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Open AccessArticle

Retrieval of Global Orbit Drift Corrected Land Surface Temperature from Long-term AVHRR Data

1
Key Laboratory of Resources and Environment Information System (LREIS), Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
3
Icube (UMR7357), UdS, CNRS, 300 Bld Sebastien Brant, BP10413, 67412 Illkirch, France
4
State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
5
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(23), 2843; https://doi.org/10.3390/rs11232843
Received: 27 September 2019 / Revised: 10 November 2019 / Accepted: 27 November 2019 / Published: 29 November 2019
(This article belongs to the Special Issue Remote Sensing of Land Surface Radiation Budget)
Advanced Very High Resolution Radiometer (AVHRR) sensors provide a valuable data source for generating long-term global land surface temperature (LST). However, changes in the observation time that are caused by satellite orbit drift restrict their wide application. Here, a generalized split-window (GSW) algorithm was implemented to retrieve the LST from the time series AVHRR data. Afterwards, a novel orbit drift correction (ODC) algorithm, which was based on the diurnal temperature cycle (DTC) model and Bayesian optimization algorithm, was also proposed for normalizing the estimated LST to the same local time. This ODC algorithm is pixel-based and it only needs one observation every day. The resulting LSTs from the six-year National Oceanic and Atmospheric Administration (NOAA)-14 satellite data were validated while using Surface Radiation Budget Network (SURFRAD) in-situ measurements. The average accuracies for LST retrieval varied from −0.4 K to 2.0 K over six stations and they also depended on the viewing zenith angle and season. The simulated data illustrate that the proposed ODC method can improve the LST estimate at a similar magnitude to the accuracy of the LST retrieval, i.e., the root-mean-square errors (RMSEs) of the corrected LSTs were 1.3 K, 2.2 K, and 3.1 K for the LST with a retrieval RMSE of 1 K, 2 K, and 3 K, respectively. This method was less sensitive to the fractional vegetation cover (FVC), including the FVC retrieval error, size, and degree of change within a neighboring area, which suggested that it could be easily updated by applying other LST expression models. In addition, ground validation also showed an encouraging correction effect. The RMSE variations of LST estimation that were introduced by ODC were within ±0.5 K, and the correlation coefficients between the corrected LST errors and original LST errors could approach 0.91. View Full-Text
Keywords: land surface temperature (LST); long-term; NOAA-AVHRR; generalized split-window (GSW); orbit drift correction (ODC); diurnal temperature cycle (DTC) land surface temperature (LST); long-term; NOAA-AVHRR; generalized split-window (GSW); orbit drift correction (ODC); diurnal temperature cycle (DTC)
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MDPI and ACS Style

Liu, X.; Tang, B.-H.; Yan, G.; Li, Z.-L.; Liang, S. Retrieval of Global Orbit Drift Corrected Land Surface Temperature from Long-term AVHRR Data. Remote Sens. 2019, 11, 2843.

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