Laboratory and field characterization of visible to near-infrared spectral reflectance of nitrate minerals from the Atacama Desert, Chile, and implications for Mars

2018 ◽  
Vol 103 (2) ◽  
pp. 197-206 ◽  
Author(s):  
Fan Wang ◽  
Brenda B. Bowen ◽  
Ji-Hye Seo ◽  
Greg Michalski
Keyword(s):  
Spectral Reflectance ◽  
Near Infrared ◽  
Atacama Desert ◽  
Infrared Spectral

scholarly journals Estimating Snowpack Density from Near-Infrared Spectral Reflectance Using a Hybrid Model

2021 ◽  
Vol 13 (20) ◽  
pp. 4089
Author(s):  
Mohamed Karim El Oufir ◽  
Karem Chokmani ◽  
Anas El Alem ◽  
Monique Bernier
Keyword(s):  
Hybrid Model ◽  
Spectral Reflectance ◽  
Near Infrared ◽  
Physical Property ◽  
Hydroelectric Power ◽  
Snow Density ◽  
Estimation Model ◽  
Infrared Spectral ◽  
Significant Performance

Improving the estimation of snow density is a key task in current snow research. Characterization of the variability of density in time and space is essential for the estimation of water equivalent, hydroelectric power production, assessment of natural hazards (avalanches, floods, etc.). Hyperspectral imaging is proving to be a promising and reliable tool for monitoring and estimating this physical property. Indeed, the spectral reflectance of snow is partly controlled by changes in its physical properties, particularly in the near-infrared (NIR) part of the spectrum. For this purpose, several models have been designed to estimate snow density from spectral information. However, none has yet achieved significant performance. One of the major difficulties is that the relationship between snow density and spectral reflectance is non-bijective (surjective). Indeed, several reflectance amplitudes can be associated with the same density and vice versa, so the correlation between density and spectral reflectance can be very poor. To resolve this issue, a hybrid snow density estimation model based on spectral data is proposed in this work. The principle behind this model is to classify the snow density prior to its estimation by means of a specific estimator corresponding to a predetermined snow density class. These additional steps eliminate the surjective relation by converting it into three bijective relations between density and spectral reflectance. The calibration step showed that the densities included within the three classes are sensitive to different spectral regions, with R2 > 0.80. The results of the cross-validation for the specific estimators were also satisfactory with R2 > 0.78 and RMSE < 36.36 kg m−3. The overall performance of the hybrid model (HM), when tested with independent data, demonstrated the effectiveness of using proximal NIR hyperspectral imagery to estimate snow density (R2 = NASH = 0.93).

scholarly journals Characterization of interferences to in situ observations of <i>δ</i><sup>13</sup>CH<sub>4</sub> and C<sub>2</sub>H<sub>6</sub> when using a cavity ring-down spectrometer at industrial sites

2017 ◽  
Vol 10 (6) ◽  
pp. 2077-2091 ◽  
Author(s):  
Sabina Assan ◽  
Alexia Baudic ◽  
Ali Guemri ◽  
Philippe Ciais ◽  
Valerie Gros ◽  
...  
Keyword(s):  
Near Infrared ◽  
Field Tests ◽  
Isotopic Signature ◽  
Ambient Conditions ◽  
Industrial Sites ◽  
Source Determination ◽  
Infrared Spectral ◽  
The Subject

Abstract. Due to increased demand for an understanding of CH4 emissions from industrial sites, the subject of cross sensitivities caused by absorption from multiple gases on δ13CH4 and C2H6 measured in the near-infrared spectral domain using CRDS has become increasingly important. Extensive laboratory tests are presented here, which characterize these cross sensitivities and propose corrections for the biases they induce. We found methane isotopic measurements to be subject to interference from elevated C2H6 concentrations resulting in heavier δ13CH4 by +23.5 ‰ per ppm C2H6 ∕ ppm CH4. Measured C2H6 is subject to absorption interference from a number of other trace gases, predominantly H2O (with an average linear sensitivity of 0.9 ppm C2H6 per  % H2O in ambient conditions). Yet, this sensitivity was found to be discontinuous with a strong hysteresis effect and we suggest removing H2O from gas samples prior to analysis. The C2H6 calibration factor was calculated using a GC and measured as 0.5 (confirmed up to 5 ppm C2H6). Field tests at a natural gas compressor station demonstrated that the presence of C2H6 in gas emissions at an average level of 0.3 ppm shifted the isotopic signature by 2.5 ‰, whilst after calibration we find that the average C2H6 : CH4 ratio shifts by +0.06. These results indicate that, when using such a CRDS instrument in conditions of elevated C2H6 for CH4 source determination, it is imperative to account for the biases discussed within this study.

Estimating olive leaf nitrogen concentration using visible and near-infrared spectral reflectance

2013 ◽  
Vol 114 (4) ◽  
pp. 426-434 ◽  
Author(s):  
Nativ Rotbart ◽  
Zeev Schmilovitch ◽  
Yafit Cohen ◽  
Victor Alchanatis ◽  
Ran Erel ◽  
...  
Keyword(s):  
Spectral Reflectance ◽  
Near Infrared ◽  
Nitrogen Concentration ◽  
Leaf Nitrogen ◽  
Olive Leaf ◽  
Leaf Nitrogen Concentration ◽  
Infrared Spectral

Characterization of the Ryugu surface by means of the variability of the near-infrared spectral slope in NIRS3 data

Icarus ◽  
2020 ◽  
Vol 351 ◽  
pp. 113959
Author(s):  
A. Galiano ◽  
E. Palomba ◽  
M. D'Amore ◽  
A. Zinzi ◽  
F. Dirri ◽  
...  
Keyword(s):  
Near Infrared ◽  
Spectral Slope ◽  
Infrared Spectral

Visible-near infrared spectral reflectance of landscape components in western Oregon

1994 ◽  
Vol 47 (2) ◽  
pp. 190-203 ◽  
Author(s):  
Samuel N. Goward ◽  
Karl F. Huemmrich ◽  
Richard H. Waring
Keyword(s):  
Spectral Reflectance ◽  
Near Infrared ◽  
Infrared Spectral

scholarly journals Visible to near-infrared multispectral images dataset for image sensors design

2020 ◽  
Vol 2020 (5) ◽  
pp. 106-1-106-7
Author(s):  
Axel Clouet ◽  
Célia Viola ◽  
Jérôme Vaillant
Keyword(s):  
Spectral Range ◽  
Spectral Reflectance ◽  
Near Infrared ◽  
Daily Life ◽  
Image Sensor ◽  
Image Sensors ◽  
Multispectral Images ◽  
Infrared Spectral Range ◽  
Infrared Spectral ◽  
Reflectance Data

In this paper we present a set of multispectral images covering the visible and near-infrared spectral range (400 nm to 1050 nm). This dataset intends to provide spectral reflectance images containing daily life objects, usable for silicon image sensor simulations. All images were taken with our acquisition bench and a particular attention was brought to processings in order to provide calibrated reflectance data. ReDFISh (Reflectance Dataset For Image sensor Simulation) is available at: http://dx.doi.org/10.18709/perscido.2020.01.ds289.

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