Comparative analysis of red-edge hyperspectral indices

Gupta, Rajendra Kumar; Vijayan, D.; Prasad, T.S.

doi:10.1016/s0273-1177(03)90545-x

Cited by 88 publications

(28 citation statements)

References 8 publications

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“…Many investigators have reported different spectral indices by extracting spectral information in the red edge region (Gitelson & Merzlyak 1994;Gupta et al 2003). In the present study, the relationship of LNA to the derivative spectra at 742 nm showed similar results over several growth seasons.…”

Section: Test Of Lnc and Lna Monitoring Modelsupporting

confidence: 76%

“…& Merzylak 1994), mND705 (Sims & gamon 2002), PRI (Gamon et al 1992), RI1dB (Gupta et al 2003), MSS-SARVI (Lyon et al 1998), PSSRb (Blackburn 1998), MSR (NiR, ReD) (Chen et al 1996), RePle (Cho & Skidmore 2006), CTR2 (Carter et al 1996), Fdλi (Dawson & Curran 1998), SDr/SDb (Wang et al 2003), and λo (Miller et al 1990). Separate linear models were fitted to determine the best-fit R 2 values for the relationships of leaf N concentration (LNC) and leaf N accumulation (LNa) to the spectral parameters.…”

Section: Wavelength (Nm)mentioning

confidence: 99%

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A non‐destructive and real‐time method of monitoring leaf nitrogen status in wheat

Yao¹,

Feng²,

Zhu³

et al. 2007

New Zealand Journal of Agricultural Research

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Non-destructive assessment of leaf nitrogen status is helpful for precision nitrogen management in crop production. This study was conducted to quantify the relationships of leaf nitrogen concentration on a leaf dry weight basis (LNC) and leaf nitrogen accumulation on a soil area basis (LNA) to canopy hyperspectral reflectance in wheat. Both LNC and LNA increased with increasing nitrogen rates, along with marked changes in canopy hyperspectral reflectance. The sensitive spectral bands for LNC and LNA occurred mostly within visible light and the near-infrared regions. The hyperspectral parameters REPle, λo and mND705 were linearly related to LNC, and MSS-SARVI, FD742, PSSRb and SDr/SDb were linearly related to LNA. Testing of the REPle and mND705 gave the R 2 values of 0.752 and 0.695 between the measured and estim ated LNC, relative errors (RE) of 14.4 and 16.5%, whereas for FD742 and SDr/SDb, the R 2 values were 0.872 and 0.828, with RE of 14.1 and 15.2% respectively.

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Section: Test Of Lnc and Lna Monitoring Modelsupporting

confidence: 76%

Section: Wavelength (Nm)mentioning

confidence: 99%

A non‐destructive and real‐time method of monitoring leaf nitrogen status in wheat

Yao¹,

Feng²,

Zhu³

et al. 2007

New Zealand Journal of Agricultural Research

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“…Different methods were designed to extract the desired wavelength position [40,60]. The theoretical analysis of [61] demonstrated that detecting inflection points (and, thus, spectral shifts) in vegetation spectra reduce the negative impacts of variations in soil brightness, irradiance conditions and atmospheric perturbations.…”

Section: Shape Descriptors: Inflection Points and Spectral Shiftsmentioning

confidence: 99%

ARCTIS — A MATLAB® Toolbox for Archaeological Imaging Spectroscopy

et al. 2014

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Imaging spectroscopy acquires imagery in hundreds or more narrow contiguous spectral bands. This offers unprecedented information for archaeological research. To extract the maximum of useful archaeological information from it, however, a number of problems have to be solved. Major problems relate to data redundancy and the visualization of the large amount of data. This makes data mining approaches necessary, as well as efficient data visualization tools. Additional problems relate to data quality. Indeed, the upwelling electromagnetic radiation is recorded in small spectral bands that are only about ten nanometers wide. The signal received by the sensor is, thus quite low compared to sensor noise and possible atmospheric perturbations. The often small, instantaneous field of view (IFOV)-essential for archaeologically relevant imaging spectrometer datasets-further limits the useful signal stemming from the ground. The combination of both effects makes radiometric smoothing techniques mandatory. The present study details the functionality of a MATLAB ® -based toolbox, called ARCTIS (ARChaeological Toolbox for Imaging Spectroscopy), for filtering, enhancing, analyzing, and visualizing imaging spectrometer datasets. The toolbox addresses the above-mentioned problems. Its 8618Graphical User Interface (GUI) is designed to allow non-experts in remote sensing to extract a wealth of information from imaging spectroscopy for archaeological research. ARCTIS will be released under creative commons license, free of charge, via website (http://luftbildarchiv.univie.ac.at).

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“…Research undertaken by Schmidt and Skidmore (2003) promoted the use of high spatial and spectral resolution data for improved mapping of salt marsh vegetation of similar structure. The hyperspectral analysis identified key regions of the electromagnetic spectrum which provided detailed information for discriminating between and identifying different wetland species (Schmidt and Skidmore, 2003 Measures of plant physiology and structure such as leaf area index, water content, plant pigment content, canopy architecture and density have been investigated extensively over the past decade (Asner, 1998;Datt, 1998;Ceccato et al, 2001;Gitelson et al, 2002;Champagne et al, 2003;Gupta et al, 2003;Merzlyak et al, 2003;Pu et al, 2003;D'Urso et al, 2004;Schlerf et al, 2005;Stimson et al, 2005;Chun-Jiang et al, 2006) using hyperspectral imagery. These applications investigate the spectral reflectance properties of plants, identifying key spectral wavebands related to specific plant physiological and structural characteristics, hence deriving sensitive vegetation spectral indices for their non-destructive estimation.…”

Section: Applications Of Hyperspectral Remote Sensing In Water Resourcesmentioning

confidence: 99%

A review of hyperspectral remote sensing and its application in vegetation and water resource studies

Govender¹,

Chetty²,

Bulcock³

2009

WSA

307

222

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Multispectral imagery has been used as the data source for water and land observational remote sensing from airborne and satellite systems since the early 1960s. Over the past two decades, advances in sensor technology have made it possible for the collection of several hundred spectral bands. This is commonly referred to as hyperspectral imagery. This review details the differences between multispectral and hyperspectral data; spatial and spectral resolutions and focuses on the application of hyperspectral imagery in water resource studies and, in particular the classification and mapping of land uses and vegetation.

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Comparative analysis of red-edge hyperspectral indices

Cited by 88 publications

References 8 publications

A non‐destructive and real‐time method of monitoring leaf nitrogen status in wheat

A non‐destructive and real‐time method of monitoring leaf nitrogen status in wheat

ARCTIS — A MATLAB® Toolbox for Archaeological Imaging Spectroscopy

A review of hyperspectral remote sensing and its application in vegetation and water resource studies

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