Characterization of Soil Water Content Using Measured Visible and Near Infrared Spectra

2006 ◽  
Vol 70 (4) ◽  
pp. 1295-1302 ◽  
Author(s):  
A. M. Mouazen ◽  
R. Karoui ◽  
J. De Baerdemaeker ◽  
H. Ramon
Keyword(s):  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Infrared Spectra ◽  
Near Infrared ◽  
Near Infrared Spectra

Laboratory Characterization of a Commercial Capacitance Sensor for Estimating Permittivity and Inferring Soil Water Content

2006 ◽  
Vol 5 (3) ◽  
pp. 1048-1064 ◽  
Author(s):  
Mike Schwank ◽  
Timothy R. Green ◽  
Christian Mätzler ◽  
Hansruedi Benedickter ◽  
Hannes Flühler
Keyword(s):  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Capacitance Sensor ◽  
Laboratory Characterization

scholarly journals Search and characterization of T-type planetary mass candidates in the σ Orionis cluster

2010 ◽  
Vol 6 (S276) ◽  
pp. 489-490
Author(s):  
Karla Peña Ramírez ◽  
Maria R. Zapatero Osorio ◽  
Victor J.S. Béjar
Keyword(s):  
Infrared Spectra ◽  
Spectral Type ◽  
Spectroscopic Data ◽  
Near Infrared ◽  
Cluster Membership ◽  
Planetary Mass ◽  
Near Infrared Spectra ◽  
Motion Measurements ◽  
Astrometric Data

AbstractWe present new photometric and astrometric data available for S Ori 70 and 73, the two T-type planetary-mass member candidates in the σ Orionis cluster (~3 ± 2 Myr, d~360 pc). S Ori 70 (J ~ 19.9 mag) has a spectral type of T5.5 ± 1.0 measured from published near-infrared spectra, while no spectroscopic data are available for S Ori 73 (J ~ 21 mag). We estimate the spectral type of S Ori 73 by using J, H, and CH4off (λc=1.575 μm, Δλ=0.112 μm) photometry and comparing the H-CH4off index of S Ori 73 with the colors of field stars and brown dwarfs of spectral types in the range F to late T. The locations of S Ori 70 and 73 in the J-H vs H-CH4off color-color diagram are consistent with spectral types T8 ± 1 and T4 ± 1, respectively. Proper motion measurements of the two sources are larger than the motion of the central σ Ori star, making their cluster membership somehow uncertain.

scholarly journals Separation of soil and canopy reflectance signatures of Mid German agricultural soils

2011 ◽  
Vol 51 (No, 7) ◽  
pp. 296-303 ◽  
Author(s):  
T. Behrens ◽  
K. Gregor ◽  
W. Diepenbrock
Keyword(s):  
Remote Sensing ◽  
Soil Properties ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Vegetation Index ◽  
Near Infrared ◽  
Field Experiments ◽  
Agricultural Soils ◽  
Soil Reflectance

Remote sensing can provide visual indications of crop growth during production season. In past, spectral optical estimations were well performed in the ability to be correlated with crop and soil properties but were not consistent within the whole production season. To better quantify vegetation properties gathered via remote sensing, models of soil reflectance under changing moisture conditions are needed. Signatures of reflected radiation were acquired for several Mid German agricultural soils in laboratory and field experiments. Results were evaluated at near-infrared spectral region at the wavelength of 850 nm. The selected soils represented different soil colors and brightness values reflecting a broad range of soil properties. At the wavelength of 850 nm soil reflectance ranged between 10% (black peat) and 74% (white quartz sand). The reflectance of topsoils varied from 21% to 32%. An interrelation was found between soil brightness rating values and spectral optical reflectance values in form of a linear regression. Increases of soil water content from 0% to 25% decreased signatures of soil reflectance at 850 nm of two different soil types about 40%. The interrelation of soil reflectance and soil moisture revealed a non-linear exponential function. Using knowledge of the individual signature of soil reflectance as well as the soil water content at the measurement, soil reflectance could be predicted. As a result, a clear separation is established between soil reflectance and reflectance of the vegetation cover if the vegetation index is known.

scholarly journals Classification of Granite Soils and Prediction of Soil Water Content Using Hyperspectral Visible and Near-Infrared Imaging

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1611 ◽  
Author(s):  
Hwan-Hui Lim ◽  
Enok Cheon ◽  
Deuk-Hwan Lee ◽  
Jun-Seo Jeon ◽  
Seung-Rae Lee
Keyword(s):  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Near Infrared ◽  
Classification Model ◽  
Projection Algorithm ◽  
Support Vector ◽  
Ann Model ◽  
Landslide Hazards ◽  
Combining Data

Soil water content is one of the most important physical indicators of landslide hazards. Therefore, quickly and non-destructively classifying soils and determining or predicting water content are essential tasks for the detection of landslide hazards. We investigated hyperspectral information in the visible and near-infrared regions (400–1000 nm) of 162 granite soil samples collected from Seoul (Republic of Korea). First, effective wavelengths were extracted from pre-processed spectral data using the successive projection algorithm to develop a classification model. A gray-level co-occurrence matrix was employed to extract textural variables, and a support vector machine was used to establish calibration models and the prediction model. The results show that an optimal correct classification rate of 89.8% could be achieved by combining data sets of effective wavelengths and texture features for modeling. Using the developed classification model, an artificial neural network (ANN) model for the prediction of soil water content was constructed. The input parameter was composed of Munsell soil color, area of reflectance (near-infrared), and dry unit weight. The accuracy in water content prediction of the developed ANN model was verified by a coefficient of determination and mean absolute percentage error of 0.91 and 10.1%, respectively.

scholarly journals Near-infrared spectra of Scapharca homodimeric hemoglobin: characterization of the deoxy and photodissociated derivatives

1996 ◽  
Vol 70 (6) ◽  
pp. 2924-2929 ◽  
Author(s):  
J. Huang ◽  
M. Leone ◽  
A. Boffi ◽  
J.M. Friedman ◽  
E. Chiancone
Keyword(s):  
Infrared Spectra ◽  
Near Infrared ◽  
Near Infrared Spectra

Near-infrared spectroscopy for soil water determination in small soil volumes

2004 ◽  
Vol 84 (3) ◽  
pp. 333-338 ◽  
Author(s):  
P. R. Bullock ◽  
X. Li ◽  
L. Leonardi
Keyword(s):  
Infrared Spectroscopy ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Water Levels ◽  
Least Squares Regression ◽  
Volumetric Soil Water Content ◽  
Volumetric Soil Water

Critical soil water levels for soil microscale processes are difficult to determine because of variability in large soil volumes and lack of techniques for logging soil water contents in small soil volumes. This study tested nearinfrared (NIR) spectroscopy for soil water content determination. Five soil horizons with a range in soil texture, soil organic carbon, carbonates, pH and horizon depth, were tested at air-dry, field capacity and 0.1 MPa tension water content. Volumetric soil water content, determined using the standard method of oven-drying and soil bulk density, was compared to NIR absorbance in various combinations and wavelengths. The NIR spectra obtained with the probe in direct contact with the soil gave better results than when the probe was separated from the soil with a glass slide. The most reliable validation results were obtained using a multivariate partial least squares regression of the full spectrum with an r2 of 0.95 and RMSE of prediction of 6.4%. Smoothing and derivatives of the spectra did not improve the validation results. The relationships for absorbance at single wavelength segments, ratios, differences and area under the curve around the 1940 nm peak were good (r2 values near 0.85 ) but poorer than the results using the full spectra. The high correlation coefficients obtained with the wide variety of soils utilized in this study suggest that NIR absorbance is a practical method for determining volumetric soil water content for small soil volumes. Key words: Near-infrared spectroscopy, soil water, Near-infrared absorbance

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