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 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.

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

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

scholarly journals Prediksi Pengoperasian Traktor di Lahan Kering Menggunakan Artificial Neural Network

2021 ◽  
Vol 12 (3) ◽  
pp. 127-133
Author(s):  
Taufik Nugraha Agassi ◽  
Yose Sebastian ◽  
Zainal Arifin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Water Content ◽  
Supervised Learning ◽  
Soil Water ◽  
Soil Water Content ◽  
Back Propagation ◽  
Learning Method ◽  
Ann Model ◽  
Dry Land

Soil water content is an important parameter in making a decision to use a tractor or not. The process of measuring soil water content and levels of field capacity in conventional which takes a long time and cannot be used in real-time to measure it is a major problem in the field. Determinants of soil water content such as ambient temperature, humidity, and rainfall can be obtained easily and quickly either by using a tool or retrieving data from the nearest BMKG station. The objective of this research is to obtain the most optimal prediction model in making decisions about tractor operation in dry land. This research uses an Artificial Neural Network (ANN) in modeling predictions of tractor operation. Prediction of tractor operation is a prediction of tractor use on a certain day using input data obtained before the day of tractor use. ANN modeling uses the back-propagation supervised learning method. The best ANN model used four hidden neurons with a learning coefficient of 0.2, a momentum of 0.8 and 20,000 iterations. This model has been able to provide optimal predictions with an accuracy value of 77%. The ANN model has been successful in predicting tractor operation on dry land using the back-propagation supervised learning method.

scholarly journals A near infrared index to assess effects of soil texture and organic carbon content on soil water content

2018 ◽  
Vol 70 (1) ◽  
pp. 151-161 ◽  
Author(s):  
I. Soltani ◽  
Y. Fouad ◽  
D. Michot ◽  
P. Bréger ◽  
R. Dubois ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Carbon Content ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Soil Texture ◽  
Near Infrared ◽  
Organic Carbon Content

Visible-Near-Infrared Spectroscopy Prediction of Soil Characteristics as Affected by Soil-Water Content

2018 ◽  
Vol 82 (6) ◽  
pp. 1333-1346 ◽  
Author(s):  
Lashya P. Marakkala Manage ◽  
Mogens Humlekrog Greve ◽  
Maria Knadel ◽  
Per Moldrup ◽  
Lis W. de Jonge ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Soil Characteristics

Soil Water Content Variation Regression Analysis Using Hyperspectral Camera in Weathered Granite Soils

2020 ◽  
Author(s):  
Hwan-hui Lim ◽  
Seung-Rae Lee ◽  
Enok Cheon ◽  
Deukhwan Lee ◽  
Seungmin Lee
Keyword(s):  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Near Infrared ◽  
Prediction Models ◽  
Physical Parameters ◽  
Least Squares Regression ◽  
Weathered Granite ◽  
Content Variation ◽  
Hyperspectral Camera

<p>Soil water content is one of the most common physical parameters that cause landslides or debris flow. Therefore, it is of very importance to determine or predict the water content variation due to infiltration of rainfall quickly and non-destructively. This study investigates the hyperspectral informations in the visible near-infrared regions (VNIR, 400nm~1000nm) of different samples of granite soils possessing varying water contents. Totally 162 granite samples were taken from 3 mountain areas. A Partial Least Squares Regression (PLSR) analysis was applied to develop calibration models and prediction models.  In the water content variation prediction model, the Area of ​​Reflectance(Near-infrared, NIR) parameter was the most suitable parameter to determine the water content. The results demonstrate that the hyperspectral camera combined with the PLSR model can be a useful and non-destructive tool for the determination of soil water content variation in the weathered granite soils that could be applied to the evaluation of possible instable area in a mountain site.</p>

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