scholarly journals In Search of a Soil Moisture Content Simulation Model: Mechanistic and Data Mining Approach Based on TDR Method Results

Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6819
Author(s):  
Andrzej Brandyk ◽  
Bartosz Szeląg ◽  
Adam Kiczko ◽  
Marcin Krukowski ◽  
Adam Kozioł ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Statistical Models ◽  
Soil Moisture Content ◽  
Simulation Models ◽  
Time Domain Reflectometry ◽  
Support Vector ◽  
Model Parameters ◽  
Physically Based ◽  
The Time Domain

Soil moisture content simulation models have continuously been an important research objective. In particular, the comparisons of the performance of different model types deserve proper attention. Therefore, the quality of selected physically-based and statistical models was analyzed utilizing the data from the Time Domain Reflectometry technique. An E-Test measurement system was applied with the reflectogram interpreted into soil volumetric moisture content by proper calibration equations. The gathered data facilitated to calibrate the physical model of Deardorff and establish parameters of: support vector machines, multivariate adaptive regression spline, and boosted trees model. The general likelihood uncertainty estimation revealed the sensitivity of individual model parameters. As it was assumed, a simple structure of statistical models was achieved but no direct physical interpretation of their parameters, contrary to a physically-based method. The TDR technique proved useful for the calibration of different soil moisture models and a satisfactory quality for their future exploitation.

scholarly journals A GNSS-IR Method for Retrieving Soil Moisture Content from Integrated Multi-Satellite Data That Accounts for the Impact of Vegetation Moisture Content

2021 ◽  
Vol 13 (13) ◽  
pp. 2442
Author(s):  
Jichao Lv ◽  
Rui Zhang ◽  
Jinsheng Tu ◽  
Mingjie Liao ◽  
Jiatai Pang ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Satellite Data ◽  
Vegetation Index ◽  
Soil Moisture Content ◽  
Support Vector ◽  
Observation Data ◽  
Multipath Effect ◽  
Mars Model ◽  
The Impact

There are two problems with using global navigation satellite system-interferometric reflectometry (GNSS-IR) to retrieve the soil moisture content (SMC) from single-satellite data: the difference between the reflection regions, and the difficulty in circumventing the impact of seasonal vegetation growth on reflected microwave signals. This study presents a multivariate adaptive regression spline (MARS) SMC retrieval model based on integrated multi-satellite data on the impact of the vegetation moisture content (VMC). The normalized microwave reflection index (NMRI) calculated with the multipath effect is mapped to the normalized difference vegetation index (NDVI) to estimate and eliminate the impact of VMC. A MARS model for retrieving the SMC from multi-satellite data is established based on the phase shift. To examine its reliability, the MARS model was compared with a multiple linear regression (MLR) model, a backpropagation neural network (BPNN) model, and a support vector regression (SVR) model in terms of the retrieval accuracy with time-series observation data collected at a typical station. The MARS model proposed in this study effectively retrieved the SMC, with a correlation coefficient (R2) of 0.916 and a root-mean-square error (RMSE) of 0.021 cm3/cm3. The elimination of the vegetation impact led to 3.7%, 13.9%, 11.7%, and 16.6% increases in R2 and 31.3%, 79.7%, 49.0%, and 90.5% decreases in the RMSE for the SMC retrieved by the MLR, BPNN, SVR, and MARS model, respectively. The results demonstrated the feasibility of correcting the vegetation changes based on the multipath effect and the reliability of the MARS model in retrieving the SMC.

scholarly journals Field Calibration of TDR to Assess the Soil Moisture of Drained Peatland Surface Layers

Water ◽  
2018 ◽  
Vol 10 (12) ◽  
pp. 1842 ◽  
Author(s):  
Tomasz Gnatowski ◽  
Jan Szatyłowicz ◽  
Bogumiła Pawluśkiewicz ◽  
Ryszard Oleszczuk ◽  
Maria Janicka ◽  
...  
Keyword(s):  
Dielectric Constant ◽  
Soil Moisture ◽  
Moisture Content ◽  
Soil Moisture Content ◽  
Mean Squared Error ◽  
Time Domain Reflectometry ◽  
Calibration Model ◽  
Surface Layers ◽  
Calibration Equation ◽  
North East

The proper monitoring of soil moisture content is important to understand water-related processes in peatland ecosystems. Time domain reflectometry (TDR) is a popular method used for soil moisture content measurements, the applicability of which is still challenging in field studies due to requirements regarding the calibration curve which converts the dielectric constant into the soil moisture content. The main objective of this study was to develop a general calibration equation for the TDR method based on simultaneous field measurements of the dielectric constant and gravimetric water content in the surface layers of degraded peatlands. Data were collected during field campaigns conducted temporarily between the years 2006 and 2016 at the drained peatland Kuwasy located in the north-east area of Poland. Based on the data analysis, a two-slopes linear calibration equation was developed as a general broken-line model (GBLM). A site-specific calibration model (SSM-D) for the TDR method was obtained in the form of a two-slopes equation describing the relationship between the soil moisture content and the dielectric constant and introducing the bioindices as covariates relating to plant species biodiversity and the state of the habitats. The root mean squared error for the GBLM and SSM-D models were equal, respectively, at 0.04 and 0.035 cm3 cm−3.

scholarly journals EFFECTS OF HYDROPHILIC POLYMERS AND ORGANIC AMENDMENTS ON ESTABLISHMENT OF KENTUCKY BLUEGRASS.

HortScience ◽  
1992 ◽  
Vol 27 (6) ◽  
pp. 588a-588
Author(s):  
A. James Downer ◽  
Ben Faber ◽  
Richard White
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Water Content ◽  
Soil Moisture Content ◽  
Organic Amendments ◽  
Kentucky Bluegrass ◽  
Soil Bulk Density ◽  
Time Domain Reflectometry ◽  
Peat Moss ◽  
Turf Quality

Three polymers (a polyacrylamide, polyacrylate and a propenoate-propenamide copolymer) and three organic amendments (peat moss, wood shavings, and composted yardwaste) were incorporated at five rates in a sandy soil to 15cm depth. Soil moisture content was determined by time domain reflectometry and gravimetrically. Only the highest polymer rates (2928kg/ha [60#/1000sq.ft.]) produced significant increases in soil moisture content and reductions of soil bulk density. Peat moss and yardwaste increased soil water content while shavings decreased water content. Turf quality scores were not affected by polymers but were initially reduced by yardwaste and shavings.

scholarly journals Is the Time-Domain Reflectometry (TDR) Technique Suitable for Moisture Content Measurement in Low-Porosity Building Materials?

2020 ◽  
Vol 12 (19) ◽  
pp. 7855 ◽  
Author(s):  
Teresa Stingl Freitas ◽  
Ana Sofia Guimarães ◽  
Staf Roels ◽  
Vasco Peixoto de Freitas ◽  
Andrea Cataldo
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Time Domain ◽  
Building Materials ◽  
Construction Materials ◽  
Gravimetric Method ◽  
Time Domain Reflectometry ◽  
Advantages And Disadvantages ◽  
The Time Domain ◽  
Low Porosity

Measuring moisture content in building materials is essential both for professional practice and for research. However, this is a very complex task, especially when long-term minor destructive measurements are desired. The time-domain reflectometry (TDR) technique is commonly used for soil moisture measurements, but its application in construction materials is considered a relatively new method, particularly for low-porosity building materials. The major obstacles to its current use in construction materials are (1) the difficulty of ensuring good contact between the TDR probe and the material, and (2) the lack of appropriate conversion functions between the measured relative permittivity and the moisture content of building materials. This paper intends to contribute to overcoming these difficulties by explaining in detail all the required steps to monitor moisture content in real-scale limestone walls. For that, a device is presented to guarantee the correct installation of the TDR probes on the walls, and a calibration procedure through the gravimetric method is proposed to avoid the use of an unsuitable calibration function developed for soil moisture measurements. In addition, the importance of the individual probe calibration is discussed, as well as TDR advantages and disadvantages for construction materials. The results obtained so far reveal that the TDR technique is suitable to detect moisture content variations in limestone, which is a low-porosity building material.

scholarly journals Irrigation Levels Affect Plant Growth and Fruit Yield of Drip-Irrigated Bell Pepper

HortScience ◽  
2004 ◽  
Vol 39 (4) ◽  
pp. 748B-748 ◽  
Author(s):  
Juan C. Diaz-Perez* ◽  
Darbie Granberry ◽  
Kenneth Seebold ◽  
David Giddings ◽  
Denne Bertrand
Keyword(s):  
Soil Moisture ◽  
Plant Growth ◽  
Moisture Content ◽  
Soil Moisture Content ◽  
Time Domain Reflectometry ◽  
Fruit Yield ◽  
Bell Pepper ◽  
Plastic Mulch ◽  
Irrigation Rate ◽  
Stage Of Development

Bell pepper (Capsicum annum L.) plants have a high demand for water and nutrients and are sensitive to water stress during the establishment period and fruit setting. High levels of irrigation are often applied in order to maximize yields. However, field observations suggest that excessive irrigations may negatively affect bell pepper plants. The objective was to evaluate the effects of irrigation rate on plant growth and fruit yield. The trial was conducted in Spring 2003 at the Coastal Plain Experiment Station, Tifton, Ga. Drip-irrigated bell pepper (`Stiletto') plants were grown on black plastic mulch in 1-m wide beds (1.8-m centers). Plants were irrigated with an amount of water that ranged from 33% to 167% the rate of evapotranspiration (ET), adjusted by crop stage of development. Soil moisture content (% by volume) over the season was continuously monitored with time domain reflectometry sensors connected to a datalogger. The results showed that the average soil moisture content for the season increased with increasing rates of irrigation. Vegetative top fresh wt. and marketable fruit yield were reduced at both, low (33% ET) and high (166% ET) rates of water application. However, irrigation rate had a stronger effect on fruit yield than on top fresh wt. Plants supplied with high irrigation rates appeared to be more chlorotic compared to plants irrigated at medium rates (100% ET). There was a tendency for higher incidences of soil borne diseases (Pythium sp., Phytophtora capsici) in plants receiving higher rates of irrigation. The conclusion is high irrigation rates (>166% ET) are not recommended since they waste water and may result in both, higher incidences of soil-borne diseases and reduced bell pepper yields.

scholarly journals SHORT TDR PROBES TO MEASURE WATER AND FERTILIZER ION GRADIENTS IN CONTAINER MEDIA

HortScience ◽  
1994 ◽  
Vol 29 (7) ◽  
pp. 742c-742
Author(s):  
Shaun F. Kelly ◽  
J.L Green ◽  
John S. Selker
Keyword(s):  
Porous Media ◽  
Soil Moisture ◽  
Moisture Content ◽  
Soil Moisture Content ◽  
Steel Wire ◽  
Field Measurements ◽  
Time Domain Reflectometry ◽  
Ion Concentrations ◽  
High Bandwidth ◽  
High Concentrations

Time Domain Reflectometry (TDR) is used to measure in situ soil moisture content and salinity of porous media. Commercially available TDR systems used for field measurements have limited use in laboratory scale experiments where short high resolution probes are needed. A short TDR probe was designed for use with high bandwidth TDR instruments currently available. The probes are designed from SMA bulkhead connectors using gold-plated stainless steel wire 0.035 inches in diameter. A 20.GHz digital sampling oscilloscope (11801; Tektronix, Beaverton, Ore.) with an SD-24 TDR sampling head is used with the probes to determine water content and ion concentrations in porous media. The 7.5- and 3.0-cm-long probes were used to measure soil moisture content and ion concentrations in laboratory columns. Fertilizer and water gradients were observed by using bromide salts brought into contact with the top of laboratory columns, 7.6 cm in diameter and 18 cm long, packed with container media [1 peat: 1 vermiculite v/v)]. Soil moisture measurements in the presence of high concentrations of salts were made by insulating the probes with Teflon heat-shrinkable tubing to minimize conductivity losses.

scholarly journals Soil Moisture Retrieval in Farmland Areas with Sentinel Multi-Source Data Based on Regression Convolutional Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 877
Author(s):  
Jian Liu ◽  
Youshuan Xu ◽  
Henghui Li ◽  
Jiao Guo
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Soil Moisture ◽  
Moisture Content ◽  
Soil Moisture Content ◽  
Remote Sensing Data ◽  
Optimal Combination ◽  
Optical Data ◽  
Support Vector ◽  
Sensing Data

As an important component of the earth ecosystem, soil moisture monitoring is of great significance in the fields of crop growth monitoring, crop yield estimation, variable irrigation, and other related applications. In order to mitigate or eliminate the impacts of sparse vegetation covers in farmland areas, this study combines multi-source remote sensing data from Sentinel-1 radar and Sentinel-2 optical satellites to quantitatively retrieve soil moisture content. Firstly, a traditional Oh model was applied to estimate soil moisture content after removing vegetation influence by a water cloud model. Secondly, support vector regression (SVR) and generalized regression neural network (GRNN) models were used to establish the relationships between various remote sensing features and real soil moisture. Finally, a regression convolutional neural network (CNNR) model is constructed to extract deep-level features of remote sensing data to increase soil moisture retrieval accuracy. In addition, polarimetric decomposition features for real Sentinel-1 PolSAR data are also included in the construction of inversion models. Based on the established soil moisture retrieval models, this study analyzes the influence of each input feature on the inversion accuracy in detail. The experimental results show that the optimal combination of R2 and root mean square error (RMSE) for SVR is 0.7619 and 0.0257 cm3/cm3, respectively. The optimal combination of R2 and RMSE for GRNN is 0.7098 and 0.0264 cm3/cm3, respectively. Especially, the CNNR model with optimal feature combination can generate inversion results with the highest accuracy, whose R2 and RMSE reach up to 0.8947 and 0.0208 cm3/cm3, respectively. Compared to other methods, the proposed algorithm improves the accuracy of soil moisture retrieval from synthetic aperture radar (SAR) and optical data. Furthermore, after adding polarization decomposition features, the R2 of CNNR is raised by 0.1524 and the RMSE of CNNR decreased by 0.0019 cm3/cm3 on average, which means that the addition of polarimetric decomposition features effectively improves the accuracy of soil moisture retrieval results.

Determining Moisture Content of Soil Layers with Time Domain Reflectometry and Micromechanics

2008 ◽  
Vol 2053 (1) ◽  
pp. 30-38 ◽  
Author(s):  
Sang Ick Lee ◽  
Dan G. Zollinger ◽  
Robert L. Lytton
Keyword(s):  
Dielectric Constant ◽  
Soil Moisture ◽  
Moisture Content ◽  
Water Content ◽  
Time Domain ◽  
Soil Moisture Content ◽  
Time Domain Reflectometry ◽  
Volumetric Water Content ◽  
Dry Density

Although the moisture condition of pavement sublayers can significantly affect pavement performance, accurate interpretation of in situ soil moisture measurements has been difficult to achieve because of the limitations of existing methods. Time domain reflectometry (TDR), originally developed to detect breaks or shorts in electrical conductors, has been used for measuring parameters related to the in situ soil moisture content. However, the apparent length method currently used to determine dielectric constant ignores other electrical properties of the conducting medium that may affect the interpretation of TDR trace to determine soil moisture. Furthermore, the existing methods for computing volumetric water content ignore the variations of dry density and determine the model parameters with assumption or regression analysis. These deficiencies can, in many cases, create a significant systematic error in the final determination of volumetric water content. To minimize these errors and improve the accuracy of moisture content estimate, a new three-step approach was proposed. The approach uses the transmission line equation to calculate the dielectric constant, conductivity, and reflectivity of a soil mixture. A micromechanics and self-consistent scheme was used to determine the volumetric moisture content and dry density on the basis of calibrated values of the solid and water dielectric constants. The system identification method was used iteratively to solve for dielectric parameters, soil moisture content, and dry density values. The validation of the new approach with ground-truth data indicated that the calculated errors were significantly less than those of existing method.

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