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Non-contact measurement of soil moisture content using thermal infrared sensor and weather variables

dc.contributor.advisorRoberge, Martinen_US
dc.contributor.committeeMemberFonstad, Terrance A.en_US
dc.contributor.committeeMemberCrowe, Trever G.en_US
dc.creatorAlshikaili, Talalen_US
dc.date.accessioned2007-03-10T16:36:16Zen_US
dc.date.accessioned2013-01-04T04:26:26Z
dc.date.available2008-03-19T08:00:00Zen_US
dc.date.available2013-01-04T04:26:26Z
dc.date.created2007en_US
dc.date.issued2007en_US
dc.date.submitted2007en_US
dc.description.abstractThe use of remote sensing technology has made it possible for the non-contact measurement of soil moisture content (SMC). Many remote sensing techniques can be used such as microwave sensors, electromagnetic waves sensors, capacitance, and thermal infrared sensors. Some of those techniques are constrained by their high fabrication cost, operation cost, size, or complexity. In this study, a thermal infrared technique was used to predict soil moisture content with the aid of using weather meteorological variables. The measured variables in the experiment were soil moisture content (%SMC), soil surface temperature (Ts) measured using thermocouples, air temperature (Ta), relative humidity (RH), solar radiation (SR), and wind speed (WS). The experiment was carried out for a total of 12 soil samples of two soil types (clay/sand) and two compaction levels (compacted/non-compacted). After data analysis, calibration models relating soil moisture content (SMC) to differential temperature (Td), relative humidity (RH), solar radiation (SR), and wind speed (WS) were generated using stepwise multiple linear regression of the calibration data set. The performance of the models was evaluated using validation data. Four mathematical models of predicting soil moisture content were generated for each soil type and configuration using the calibration data set. Among the four models, the best model for each soil type and configuration was determined by comparing root mean of squared errors of calibration (RMSEC) and root mean of squared errors of validation (RMSEV) values. Furthermore, a calibration model for the thermal infrared sensor was developed to determine the corrected soil surface temperature as measured by the sensor (Tir) instead of using the thermocouples. The performance of the thermal infrared sensor to predict soil moisture content was then tested for sand compacted and sand non-compacted soils and compared to the predictive performance of the thermocouples. This was achieved by using the measured soil surface temperature by the sensor (Tir), instead of the measured soil surface temperature using the thermocouples to determine the soil-minus-air temperature (Td). The sensor showed comparable prediction performance, relative to thermocouples. Overall, the models developed in this study showed high prediction performance when tested with the validation data set. The best models to predict SMC for compacted clay soil, non-compacted clay soil, and compacted sandy soil were three-variable models containing three predictive variables; Td, RH, and SR. On the other hand, the best model to predict SMC for compacted sandy soil was a two-variable model containing Td, and RH. The results showed that the prediction performance of models for predicting SMC for the sandy soils was superior to those of clay soils.en_US
dc.identifier.urihttp://hdl.handle.net/10388/etd-03102007-163616en_US
dc.language.isoen_USen_US
dc.subjectstepwise multiple linear regressionen_US
dc.subjectremote sensingen_US
dc.subjectsoil moistureen_US
dc.subjectthermal infrareden_US
dc.subjectdifferential temperatureen_US
dc.subjectpredictive performanceen_US
dc.subjectsoil surface-minus-air temperatureen_US
dc.subjectroot mean of squared errorsen_US
dc.subjectcalibrationen_US
dc.subjectvalidationen_US
dc.subjectnon-contacten_US
dc.titleNon-contact measurement of soil moisture content using thermal infrared sensor and weather variablesen_US
dc.type.genreThesisen_US
dc.type.materialtexten_US
thesis.degree.departmentAgricultural and Bioresource Engineeringen_US
thesis.degree.disciplineAgricultural and Bioresource Engineeringen_US
thesis.degree.grantorUniversity of Saskatchewanen_US
thesis.degree.levelMastersen_US
thesis.degree.nameMaster of Science (M.Sc.)en_US

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