scholarly journals Spectral Analysis and Sensitive Waveband Determination Based on Nitrogen Detection of Different Soil Types Using Near Infrared Sensors

Sensors ◽  
2018 ◽  
Vol 18 (2) ◽  
pp. 523 ◽  
Author(s):  
Shupei Xiao ◽  
Yong He ◽  
Tao Dong ◽  
Pengcheng Nie
Keyword(s):  
Spectral Analysis ◽  
Near Infrared ◽  
Soil Types ◽  
Infrared Sensors ◽  
Nitrogen Detection

scholarly journals Research on the Effects of Drying Temperature on Nitrogen Detection of Different Soil Types by Near Infrared Sensors

Sensors ◽  
2018 ◽  
Vol 18 (2) ◽  
pp. 391 ◽  
Author(s):  
Pengcheng Nie ◽  
Tao Dong ◽  
Yong He ◽  
Shupei Xiao
Keyword(s):  
Near Infrared ◽  
Soil Types ◽  
Infrared Sensors ◽  
Drying Temperature ◽  
Nitrogen Detection

scholarly journals Spectral analysis as an extra method to soil type discrimination

2020 ◽  
Vol 12 (2) ◽  
pp. 153-156
Author(s):  
M. Todorova ◽  
S. Atanassova ◽  
M. Georgiev ◽  
L. Pleshkuza
Keyword(s):  
Spectral Analysis ◽  
Soil Type ◽  
Quantitative Method ◽  
Diffuse Reflectance ◽  
Near Infrared ◽  
Soil Analysis ◽  
Soil Samples ◽  
Soil Types ◽  
Spectral Information ◽  
Future Studies

Abstract. The purpose of the study was to test near infrared soil spectra as an extra method for three soil types (Fluvisols, Vertisols and Solonchaks) discrimination from different regions of South Bulgaria. The diffuse reflectance spectra of 177 soil samples (from the 0-20cm layers): 50 samples of Fluvisols soil type, 78 samples of Vertisols soil type and 48 samples of Solonchaks soil type were obtained using a Spectrum NIRQuest (OceanOptics, Inc.) working within the range from 900 to 1700 nm. Soft independent modelling of class analogy (SIMCA) was performed to classify samples according to their taxonomic classes. The results obtained showed that the soil samples are separated accurately according to their soil type based on their spectral information. All this could be used in the future studies related to the application of the NIRS method as a qualitative or quantitative method for soil analysis and also for the purposes of precision farming.

Detecting cadmium contamination in loessal soils using near-infrared spectroscopy in the Xiaoqinling gold area

2021 ◽  
pp. 0958305X2110301
Author(s):  
Min Yang ◽  
Youning Xu ◽  
Haixing Shang ◽  
Abdullah Abdullah ◽  
Wen Zhang
Keyword(s):  
Heavy Metal ◽  
Spectral Analysis ◽  
Near Infrared ◽  
Heavy Metal Contamination ◽  
Northwest China ◽  
Peak Shift ◽  
Hydroxyl Group ◽  
Band Shift ◽  
Heavy Metal Cations ◽  
Field Reflectance

Loess is an important soil type that is widespread in the Loess Plateau of northwest China. However, mining exploitation, beneficiation, and metallurgy have led to inorganic contamination of soils that threatens the health of residents. The regular absorption peak shift of near-infrared (NIR) spectra in loessal soils represents a new method of soil environmental assessment based on field reflectance spectroscopy and hyperspectral remote sensing. Specifically, the NIR features of loessal soil will shift in response to changes in the soil composition and microstructure induced by heavy metal pollution. This study collected 27 samples from notable regions in the study area. Mid-infrared (MIR) spectral analysis, NIR spectral analysis, modified seven-step Tessier sequential extraction, and X-ray diffraction were used to analyze the band shift phenomenon of MIR and NIR features. The alignment of NIR bands was determined via the correlation between NIR and MIR bands associated with the vibration variations of the hydroxyl group. The correlations established by NIR band positions and exchangeable Cd cations were also analyzed. The results were then discussed according to the mineralogical characteristics of the heavy metal cations adsorbed on the surface and interlayer sites of clay minerals. These results can be used as a reference for the application of NIR technology to detecting heavy metal contamination in the soil of mining regions.

scholarly journals HyTexiLa: High Resolution Visible and Near Infrared Hyperspectral Texture Images

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2045 ◽  
Author(s):  
Haris Khan ◽  
Sofiane Mihoubi ◽  
Benjamin Mathon ◽  
Jean-Baptiste Thomas ◽  
Jon Hardeberg
Keyword(s):  
Spectral Analysis ◽  
High Resolution ◽  
Matrix Factorization ◽  
Local Binary Pattern ◽  
Near Infrared ◽  
Texture Classification ◽  
Hyperspectral Images ◽  
Close Range ◽  
Local Binary Pattern Feature ◽  
Non Negative Matrix Factorization

We present a dataset of close range hyperspectral images of materials that span the visible and near infrared spectrums: HyTexiLa (Hyperspectral Texture images acquired in Laboratory). The data is intended to provide high spectral and spatial resolution reflectance images of 112 materials to study spatial and spectral textures. In this paper we discuss the calibration of the data and the method for addressing the distortions during image acquisition. We provide a spectral analysis based on non-negative matrix factorization to quantify the spectral complexity of the samples and extend local binary pattern operators to the hyperspectral texture analysis. The results demonstrate that although the spectral complexity of each of the textures is generally low, increasing the number of bands permits better texture classification, with the opponent band local binary pattern feature giving the best performance.

scholarly journals Application of near infrared sensors to preconcentration of hydrothermally-formed copper ore

2016 ◽  
Vol 85 ◽  
pp. 148-167 ◽  
Author(s):  
Shekwonyadu Iyakwari ◽  
Hylke J. Glass ◽  
Gavyn K. Rollinson ◽  
Przemyslaw B. Kowalczuk
Keyword(s):  
Near Infrared ◽  
Infrared Sensors ◽  
Copper Ore
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