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Atmosphere 2018, 9(9), 334; https://doi.org/10.3390/atmos9090334

Gap-Filling of MODIS Time Series Land Surface Temperature (LST) Products Using Singular Spectrum Analysis (SSA)

1
Department of Geography, Yazd University, Yazd 8915818411, Iran
2
Institute for Meteorological Research, Bustadavegur 9, IS-150 Reykjavik, Iceland
3
Department of Physics, University of Iceland and Icelandic Meteorological Office, Bustadavegur 9, IS-150 Reykjavik, Iceland
4
College of Natural resources and Desert, Yazd University, Yazd 8915818411, Iran
5
Department of Environmental Science and Engineering, Jiangwan Campus, Fudan University, 2005 Songhu Road, Yangpu District, Shanghai 200438, China
*
Authors to whom correspondence should be addressed.
Received: 21 June 2018 / Revised: 8 August 2018 / Accepted: 15 August 2018 / Published: 23 August 2018
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Abstract

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. View Full-Text
Keywords: gap filling; M-SSA; Monte Carlo test; time series; MODIS LST gap filling; M-SSA; Monte Carlo test; time series; MODIS LST
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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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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