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Integration of land based embedded and remote sensed temperature for daily temperature mapping

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TitleInfo
Title
Integration of land based embedded and remote sensed temperature for daily temperature mapping
Name (type = personal)
NamePart (type = family)
Farzan
NamePart (type = given)
Farbod
DisplayForm
farbod farzan
Role
RoleTerm (authority = RULIB)
author
Name (type = personal)
NamePart (type = family)
Hill
NamePart (type = given)
David J
DisplayForm
David J Hill
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
chair
Name (type = personal)
NamePart (type = family)
Mazurek
NamePart (type = given)
Monica
DisplayForm
Monica Mazurek
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
Guo
NamePart (type = given)
Qizhong
DisplayForm
Qizhong Guo
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = corporate)
NamePart
Rutgers University
Role
RoleTerm (authority = RULIB)
degree grantor
Name (type = corporate)
NamePart
Graduate School - New Brunswick
Role
RoleTerm (authority = RULIB)
school
TypeOfResource
Text
Genre (authority = marcgt)
theses
OriginInfo
DateCreated (qualifier = exact)
2012
DateOther (qualifier = exact); (type = degree)
2012-01
CopyrightDate (qualifier = exact)
2012
Place
PlaceTerm (type = code)
xx
Language
LanguageTerm (authority = ISO639-2b); (type = code)
eng
Abstract (type = abstract)
Temperature variability is an important driver of many important global and regional processes, which has inspired researchers to understand and predict the spatial variability of surface air temperature. This importance has increased demand for quality, high resolution gridded climatological datasets that deliver detailed information on the variability of temperature at regional scales. Several interpolation and extrapolation techniques have been introduced that use point data sources (land-based data from weather stations). However, the scarcity of weather stations with long-term records and good spatial coverage and the impacts of a non-stationary climate limits these traditional methods. Through the analysis of existing ground-based temperature sensors we have shown that there are inadequate ground-based measurements to estimate the spatial variability of daily min/max temperature. Furthermore, we have shown that existing interpolation methods are insufficiently accurate to estimate the local temperature at ungauged locations because they cannot capture anthropogenic (e.g. urban heat island) or microclimatological (e.g. cold air pooling) effects. This result implies that, in general, ground-based temperature measurements are too sparse to capture the spatial variability of temperature. Together with satellite observations, gridded meteorological variables can provide important information of the complex interactions of these features in order to accurately map temperature across broad regions. Satellite remote-sensing is another way for acquisition of land surface temperature (LST) data. However, due to technical constraints, satellite thermal sensors are incapable to supply both spatially and temporally dense LST image data. The reason for this is that the spatial and temporal resolutions of a satellite thermal sensor are anti-correlated, meaning that a high spatial resolution is related with low temporal resolution and vice versa. The trade-off between spatial and temporal resolution of satellite data, encouraged us to apply the Moderate Resolution Imaging Spectroradiometer (MODIS) as a source of remote-sensed land surface temperature data to capture many of the rapid biological and meteorological changes that MODIS (Spatial Resolution [bands 20-23]: 1km, 5km) observes in every 1 to 2 days. This work develops a new method for integrating remote sensed and ground-based observations of temperature to account for anthropogenic and microclimatological impacts on the surface air temperature. This method is based on a mathematical function that relates the temperature at each point in space as a summation of the remote-sensed measurement at that location and a spatially dependent bias term, which is calculated using the ground based measurements. This model combines the spatial patterns captured within the remote-sensed measurements with the high accuracy of the land-based embedded sensors to construct continuous maps of daily min/max temperature over broad regions. Thus, this model is able to capture the underlying spatial variability of temperature better than other traditional spatial methods.
Subject (authority = RUETD)
Topic
Civil and Environmental Engineering
RelatedItem (type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
Identifier
ETD_3705
PhysicalDescription
Form (authority = gmd)
electronic resource
InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
xii, 113 p. : ill.
Note (type = degree)
M.S.
Note (type = bibliography)
Includes bibliographical references
Note (type = statement of responsibility)
by Farbod Farzan
Subject (authority = ETD-LCSH)
Topic
Earth temperature—Remote sensing
Identifier (type = hdl)
http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000064083
RelatedItem (type = host)
TitleInfo
Title
Graduate School - New Brunswick Electronic Theses and Dissertations
Identifier (type = local)
rucore19991600001
Location
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NjNbRU
Identifier (type = doi)
doi:10.7282/T30G3J6C
Genre (authority = ExL-Esploro)
ETD graduate
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Rights

RightsDeclaration (ID = rulibRdec0006)
The author owns the copyright to this work.
RightsHolder (type = personal)
Name
FamilyName
farzan
GivenName
farbod
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2011-11-16 16:10:57
AssociatedEntity
Name
farbod farzan
Role
Copyright holder
Affiliation
Rutgers University. Graduate School - New Brunswick
AssociatedObject
Type
License
Name
Author Agreement License
Detail
I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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