Estimating leaf chlorophyll of barley at different growth stages using spectral indices to reduce soil background and canopy structure effects

Yu, Kang; Lenz-Wiedemann, Victoria I. S.; Chen, Xinping; Bareth, Georg

doi:10.1016/j.isprsjprs.2014.08.005

Cited by 120 publications

(70 citation statements)

References 81 publications

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“…Some studies have demonstrated the feasibility of estimating chlorophyll content from vegetation indices calculated from measurements of reflectance at specific wavelengths (Schlemmer et al, 2005;Yu et al, 2014;Martinez and Ramos, 2015). The first vegetation indexes were based on the contrast between the near infrared reflectance values and the strong absorption by chlorophyll in the red part of the spectrum.…”

Section: Introductionmentioning

confidence: 99%

Relationship between crop nutritional status, spectral measurements and Sentinel 2 images

Martínez

2017

Agron. colomb.

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RESUMENIn order to monitor the nutritional status of some crops based on plant spectroscopy and Sentinel 2 satellite images in Colombia, spectral reflectance data were taken between 350 and 2,500 nm with a FieldSpec 4 spectrometer in rubber, rice, sugar cane, maize, soybean, cashew, oil palm crops, pastures and natural savanna. Furthermore contents of mineral nutrients in leaves were determined. Several vegetation indexes and red edge positions were calculated using various methods from spectral data and Sentinel 2 satellite images and were correlated with leaf nutrient content. The results showed correlations between spectral indices, mainly those involving a spectral response in the red-edge range with the N, P, K and Cu although the best correlation coefficients were for N. First reflectance derivatives, transformations by the State Normal Variate and second reflectance derivatives showed great potential to monitor N content in crops. The green model index and the red-edge model computed from Sentinel 2 images had the best performance to monitor N content, although in the study area, presence of clouds affected the use of these images. The Sentinel 2 images allowed calculating some vegetation indexes obtained with other images, such as Landsat or SPOT, but additionally other indexes and calculations based on the bands of the red-edge, which is a great contribution to obtain more information of crops on their spatial and temporal variability Con el fin de monitorear el estado nutricional de algunos cultivos con base en espectroscopía de plantas e imágenes satelitales 2 en Colombia, se tomaron datos de reflectancia entre 350 y 2.500 nm con un espectrómetro FieldSpec 4 en cultivos de caucho, arroz, caña de azúcar, maíz, soya, marañón y palma de aceite, en pasturas y sabanas naturales y se determinó el contenido de nutrientes minerales en hojas. Se calcularon varios índices de vegetación y posiciones de borde rojo usando varios métodos, a partir de datos espectrales e imágenes de satélite Sentinel 2 y se correlacionaron con el contenido de nutrientes en las hojas. Los resultados mostraron correlaciones entre índices espectrales, principalmente aquellos que involucraron la respuesta espectral en el rango de borde rojo, con N, P, K y Cu aunque los mejores coeficientes de correlación fueron para N. La primera derivada de la reflectancia, su transformación por la state normal variate y la segundas derivadas mostraron un gran potencial para monitorear el contenido de N en los cultivos. El índice del modelo verde y el modelo de borde rojo calculados a partir de imágenes Sentinel 2 tuvieron el mejor desempeño para monitorear el contenido de N, aunque en las condiciones del área de estudio la presencia de nubes afectó el uso de estas imágenes. Las imágenes Sentinel 2 permitieron calcular algunos índices de vegetación que se obtienen con otras imágenes, como Landsat o SPOT, pero adicionalmente otros índices y cálculos basados en las bandas del borde rojo, lo cual es una gran contribución para obtener más informac...

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Section: Introductionmentioning

confidence: 99%

Relationship between crop nutritional status, spectral measurements and Sentinel 2 images

Martínez

2017

Agron. colomb.

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“…Some of the most used formulations for two and three spectral bands, indicated by le Maire et al (2004), Yu et al (2014) and Verrelst et al (2015), were tested (Table 1): difference indices (DI); ratio indices (RI); normalized DI (NDI); difference of RI (DRI); and ratio of DI (RDI). Table 1.…”

Section: Optimization Of Vegetation Indices For Discrimination Of Dismentioning

confidence: 99%

Assessing Changes in Potato Canopy Caused by Late Blight in Organic Production Systems Through Uav-Based Pushbroom Imaging Spectrometer

Franceschini

Bartholomeus

Apeldoorn

et al. 2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Commission I, ICWG I/IIKEY WORDS: hyperspectral imagery; Vis-NIR spectroscopy; organic potato production; late blight. ABSTRACT:Productivity of cropping systems can be constrained simultaneously by different limiting factors and approaches allowing to indicate and identify plants under stress in field conditions can be valuable for farmers and breeders. In organic production systems, sensing solutions are not frequently studied, despite their potential for crop traits retrieval and stress assessment. In this study, spectral data in the optical domain acquired using a pushbroom spectrometer on board of a unmanned aerial vehicle is used to evaluate the potential of this information for assessment of late blight (Phytophthora infestans) incidence on potato (Solanum tuberosum) under organic cultivation. Vegetation indices formulations with two and three spectral bands were tested for the complete range of the spectral information acquired (i.e., from 450 to 900 nm, with 10 nm of spectral resolution). This evaluation concerned the discrimination between plots cultivated with only one resistant potato variety in contrast with plots with a variety mixture, with resistant and susceptible cultivars. Results indicated that indices based on three spectral bands performed better and optimal wavelengths (i.e., near 490, 530 and 670 nm) are not only related to chlorophyll content but also to other leaf pigments like carotenoids.

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“…Data along the profile can also be consolidated using maximum likelihood classification, discriminant analysis, principal components analysis, regression analysis, wavelet analysis, various decision tree analyses, support vector machine analysis, and artificial neural network analyses (Agarwal et al, 2013;Bai et al, 2012;Bajwa et al, 2004;Banskota et al, 2011;Cheng et al, 2014;Cho et al, 2012;Eddy et al, 2014;Farrell and Mersereau, 2005;Goel et al, 2003;Koger et al, 2003;Lewis, 2002;Mirik et al, 2006;Naidoo et al, 2012;Nooni et al, 2014;Ranganathan and Borges, 2010;Shafri et al, 2007;Sun et al, 2014;Villa et al, 2011;Xu and Gong, 2007;Yu et al, 2014;Zhang et al, 2006).…”

Section: Dimension Reduction Practicesmentioning

confidence: 99%

“…Flavonoids and luteins also help with energy dissipation Rascher et al, 2007). In this study, I estimated the concentrations of carotenoids, anthocyanins, and chlorophylls, because they encompass the major groups of pigments in terrestrial plants, and the equations for estimating these three pigments are relatively welldefined in the remote sensing literature (Gitelson et al, 2006;Merzlyak et al, 2003;Yu et al, 2014).…”

Section: Variability In Pigment Contentmentioning

confidence: 99%

“…When using spectral reflectances to estimate pigment levels, we need to use multiple wavelengths (Blackburn, 2006), because absorption bands can be affected by more than one chemical constituent, and one chemical constituent can influence a broad spectral region (Kokaly and Clark, 1999). Many vegetation indices using multiple bands are available to estimate pigment levels (see Yu et al (2014) for examples).…”

Section: Remote Sensing Of Pigmentsmentioning

confidence: 99%

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Assessing the Role of Invasive Species in Successional Plant Communities Using Hyperspectral Remote Sensing

Aneece¹

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I AbstractBiodiversity has been correlated with ecosystem stability and function. Invasive species can decrease biodiversity and cause ecological and economical damage. These species are especially prevalent in abandoned agricultural fields, and can disrupt secondary successional dynamics. Although field surveys are used to assess the impacts of these species, remote sensing can be more efficient, especially at large spatial extents. I examined the ability to use ground-level hyperspectral remote sensing to study invasive species and their effects on community properties at the Blandy Experimental Farm (BEF) in north-central Virginia. Their effects on ecosystem properties could not be assessed using ground-level remote sensing in this system; thus, I used leaf and soil measurements to assess their effects on secondary succession.I found that remote sensing can be used to differentiate among plant communities.The most influential species to community discriminability are considered invasive, suggesting that these species can strongly influence species compositions and other community properties. The most influential wavelengths for discrimination were distributed throughout the spectral profile and corresponded with plant physiological and structural elements.Thus, spectral differences across species were large enough to be used in aggregate to differentiate plant communities. Additionally, these differences were large enough to differentiate individual species, but discriminability varied by species. The two thistle species that are similar phylogenetically and structurally were readily distinguished amongst each other. However, the shrubby buckthorn was difficult to distinguish from the oriental bittersweet vine despite phylogenetic distance and differences in structure. This was likely due to physical overlap in the field and thus the difficulty in obtaining pure signatures for discrimination. Discriminability also differed by the spectral region examined. Buckthorn and oriental bittersweet were least discriminable in the 550-599 nm and 650-699 nm regions, due 2 to greater intraspecific variability of spectral data in these regions.Additionally, remote sensing can be used to estimate higher order community properties like species diversity, and thus assess the effects of invasive species on plant diversity.However, correlations between species diversity and spectral diversity varied by the spectral transformation technique used and the spectral region examined. There was a strong positive correlation between the two in the visible region when band depth was used and in the near-infrared region when first derivatives were used. There were no strong correlations in the red edge, due to high intraspecific variability in chlorophyll content.Lastly, I assessed the effects of these exotic invasive species on ecosystem properties, specifically secondary succession, using leaf and soil data. Differences in soil characteristics were larger across fields and stages than across species. However, there were species-level ...

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Estimating leaf chlorophyll of barley at different growth stages using spectral indices to reduce soil background and canopy structure effects

Cited by 120 publications

References 81 publications

Relationship between crop nutritional status, spectral measurements and Sentinel 2 images

Relationship between crop nutritional status, spectral measurements and Sentinel 2 images

Assessing Changes in Potato Canopy Caused by Late Blight in Organic Production Systems Through Uav-Based Pushbroom Imaging Spectrometer

Assessing the Role of Invasive Species in Successional Plant Communities Using Hyperspectral Remote Sensing

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