Soil Moisture Evaluation Using a Calibrated Sensor Network and a Soil-Vegetation-Atmospheric-Transfer Model.

Persistent Link:
http://hdl.handle.net/10150/191282
Title:
Soil Moisture Evaluation Using a Calibrated Sensor Network and a Soil-Vegetation-Atmospheric-Transfer Model.
Author:
Hymer, Daniel Craig.
Issue Date:
1998
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
Recent studies have proposed that images from Synthetic Aperture Radar (SAR) sensors can be used to map spatially distributed soil moisture patterns within 5 cm of the surface. Unfortunately, many hydrologic applications require vadose zone soil moisture measurements rather than surface soil moisture measured by the SAR sensor. By combining SAR-derived surface soil moisture maps with a Soil-Vegetation-Atmosphere- Transfer (SVAT) model, it may be possible to obtain spatially distributed, temporally continuous information on vadose zone soil moisture. The first step in developing such a combined approach is to investigate the accuracy and precision of a SVAT model to estimate surface and vadose zone soil moisture over time. In this experiment, we evaluated the Simultaneous Heat and Water (SHAW) model by comparing its soil moisture estimates to a calibrated, one year, hourly soil moisture data set at three different depths under bare soil and shrub cover surfaces. Analysis indicated that the SHAW model overestimated soil moisture at each depth by an average of 0.02 M^3M&-3 under bare soil and underestimated soil moisture at each depth under shrub cover by an average of 0.02 m^3m^-3 . Based on this research, future studies should focus on calibration of the SHAW model and the assimilation of remotely sensed data as a primary model input.
Type:
Thesis-Reproduction (electronic); text
LCSH Subjects:
Hydrology.; Soil moisture.; Plants -- Effect of soil moisture on.
Degree Name:
M.S.
Degree Level:
masters
Degree Program:
Soil, Water and Environmental Science; Graduate College
Degree Grantor:
University of Arizona
Committee Chair:
Moran, Susan

Full metadata record

DC FieldValue Language
dc.language.isoenen_US
dc.titleSoil Moisture Evaluation Using a Calibrated Sensor Network and a Soil-Vegetation-Atmospheric-Transfer Model.en_US
dc.creatorHymer, Daniel Craig.en_US
dc.contributor.authorHymer, Daniel Craig.en_US
dc.date.issued1998en_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.description.abstractRecent studies have proposed that images from Synthetic Aperture Radar (SAR) sensors can be used to map spatially distributed soil moisture patterns within 5 cm of the surface. Unfortunately, many hydrologic applications require vadose zone soil moisture measurements rather than surface soil moisture measured by the SAR sensor. By combining SAR-derived surface soil moisture maps with a Soil-Vegetation-Atmosphere- Transfer (SVAT) model, it may be possible to obtain spatially distributed, temporally continuous information on vadose zone soil moisture. The first step in developing such a combined approach is to investigate the accuracy and precision of a SVAT model to estimate surface and vadose zone soil moisture over time. In this experiment, we evaluated the Simultaneous Heat and Water (SHAW) model by comparing its soil moisture estimates to a calibrated, one year, hourly soil moisture data set at three different depths under bare soil and shrub cover surfaces. Analysis indicated that the SHAW model overestimated soil moisture at each depth by an average of 0.02 M^3M&-3 under bare soil and underestimated soil moisture at each depth under shrub cover by an average of 0.02 m^3m^-3 . Based on this research, future studies should focus on calibration of the SHAW model and the assimilation of remotely sensed data as a primary model input.en_US
dc.description.notehydrology collectionen_US
dc.typeThesis-Reproduction (electronic)en_US
dc.typetexten_US
dc.subject.lcshHydrology.en_US
dc.subject.lcshSoil moisture.en_US
dc.subject.lcshPlants -- Effect of soil moisture on.en_US
thesis.degree.nameM.S.en_US
thesis.degree.levelmastersen_US
thesis.degree.disciplineSoil, Water and Environmental Scienceen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.chairMoran, Susanen_US
dc.identifier.oclc220935617en_US
All Items in UA Campus Repository are protected by copyright, with all rights reserved, unless otherwise indicated.