A novel spectral index for estimating fractional cover of non-photosynthetic vegetation using near-infrared bands of Sentinel satellite

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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The dust content of the Crab Nebula

Monthly Notices of the Royal Astronomical Society ◽

10.1093/mnras/stz1533 ◽

2019 ◽

Vol 488 (1) ◽

pp. 164-182 ◽

Cited By ~ 4

Author(s):

I De Looze ◽

M J Barlow ◽

R Bandiera ◽

A Bevan ◽

M F Bietenholz ◽

...

Keyword(s):

Spectral Index ◽

Near Infrared ◽

Crab Nebula ◽

Dust Emission ◽

Infrared Emission ◽

Small Contribution ◽

Dust Extinction ◽

Dust Mass ◽

The Crab Nebula ◽

Dust Condensation

ABSTRACT We have modelled the near-infrared to radio images of the Crab Nebula with a Bayesian SED model to simultaneously fit its synchrotron, interstellar (IS), and supernova dust emission. We infer an IS dust extinction map with an average AV = 1.08 ± 0.38 mag, consistent with a small contribution (${\lesssim }22{{\ \rm per\ cent}}$) to the Crab’s overall infrared emission. The Crab’s supernova dust mass is estimated to be between 0.032 and 0.049 M⊙ (for amorphous carbon grains) with an average dust temperature Tdust = 41 ± 3 K, corresponding to a dust condensation efficiency of 8–12 ${{\ \rm per\ cent}}$. This revised dust mass is up to an order of magnitude lower than some previous estimates, which can be attributed to our different IS dust corrections, lower SPIRE flux densities, and higher dust temperatures than were used in previous studies. The dust within the Crab is predominantly found in dense filaments south of the pulsar, with an average V-band dust extinction of AV = 0.20–0.39 mag, consistent with recent optical dust extinction studies. The modelled synchrotron power-law spectrum is consistent with a radio spectral index αradio = 0.297 ± 0.009 and an infrared spectral index αIR = 0.429 ± 0.021. We have identified a millimetre excess emission in the Crab’s central regions, and argue that it most likely results from two distinct populations of synchrotron emitting particles. We conclude that the Crab’s efficient dust condensation (8–12 ${{\ \rm per\ cent}}$) provides further evidence for a scenario where supernovae can provide substantial contributions to the IS dust budgets in galaxies.

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The near-infrared spectral index of Sagittarius A* derived fromKs- andH-band flare statistics

Astronomy and Astrophysics ◽

10.1051/0004-6361/201016134 ◽

2011 ◽

Vol 532 ◽

pp. A26 ◽

Cited By ~ 21

Author(s):

M. Bremer ◽

G. Witzel ◽

A. Eckart ◽

M. Zamaninasab ◽

R. M. Buchholz ◽

...

Keyword(s):

Spectral Index ◽

Near Infrared ◽

Sagittarius A ◽

Infrared Spectral

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The optical and NIR spectrum of the Crab pulsar with X-shooter

Astronomy and Astrophysics ◽

10.1051/0004-6361/201935086 ◽

2019 ◽

Vol 629 ◽

pp. A140

Author(s):

J. Sollerman ◽

J. Selsing ◽

P. M. Vreeswijk ◽

P. Lundqvist ◽

A. Nyholm

Keyword(s):

Spectral Index ◽

Near Infrared ◽

Maximum Velocity ◽

Noise Spectrum ◽

Emission Lines ◽

High Signal ◽

Electromagnetic Spectrum ◽

Crab Pulsar ◽

Very Large Telescope ◽

Nir Emission

Context. Pulsars are well studied all over the electromagnetic spectrum, and the Crab pulsar may be the most studied object in the sky. Nevertheless, a high-quality optical to near-infrared (NIR) spectrum of the Crab or any other pulsar has not been published to date. Aims. Obtaining a properly flux-calibrated spectrum enables us to measure the spectral index of the pulsar emission, without many of the caveats from previous studies. This was the main aim of this project, but in addition we could also detect absorption and emission features from the pulsar and nebula over an unprecedentedly wide wavelength range. Methods. A spectrum was obtained with the X-shooter spectrograph on the Very Large Telescope. Special care was given to the flux-calibration of these data. Results. A high signal-to-noise spectrum of the Crab pulsar was obtained from 300 nm to 2400 nm. The spectral index fit to this spectrum is flat with αν = 0.16 ± 0.07. For the emission lines we measured a maximum velocity of ∼1600 km s−1, whereas the absorption lines from the material between us and the pulsar is unresolved at the ∼50 km s−1 resolution. A number of diffuse interstellar bands and a few NIR emission lines that have previously not been reported from the Crab are highlighted.

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USING LEGACY SOIL DATA FOR STANDARDIZING PREDICTIONS OF TOPSOIL CLAY CONTENT OBTAINED FROM VNIR/SWIR HYPERSPECTRAL AIRBORNE IMAGES

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xl-3-w3-439-2015 ◽

2015 ◽

Vol XL-3/W3 ◽

pp. 439-444

Author(s):

C. Gomez ◽

A. Gholizadeh ◽

L. Borůvka ◽

P. Lagacherie

Keyword(s):

Soil Properties ◽

Spectral Index ◽

Near Infrared ◽

Soil Property ◽

Clay Content ◽

Short Wave ◽

Soil Samples ◽

Spectral Bands ◽

Large Sets ◽

The One

Mapping of topsoil properties using Visible, Near-Infrared and Short Wave Infrared (VNIR/SWIR) hyperspectral imagery requires large sets of ground measurements for calibrating the models that estimate soil properties. To avoid collecting such expensive data, we proposed a procedure including two steps that involves only legacy soil data that were collected over and?or around the study site: 1) estimation of a soil property using a spectral index of the literature and 2) standardisation of the estimated soil property using legacy soil data. This approach was tested for mapping clay contents in a Mediterranean region in which VNIR/SWIR AISA-DUAL hyperspectral airborne data were acquired. The spectral index was the one proposed by Levin et al (2007) using the spectral bands at 2209, 2133 and 2225 nm. Two legacy soil databases were tested as inputs of the procedure: the Focused-Legacy database composed of 67 soil samples collected in 2000 over the study area, and the No-Focused-Legacy database composed of 64 soil samples collected between 1973 and 1979 around but outside of the study area. The results were compared with those obtained from 120 soil samples collected over the study area during the hyperspectral airborne data acquisition, which were considered as a reference. Our results showed that: 1) the spectral index with no further standardisation offered predictions with high accuracy in term of coefficient of correlation r (0.71), but also high bias (−414 g/kg) and SEP (439 g/kg), 2) the standardisation using both legacy soil databases allowed an increase of accuracy (r = 0.76) and a reduction of bias and SEP and 3) a better standardisation was obtained by using the Focused-Legacy database rather than the No-Focused-Legacy database. Finally, the clay predicted map obtained with standardisation using the Focused-Legacy database showed pedologically-significant soil spatial structures with clear short-scale variations of topsoil clay contents in specific areas. This study, associated with the coming availability of a next generation of hyperspectral VNIR/SWIR satellite data for the entire globe, paves the way for inexpensive methods for delivering high resolution soil properties maps.

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Salinity Monitoring at Saline Sites with Visible–Near-Infrared Spectral Data

Minerals ◽

10.3390/min11101086 ◽

2021 ◽

Vol 11 (10) ◽

pp. 1086

Author(s):

Wei Li ◽

Jing Liu ◽

Nisha Bao ◽

Xinqi Mao ◽

Yachun Mao ◽

...

Keyword(s):

Neural Network ◽

Spectral Data ◽

Spectral Index ◽

Near Infrared ◽

Rbf Neural Network ◽

Salt Content ◽

Saline Soils ◽

Spectral Indices ◽

Inversion Model ◽

Neural Network Algorithm

To address the global phenomenon of the salinisation of large land areas, a quantitative inversion model of the salinity of saline soils and soil visible–near-infrared (NIR) spectral data was developed by considering saline soils in Zhenlai County, Jilin Province, China as the research object. The original spectral data were first subjected to Savitzky–Golay (SG) smoothing, multiplicative scattering correction (MSC) pre-processing, and a combined transformation technique. The pre-processed spectral data were then analysed to construct the difference index (DI), ratio index (RI), and normalised difference index (NDI), and the Spearman rank correlation coefficient (r) between these three spectral indices and the salt content in the samples was calculated, while a combined spectral index (r > 0.8) was eventually selected as a sensitive spectral index. Finally, a quantitative inversion model for the salinity of saline soils was developed, and the model’s accuracy was evaluated based on partial least squares regression (PLSR), the random forest (RF) algorithm, and the radial basis function (RBF) neural network algorithm. The results indicated that the inversion of soil salt content using the selected combination of spectral indices based on the RBF neural network algorithm was the most effective, with the prediction model yielding an R2 value of 0.950, a root mean square error (RMSE) of 1.014, and a relative percentage deviation (RPD) of 4.479, which suggested a good prediction effect.

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Comparison of Radio and Optical Polarization in Extended Extragalactic Radio Sources: I. Observations

Symposium - International Astronomical Union ◽

10.1017/s0074180900190825 ◽

1990 ◽

Vol 140 ◽

pp. 449-450

Author(s):

H.-J. Röser ◽

K. Meisenheimer

Keyword(s):

Spectral Index ◽

Hot Spots ◽

Near Infrared ◽

Detailed Comparison ◽

Radio Sources ◽

Optical Polarization ◽

Extragalactic Radio Sources ◽

Flux Measurements ◽

Synchrotron Light ◽

Infrared Frequencies

We presented new observations in various wavebands of those extragalactic radio sources for which the emission of optical synchrotron light is established by polarimetric investigations (jets of M 87 and 3C 273, hot spots in the lobes of 3C 20, 3C 33 and Pictor A). In addition to optical spectral index and polarization maps, we collected flux measurements at radio, millimetre, and near-infrared frequencies. Our observations result in accurate synchrotron spectra between 109 and 1015 Hz. For some sources a detailed comparison between radio and optical polarization is possible.

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Near infrared variability of Sgr A* - spectral index measurements

Proceedings of the International Astronomical Union ◽

10.1017/s1743921314000738 ◽

2013 ◽

Vol 9 (S303) ◽

pp. 274-282 ◽

Cited By ~ 2

Author(s):

G. Witzel ◽

M. Morris ◽

A. Ghez ◽

L. Meyer ◽

E. Becklin ◽

...

Keyword(s):

Spectral Index ◽

Near Infrared ◽

Time Lag ◽

Time Variable ◽

Time Variability ◽

Data Set ◽

Sagittarius A ◽

State Process ◽

Sgr A ◽

High Cadence

AbstractWe discuss observations of Sagittarius A* with NACO@VLT in K-band and recent synchronous observations with NIRC2@Keck II and OSIRIS@Keck I in L′-band and H-band, respectively. The variability of Sagittarius A* in the near infrared is a continuous one-state process that can be described by a pure red-noise process having a timescale of a few hours. We describe this process and its properties in detail. Our newest observations with the Keck telescopes represent the first truly synchronous high cadence data set to test for time variability of the spectral index within the near infrared. We discovered a time-variable spectral index that might be interpreted as a time lag of the L′-band with respect to the H-band.

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Winter wheat yield estimation model with MODIS normalized near-infrared spectral index

2008 International Workshop on Earth Observation and Remote Sensing Applications ◽

10.1109/eorsa.2008.4620316 ◽

2008 ◽

Cited By ~ 1

Author(s):

Wenpeng Lin ◽

Min Zhao ◽

Yunlong Liu ◽

Jun Gao ◽

Chenli Wang

Keyword(s):

Winter Wheat ◽

Spectral Index ◽

Near Infrared ◽

Wheat Yield ◽

Yield Estimation ◽

Estimation Model ◽

Infrared Spectral ◽

Winter Wheat Yield

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Evaluating the Hyperspectral Sensitivity of the Differenced Normalized Burn Ratio for Assessing Fire Severity

Remote Sensing ◽

10.3390/rs13224611 ◽

2021 ◽

Vol 13 (22) ◽

pp. 4611

Author(s):

Max J. van Gerrevink ◽

Sander Veraverbeke

Keyword(s):

Spectral Index ◽

Field Data ◽

Near Infrared ◽

Environmental Changes ◽

Fire Severity ◽

Hyperspectral Data ◽

Landsat 8 ◽

Fire Emissions ◽

Differenced Normalized Burn Ratio ◽

Normalized Burn Ratio

Fire severity represents fire-induced environmental changes and is an important variable for modeling fire emissions and planning post-fire rehabilitation. Remotely sensed fire severity is traditionally evaluated using the differenced normalized burn ratio (dNBR) derived from multispectral imagery. This spectral index is based on bi-temporal differenced reflectance changes caused by fires in the near-infrared (NIR) and short-wave infrared (SWIR) spectral regions. Our study aims to evaluate the spectral sensitivity of the dNBR using hyperspectral imagery by identifying the optimal bi-spectral NIR SWIR combination. This assessment made use of a rare opportunity arising from the pre- and post-fire airborne image acquisitions over the 2013 Rim and 2014 King fires in California with the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) sensor. The 224 contiguous bands of this sensor allow for 5760 unique combinations of the dNBR at a high spatial resolution of approximately 15 m. The performance of the hyperspectral dNBR was assessed by comparison against field data and the spectral optimality statistic. The field data is composed of 83 in situ measurements of fire severity using the Geometrically structured Composite Burn Index (GeoCBI) protocol. The optimality statistic ranges between zero and one, with one denoting an optimal measurement of the fire-induced spectral change. We also combined the field and optimality assessments into a combined score. The hyperspectral dNBR combinations demonstrated strong relationships with GeoCBI field data. The best performance of the dNBR combination was derived from bands 63, centered at 0.962 µm, and 218, centered at 2.382 µm. This bi-spectral combination yielded a strong relationship with GeoCBI field data of R2 = 0.70 based on a saturated growth model and a median spectral index optimality statistic of 0.31. Our hyperspectral sensitivity analysis revealed optimal NIR and SWIR bands for the composition of the dNBR that are outside the ranges of the NIR and SWIR bands of the Landsat 8 and Sentinel-2 sensors. With the launch of the Precursore Iperspettrale Della Missione Applicativa (PRISMA) in 2019 and several planned spaceborne hyperspectral missions, such as the Environmental Mapping and Analysis Program (EnMAP) and Surface Biology and Geology (SBG), our study provides a timely assessment of the potential and sensitivity of hyperspectral data for assessing fire severity.

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