Preprint Article Version 1 This version is not peer-reviewed

Reconstruction of the Cloud-Free Time Series Satellite Observations of Land Surface Temperature (LST) Using Singular Spectrum Analysis (SSA)

Version 1 : Received: 20 June 2018 / Approved: 20 June 2018 / Online: 20 June 2018 (16:25:07 CEST)

How to cite: Ghafarian Malamiri, H.R.; Rousta, I.; Olafsson, H.; Zare, H.; Zhang, H. Reconstruction of the Cloud-Free Time Series Satellite Observations of Land Surface Temperature (LST) Using Singular Spectrum Analysis (SSA). Preprints 2018, 2018060327 (doi: 10.20944/preprints201806.0327.v1). Ghafarian Malamiri, H.R.; Rousta, I.; Olafsson, H.; Zare, H.; Zhang, H. Reconstruction of the Cloud-Free Time Series Satellite Observations of Land Surface Temperature (LST) Using Singular Spectrum Analysis (SSA). Preprints 2018, 2018060327 (doi: 10.20944/preprints201806.0327.v1).

Abstract

Land Surface Temperature (LST) is a basic parameter in energy exchange between the land and atmosphere and is frequently used in many sciences such as climatology, hydrology, agriculture, ecology, etc. LST time series data have usually deficient, missing and unacceptable data caused by the presence of clouds in images, presence of dust in atmosphere and sensor failure. In this study, 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 MODIS with horizontal number 22 and vertical number 05 (h22v05). This image involved a large part of Iran and Turkmenistan and Caspian Sea. In this study, MODIS LST sensor (MOD11A1) was used during 2015 with 1×1 Km spatial resolution and day/night LST data (daily temporal resolution). The results of the data quality showed that cloud cover caused 36.37% of missing data in the studied time series with 730 day/night LST images. Further, the results of SSA algorithm in reconstruction of LST images indicated the Root Mean Square Error (RMSE) of 2.95 K between the original and reconstructed data in LST time series in the study region. In general, the findings showed that SSA algorithm using spatio-temporal interpolation in LST time series can be effectively used to resolve the problem of missing data caused by cloud cover.

Subject Areas

Gap filling, M-SSA, Monte Carlo test, Time series, LST

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