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Journal articles on the topic 'Soil property estimation'
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1
Fredlund, Delwyn G., Daichao Sheng, and Jidong Zhao. "Estimation of soil suction from the soil-water characteristic curve." Canadian Geotechnical Journal 48, no. 2 (February 2011): 186–98. http://dx.doi.org/10.1139/t10-060.
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Soil-water characteristic curves (SWCCs) are routinely used for the estimation of unsaturated soil property functions (e.g., permeability functions, water storage functions, shear strength functions, and thermal property functions). This paper examines the possibility of using the SWCC for the estimation of in situ soil suction. The paper focuses on the limitations of estimating soil suctions from the SWCC and also suggests a context under which soil suction estimations should be used. The potential range of estimated suction values is known to be large because of hysteresis between drying and wetting SWCCs. For this, and other reasons, the estimation of in situ suctions from the SWCC has been discouraged. However, a framework is suggested in this paper for estimating the median value for in situ soil suction along with a likely range of soil suction values (i.e., maximum and minimum values). The percentage error in the estimation of soil suction from the SWCC is shown to be lowest for sand soils and highest for clay soils.
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Fredlund, Delwyn G. "The 1999 R.M. Hardy Lecture: The implementation of unsaturated soil mechanics into geotechnical engineering." Canadian Geotechnical Journal 37, no. 5 (October 1, 2000): 963–86. http://dx.doi.org/10.1139/t00-026.
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The implementation of unsaturated soil mechanics into geotechnical engineering practice requires that there be a paradigm shift from classical soil mechanics methodology. The primary drawback to implementation has been the excessive costs required to experimentally measure unsaturated soil properties. The use of the soil-water characteristic curve has been shown to be the key to the implementation of unsaturated soil mechanics. Numerous techniques have been proposed and studied for the assessment of the soil-water characteristic curves. These techniques range from direct laboratory measurement to indirect estimation from grain-size curves and knowledge-based database systems. The soil-water characteristic curve can then be used for the estimation of unsaturated soil property functions. Theoretically based techniques have been proposed for the estimation of soil property functions such as (i) coefficient of permeability, (ii) water storage modulus, and (iii) shear strength. Gradually these estimations are producing acceptable procedures for geotechnical engineering practices for unsaturated soils. The moisture flux ground surface boundary condition is likewise becoming a part of the solution of most problems involving unsaturated soils. The implementation process for unsaturated soils will still require years of collaboration between researchers and practicing geotechnical engineers.Key words: unsaturated soil mechanics, soil suction, unsaturated soil property functions, negative pore-water pressure, matric suction, soil-water characteristic curve.
3
Ng, Set Foong, Pei Eng Ch’ng, Yee Ming Chew, and Kok Shien Ng. "Applying the Method of Lagrange Multipliers to Derive an Estimator for Unsampled Soil Properties." Scientific Research Journal 11, no. 1 (June 1, 2014): 15. http://dx.doi.org/10.24191/srj.v11i1.5416.
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Soil properties are very crucial for civil engineers to differentiate one type of soil from another and to predict its mechanical behavior. However, it is not practical to measure soil properties at all the locations at a site. In this paper, an estimator is derived to estimate the unknown values for soil properties from locations where soil samples were not collected. The estimator is obtained by combining the concept of the ‘Inverse Distance Method’ into the technique of ‘Kriging’. The method of Lagrange Multipliers is applied in this paper. It is shown that the estimator derived in this paper is an unbiased estimator. The partiality of the estimator with respect to the true value is zero. Hence, the estimated value will be equal to the true value of the soil property. It is also shown that the variance between the estimator and the soil property is minimised. Hence, the distribution of this unbiased estimator with minimum variance spreads the least from the true value. With this characteristic of minimum variance unbiased estimator, a high accuracy estimation of soil property could be obtained.
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Ng, Set Foong, Pei Eng Ch’ng, Yee Ming Chew, and Kok Shien Ng. "Applying the Method of Lagrange Multipliers to Derive an Estimator for Unsampled Soil Properties." Scientific Research Journal 11, no. 1 (June 1, 2014): 15. http://dx.doi.org/10.24191/srj.v11i1.9398.
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Soil properties are very crucial for civil engineers to differentiate one type of soil from another and to predict its mechanical behavior. However, it is not practical to measure soil properties at all the locations at a site. In this paper, an estimator is derived to estimate the unknown values for soil properties from locations where soil samples were not collected. The estimator is obtained by combining the concept of the ‘Inverse Distance Method’ into the technique of ‘Kriging’. The method of Lagrange Multipliers is applied in this paper. It is shown that the estimator derived in this paper is an unbiased estimator. The partiality of the estimator with respect to the true value is zero. Hence, the estimated value will be equal to the true value of the soil property. It is also shown that the variance between the estimator and the soil property is minimised. Hence, the distribution of this unbiased estimator with minimum variance spreads the least from the true value. With this characteristic of minimum variance unbiased estimator, a high accuracy estimation of soil property could be obtained.
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Ortenzi, Sofia, Martina Mangoni, and Lucio Di Matteo. "Estimating moisture content and hydraulic properties of unsaturated sandy soils of Tiber River (Central Italy): integrating data from calibrated PR2/6 probe and hydraulic property estimator." Acque Sotterranee - Italian Journal of Groundwater 11, no. 1 (March 31, 2022): 17–25. http://dx.doi.org/10.7343/as-2022-541.
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The correct estimation of soil moisture data is essential in soil-water management and estimating the hydraulic properties of unsaturated soils. The increased use of Multi-Sensor Capacitance Probes (MCAPs) requires careful calibration. Without accurate calibration, the use of MCAPs leads to incorrect water content estimation, making them of no practical use. This work presents the specific calibration equations for the correct use of the PR2/6 profile probe on sands of different nature. As the iron oxides content of the Tiber River basin sands increases, the calibration lines slope increases, allowing the understanding of the different electromagnetic responses. As for other sands worldwide, sands with high iron oxides content show a relative high specific surface than quartz or calcareous sands, responsible for more adhesive water (e.g., high permittivity values). The water content data are integrated with a hydraulic property estimator allowing the estimation of the hydraulic conductivity of soils. Applying the manufacturer equation of the PR2/6 profile probe instead of the specific equation leads to an overestimation of the hydraulic conductivity values up to two orders of magnitude, making therefore rather incorrect the understanding of the phenomena occurring in the unsaturated zone.
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Mattikalli, N. M., E. T. Engman, L. R. Ahuja, and T. J. Jackson. "Microwave remote sensing of soil moisture for estimation of profile soil property." International Journal of Remote Sensing 19, no. 9 (January 1998): 1751–67. http://dx.doi.org/10.1080/014311698215234.
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Odgers, Nathan P., Alex B. McBratney, and Budiman Minasny. "Digital soil property mapping and uncertainty estimation using soil class probability rasters." Geoderma 237-238 (January 2015): 190–98. http://dx.doi.org/10.1016/j.geoderma.2014.09.009.
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Cho, Yongjin, Kenneth A. Sudduth, and Scott T. Drummond. "Profile Soil Property Estimation Using a VIS-NIR-EC-Force Probe." Transactions of the ASABE 60, no. 3 (2017): 683–92. http://dx.doi.org/10.13031/trans.12049.
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Abstract. Combining data collected in-field from multiple soil sensors has the potential to improve the efficiency and accuracy of soil property estimates. Optical diffuse reflectance spectroscopy (DRS) has been used to estimate many important soil properties, such as soil carbon, water content, and texture. Other common soil sensors include penetrometers that measure soil strength and apparent electrical conductivity (ECa) sensors. Previous field research has related these sensor measurements to soil properties such as bulk density, water content, and texture. A commercial instrument that can simultaneously collect reflectance spectra, ECa, and soil strength data is now available. The objective of this research was to relate laboratory-measured soil properties, including bulk density (BD), total organic carbon (TOC), water content (WC), and texture fractions to sensor data from this instrument. At four field sites in mid-Missouri, profile sensor measurements were obtained to 0.9 m depth, followed by collection of soil cores at each site for laboratory measurements. Using only DRS data, BD, TOC, and WC were not well-estimated (R2 = 0.32, 0.67, and 0.40, respectively). Adding ECa and soil strength data provided only a slight improvement in WC estimation (R2 = 0.47) and little to no improvement in BD and TOC estimation. When data were analyzed separately by major land resource area (MLRA), fusion of data from all sensors improved soil texture fraction estimates. The largest improvement compared to reflectance alone was for MLRA 115B, where estimation errors for the various soil properties were reduced by approximately 14% to 26%. This study showed promise for in-field sensor measurement of some soil properties. Additional field data collection and model development are needed for those soil properties for which a combination of data from multiple sensors is required. Keywords: NIR spectroscopy, Precision agriculture, Reflectance spectra, Soil properties, Soil sensing.
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Zhang, Feixia, G. Ward Wilson, and D. G. Fredlund. "Permeability function for oil sands tailings undergoing volume change during drying." Canadian Geotechnical Journal 55, no. 2 (February 2018): 191–207. http://dx.doi.org/10.1139/cgj-2016-0486.
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The coefficient of permeability function is an important unsaturated soil property required when modeling seepage and contaminant transport phenomena. Inaccuracies in the estimation of the permeability function can lead to significant errors in numerical modeling results. Changes in void ratio and degree of saturation are factors that influence the permeability function. Presently available methodologies for estimating the unsaturated permeability function make the assumption that there is no volume change as soil suction is changed. As a result, volume changes are interpreted as changes in degree of saturation. The commonly used estimation techniques for the permeability function are reasonable for soils such as sands that experience little volume change as soil suction is changed. On the other hand, inaccurate results are generated when soils undergo volume change as is the case with oil sands tailings. Revisions to previous methodologies are proposed to render the estimation of the permeability function more suitable for simulating the drying process associated with soils that undergo high volume changes. The revised methodology independently analyzes the effect of volume changes (i.e., changes in void ratio) and degree of saturation changes (i.e., changes in S-SWCC (degree of saturation - soil-water characteristic curve)). Laboratory data on thickened oil sands tailings are presented and interpreted within the context of the proposed methodology.
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Mohanty, Binayak P. "Soil Hydraulic Property Estimation Using Remote Sensing: A Review." Vadose Zone Journal 12, no. 4 (November 2013): vzj2013.06.0100. http://dx.doi.org/10.2136/vzj2013.06.0100.
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K. S. Lee, K. A. Sudduth, S. T. Drummond, D. H. Lee, N. R. Kitchen, and S. O. Chung. "Calibration Methods for Soil Property Estimation Using Reflectance Spectroscopy." Transactions of the ASABE 53, no. 3 (2010): 675–84. http://dx.doi.org/10.13031/2013.30059.
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Tan, Chee Ghuan, Taksiah Abdul Majid, Kamar Shah Ariffin, and Norazura Muhamad Bunnori. "Effects of Site Classification on Empirical Correlation between Shear Wave Velocity and Standard Penetration Resistance for Soils." Applied Mechanics and Materials 284-287 (January 2013): 1305–10. http://dx.doi.org/10.4028/www.scientific.net/amm.284-287.1305.
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In seismic engineering, the dynamic property of the soil is one of the most important aspects in ground response analysis. Dynamic property is significantly affected by local soil deposits. Shear wave velocity (Vs) of soil is one of the main parameters in determining the amplification factor on ground surface. It is not economically feasible to measure Vs for all sites. Therefore, a reliable empirical correlation between Vs and standard penetration resistance (Nspt) will be useful since Nspt data are easily obtainable in construction industry. This study aims to develop an empirical correlation between Vs and Nspt for all soils by considering the effect of site classification according to the Uniform Building Code. New empirical correlations for all soils are presented in this study and well compared with the previous study to evaluate prediction capability. Results show that site classification has a significant impact on the Vs estimation, and that the proposed correlations are the most appropriate for estimating the Vs profile in the studied area compared with existing correlations.
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Cho, Yongjin, Kenneth A. Sudduth, and Sun-Ok Chung. "Soil physical property estimation from soil strength and apparent electrical conductivity sensor data." Biosystems Engineering 152 (December 2016): 68–78. http://dx.doi.org/10.1016/j.biosystemseng.2016.07.003.
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Cho, Yongjin, Alexander H. Sheridan, Kenneth A. Sudduth, and Kristen S. Veum. "Comparison of Field and Laboratory VNIR Spectroscopy for Profile Soil Property Estimation." Transactions of the ASABE 60, no. 5 (2017): 1503–10. http://dx.doi.org/10.13031/trans.12299.
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Abstract. In-field, in-situ data collection with soil sensors has potential to improve the efficiency and accuracy of soil property estimates. Optical diffuse reflectance spectroscopy (DRS) has been used to estimate important soil properties, such as soil carbon, nitrogen, water content, and texture. Most previous work has focused on laboratory-based visible and near-infrared (VNIR) spectroscopy using dried soil. The objective of this research was to compare estimates of laboratory-measured soil properties from a laboratory DRS spectrometer and an in-situ profile DRS spectrometer. Soil cores were obtained to approximately 1 m depth from treatment blocks representing variability in topsoil depth located at the South Farm Research Center of the University of Missouri. Soil cores were split by horizon, and samples were scanned with the laboratory DRS spectrometer in both field-moist and oven-dried conditions. In-situ profile DRS spectrometer scans were obtained at the same sampling locations. Soil properties measured in the laboratory from the cores were bulk density, total organic carbon (TOC), total nitrogen (TN), particulate organic matter carbon and nitrogen (POM-C and POM-N), water content, and texture fractions. The best estimates of TOC, TN, and bulk density were from the laboratory DRS spectra on dry soil (R2 = 0.94, 0.91, and 0.71, respectively). Estimation errors with the field DRS system were at most 25% higher for these soil properties. For POM-C and POM-N, the field system provided estimates of similar accuracy to the best (dry soil) laboratory measurements. Clay and silt texture fraction estimates were most accurate from laboratory DRS spectra on field-moist soil (R2 = 0.91 and 0.93, respectively). Estimation errors for clay and silt were almost doubled with the field DRS system. Considering the efficiency advantages, in-field, in-situ DRS appears to be a viable alternative to laboratory DRS for TOC, TN, POM-C, POM-N, and bulk density estimates, but perhaps not for soil texture estimates. Keywords: In-situ sensing, Precision agriculture, Reflectance spectra, Soil properties, Soil spectroscopy.
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Fredlund, Delwyn G., and Sandra L. Houston. "Protocol for the assessment of unsaturated soil properties in geotechnical engineering practice." Canadian Geotechnical Journal 46, no. 6 (June 2009): 694–707. http://dx.doi.org/10.1139/t09-010.
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The implementation of unsaturated soil mechanics into routine geotechnical engineering practice requires an evaluation of methodologies that may be used for the assessment of “unsaturated soil property functions.” Guidelines and recommendations need to be provided to practicing engineers. The guidelines need to take the form of “engineering protocols” that define acceptable standards for engineering practice. “Engineering protocols” for unsaturated soils engineering practice can be divided into “preliminary design” protocols and “final design” protocols. Both design levels involve the use of a variety of estimation procedures that have been proposed for various classes of geotechnical problems (e.g., unsaturated flow, shear strength, volume change, and distortion). The hierarchy in methodologies is based mainly on the costs and risks associated with a particular engineering project. In this paper, “hierarchical levels” are suggested that take into consideration the cost of various direct and indirect methodologies for the determination of unsaturated soil properties. Recommendations and suggestions are provided for methods for the determination and use of the soil-water characteristic curves (SWCC) and consequently, for the computation of unsaturated soil property functions (USPFs). Primary attention is given to estimation procedures best known to the authors and most appropriate for geotechnical engineering practice.
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Reidy, B., I. Simo, P. Sills, and R. E. Creamer. "Pedotransfer functions for Irish soils – estimation of bulk density (<i>ρ</i><sub>b</sub>) per horizon type." SOIL 2, no. 1 (January 18, 2016): 25–39. http://dx.doi.org/10.5194/soil-2-25-2016.
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Abstract. Soil bulk density is a key property in defining soil characteristics. It describes the packing structure of the soil and is also essential for the measurement of soil carbon stock and nutrient assessment. In many older surveys this property was neglected and in many modern surveys this property is omitted due to cost both in laboratory and labour and in cases where the core method cannot be applied. To overcome these oversights pedotransfer functions are applied using other known soil properties to estimate bulk density. Pedotransfer functions have been derived from large international data sets across many studies, with their own inherent biases, many ignoring horizonation and depth variances. Initially pedotransfer functions from the literature were used to predict different horizon type bulk densities using local known bulk density data sets. Then the best performing of the pedotransfer functions were selected to recalibrate and then were validated again using the known data. The predicted co-efficient of determination was 0.5 or greater in 12 of the 17 horizon types studied. These new equations allowed gap filling where bulk density data were missing in part or whole soil profiles. This then allowed the development of an indicative soil bulk density map for Ireland at 0–30 and 30–50 cm horizon depths. In general the horizons with the largest known data sets had the best predictions, using the recalibrated and validated pedotransfer functions.
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Ren, Jianhua, Xiaojie Li, Sijia Li, Honglei Zhu, and Kai Zhao. "Quantitative Analysis of Spectral Response to Soda Saline-AlkaliSoil after Cracking Process: A Laboratory Procedure to Improve Soil Property Estimation." Remote Sensing 11, no. 12 (June 13, 2019): 1406. http://dx.doi.org/10.3390/rs11121406.
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Cracking on the surface of soda saline-alkali soil is very common. In most previous studies, spectral prediction models of soil salinity were less accurate since spectral measurements were usually performed on 2 mm soil samples which cannot represent true soil surface condition very well. The objective of our research is to provide a procedure to improve soil property estimation of soda saline-alkali soil based on spectral measurement considering the texture feature of the soil surface with cracks. To achieve this objective, a cracking test was performed with 57 soil samples from Songnen Plain of China, the contrast (CON) texture feature of crack images of soil samples was then extracted from grey level co-occurrence matrix (GLCM). The original reflectance was then measured and the mixed reflectance considering the CON texture feature was also calculated from both the block soil samples (soil blocks separated by crack regions) and the comparison soil samples (soil powders with 2 mm particle size). The results of analysis between spectra and the main soil properties indicate that surface cracks can reduce the overall reflectivity of the soda saline-alkali soil and thus increasing the spectral difference among the block soil samples with different salinity levels. The results also show that both univariate and multivariate linear regression models considering the CON texture feature can greatly improve the prediction accuracy of main soil properties of soda saline-alkali soils, such as Na+, EC and salinity, which also can reduce the intensity of field spectral measurements under natural condition.
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Kawamura, Kensuke, Tomohiro Nishigaki, Andry Andriamananjara, Hobimiarantsoa Rakotonindrina, Yasuhiro Tsujimoto, Naoki Moritsuka, Michel Rabenarivo, and Tantely Razafimbelo. "Using a One-Dimensional Convolutional Neural Network on Visible and Near-Infrared Spectroscopy to Improve Soil Phosphorus Prediction in Madagascar." Remote Sensing 13, no. 8 (April 15, 2021): 1519. http://dx.doi.org/10.3390/rs13081519.
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As a proximal soil sensing technique, laboratory visible and near-infrared (Vis-NIR) spectroscopy is a promising tool for the quantitative estimation of soil properties. However, there remain challenges for predicting soil phosphorus (P) content and availability, which requires a reliable model applicable for different land-use systems to upscale. Recently, a one-dimensional convolutional neural network (1D-CNN) corresponding to the spectral information of soil was developed to considerably improve the accuracy of soil property predictions. The present study investigated the predictive ability of a 1D-CNN model to estimate soil available P (oxalate-extractable P; Pox) content in soils by comparing it with partial least squares (PLS) and random forest (RF) regressions using soil samples (n = 318) collected from natural (forest and non-forest) and cultivated (upland and flooded rice fields) systems in Madagascar. Overall, the 1D-CNN model showed the best predictive accuracy (R2 = 0.878) with a highly accurate prediction ability (ratio of performance to the interquartile range = 2.492). Compared to the PLS model, the RF and 1D-CNN models indicated 4.37% and 23.77% relative improvement in root mean squared error values, respectively. Based on a sensitivity analysis, the important wavebands for predicting soil Pox were associated with iron (Fe) oxide, organic matter (OM), and water absorption, which were previously known wavelength regions for estimating P in soil. These results suggest that 1D-CNN corresponding spectral signatures can be expected to significantly improve the predictive ability for estimating soil available P (Pox) from Vis-NIR spectral data. Rapid and accurate estimation of available P content in soils using our results can be expected to contribute to effective fertilizer management in agriculture and the sustainable management of ecosystems. However, the 1D-CNN model will require a large dataset to extend its applicability to other regions of Madagascar. Thus, further updates should be tested in future studies using larger datasets from a wide range of ecosystems in the tropics.
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Fredlund, Delwyn G., and Murray D. Fredlund. "Application of ‘Estimation Procedures’ in Unsaturated Soil Mechanics." Geosciences 10, no. 9 (September 11, 2020): 364. http://dx.doi.org/10.3390/geosciences10090364.
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The application of unsaturated soil mechanics in routine geotechnical engineering applications requires the determination of unsaturated soil properties. Unfortunately, the cost of direct measurement of unsaturated soil properties goes beyond the financial budget of most clients. A solution has arisen, however, that involves the measurement of two less costly soil properties functions that can be used in conjunction with a series of assumptions and estimation methodologies. The two laboratory tests involve measurement of the: (i) gravimetric water content versus soil suction, referred to as the soil-water characteristic curve (w-SWCC) and (ii) water content versus void ratio, referred to as the shrinkage curve (SC). These two unsaturated soil property relationships can be used along with saturated soil properties to extend unsaturated soil properties over the full range of soil suctions. “Estimation procedures” have been developed and verified for all physical properties of interest in unsaturated soil mechanics. The use of estimation procedures has meant that the geotechnical engineer must operate within a new paradigm. The new paradigm provides sufficient accuracy for most geotechnical engineering applications. The net result is an increased decision-making capability for geotechnical engineers.
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Xu, Zhengyuan, Shengbo Chen, Peng Lu, Zibo Wang, Anzhen Li, Qinghong Zeng, and Liwen Chen. "Optimizing a Standard Spectral Measurement Protocol to Enhance the Quality of Soil Spectra: Exploration of Key Variables in Lab-Based VNIR-SWIR Spectral Measurement." Remote Sensing 14, no. 7 (March 23, 2022): 1558. http://dx.doi.org/10.3390/rs14071558.
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The method of proximal VNIR-SWIR (with a spectral region of 400–2500 nm) spectroscopy in a laboratory setting has been widely employed in soil property estimations. Increasing attention has been focused recently on establishing an agreed-upon protocol for soil spectral measurement, fueled by the recognition that studies carried out under different laboratory settings have made future data sharing and model comparisons difficult. This study aimed to explore the key factors in a lab-based spectral measurement procedure to provide recommendations for enhancing the spectra quality and promoting the development of the spectral measurement protocol. To this aim, with the support of the standard spectral laboratory at Jilin University, China, we designed and performed control experiments on four key factors—the light interference in the measurement course, soil temperature, soil moisture, and soil particle size—to quantify the variation in the spectra quality by the subsequent estimation accuracies of different estimation models developed with different spectra obtained from control groups. The results showed that (1) the soil–probe contact measurement derived the optimum spectra quality and estimation accuracy; however, close-non-contact measurement also achieved acceptable results; (2) sieving the soil sample into particle sizes below 1 mm and drying before spectral measurement effectively enhanced spectra quality and estimation accuracy; (3) the variation in soil temperature did not have a distinct influence on spectra quality, and the estimation accuracies of models developed based on soil samples at 20–50 °C were all acceptable. Moreover, a 30-min warm-up of the spectrometer and contact probe was found to be effective. We carried out a complete and detailed control experiment process, the results of which offer a guide for optimizing the process of laboratory-based soil proximal spectral measurement to enhance spectra quality and corresponding estimation accuracy. Furthermore, we present theoretical support for the development of the spectral measurement protocol. We also present optional guidance with relatively lower accuracy but effective results, which are save time and are low cost for future spectral measurement projects.
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Fredlund, Murray D., G. Ward Wilson, and Delwyn G. Fredlund. "Use of the grain-size distribution for estimation of the soil-water characteristic curve." Canadian Geotechnical Journal 39, no. 5 (October 1, 2002): 1103–17. http://dx.doi.org/10.1139/t02-049.
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The implementation of unsaturated soil mechanics into engineering practice is dependent, to a large extent, upon an ability to estimate unsaturated soil property functions. The soil-water characteristic curve (SWCC), along with the saturated soil properties, has proven to provide a satisfactory basis for estimating the permeability function and shear strength functions for an unsaturated soil. The volume change functions have not been totally defined nor applied in geotechnical engineering. The objective of this paper is to present a procedure for estimating the SWCC from information on the grain-size distribution and the volumemass properties of a soil. SWCCs represent a continuous water content versus soil suction relationship. The proposed method provides an approximate means of estimating the desorption curve corresponding to a soil initially slurried near the liquid limit. The effects of stress history, fabric, confining pressure, and hysteresis are not addressed. A database of published data is used to verify the proposed procedure. The database contains independent measurements of the grain-size distribution and the SWCC. The level of fit between the estimated and measured SWCCs is analyzed statistically. The proposed procedure is compared to previously proposed methods for predicting the SWCC from the grain-size distribution. The results show that the proposed procedure is somewhat superior to previous methods.Key words: soil-water characteristic curve, grain-size distribution, volume-mass properties, pedo-transfer function, unsaturated soil property functions.
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KATSUMA, Shinya, Ryosuke YAJIMA, Shunsuke HAMASAKI, Pang-jo CHUN, Keiji NAGATANI, Genki YAMAUCHI, Takeshi HASHIMOTO, Atsushi YAMASHITA, and Hajime ASAMA. "Soil Property Estimation Using 3-D Measurement Data for Autonomous Excavation in Consideration with Soil Properties." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2020 (2020): 2A2—A11. http://dx.doi.org/10.1299/jsmermd.2020.2a2-a11.
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Reidy, B., I. Simo, P. Sills, and R. E. Creamer. "Pedotransfer functions for Irish soils – estimation of bulk density (ρ<sub>b</sub>) per horizon type." SOIL Discussions 2, no. 2 (October 9, 2015): 1039–74. http://dx.doi.org/10.5194/soild-2-1039-2015.
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Abstract. Soil bulk density is a key property in defining soil characteristics. It describes the packing structure of the soil and is also essential for the measurement of soil carbon stock and nutrient assessment. In many older surveys this property was neglected and in many modern surveys this property is omitted due to cost both in laboratory and labour and in cases where the core method cannot be applied. To overcome these oversights pedotransfer functions are applied using other known soil properties to estimate bulk density. Pedotransfer functions have been derived from large international datasets across many studies, with their own inherent biases, many ignoring horizonation and depth variances. Initially pedotransfer functions from the literature were used to predict different horizon types using local known bulk density datasets. Then the best performing of the pedotransfer functions, were selected to recalibrate and then were validated again using the known data. The predicted co-efficient of determination was 0.5 or greater in 12 of the 17 horizon types studied. These new equations allowed gap filling where bulk density data was missing in part or whole soil profiles. This then allowed the development of an indicative soil bulk density map for Ireland at 0–30 and 30–50 cm horizon depths. In general the horizons with the largest known datasets had the best predictions, using the recalibrated and validated pedotransfer functions.
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Angelopoulou, Theodora, Sabine Chabrillat, Stefano Pignatti, Robert Milewski, Konstantinos Karyotis, Maximilian Brell, Thomas Ruhtz, Dionysis Bochtis, and George Zalidis. "Evaluation of Airborne HySpex and Spaceborne PRISMA Hyperspectral Remote Sensing Data for Soil Organic Matter and Carbonates Estimation." Remote Sensing 15, no. 4 (February 17, 2023): 1106. http://dx.doi.org/10.3390/rs15041106.
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Remote sensing and soil spectroscopy applications are valuable techniques for soil property estimation. Soil organic matter (SOM) and calcium carbonate are important factors in soil quality, and although organic matter is well studied, calcium carbonates require more investigation. In this study, we validated the performance of laboratory soil spectroscopy for estimating the aforementioned properties with referenced in situ data. We also examined the performance of imaging spectroscopy sensors, such as the airborne HySpex and the spaceborne PRISMA. For this purpose, we applied four commonly used machine learning algorithms and six preprocessing methods for the evaluation of the best fitting algorithm.. The study took place over crop areas of Amyntaio in Northern Greece, where extensive soil sampling was conducted. This is an area with a very variable mineralogical environment (from lignite mine to mountainous area). The SOM results were very good at the laboratory scale and for both remote sensing sensors with R2 = 0.79 for HySpex and R2 = 0.76 for PRISMA. Regarding the calcium carbonate estimations, the remote sensing accuracy was R2 = 0.82 for HySpex and R2 = 0.36 for PRISMA. PRISMA was still in the commissioning phase at the time of the study, and therefore, the acquired image did not cover the whole study area. Accuracies for calcium carbonates may be lower due to the smaller sample size used for the modeling procedure. The results show the potential for using quantitative predictions of SOM and the carbonate content based on soil and imaging spectroscopy at the air and spaceborne scales and for future applications using larger datasets.
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TISCHLER, M., M. GARCIA, C. PETERSLIDARD, M. MORAN, S. MILLER, D. THOMA, S. KUMAR, and J. GEIGER. "A GIS framework for surface-layer soil moisture estimation combining satellite radar measurements and land surface modeling with soil physical property estimation." Environmental Modelling & Software 22, no. 6 (June 2007): 891–98. http://dx.doi.org/10.1016/j.envsoft.2006.05.022.
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Varouchakis, E. A., G. V. Giannakis, M. A. Lilli, E. Ioannidou, N. P. Nikolaidis, and G. P. Karatzas. "Development of a statistical tool for the estimation of riverbank erosion probability." SOIL 2, no. 1 (January 15, 2016): 1–11. http://dx.doi.org/10.5194/soil-2-1-2016.
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Abstract. Riverbank erosion affects river morphology and local habitat, and results in riparian land loss, property and infrastructure damage, and ultimately flood defence weakening. An important issue concerning riverbank erosion is the identification of the vulnerable areas in order to predict river changes and assist stream management/restoration. An approach to predict areas vulnerable to erosion is to quantify the erosion probability by identifying the underlying relations between riverbank erosion and geomorphological or hydrological variables that prevent or stimulate erosion. In the present work, a statistical methodology is proposed to predict the probability of the presence or absence of erosion in a river section. A physically based model determines the locations vulnerable to erosion by quantifying the potential eroded area. The derived results are used to determine validation locations for the evaluation of the statistical tool performance. The statistical tool is based on a series of independent local variables and employs the logistic regression methodology. It is developed in two forms, logistic regression and locally weighted logistic regression, which both deliver useful and accurate results. The second form, though, provides the most accurate results as it validates the presence or absence of erosion at all validation locations. The proposed tool is easy to use and accurate and can be applied to any region and river.
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Zolfaghari Nia, Masoud, Mostafa Moradi, Gholamhosein Moradi, and Ruhollah Taghizadeh-Mehrjardi. "Machine Learning Models for Prediction of Soil Properties in the Riparian Forests." Land 12, no. 1 (December 22, 2022): 32. http://dx.doi.org/10.3390/land12010032.
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Spatial variability of soil properties is a critical factor for the planning, management, and exploitation of soil resources. Thus, the use of different digital soil mapping models to provide accuracy plays a crucial role in providing soil physicochemical properties maps. Soil spatial variability in forest stands is not well-known in Iran. Meanwhile, riparian buffers are important for several services such as providing high water quality, nutrient recycling, and buffering agricultural production. Accordingly, in this research, 103 soil samples were taken using the Latin hypercubic method in the Maroon riparian forest of Behbahan and agricultural lands in the vicinity of the forest to evaluate the spatial variability of soil nitrogen, potassium, organic carbon, C:N ratio, pH, calcium carbonate, sand, silt, clay, and bulk density. Different machine learning models, including artificial neural networks, random forest, cubist regression tree, and k-nearest neighbor were used to compare the estimation of soil properties. Moreover, three main sources of spatial information including remote sensing images, digital elevation model, and climate parameters were used as ancillary data. Our results indicated that the random forest model has the best results in estimating soil pH, nitrogen, potassium, and bulk density. In contrast, the cubist regression tree indicated the best estimation for organic carbon, C:N ratio, phosphorous, and clay. Further, artificial neural networks showed the best estimation for calcium carbonate, sand, and silt contents. Our results revealed that geospatial information such as terrain parameters, climate parameters, and satellite images could be well used as ancillary data for the spatial mapping of soil physiochemical properties in riparian forests and agricultural lands. In conclusion, a specific machine learning model needs to be used for each soil property to provide highly accurate maps with less error.
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Neyshabouri, Mohammad Reza, Mehdi Rahmati, Claude Doussan, and Boshra Behroozinezhad. "Simplified estimation of unsaturated soil hydraulic conductivity using bulk electrical conductivity and particle size distribution." Soil Research 51, no. 1 (2013): 23. http://dx.doi.org/10.1071/sr12158.
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Unsaturated soil hydraulic conductivity K is a fundamental transfer property of soil but its measurement is costly, difficult, and time-consuming due to its large variations with water content (θ) or matric potential (h). Recently, C. Doussan and S. Ruy proposed a method/model using measurements of the electrical conductivity of soil core samples to predict K(h). This method requires the measurement or the setting of a range of matric potentials h in the core samples—a possible lengthy process requiring specialised devices. To avoid h estimation, we propose to simplify that method by introducing the particle-size distribution (PSD) of the soil as a proxy for soil pore diameters and matric potentials, with the Arya and Paris (AP) model. Tests of this simplified model (SM) with laboratory data on a broad range of soils and using the AP model with available, previously defined parameters showed that the accuracy was lower for the SM than for the original model (DR) in predicting K (RMSE of logK = 1.10 for SM v. 0.30 for DR; K in m s–1). However, accuracy was increased for SM when considering coarse- and medium-textured soils only (RMSE of logK = 0.61 for SM v. 0.26 for DR). Further tests with 51 soils from the UNSODA database and our own measurements, with estimated electrical properties, confirmed good agreement of the SM for coarse–medium-textured soils (<35–40% clay). For these textures, the SM also performed well compared with the van Genuchten–Mualem model. Error analysis of SM results and fitting of the AP parameter showed that most of the error for fine-textured soils came from poorer adequacy of the AP model’s previously defined parameters for defining the water retention curve, whereas this was much less so for coarse-textured soils. The SM, using readily accessible soil data, could be a relatively straightforward way to estimate, in situ or in the laboratory, K(h) for coarse–medium-textured soils. This requires, however, a prior check of the predictive efficacy of the AP model for the specific soil investigated, in particular for fine-textured/structured soils and when using previously defined AP parameters.
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Azan Basallo, Yasser, FRANCISCO ANTONIO HORTA RANGEL, JULIO CESAR LEAL VACA, JOSE MIGUEL SORIA UGALDE, and Jorge Luis Morales Martínez. "ALTERNATIVE ESTIMATION OF THE SOIL WATER RETENTION CURVE BASED ON THE ARYA-PARIS MODEL." DYNA ENERGIA Y SOSTENIBILIDAD 12, no. 1 (April 12, 2023): [11P.]. http://dx.doi.org/10.6036/es10775.
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ABSTRACT: The physical and chemical characteristics of a soil determine its hydraulic properties, such as hydraulic conductivity, infiltration, and moisture. Due to the usefulness of a soil's moisture retention characteristic curve, several methodologies have been developed to obtain it. Mathematical models for the prediction of this hydraulic property constitute a type of methodologies that is economical and very affordable. One of the most practical models is the model of Arya and Paris (1981), which, however, requires an adjustment parameter, which limits its applicability in some cases, and, in addition, underestimates the moisture content by not considering the water remaining in the soil menisci. The present study proposes an alternative model based on the model of Arya and Paris, but that attempts to circumvent the disadvantages mentioned above, assuming a pore network formed by conical pores. The predictions of the moisture retention curve of our alternative model were compared with those of the original model through the root mean square error, using experimental data from 100 soils of three different textural classes, taken from the UNSODA database. The results by texture indicate that the alternative model generally produces better estimates for all three textures; however, for 17% of the soils the Arya and Paris model performed better. Keywords: water retention curve, retention, soil, porosity, volume.
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Yoon, Gil Lim, Byung Tak Kim, and Sang Soo Jeon. "Empirical correlations of compression index for marine clay from regression analysis." Canadian Geotechnical Journal 41, no. 6 (December 1, 2004): 1213–21. http://dx.doi.org/10.1139/t04-057.
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Single and multiple regression models to estimate the compression index of marine clay in coastal areas in Korea were investigated based on soil property data from more than 1200 consolidation tests on undisturbed samples. Site-specific empirical correlations were proposed to estimate the compression index in terms of both single and multiple soil properties. The proposed regression equations were then compared with the existing empirical equations. It was found that the compression index predicted by a simple linear regression model involving the natural water content, natural void ratio, and liquid limit can reasonably evaluate the real soil compression index. These regression equations may allow a preliminary estimation of the ground settlement for marine clay. It was also recognized that the applications of empirical equations suggested in previous studies result in large uncertainties in estimating the compression index of marine clayey soil in the coastal zone in Korea.Key words: settlement, compression index, regression, statistical analysis, consolidation.
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Aashifa, M. A. R., and P. Loganathan. "Preliminary Studies on Existing Scenario of Selected Soil Property in Cheddikulam DS Division Vavuniya, Sri Lanka." International Journal of Environment 5, no. 4 (January 13, 2017): 1–11. http://dx.doi.org/10.3126/ije.v5i4.16389.
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This study was conducted to quantify the spatial variability of soil properties, use this information to produce accurate map by means of ordinary kriging and find the ways to reclaim the problem soil and make suggestions to cultivate the crop variety which is suitable for the existing soil property.70 sampling points were selected for that research using stratified random sampling method. Stratification was based on the type of land cover, and following land cover patterns were identified forest patches, agriculture land patches, grass land patches and catchments. Sampling points were randomly selected from each land cover types. Minimum distance between two adjacent sampling points was 500m. Soil samples were analyzed for pH, EC, exchangeable K, available P. In each location, soils were collected from top to - 30 cm depth (root zone) using a core sampler and sub soil samples were collected around the geo-reference point to obtain a composite sample. Geostatistical tool of the software (ArcGIS 10.2.2. trail version) was used to construct semi-variograms and spatial structure analysis for the variables. Geostatistical estimation had done by kriging. 13% of agriculture land area was acidic soil and 5.7% alkaline soil. 13% of agriculture land area was identified as saline soil. 67.11% of agriculture lands contain more phosphorous concentration than the optimum range. 3.4% agriculture lands contain higher potassium concentration than the optimum range. 98% of forest lands and 100% of grass lands contains phosphorous concentration higher than the optimum range. But forest lands and catchments shows lower level of potassium concentration. 22% of grass lands contain higher potassium than the optimum level. Agriculture practices leads to change in the soil hence identified soil problems should be reclaimed in order to maintain the fertility of soil for sustainable production. Proper management of soil can be a better solution for supporting the successful agricultural activity of community in future and socio-economic development of this region.INTERNATIONAL JOURNAL OF ENVIRONMENTVolume-5, Issue-4, Sep-Nov 2016, page : 1-11
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Bahrawi, Jarbou A., Mohamed Elhag, Amal Y. Aldhebiani, Hanaa K. Galal, Ahmad K. Hegazy, and Ebtisam Alghailani. "Soil Erosion Estimation Using Remote Sensing Techniques in Wadi Yalamlam Basin, Saudi Arabia." Advances in Materials Science and Engineering 2016 (2016): 1–8. http://dx.doi.org/10.1155/2016/9585962.
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Soil erosion is one of the major environmental problems in terms of soil degradation in Saudi Arabia. Soil erosion leads to significant on- and off-site impacts such as significant decrease in the productive capacity of the land and sedimentation. The key aspects influencing the quantity of soil erosion mainly rely on the vegetation cover, topography, soil type, and climate. This research studies the quantification of soil erosion under different levels of data availability in Wadi Yalamlam. Remote Sensing (RS) and Geographic Information Systems (GIS) techniques have been implemented for the assessment of the data, applying the Revised Universal Soil Loss Equation (RUSLE) for the calculation of the risk of erosion. Thirty-four soil samples were randomly selected for the calculation of the erodibility factor, based on calculating theK-factor values derived from soil property surfaces after interpolating soil sampling points. Soil erosion risk map was reclassified into five erosion risk classes and 19.3% of the Wadi Yalamlam is under very severe risk (37,740 ha). GIS and RS proved to be powerful instruments for mapping soil erosion risk, providing sufficient tools for the analytical part of this research. The mapping results certified the role of RUSLE as a decision support tool.
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LU, GUI-NING, CHEN YANG, XUE-QIN TAO, XIAO-YUN YI, and ZHI DANG. "ESTIMATION OF SOIL SORPTION COEFFICIENTS OF POLYCYCLIC AROMATIC HYDROCARBONS BY QUANTUM CHEMICAL DESCRIPTORS." Journal of Theoretical and Computational Chemistry 07, no. 01 (February 2008): 67–79. http://dx.doi.org/10.1142/s0219633608003599.
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Quantitative structure–property relationship (QSPR) modeling is a powerful approach for predicting environmental behavior of organic pollutants with their structure descriptors. This study reports an optimal QSPR model for estimating logarithmic soil sorption coefficients (log K OC ) of polycyclic aromatic hydrocarbons (PAHs). Quantum chemical descriptors computed using density functional theory at the B3LYP/6-31G(d) level and partial least squares (PLS) analysis with an optimizing procedure were used to generate QSPR models for log K OC of PAHs. The correlation coefficient of the optimal model was 0.993, and the results of a cross-validation test ([Formula: see text]) showed this optimal model had high fitting precision and good predicting ability. The log K OC values predicted by the optimal model are very close to those observed. The PLS analysis indicated that PAHs with larger electronic spatial extent tend to more easily adsorb and accumulate in soils and sediments, whereas those with higher molecular total energy and larger energy gap between the lowest unoccupied and the highest occupied molecular orbital adsorb and accumulate in soils and sediments less readily.
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BITTAR, ROBERTO DIB, SUELI MARTINS DE FREITAS ALVES, and FRANCISCO RAMOS DE MELO. "ESTIMATION OF PHYSICAL AND CHEMICAL SOIL PROPERTIES BY ARTIFICIAL NEURAL NETWORKS." Revista Caatinga 31, no. 3 (July 2018): 704–12. http://dx.doi.org/10.1590/1983-21252018v31n320rc.
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ABSTRACT Soil physical and chemical analyses are relatively high-cost and time-consuming procedures. In the search for alternatives to predict these properties from a reduced number of soil samples, the use of Artificial Neural Networks (ANN) has been pointed out as a great computational technique to solve this problem by means of experience. This tool also has the ability to acquire knowledge and then apply it. This study aimed at using ANNs to estimate the physical and chemical properties of soil. The data came from the physical and chemical analysis of 120 sampling points, which were submitted to descriptive analysis, geostatistical analysis, and ANNs training and analysis. In the geostatistical analysis, the semivariogram model that best fitted the experimental variogram was verified for each soil property, and the ordinary kriging was used as an interpolation method. The ANNs were trained and selected based on their assertiveness in the mapping of considered standards, and then used to estimate all soil properties. The mean errors of ordinary kriging estimates were compared to those of ANNs and then compared to the original values using Student's t-Test. The results showed that the ANN had an assertiveness compatible with ordinary kriging. Therefore, such technique is a promising tool to estimate soil properties using a reduced number of soil samples.
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Nakhaei, Mohammad, and Jiří Šimůnek. "Parameter estimation of soil hydraulic and thermal property functions for unsaturated porous media using the HYDRUS-2D code." Journal of Hydrology and Hydromechanics 62, no. 1 (March 1, 2014): 7–15. http://dx.doi.org/10.2478/johh-2014-0008.
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Abstract Knowledge of soil hydraulic and thermal properties is essential for studies involving the combined effects of soil temperature and water input on water flow and redistribution processes under field conditions. The objective of this study was to estimate the parameters characterizing these properties from a transient water flow and heat transport field experiment. Real-time sensors built by the authors were used to monitor soil temperatures at depths of 40, 80, 120, and 160 cm during a 10-hour long ring infiltration experiment. Water temperatures and cumulative infiltration from a single infiltration ring were monitored simultaneously. The soil hydraulic parameters (the saturated water content θ s, empirical shape parameters α and n, and the saturated hydraulic conductivity Ks) and soil thermal conductivity parameters (coefficients b1, b2, and b3 in the thermal conductivity function) were estimated from cumulative infiltration and temperature measurements by inversely solving a two-dimensional water flow and heat transport using HYDRUS-2D. Three scenarios with a different, sequentially decreasing number of optimized parameters were considered. In scenario 1, seven parameters (θ s, Ks, α, n, b1, b2, and b3) were included in the inverse problem. The results indicated that this scenario does not provide a unique solution. In scenario 2, six parameters (Ks, α, n, b1, b2, and b3) were included in the inverse problem. The results showed that this scenario also results in a non-unique solution. Only scenario 3, in which five parameters (α, n, b1, b2, and b3) were included in the inverse problem, provided a unique solution. The simulated soil temperatures and cumulative infiltration during the ring infiltration experiment compared reasonably well with their corresponding observed values.
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Fredlund, Delwyn G. "State of practice for use of the soil-water characteristic curve (SWCC) in geotechnical engineering." Canadian Geotechnical Journal 56, no. 8 (August 2019): 1059–69. http://dx.doi.org/10.1139/cgj-2018-0434.
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Routine geotechnical engineering practice has witnessed a significant increase in the usage of unsaturated soil mechanics principles. Laboratory measurement of the soil-water characteristic curve (SWCC) for a soil has been labelled as a primary reason for the improved understanding of unsaturated soil behaviour. Laboratory measurement of the “shrinkage curve” has yielded further insight into the estimation of unsaturated soil property functions (USPFs). The USPFs provide the necessary information for the simultaneous numerical modeling of the saturated and unsaturated portions of the soil profile. This paper presents a state-of-practice summary of the engineering protocols that have emerged amidst the numerous research studies reported over the past couple of decades. It also introduces issues related to hysteresis associated with the SWCC and suggests a pathway forward.
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Almeida, Karla Silva Santos Alvares de, Luciano Da Silva Souza, Vital Pedro Da Silva Paz, Maurício Antônio Coelho Filho, and Eduardo Holzapfel Hoces. "Models for moisture estimation in different horizons of yellow argisol using TDR." Semina: Ciências Agrárias 38, no. 4 (August 4, 2017): 1727. http://dx.doi.org/10.5433/1679-0359.2017v38n4p1727.
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The determination of soil moisture is very important because it is the property with the most influence on the dielectric constant of the medium. Time-domain reflectometry (TDR) is an indirect technique used to estimate the water content of the soil (?) based on its dielectric constant (Ka). Like any other technique, it has advantages and disadvantages. Among the major disadvantages is the need for calibration, which requires consideration of the soil characteristics. This study aimed to perform the calibration of a TDR100 device to estimate the volumetric water content of four horizons of a Yellow Argisol. Calibration was performed under laboratory conditions using disturbed soil samples contained in PVC columns. The three rods of the handcrafted probes were vertically installed in the soil columns. Weight measurements with digital scales and daily readings of the dielectric constant with the TDR device were taken. For all soil horizons evaluated, the best fits between the dielectric constant and the volumetric water content were related to the cubic polynomial model. The Ledieu model overestimated by approximately 68 % the volumetric water content in the A and AB horizons, and underestimating by 69 % in Bt2, in relation to volumetric water content obtained by gravimetry. The underestimation by linear, Topp, Roth, and Malicki models ranged from 50 % to 85 % for all horizons.
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Chang, Chen-Chao, and Dong-Hui Cheng. "Predicting the soil water retention curve from the particle size distribution based on a pore space geometry containing slit-shaped spaces." Hydrology and Earth System Sciences 22, no. 9 (September 4, 2018): 4621–32. http://dx.doi.org/10.5194/hess-22-4621-2018.
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Abstract. Traditional models employed to predict the soil water retention curve (SWRC) from the particle size distribution (PSD) always underestimate the water content in the dry range of the SWRC. Using the measured physical parameters of 48 soil samples from the UNSODA unsaturated soil hydraulic property database, these errors were proven to originate from an inaccurate estimation of the pore size distribution. A method was therefore proposed to improve the estimation of the water content at high suction heads using a pore model comprising a circle-shaped central pore connected to slit-shaped spaces. In this model, the pore volume fraction of the minimum pore diameter range and the corresponding water content were accordingly increased. The predicted SWRCs using the improved method reasonably approximated the measured SWRCs, which were more accurate than those obtained using the traditional method and the scaling approach in the dry range of the SWRC.
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Gnatowski, Tomasz. "Analysis of thermal diffusivity data determined for selected organic topsoil layer." Annals of Warsaw University of Life Sciences - SGGW. Land Reclamation 41, no. 2 (January 1, 2009): 95–107. http://dx.doi.org/10.2478/v10060-008-0053-y.
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Analysis of thermal diffusivity data determined for selected organic topsoil layer Thermal diffusivity (KT) is a very complex soil property. However, proper estimation of this parameter is very important for the study of thermal processes in the soil. Nevertheless, the thermal diffusivity of peat and organic soils is not well characterized. The purpose of this study was to evaluate different methods for the assessment of the thermal diffusivity of selected organic topsoil layer. The first group of methods included calculation procedures developed from analytical solution of the heat transfer equation. For the determination of thermal diffusivity the distribution of the soil temperature at two depths was required. The second group of methods was based on the classical definition of thermal diffusivity where the quantification of thermal conductivity and heat capacity is required. The measurements and calculations clearly suggest that methods based on the phase equation should be not considered as appropriate methods for thermal diffusivity determination for organic soils. The other methods considered lead to results which were comparable to the experimental KT data.
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Ouerghemmi, W., C. Gomez, S. Nacer, and P. Lagacherie. "SEMI-BLIND SOURCE SEPARATION FOR ESTIMATION OF CLAY CONTENT OVER SEMI-VEGETATED AREAS, FROM VNIR/SWIR HYPERSPECTRAL AIRBORNE DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-3/W3 (August 19, 2015): 413–17. http://dx.doi.org/10.5194/isprsarchives-xl-3-w3-413-2015.
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The applicability of Visible, Near-Infrared and Short Wave Infrared (VNIR/SWIR) hyperspectral imagery for soil property mapping decreases when surfaces are partially covered by vegetation. The objective of this research was to develop and evaluate a methodology based on the “double-extraction” technique, for clay content estimation over semi-vegetated surfaces using VNIR/SWIR hyperspectral airborne data. The “double-extraction” technique initially proposed by Ouerghemmi et al. (2011) consists of 1) an extraction of a soil reflectance spectrum <i>s</i><sub>soil</sub> from semi-vegetated spectra using a Blind Source Separation technique, and 2) an extraction of clay content from the soil reflectance spectrum <i>s</i><sub>soil</sub>, using a multivariate regression method. In this paper, the Source Separation approach is Semi-Blind thanks to the integration of field knowledge in Source Separation model. And the multivariate regression method is a partial least squares regression (PLSR) model. This study employed VNIR/SWIR HyMap airborne data acquired in a French Mediterranean region over an area of 24 km<sup>2</sup>. <br><br> Our results showed that our methodology based on the “double-extraction” technique is accurate for clay content estimation when applied to pixels under a specific Cellulose Absorption Index threshold. Finally the clay content can be estimated over around 70% of the semi-vegetated pixels of our study area, which may offer an extension of soil properties mapping, at the moment restricted to bare soils.
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Rahman, Arafat, MJ Uddin, Md Raisuddin Sikder, Humyra B. Murshed, JA Faysal, Mohiyuddeen Ahmad, and ASM Mohiuddin. "Soil properties and carbon stock along the toposequence of Lalmai hill ecosystem of Bangladesh." Dhaka University Journal of Biological Sciences 30, no. 2 (July 9, 2021): 331–43. http://dx.doi.org/10.3329/dujbs.v30i2.54658.
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A study was carried out in the Lalmai hill ecosystem of Bangladesh regarding their soil properties and soil organic carbon (SOC) stocks. The Lalmai hill ecosystem consists of three toposequence arrangements as hills, piedmonts, and floodplains. Forty-five soil samples covering nine soil profiles were selected to conduct the present study. Soil samples were collected at five different depths of 0-20 cm, 20-40 cm, 40-60 cm, 60-80 cm and 80-100 cm intervals from each pit of the study sites. Soil pH, percent SOC, percent total nitrogen (TN), bulk density, cation exchange capacity (CEC), particle size distribution, and SOC storage (kg/m2) dataset indicates that piedmont deposits and floodplain soils are more enriched than the upper hill soils. Regarding SOC storage, the post hoc test indicates that hill soils are significantly different from the other two physiographic units, but there is no significant difference between piedmont deposits and floodplain soils. The soil property varies differently depending on their depth level at different physiographic units. Estimation on SOC stock revealed that 2.01Tg, 21.75Tg, 12.68Tg carbon remains in the hill soils, piedmont soils, and estuarine floodplain soils, respectively. The total SOC stock was estimated at 36.44 Tg in the Lalmai hill ecosystem of Bangladesh, where piedmont deposits contained the highest level of SOC stock. It is assumed that more clay-organic substances are washed in at the foot of piedmont units due to the well-drained nature of upper Pleistocene hill soils. Thus, fine soil textural nature, diverse land and land cover accelerates to sequester more carbon in piedmont zone rather than hill or floodplain zones.
Dhaka Univ. J. Biol. Sci. 30(2): 331-343, 2021 (July)
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Wilson, Brian R., Phoebe Barnes, Terry B. Koen, Subhadip Ghosh, and Dacre King. "Measurement and estimation of land-use effects on soil carbon and related properties for soil monitoring: a study on a basalt landscape of northern New South Wales, Australia." Soil Research 48, no. 5 (2010): 421. http://dx.doi.org/10.1071/sr09146.
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There is a growing need for information relating to soil condition, its current status, and the nature and direction of change in response to management pressures. Monitoring is therefore being promoted regionally, nationally, and internationally to assess and evaluate soil condition for the purposes of reporting and prioritisation of funding for natural resource management. Several technical and methodological obstacles remain that impede the broad-scale implementation of measurement and monitoring schemes, and we present a dataset designed to (i) assess the optimum size of sample site for soil monitoring, (ii) determine optimum sample numbers required across a site to estimate soil properties to known levels of precision and confidence, and (iii) assess differences in the selected soil properties between a range of land-use types across a basalt landscape of northern NSW. Sample site size was found to be arbitrary and a sample area 25 by 25 m provided a suitable estimate of soil properties at each site. Calculated optimum sample numbers differed between soil property, depth, and land use. Soil pH had a relatively low variability across the sites studied, whereas carbon, nitrogen, and bulk density had large variability. Variability was particularly high for woodland soils and in the deeper soil layers. A sampling intensity of 10 samples across a sampling area 25 by 25 m was found to yield adequate precision and confidence in the soil data generated. Clear and significant differences were detected between land-use types for the various soil properties determined but these effects were restricted to the near-surface soil layers (0–50 and 50–100 mm). Land use has a profound impact on soil properties near to the soil surface, and woodland soils at these depths had significantly higher carbon, nitrogen, and pH and lower bulk density than the other land uses. Soil properties between the other non-woodland land-use types were largely similar, apart from a modestly higher carbon content and higher soil acidity under improved pasture. Data for soil carbon assessment should account for equivalent mass, since this significantly modified carbon densities, particularly for the lighter woodland soils. Woodland soils had larger quantities of carbon (T/ha corrected for equivalent mass) than any other land-use type, and in order to maintain the largest quantity of carbon in this landscape, retaining trees and woodland is the most effective option. Results from this work are being used to inform further development the NSW Statewide Soil Monitoring Program.
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Jiang, Xueqin, Shanjun Luo, Qin Ye, Xican Li, and Weihua Jiao. "Hyperspectral Estimates of Soil Moisture Content Incorporating Harmonic Indicators and Machine Learning." Agriculture 12, no. 8 (August 10, 2022): 1188. http://dx.doi.org/10.3390/agriculture12081188.
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Soil is one of the most significant natural resources in the world, and its health is closely related to food security, ecological security, and water security. It is the basic task of soil environmental quality assessment to monitor the temporal and spatial variation of soil properties scientifically and reasonably. Soil moisture content (SMC) is an important soil property, which plays an important role in agricultural practice, hydrological process, and ecological balance. In this paper, a hyperspectral SMC estimation method for mixed soil types was proposed combining some spectral processing technologies and principal component analysis (PCA). The original spectra were processed by wavelet packet transform (WPT), first-order differential (FOD), and harmonic decomposition (HD) successively, and then PCA dimensionality reduction was used to obtain two groups of characteristic variables: WPT-FOD-PCA (WFP) and WPT-FOD-HD-PCA (WFHP). On this basis, three regression models of principal component regression (PCR), partial least squares regression (PLSR), and back propagation (BP) neural network were applied to compare the SMC predictive ability of different parameters. Meanwhile, we also compared the results with the estimates of conventional spectral indices. The results indicate that the estimation results based on spectral indices have significant errors. Moreover, the BP models (WFP-BP and WFHP-BP) show more accurate results when the same variables are selected. For the same regression model, the choice of variables is more important. The three models based on WFHP (WFHP-PCR, WFHP-PLSR, and WFHP-BP) all show high accuracy and maintain good consistency in the prediction of high and low SMC values. The optimal model was determined to be WFHP-BP with an R2 of 0.932 and a prediction error below 2%. This study can provide information on farm entropy before planting crops on arable land as well as a technical reference for estimating SMC from hyperspectral images (satellite and UAV, etc.).
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Pegalajar, M. C., L. G. B. Ruiz, and D. Criado-Ramón. "Munsell Soil Colour Classification Using Smartphones through a Neuro-Based Multiclass Solution." AgriEngineering 5, no. 1 (February 10, 2023): 355–68. http://dx.doi.org/10.3390/agriengineering5010023.
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Colour is a property widely used in many fields to extract information in several ways. In soil science, colour provides information regarding the chemical and physical characteristics of soil, such as genesis, composition, and fertility, amongst others. Thus, accurate estimation of soil colour is essential for many disciplines. To achieve this, experts traditionally rely on comparing Munsell colour charts with soil samples, which is a laborious process. In this study, we proposed using artificial neural networks to catalogue soil colour with a two-step classification. Firstly, the hue variable is estimated, and then the remaining two coordinates, value and chroma. Our experiments were conducted using three different, common cameras (one digital camera and two mobile phones). The results of our tests showed a 20% improvement in classification accuracy using the lowest-quality camera and an average accuracy of over 90%.
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Andersen, Lars Vabbersgaard, John Dalsgaard Sørensen, Sun-Bin Kim, Jin-Hak Yi, Gil-Lim Yoon, and Lance Manuel. "Influence of Characteristic-Soil-Property-Estimation Approach on the Response of Monopiles for Offshore Wind Turbines." Journal of Ocean and Wind Energy 2, no. 3 (August 1, 2015): 160–67. http://dx.doi.org/10.17736/jowe.2015.jcr39.
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Thnh, Nguyen Trung, Hichem Sahli, and Dinh Nho Ho. "Finite-Difference Methods and Validity of a Thermal Model for Landmine Detection With Soil Property Estimation." IEEE Transactions on Geoscience and Remote Sensing 45, no. 3 (March 2007): 656–74. http://dx.doi.org/10.1109/tgrs.2006.888862.
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Motoyama, Hiroki, and Muneo Hori. "Construction and Usefulness Verification of Modeling Method of Subsurface Soil Layers for Numerical Analysis of Urban Area Ground Motion." GeoHazards 3, no. 2 (May 9, 2022): 242–51. http://dx.doi.org/10.3390/geohazards3020013.
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Estimation of urban seismic damage using numerical simulation needs an automatic modeling method of surface layers and residential buildings. This study focuses on modeling of surface layers and shows a method of constructing models by interpolating boring data. An important property of the modeling method is robustness, that means that the method works for boring data with inconsistent soil layers. To satisfy this, we developed the method using artificial layers. We applied the method to a test site and checked its robustness. This test also showed that the method gave realistic models. Finally, we applied the method to the estimation of urban seismic damage and discussed the usefulness by comparing the result with one obtained by a conventional method.
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Castaldi, Fabio, Sabine Chabrillat, and Bas van Wesemael. "Sampling Strategies for Soil Property Mapping Using Multispectral Sentinel-2 and Hyperspectral EnMAP Satellite Data." Remote Sensing 11, no. 3 (February 4, 2019): 309. http://dx.doi.org/10.3390/rs11030309.
Full textAbstract:
Designing a sampling strategy for soil property mapping from remote sensing imagery entails making decisions about sampling pattern and number of samples. A consistent number of ancillary data strongly related to the target variable allows applying a sampling strategy that optimally covers the feature space. This study aims at evaluating the capability of multispectral (Sentinel-2) and hyperspectral (EnMAP) satellite data to select the sampling locations in order to collect a calibration dataset for multivariate statistical modelling of the Soil Organic Carbon (SOC) content in the topsoil of croplands. We tested different sampling strategies based on the feature space, where the ancillary data are the spectral bands of the Sentinel-2 and of simulated EnMAP satellite data acquired in Demmin (north-east Germany). Some selection algorithms require setting the number of samples in advance (random, Kennard-Stones and conditioned Latin Hypercube algorithms) where others automatically provide the ideal number of samples (Puchwein, SELECT and Puchwein+SELECT algorithm). The SOC content and the spectra extracted at the sampling locations were used to build random forest (RF) models. We evaluated the accuracy of the RF estimation models on an independent dataset. The lowest Sentinel-2 normalized root mean square error (nRMSE) for the validation set was obtained using Puchwein (nRMSE: 8.7%), and Kennard-Stones (9.2%) algorithms. The most efficient sampling strategies, expressed as the ratio between accuracy and number of samples per hectare, were obtained using Puchwein with EnMAP and Puchwein+SELECT algorithm with Sentinel-2 data. Hence, Sentinel-2 and EnMAP data can be exploited to build a reliable calibration dataset for SOC mapping. For EnMAP, the different selection algorithms provided very similar results. On the other hand, using Puchwein and Kennard-Stones algorithms, Sentinel-2 provided a more accurate estimation than the EnMAP. The calibration datasets provided by EnMAP data provided lower SOC variability and lower prediction accuracy compared to Sentinel-2. This was probably due to EnMAP coarser spatial resolution (30 m) less adequate for linkage to the sampling performed at 10 m scale.
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Zhang, Feixia, and D. G. Fredlund. "Examination of the estimation of relative permeability for unsaturated soils." Canadian Geotechnical Journal 52, no. 12 (December 2015): 2077–87. http://dx.doi.org/10.1139/cgj-2015-0043.
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The unsaturated permeability function is an important soil property function used in the numerical modeling of saturated–unsaturated soil systems. The permeability function is generally predicted by integrating along the soil-water characteristic curve (SWCC) starting at saturated soil conditions. The integration is based on a particular integral formula. The Fredlund–Xing–Huang permeability function is a flexible integration technique used for calculating the unsaturated permeability function. The original permeability theory published by Fredlund, Xing, and Huang in 1994 specified that the air-entry value (AEV), ψaev, be used as the lower limit of the integration when calculating the permeability function. However, as there was no analytical procedure available for the calculation of the AEV on the SWCC, it became common practice to start the integration procedure from a value near zero. The assumption was made that the error associated with starting the integration from an arbitrary low value was minimal. While this might be the case in some situations, the error can be quite substantial in other situations. This paper undertakes a study of the effect of the lower limit of integration on the calculation of the permeability function. Comparisons are made between starting the integration from various values below the AEV and starting the integration from the calculated AEV, ψaev. A mathematical algorithm is also proposed for the calculation of the AEV for integration purposes. The results show that the relative coefficient of permeability can be significantly underestimated when the lower limit of integration is smaller than the AEV. The recommendation is that the AEV always be used as the lower limit of integration in the Fredlund–Xing–Huang permeability equation.
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Shang, Songhao. "Log-Cubic Method for Generation of Soil Particle Size Distribution Curve." Scientific World Journal 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/579460.
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Particle size distribution (PSD) is a fundamental physical property of soils. Traditionally, the PSD curve was generated by hand from limited data of particle size analysis, which is subjective and may lead to significant uncertainty in the freehand PSD curve and graphically estimated cumulative particle percentages. To overcome these problems, a log-cubic method was proposed for the generation of PSD curve based on a monotone piecewise cubic interpolation method. The log-cubic method and commonly used log-linear and log-spline methods were evaluated by the leave-one-out cross-validation method for 394 soil samples extracted from UNSODA database. Mean error and root mean square error of the cross-validation show that the log-cubic method outperforms two other methods. What is more important, PSD curve generated by the log-cubic method meets essential requirements of a PSD curve, that is, passing through all measured data and being both smooth and monotone. The proposed log-cubic method provides an objective and reliable way to generate a PSD curve from limited soil particle analysis data. This method and the generated PSD curve can be used in the conversion of different soil texture schemes, assessment of grading pattern, and estimation of soil hydraulic parameters and erodibility factor.