An accurate estimate of monthly mean land surface temperatures from MODIS clear-sky retrievals

Chen, X; Su, Z; Ma, Y; Cleverly, J; Liddell, M

An accurate estimate of monthly mean land surface temperatures from MODIS clear-sky retrievals

Chen, X Su, Z Ma, Y Cleverly, J

Liddell, M

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Publication Type:: Journal Article
Citation:: Journal of Hydrometeorology, 2017, 18 (10), pp. 2827 - 2847
Issue Date:: 2017-10-01

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Field	Value	Language
dc.contributor.author	Chen, X	en_US
dc.contributor.author	Su, Z	en_US
dc.contributor.author	Ma, Y	en_US
dc.contributor.author	Cleverly, J https://orcid.org/0000-0002-2731-7150	en_US
dc.contributor.author	Liddell, M	en_US
dc.date.issued	2017-10-01	en_US
dc.identifier.citation	Journal of Hydrometeorology, 2017, 18 (10), pp. 2827 - 2847	en_US
dc.identifier.issn	1525-755X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/123401
dc.description.abstract	© 2017 American Meteorological Society. MODIS thermal sensors can provide us with global land surface temperature (LST) several times each day, but have difficulty in obtaining information from the land surface in cloudy situations. As a result, the monthly day or night LST products [Terra monthly day LST (TMD), Terra monthly night LST (TMN), Aqua monthly day LST (AMD), and Aqua monthly night LST (AMN)] are the average LST values calculated over a variable number of clear-sky days in a month. Is it possible to derive an accurate estimate of monthly mean LST based on averaging of the multidaily overpasses of MODISsensors? In situ ground measurements and ERA-Interim reanalyses data, both of which provide continuous information in either clear or cloudy conditions, have been used to validate the approach. Using LST measurements from 156 ground flux towers, it was found that the three mean values LST(AMD,AMN), LST(TMD,TMN), and LST(AMD,AMN,TMD,TMN) (mean biases of 0.19, 0.59, and 0.40 K, respectively) can all provide a reliable estimate of all-sky monthly mean LST. Of the three means, we recommend the use of LST(AMD,AMN,TMD,TMN) for monthly mean LST in climate studies as it provides the most complete coverage.When retrievals from either Terra or Aqua are not available, then either LST(AMD,AMN) or LST(TMD,TMN) may be used to fill the gaps. The intrinsic error in the MODIS monthly mean LST cannot be explained from monthly mean view time, view angle, and clear-sky ratio. MODIS monthly LST calculated using this approach LST(AMD,AMN,TMD,TMN) (RMSE52.65, mean bias < ±1K) will have wide applicability for climate studies and numerical model evaluation.	en_US
dc.relation.ispartof	Journal of Hydrometeorology	en_US
dc.relation.isbasedon	10.1175/JHM-D-17-0009.1	en_US
dc.subject.classification	Meteorology & Atmospheric Sciences	en_US
dc.title	An accurate estimate of monthly mean land surface temperatures from MODIS clear-sky retrievals	en_US
dc.type	Journal Article
utslib.citation.volume	10	en_US
utslib.citation.volume	18	en_US
utslib.for	0401 Atmospheric Sciences	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Life Sciences
utslib.copyright.status	closed_access
pubs.issue	10	en_US
pubs.publication-status	Published	en_US
pubs.volume	18	en_US

Abstract:

© 2017 American Meteorological Society. MODIS thermal sensors can provide us with global land surface temperature (LST) several times each day, but have difficulty in obtaining information from the land surface in cloudy situations. As a result, the monthly day or night LST products [Terra monthly day LST (TMD), Terra monthly night LST (TMN), Aqua monthly day LST (AMD), and Aqua monthly night LST (AMN)] are the average LST values calculated over a variable number of clear-sky days in a month. Is it possible to derive an accurate estimate of monthly mean LST based on averaging of the multidaily overpasses of MODISsensors? In situ ground measurements and ERA-Interim reanalyses data, both of which provide continuous information in either clear or cloudy conditions, have been used to validate the approach. Using LST measurements from 156 ground flux towers, it was found that the three mean values LST(AMD,AMN), LST(TMD,TMN), and LST(AMD,AMN,TMD,TMN) (mean biases of 0.19, 0.59, and 0.40 K, respectively) can all provide a reliable estimate of all-sky monthly mean LST. Of the three means, we recommend the use of LST(AMD,AMN,TMD,TMN) for monthly mean LST in climate studies as it provides the most complete coverage.When retrievals from either Terra or Aqua are not available, then either LST(AMD,AMN) or LST(TMD,TMN) may be used to fill the gaps. The intrinsic error in the MODIS monthly mean LST cannot be explained from monthly mean view time, view angle, and clear-sky ratio. MODIS monthly LST calculated using this approach LST(AMD,AMN,TMD,TMN) (RMSE52.65, mean bias < ±1K) will have wide applicability for climate studies and numerical model evaluation.

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http://hdl.handle.net/10453/123401