scholarly journals Application of the TDR Soil Moisture Sensor for Terramechanical Research

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2116 ◽  
Author(s):  
Jarosław Pytka ◽  
Piotr Budzyński ◽  
Mariusz Kamiński ◽  
Tomasz Łyszczyk ◽  
Jerzy Józwik
Keyword(s):  
Soil Moisture ◽  
Optimal Solution ◽  
Time Domain Reflectometry ◽  
Moisture Sensor ◽  
Soil Moisture Data ◽  
Flight Operations ◽  
Before And After ◽  
Vehicle Mobility ◽  
Field Sensor

This paper presents examples of the application of the TDR (Time-Domain Reflectometry) sensor in terramechanical research. Examples include the determination of soil moisture content during off-road vehicle mobility tests, the determination of snow density before and after the wheeling of a snow grooming machine and an airplane, as well as the monitoring of turf moisture on a grassy airfield for the analysis and prediction of safe and efficient flight operations (takeoff and landing). A handheld TDR meter was used in these experiments. Soil moisture data were correlated with the vehicle mobility index and a simple model for this correlation was derived. Using grassy airfield research, soil moisture data were related to meteorological impacts (precipitation, sunlight, etc.). Generally, it was concluded that the TDR meter, in its handheld version, was a useful tool in the performed research, but a field sensor that operates autonomically would be an optimal solution for the subject applications.

Estimation of Soil Moisture Content Using Deep Learning and High-Resolution Satellite Imagery (Sentinel-1 and 2)

2020 ◽  
Author(s):  
Soo-Jin Lee ◽  
Yang-Won Lee
Keyword(s):  
Soil Moisture ◽  
Deep Learning ◽  
High Resolution ◽  
Forest Fires ◽  
Optimal Solution ◽  
Local Scale ◽  
Soil Moisture Data ◽  
Local Optimal Solution ◽  
Salinity Data

<p>Soil moisture is an important factor affecting global circulation (climate, carbon, and water), disasters (drought, floods, and forest fires), and crop growth, so the production of soil moisture data is important. Currently, satellite-based soil moisture data is available from NASA’ SMAP (Soil Moisture Active Passive) and ESA’ SMOS (Soil Moisture and Ocean Salinity) data. Since these data are based on passive microwave sensor, they have low spatial resolution. Therefore, it is difficult to observe the distribution of soil moisture on a local scale. The purpose of this study is to produce high resolution soil moisture for monitoring on a local scale. For this purpose, we performed soil moisture modeling using high resolution satellite data (Sentinel-1 SAR (synthetic-aperture radar), Sentinel-2 MSI (multispectral instrument)) and deep learning. Deep learning is a method improving the problems of traditional neural networks such as overfitting, gradient vanishing, and local optimal solution through development of learning methods such as dropout, ReLU (Rectified Linear Unit), and so on. Recently, it has been used for estimation of surface hydrologic factors (soil moisture, evapotranspiration, etc.). The study area is an agricultural area located in Manitoba and Saskatoon, Canada. In-situ soil moisture data was constructed from RISMA (Real-Time In-Situ Soil Monitoring for Agriculture). In order to develop an optimal soil moisture model, various condition experiments on hyper-parameters affecting the performance of model were carried out and their performance was evaluated.</p>

scholarly journals Influência da Densidade na Estimativa da Umidade Volumétrica em um Latossolo Vermelho-Amarelo (Influence of Density in Estimation of Volumetric Moisture an Oxisol)

2013 ◽  
Vol 5 (5) ◽  
pp. 1056 ◽  
Author(s):  
Carlos Alexandre Barros de Almeida ◽  
Antonio Celso Dantas Antonino ◽  
Rejane Magalhaes de Mendonça Pimentel ◽  
Carlos Alberto Brayner de Oliveira Lira ◽  
José Romualdo de Sousa Lima
Keyword(s):  
Dielectric Constant ◽  
Soil Moisture ◽  
Bulk Density ◽  
Time Domain ◽  
Time Domain Reflectometry ◽  
Calibration Equation ◽  
Moisture Sensor ◽  
Soil Moisture Sensor ◽  
Volumetric Soil Moisture ◽  
Use Of Time

A estimativa da umidade volumétrica do solo pode ser realizada por vários métodos, entre eles destaca-se o uso da Reflectometria no Domínio do Tempo (TDR). Este tem como uso padrão, uma equação que relaciona a constante dielétrica do meio com a umidade sugerida pelo manual do fabricante. Este estudo objetivou avaliar a medição a umidade volumétrica do solo pelo sensor CS616. Na sua realização foi feita a calibração deste sensor em laboratório, para quatro camadas em um Latossolo Vermelho-Amarelo que apresentam densidades diferentes. Foram utilizados cinco métodos diferentes, três consagrados pela literatura e outros dois sugeridos por esse estudo. Os resultados permitiram concluir que nesse solo há uma grande disparidade entre os resultados encontrados durante a calibração do sensor e que a densidade do solo é um parâmetro importante nas medições de umidade do solo.Palavras-chave: reflectometria no domínio do tempo, medição direta da água no solo, equação de calibração Influence of Density in Estimation of Volumetric Moisture an Oxisol ABSTRACTThe estimation of volumetric soil moisture can be accomplished by various methods, among them stands out the use of Time Domain Reflectometry (TDR). This standard is to use an equation that relates the dielectric constant of the medium with humidity suggested by the manufacturer's manual. This study aimed to evaluate the measured volumetric soil moisture sensor for the CS616. In its realization was made to calibrate this sensor in the laboratory for four layers in an Latossolo Vermelho-Amarelo which have different densities. Was used five different methods, the literature established three and two others suggested by this study. The results showed that this soil there is great disparity between the results obtained during calibration of the sensor and the bulk density is an important parameter in measurements of soil moisture.Keywords: time domain reflectometry, direct measurement of soil water, calibration equation

scholarly journals The value of satellite remote sensing soil moisture data and the DISPATCH algorithm in irrigation fields

2018 ◽  
Vol 22 (11) ◽  
pp. 5889-5900 ◽  
Author(s):  
Mireia Fontanet ◽  
Daniel Fernàndez-Garcia ◽  
Francesc Ferrer
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Land Surface ◽  
Vegetation Index ◽  
Spatial Scales ◽  
Sprinkler Irrigation ◽  
Irrigation Scheduling ◽  
Time Domain Reflectometry ◽  
Rainfall Events ◽  
Soil Moisture Data

Abstract. Soil moisture measurements are needed in a large number of applications such as hydro-climate approaches, watershed water balance management and irrigation scheduling. Nowadays, different kinds of methodologies exist for measuring soil moisture. Direct methods based on gravimetric sampling or time domain reflectometry (TDR) techniques measure soil moisture in a small volume of soil at few particular locations. This typically gives a poor description of the spatial distribution of soil moisture in relatively large agriculture fields. Remote sensing of soil moisture provides widespread coverage and can overcome this problem but suffers from other problems stemming from its low spatial resolution. In this context, the DISaggregation based on Physical And Theoretical scale CHange (DISPATCH) algorithm has been proposed in the literature to downscale soil moisture satellite data from 40 to 1 km resolution by combining the low-resolution Soil Moisture Ocean Salinity (SMOS) satellite soil moisture data with the high-resolution Normalized Difference Vegetation Index (NDVI) and land surface temperature (LST) datasets obtained from a Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. In this work, DISPATCH estimations are compared with soil moisture sensors and gravimetric measurements to validate the DISPATCH algorithm in an agricultural field during two different hydrologic scenarios: wet conditions driven by rainfall events and wet conditions driven by local sprinkler irrigation. Results show that the DISPATCH algorithm provides appropriate soil moisture estimates during general rainfall events but not when sprinkler irrigation generates occasional heterogeneity. In order to explain these differences, we have examined the spatial variability scales of NDVI and LST data, which are the input variables involved in the downscaling process. Sample variograms show that the spatial scales associated with the NDVI and LST properties are too large to represent the variations of the average soil moisture at the site, and this could be a reason why the DISPATCH algorithm does not work properly in this field site.

scholarly journals Analysis of spatial variability of near-surface soil moisture to increase rainfall-runoff modelling accuracy in SW Hungary

2015 ◽  
Vol 7 (1) ◽  
Author(s):  
P. Hegedüs ◽  
S. Czigány ◽  
E. Pirkhoffer ◽  
L. Balatonyi ◽  
R. Hickey
Keyword(s):  
Soil Moisture ◽  
Surface Soil ◽  
Watershed Scale ◽  
Surface Soil Moisture ◽  
Near Surface ◽  
Moisture Sensor ◽  
Soil Moisture Sensor ◽  
Soil Moisture Data ◽  
Data Points ◽  
Monitoring Stations

AbstractBetween September 5, 2008 and September 5, 2009, near-surface soil moisture time series were collected in the northern part of a 1.7 km2 watershed in SWHungary at 14 monitoring locations using a portable TDR-300 soil moisture sensor. The objectives of this study are to increase the accuracy of soil moisture measurement at watershed scale, to improve flood forecasting accuracy, and to optimize soil moisture sensor density.According to our results, in 10 of 13 cases, a strong correlation exists between the measured soil moisture data of Station 5 and all other monitoring stations; Station 5 is considered representative for the entire watershed. Logically, the selection of the location of the representative measurement point(s) is essential for obtaining representative and accurate soil moisture values for the given watershed. This could be done by (i) employing monitoring stations of higher number at the exploratory phase of the monitoring, (ii) mapping soil physical properties at watershed scale, and (iii) running cross-relational statistical analyses on the obtained data.Our findings indicate that increasing the number of soil moisture data points available for interpolation increases the accuracy of watershed-scale soil moisture estimation. The data set used for interpolation (and estimation of mean antecedent soil moisture values) could be improved (thus, having a higher number of data points) by selecting points of similar properties to the measurement points from the DEM and soil databases. By using a higher number of data points for interpolation, both interpolation accuracy and spatial resolution have increased for the measured soil moisture values for the Pósa Valley.

scholarly journals Determination of soil density by theoretical and experimental methods

2021 ◽  
Vol 344 (1) ◽  
pp. 60-63
Author(s):  
A. V. Lavrov ◽  
M. A. Litvinov
Keyword(s):  
Soil Moisture ◽  
Harmful Effect ◽  
Soil Density ◽  
Steel Cylinder ◽  
Soil Hardness ◽  
Three Samples ◽  
Before And After ◽  
Almost All ◽  
Maximum Contact

Relevance. According our researches it was found that almost all models oftractors and self-propelled machines has created the maximum contact pressure ofthe movers on the soil above the permissible values. In such way, in view ofthe extreme topicality of the saving soil fertility during evaluating theagrotechnical indicators of a self-propelled selection seeder, it is necessary, first ofall, to make researches to determine the harmful effect of propellers on the soil.Methods. Theoretical researches of determining the soil hardness and density werecarried out using the dependence of density on hardness. During the tests of theself-propelled selection seeder, soil moisture was measured at a depth of 3 inches (7.6 cm) and it was 20%. To measure soil moisture, It was used a digital device, itwas a universal moisture meter TK100. Samples were taken before and after eachpass of the self-propelled seeder with the front and rear wheels. Hardness wasmeasured for each sample.The Kachinskys method was used to measure soil density as the experimentalmethod. To take soil samples, a 100 cubic meter drill (steel cylinder) was used.Soil samples were taken according to the method described above. At the sametime, for each case, three samples were taken to exclude random deviations in soildensity indicators. In the laboratory, the samples were weighed on a VK 3000.1electronic balance with a measurement error of 0.1 grams.Results. The soil density was determined by calculation and experimental methodsin three zones: before the seeder pass and after each its pass in the track behind thefront and rear wheels at a depth of 7.6 cm. The results obtained differ from eachother by a maximum of 6.2%.

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