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

Abstract

High 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) LST is one of such high spatiotemporal datasets that are widely used. But, it has large amount of missing values primarily because of clouds. Gapfilling the missing values is an important approach to create high spatiotemporal LST datasets. However current gapfilling methods have limitations in terms of accuracy and time required 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 a combination of daily merging, spatiotemporal gapfilling, and temporal interpolation methods, to create a 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 for the conterminous U.S. in 2010. The evaluation of the gapfilled LST product indicates that its root mean squared error (RMSE) to be 3.3K for mid-daytime (1:30 pm) and 2.7K for mid-13 nighttime (1:30 am) observations. The method can be easily extended to other years and regions and is also applicable to other satellitemore » products. This seamless daily (mid-daytime and mid-nighttime) LST product with 1 km spatial resolution is of great value for studying effects of urbanization (e.g., urban heat island) and the related impacts on people, ecosystems, energy systems and other infrastructure for cities.« less

Authors:
; ORCiD logo; ;
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1415692
Report Number(s):
PNNL-SA-126568
Journal ID: ISSN 0034-4257; KP1703030
DOE Contract Number:
AC05-76RL01830
Resource Type:
Journal Article
Resource Relation:
Journal Name: Remote Sensing of Environment; Journal Volume: 206; Journal Issue: C
Country of Publication:
United States
Language:
English
Subject:
MODIS; Land surface temperature; gapfilling; interpolation; urbanization

Citation Formats

Li, Xiaoma, Zhou, Yuyu, Asrar, Ghassem R., and Zhu, Zhengyuan. Creating a seamless 1 km resolution daily land surface temperature dataset for urban and surrounding areas in the conterminous United States. United States: N. p., 2018. Web. doi:10.1016/j.rse.2017.12.010.
Li, Xiaoma, Zhou, Yuyu, Asrar, Ghassem R., & Zhu, Zhengyuan. Creating a seamless 1 km resolution daily land surface temperature dataset for urban and surrounding areas in the conterminous United States. United States. doi:10.1016/j.rse.2017.12.010.
Li, Xiaoma, Zhou, Yuyu, Asrar, Ghassem R., and Zhu, Zhengyuan. Thu . "Creating a seamless 1 km resolution daily land surface temperature dataset for urban and surrounding areas in the conterminous United States". United States. doi:10.1016/j.rse.2017.12.010.
@article{osti_1415692,
title = {Creating a seamless 1 km resolution daily land surface temperature dataset for urban and surrounding areas in the conterminous United States},
author = {Li, Xiaoma and Zhou, Yuyu and Asrar, Ghassem R. and Zhu, Zhengyuan},
abstractNote = {High 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) LST is one of such high spatiotemporal datasets that are widely used. But, it has large amount of missing values primarily because of clouds. Gapfilling the missing values is an important approach to create high spatiotemporal LST datasets. However current gapfilling methods have limitations in terms of accuracy and time required 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 a combination of daily merging, spatiotemporal gapfilling, and temporal interpolation methods, to create a 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 for the conterminous U.S. in 2010. The evaluation of the gapfilled LST product indicates that its root mean squared error (RMSE) to be 3.3K for mid-daytime (1:30 pm) and 2.7K for mid-13 nighttime (1:30 am) observations. The method can be easily extended to other years and regions and is also applicable to other satellite products. This seamless daily (mid-daytime and mid-nighttime) LST product with 1 km spatial resolution is of great value for studying effects of urbanization (e.g., urban heat island) and the related impacts on people, ecosystems, energy systems and other infrastructure for cities.},
doi = {10.1016/j.rse.2017.12.010},
journal = {Remote Sensing of Environment},
number = C,
volume = 206,
place = {United States},
year = {Thu Mar 01 00:00:00 EST 2018},
month = {Thu Mar 01 00:00:00 EST 2018}
}