Titill: | Gap-Filling of MODIS Time Series Land Surface Temperature (LST) Products Using Singular Spectrum Analysis (SSA) |
Höfundur: |
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Útgáfa: | 2018-08-23 |
Tungumál: | Enska |
Umfang: | 334 |
Háskóli/Stofnun: | Háskóli Íslands (HÍ) University of Iceland (UI) |
Svið: | School of Engineering and Natural Sciences (UI) Verkfræði- og náttúruvísindasvið (HÍ) |
Deild: | Raunvísindadeild (HÍ) Faculty of Physical Sciences (UI) |
Birtist í: | Atmosphere;9(9) |
ISSN: | 2073-4433 |
DOI: | 10.3390/atmos9090334 |
Efnisorð: | Gap filling; M-SSA; Monte Carlo test; Time series; MODIS LST; Veðurathuganir; Veðurfarsrannsóknir; Gervitungl; Hitamælingar |
URI: | https://hdl.handle.net/20.500.11815/1465 |
Tilvitnun:Ghafarian Malamiri, H.R.; Rousta, I.; Olafsson, H.; Zare, H.; Zhang, H. Gap-Filling of MODIS Time Series Land Surface Temperature (LST) Products Using Singular Spectrum Analysis (SSA). Atmosphere 2018, 9, 334.
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Útdráttur:Land surface temperature (LST) is a basic parameter in energy exchange between the
land and the atmosphere, and is frequently used in many sciences such as climatology, hydrology,
agriculture, ecology, etc. Time series of satellite LST data have usually deficient, missing, and
unacceptable data caused by the presence of clouds in images, the presence of dust in the atmosphere,
and sensor failure. In this study, the singular spectrum analysis (SSA) algorithm was used to resolve
the problem of missing and outlier data caused by cloud cover. The region studied in the present
research included an image frame of the Moderate Resolution Imaging Spectroradiometer (MODIS)
with horizontal number 22 and vertical number 05 (h22v05). This image involved a large part of
Iran, Turkmenistan, and the Caspian Sea. In this study, MODIS LST products (MOD11A1) were
used during 2015 with approximately 1 km × 1 km spatial resolution and day/night LST data (daily
temporal resolution). On average, the data have 36.37% gaps in each pixel profile with 730 day/night
LST data. The results of the SSA algorithm in the reconstruction of LST images indicated a root mean
square error (RMSE) of 2.95 Kelvin (K) between the original and reconstructed LST time series data
in the study region. In general, the findings showed that the SSA algorithm using spatio-temporal
interpolation can be effectively used to resolve the problem of missing data caused by cloud cover.
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Athugasemdir:Publisher's version (útgefin grein).
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Leyfi:This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (http://creativecommons.org/licenses/by/4.0/).
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