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Article: Creating a seamless 1 km resolution daily land surface temperature dataset for urban and surrounding areas in the conterminous United States

TitleCreating a seamless 1 km resolution daily land surface temperature dataset for urban and surrounding areas in the conterminous United States
Authors
KeywordsGapfilling
Interpolation
Land surface temperature
MODIS
Urbanization
Issue Date2018
Citation
Remote Sensing of Environment, 2018, v. 206, p. 84-97 How to Cite?
AbstractHigh spatiotemporal land surface temperature (LST) datasets are increasingly needed in a variety of fields such as ecology, hydrology, meteorology, epidemiology, and energy systems. Moderate Resolution Imaging Spectroradiometer (MODIS) daily LST is one of such high spatiotemporal datasets that are widely used. But, it has a large amount of missing values primarily because of clouds, shadows, and other atmospheric conditions. Gapfilling the missing values is an important approach to create seamless high spatiotemporal LST datasets. However, current gapfilling methods have limitations in terms of accuracy and efficiency to assemble the data over large areas (e.g., national and continental levels). In this study, we developed a 3-step hybrid method by integrating daily merging (gapfilling missing values at one overpass using values at the other three overpasses each day), spatiotemporal gapfilling (estimating missing values based on values of their spatial and temporal neighbors), and temporal interpolation (gapfilling missing values based on values of their neighboring days), to create a seamless high spatiotemporal LST dataset using the four daily LST observations from the two MODIS instruments on Terra and Aqua satellites. We applied this method in urban and surrounding areas in the conterminous U.S. in 2010. The evaluation of the gapfilled LST product indicates its root mean squared error (RMSE) to be 3.3 K for mid-daytime (1:30 pm) and 2.7 K for mid-nighttime (1:30 am) observations. The method can be easily extended to other years and regions and is also applicable to other satellite products for large areas. This seamless daily (mid-daytime and mid-nighttime) LST product with 1 km spatial resolution is of great value for studying urban climate (e.g., quantifying surface urban heat island intensity, creating seamless high spatiotemporal air temperature dataset) and the related impacts on people (e.g., health and mortality), ecosystems (e.g., phenology), and energy systems (e.g., building energy use).
Persistent Identifierhttp://hdl.handle.net/10722/329483
ISSN
2023 Impact Factor: 11.1
2023 SCImago Journal Rankings: 4.310
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorLi, Xiaoma-
dc.contributor.authorZhou, Yuyu-
dc.contributor.authorAsrar, Ghassem R.-
dc.contributor.authorZhu, Zhengyuan-
dc.date.accessioned2023-08-09T03:33:07Z-
dc.date.available2023-08-09T03:33:07Z-
dc.date.issued2018-
dc.identifier.citationRemote Sensing of Environment, 2018, v. 206, p. 84-97-
dc.identifier.issn0034-4257-
dc.identifier.urihttp://hdl.handle.net/10722/329483-
dc.description.abstractHigh spatiotemporal land surface temperature (LST) datasets are increasingly needed in a variety of fields such as ecology, hydrology, meteorology, epidemiology, and energy systems. Moderate Resolution Imaging Spectroradiometer (MODIS) daily LST is one of such high spatiotemporal datasets that are widely used. But, it has a large amount of missing values primarily because of clouds, shadows, and other atmospheric conditions. Gapfilling the missing values is an important approach to create seamless high spatiotemporal LST datasets. However, current gapfilling methods have limitations in terms of accuracy and efficiency to assemble the data over large areas (e.g., national and continental levels). In this study, we developed a 3-step hybrid method by integrating daily merging (gapfilling missing values at one overpass using values at the other three overpasses each day), spatiotemporal gapfilling (estimating missing values based on values of their spatial and temporal neighbors), and temporal interpolation (gapfilling missing values based on values of their neighboring days), to create a seamless high spatiotemporal LST dataset using the four daily LST observations from the two MODIS instruments on Terra and Aqua satellites. We applied this method in urban and surrounding areas in the conterminous U.S. in 2010. The evaluation of the gapfilled LST product indicates its root mean squared error (RMSE) to be 3.3 K for mid-daytime (1:30 pm) and 2.7 K for mid-nighttime (1:30 am) observations. The method can be easily extended to other years and regions and is also applicable to other satellite products for large areas. This seamless daily (mid-daytime and mid-nighttime) LST product with 1 km spatial resolution is of great value for studying urban climate (e.g., quantifying surface urban heat island intensity, creating seamless high spatiotemporal air temperature dataset) and the related impacts on people (e.g., health and mortality), ecosystems (e.g., phenology), and energy systems (e.g., building energy use).-
dc.languageeng-
dc.relation.ispartofRemote Sensing of Environment-
dc.subjectGapfilling-
dc.subjectInterpolation-
dc.subjectLand surface temperature-
dc.subjectMODIS-
dc.subjectUrbanization-
dc.titleCreating a seamless 1 km resolution daily land surface temperature dataset for urban and surrounding areas in the conterminous United States-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.rse.2017.12.010-
dc.identifier.scopuseid_2-s2.0-85038838239-
dc.identifier.volume206-
dc.identifier.spage84-
dc.identifier.epage97-
dc.identifier.isiWOS:000427342700007-

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