kaolin throughout the grapevine cycle and support decisions about the re-application timing.
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Adams, Jennifer, Philip Lewis, and Mathias Disney. "Decoupling Canopy Structure and Leaf Biochemistry: Testing the Utility of Directional Area Scattering Factor (DASF)." Remote Sensing 10, no. 12 (November 29, 2018): 1911. http://dx.doi.org/10.3390/rs10121911.
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Biochemical properties retrieved from remote sensing data are crucial sources of information for many applications. However, leaf and canopy scattering processes must be accounted for to reliably estimate information on canopy biochemistry, carbon-cycle processes and energy exchange. A coupled leaf-canopy model based on spectral invariants theory has been proposed, that uses the so-called Directional Area Scattering Factor (DASF) to correct hyperspectral remote sensing data for canopy structural effects. In this study, the reliability of DASF to decouple canopy structure and biochemistry was empirically tested using simulated reflectance spectra modelled using a Monte Carlo Ray Tracing (MCRT) radiative transfer model. This approach allows all canopy and radiative properties to be specified a priori. Simulations were performed under idealised conditions of directional-hemispherical reflectance, isotropic Lambertian leaf reflectance and transmittance and sufficiently dense (high LAI) canopies with black soil where the impact of canopy background is negligible, and also departures from these conditions. It was shown that both DASF and total canopy scattering could be accurately extracted under idealised conditions using information from both the full 400–2500 nm spectral interval and the 710–790 nm interval alone, even given no prior knowledge of leaf optical properties. Departures from these idealised conditions: varying view geometry, bi-directional reflectance, LAI and soil effects, were tested. We demonstrate that total canopy scattering could be retrieved under conditions of varying view geometry and bi-directional reflectance, but LAI and soil effects were shown to reduce the accuracy with which the scattering can be modelled using the DASF approach. We show that canopy architecture, either homogeneous or heterogeneous 3D arrangements of canopy scattering elements, has important influences over DASF and consequently the accuracy of retrieval of total canopy scattering. Finally, although DASF and total canopy scattering could be retrieved to within 2.4% of the modelled total canopy scattering signal given no prior knowledge of leaf optical properties, spectral invariant parameters were not accurately retrieved from the simulated signal. This has important consequences since these parameters are quite widely used in canopy reflectance modelling and have the potential to help derive new, more accurate canopy biophysical information. Understanding and quantifying the limitations of the DASF approach as we have done here, is an important step in allowing the wider use of these methods for decoupling canopy structure and biochemistry.
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INADA, Katsumi. "Spectral Ratio of Reflectance for Estimating Chlorophyll Content of Leaf." Japanese journal of crop science 54, no. 3 (1985): 261–72. http://dx.doi.org/10.1626/jcs.54.261.
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Xue, Lihong, Weixing Cao, Weihong Luo, Tingbo Dai, and Yan Zhu. "Monitoring Leaf Nitrogen Status in Rice with Canopy Spectral Reflectance." Agronomy Journal 96, no. 1 (January 2004): 135–42. http://dx.doi.org/10.2134/agronj2004.1350.
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Xue, Lihong, Weixing Cao, Weihong Luo, Tingbo Dai, and Yan Zhu. "Monitoring Leaf Nitrogen Status in Rice with Canopy Spectral Reflectance." Agronomy Journal 96, no. 1 (2004): 135. http://dx.doi.org/10.2134/agronj2004.0135.
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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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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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Habibullah, Mohammad, Mohammad Reza Mohebian, Raju Soolanayakanahally, Ali Newaz Bahar, Sally Vail, Khan A. Wahid, and Anh Dinh. "Low-Cost Multispectral Sensor Array for Determining Leaf Nitrogen Status." Nitrogen 1, no. 1 (August 25, 2020): 67–80. http://dx.doi.org/10.3390/nitrogen1010007.
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A crop’s health can be determined by its leaf nutrient status; more precisely, leaf nitrogen (N) level, is a critical indicator that carries a lot of worthwhile nutrient information for classifying the plant’s health. However, the existing non-invasive techniques are expensive and bulky. The aim of this study is to develop a low-cost, quick-read multi-spectral sensor array to predict N level in leaves non-invasively. The proposed sensor module has been developed using two reflectance-based multi-spectral sensors (visible and near-infrared (NIR)). In addition, the proposed device can capture the reflectance data at 12 different wavelengths (six for each sensor). We conducted the experiment on canola leaves in a controlled greenhouse environment as well as in the field. In the greenhouse experiment, spectral data were collected from 87 leaves of 24 canola plants, subjected to varying levels of N fertilization. Later, 42 canola cultivars were subjected to low and high nitrogen levels in the field experiment. The k-nearest neighbors (KNN) algorithm was employed to model the reflectance data. The trained model shows an average accuracy of 88.4% on the test set for the greenhouse experiment and 79.2% for the field experiment. Overall, the result concludes that the proposed cost-effective sensing system can be viable in determining leaf nitrogen status.
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Xie, Mengmeng, Zhongqiang Wang, Alfredo Huete, Luke A. Brown, Heyu Wang, Qiaoyun Xie, Xinpeng Xu, and Yanling Ding. "Estimating Peanut Leaf Chlorophyll Content with Dorsiventral Leaf Adjusted Indices: Minimizing the Impact of Spectral Differences between Adaxial and Abaxial Leaf Surfaces." Remote Sensing 11, no. 18 (September 15, 2019): 2148. http://dx.doi.org/10.3390/rs11182148.
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Relatively little research has assessed the impact of spectral differences among dorsiventral leaves caused by leaf structure on leaf chlorophyll content (LCC) retrieval. Based on reflectance measured from peanut adaxial and abaxial leaves and LCC measurements, this study proposed a dorsiventral leaf adjusted ratio index (DLARI) to adjust dorsiventral leaf structure and improve LCC retrieval accuracy. Moreover, the modified Datt (MDATT) index, which was insensitive to leaves structure, was optimized for peanut plants. All possible wavelength combinations for the DLARI and MDATT formulae were evaluated. When reflectance from both sides were considered, the optimal combination for the MDATT formula was ( R 723 − R 738 ) / ( R 723 − R 722 ) with a cross-validation R2cv of 0.91 and RMSEcv of 3.53 μg/cm2. The DLARI formula provided the best performing indices, which were ( R 735 − R 753 ) / ( R 715 − R 819 ) for estimating LCC from the adaxial surface (R2cv = 0.96, RMSEcv = 2.37 μg/cm2) and ( R 732 − R 754 ) / ( R 724 − R 773 ) for estimating LCC from reflectance of both sides (R2cv = 0.94, RMSEcv = 2.81 μg/cm2). A comparison with published vegetation indices demonstrated that the published indices yielded reliable estimates of LCC from the adaxial surface but performed worse than DLARIs when both leaf sides were considered. This paper concludes that the DLARI is the most promising approach to estimate peanut LCC.
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Naik, B. Balaji, H. R. Naveen, G. Sreenivas, K. Karun Choudary, D. Devkumar, and J. Adinarayana. "Identification of Water and Nitrogen Stress Indicative Spectral Bands Using Hyperspectral Remote Sensing in Maize During Post-Monsoon Season." Journal of the Indian Society of Remote Sensing 48, no. 12 (October 14, 2020): 1787–95. http://dx.doi.org/10.1007/s12524-020-01200-w.
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AbstractRealization of agricultural crop condition through field survey is quite expensive, time consuming and sometimes not practical for remote locations. Optical remote sensing techniques can provide information on real condition of the crops by observing spectral reflectance at different crop growth phases and is less expensive and less time consuming. Hyperspectral remote sensing provides a unique opportunity for non-destructive, timely and accurate estimation of crop biophysical and biochemical properties. In this study, a field experiment was conducted to identify the water and nitrogen stress indicative spectral bands using ground-based hyperspectral data and to assess the predictive capability of selective bands on yield of maize under water and nitrogen stress environment. The experiment comprised of three irrigation scheduling treatments based on IW/CPE ration of 0.6, 0.8 and 1.2 and three nitrogen level treatments, i.e., 100, 200 and 300 kg of N ha−1, respectively, with three replications in a split plot design. The spectral reflectance was measured before irrigation at tasseling and dough stage of the maize crop using portable field spectroradiometer. The results of stepwise multiple linear regression indicated the highest predicting capability of spectral bands 540 nm, 780 nm and 860 nm for leaf nitrogen and 700 nm, 740 nm and 860 nm for leaf water content. The derived biophysical parameters based on spectral reflectance viz. relative leaf water content (%), leaf area index and leaf nitrogen contentment (%) at tasseling stage of maize crop accounted for 80%, 61% and 66% variation in grain yield, respectively.
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Savé, R., J. Peñuelas, I. Filella, and C. Olivella. "Water Relations, Hormonal Level, and Spectral Reflectance of Gerbera jamesonii BoluS Subjected to Chilling Stress." Journal of the American Society for Horticultural Science 120, no. 3 (May 1995): 515–19. http://dx.doi.org/10.21273/jashs.120.3.515.
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One-year-old gerbera plants subjected to 1 night at 5C had reduced leaf water losses and chlorophyll content and increased root hydraulic resistance, but stomatal conductance and leaf water potential did not change. After 3 nights, leaf water potential had decreased and leaf reflectance in the visible and the near-infrared had increased. Similarly, abscisic acid (ABA) in leaves had increased and cytokinins (CK) in leaves and roots had decreased, but ABA levels in roots did not change. After 4 days at 20C, root hydraulic resistance, reflectance and leaf water loss returned to their initial values, but leaf water potential and chlorophyll content remained lower. Leaf ABA levels reached values lower than the initial, while root ABA and leaf CK levels retained the initial values. These data suggest that in the gerbera plants studied, 3 nights at 5C produced a reversible strain but otherwise plants remained uninjured, so this gerbera variety could be cultured with low energetic inputs under Mediterranean conditions. The results may indicate that ABA and CK were acting as synergistic signals of the chilling stress. Spectral reflectance signals seemed to be useful as plant chilling injury indicators at ground level.
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D. K. DAS, S. PRADHAN, V.K. SEHGAL, R.N. SAHOO, V.K. GUPTA, and R. SINGH. "Spectral reflectance characteristics of healthy and yellow mosaic virus infected soybean (Glycine max L.) leaves in a semiarid environment." Journal of Agrometeorology 15, no. 1 (June 1, 2013): 36–38. http://dx.doi.org/10.54386/jam.v15i1.1435.
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Field experiments were conducted during Kharif 2009 and 2010 at IARI farm, New Delhi to study the spectral reflectance characteristics of YMV susceptible (JS-335) and tolerant (Pusa-9814) varieties. In both the years, 90-100% leaves of the variety, JS-335 and 5-10% leaves of the variety, Pusa-9814 were infected with the disease in the field. In order to characterize spectral reflectance of healthy andYMV infected soybean crop, soybean leaves were collected from YMV-infected crop (JS-335) and healthy crop (Pusa-9814) and taken to laboratory for reflectance measurement under controlled condition. Leaf chlorophyll content was measured using DMSO method. Normalized Difference Vegetation Index (NDVI), Ratio Vegetation Index (RVI), Greeness Index (GI), Photochemical Reflectance Index (PRI) and Leaf Moisture Vegetation Index 1 (LMVI1) were computed and it was observed that NDVI was found to be useful in detecting yellow mosaic virus infected soybean.
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Meng, Ran, Zhengang Lv, Jianbing Yan, Gengshen Chen, Feng Zhao, Linglin Zeng, and Binyuan Xu. "Development of Spectral Disease Indices for Southern Corn Rust Detection and Severity Classification." Remote Sensing 12, no. 19 (October 4, 2020): 3233. http://dx.doi.org/10.3390/rs12193233.
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Southern Corn Rust (SCR) is one of the most destructive diseases in corn production, significantly affecting corn quality and yields globally. Field-based fast, nondestructive diagnosis of SCR is critical for smart agriculture applications to reduce pesticide use and ensure food safety. The development of spectral disease indices (SDIs), based on in situ leaf reflectance spectra, has proven to be an effective method in detecting plant diseases in the field. However, little is known about leaf spectral signatures that can assist in the accurate diagnosis of SCR, and no SDIs-based model has been reported for the field-based SCR monitoring. Here, to address those issues, we developed SDIs-based monitoring models to detect SCR-infected leaves and classify SCR damage severity. In detail, we first collected in situ leaf reflectance spectra (350–2500 nm) of healthy and infected corn plants with three severity levels (light, medium, and severe) using a portable spectrometer. Then, the RELIEF-F algorithm was performed to select the most discriminative features (wavelengths) and two band normalized differences for developing SDIs (i.e., health index and severity index) in SCR detection and severity classification, respectively. The leaf reflectance spectra, most sensitive to SCR detection and severity classification, were found in the 572 nm, 766 nm, and 1445 nm wavelength and 575 nm, 640 nm, and 1670 nm wavelength, respectively. These spectral features were associated with leaf pigment and leaf water content. Finally, by employing a support vector machine (SVM), the performances of developed SCR-SDIs were assessed and compared with 38 stress-related vegetation indices (VIs) identified in the literature. The SDIs-based models developed in this study achieved an overall accuracy of 87% and 70% in SCR detection and severity classification, 1.1% and 8.3% higher than the other best VIs-based model under study, respectively. Our results thus suggest that the SCR-SDIs is a promising tool for fast, nondestructive diagnosis of SCR in the field over large areas. To our knowledge, this study represents one of the first few efforts to provide a theoretical basis for remote sensing of SCR at field and larger scales. With the increasing use of unmanned aerial vehicles (UAVs) with hyperspectral measurement capability, more studies should be conducted to expand our developed SCR-SDIs for SCR monitoring at different study sites and growing stages in the future.