Retrieval of All-Weather 1 km Land Surface Temperature from Combined MODIS and AMSR2 Data over the Tibetan Plateau

Land surface temperature (LST) is one of the most valuable variables for applications relating to hydrological processes, drought monitoring and climate change. LST from satellite data provides consistent estimates over large scales but is only available for cloud-free pixels, greatly limiting appli...

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Autores principales:	Yanmei Zhong, Lingkui Meng, Zushuai Wei, Jian Yang, Weiwei Song, Mohammad Basir
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	land surface temperature all-weather fusion random forest AMSR-2 Tibetan Plateau Science Q
Acceso en línea:	https://doaj.org/article/df63c917d5994317ae9723f4e9cd8504
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spelling	oai:doaj.org-article:df63c917d5994317ae9723f4e9cd85042021-11-25T18:54:28ZRetrieval of All-Weather 1 km Land Surface Temperature from Combined MODIS and AMSR2 Data over the Tibetan Plateau10.3390/rs132245742072-4292https://doaj.org/article/df63c917d5994317ae9723f4e9cd85042021-11-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/22/4574https://doaj.org/toc/2072-4292Land surface temperature (LST) is one of the most valuable variables for applications relating to hydrological processes, drought monitoring and climate change. LST from satellite data provides consistent estimates over large scales but is only available for cloud-free pixels, greatly limiting applications over frequently cloud-covered regions. With this study, we propose a method for estimating all-weather 1 km LST by combining passive microwave and thermal infrared data. The product is based on clear-sky LST retrieved from Moderate-resolution Imaging Spectroradiometer (MODIS) thermal infrared measurements complemented by LST estimated from the Advanced Microwave Scanning Radiometer Version 2 (AMSR2) brightness temperature to fill gaps caused by clouds. Terrain, vegetation conditions, and AMSR2 multiband information were selected as the auxiliary variables. The random forest algorithm was used to establish the non-linear relationship between the auxiliary variables and LST over the Tibetan Plateau. To assess the error of this method, we performed a validation experiment using clear-sky MODIS LST and in situ measurements. The estimated all-weather LST approximated MODIS LST with an acceptable error, with a coefficient of correlation (r) between 0.87 and 0.99 and a root mean square error (RMSE) between 2.24 K and 5.35 K during the day. At night-time, r was between 0.89 and 0.99 and the RMSE was between 1.02 K and 3.39 K. The error between the estimated LST and in situ LST was also found to be acceptable, with the RMSE for cloudy pixels between 5.15 K and 6.99 K. This method reveals a significant potential to derive all-weather 1 km LST using AMSR2 and MODIS data at a regional and global scale, which will be explored in the future.Yanmei ZhongLingkui MengZushuai WeiJian YangWeiwei SongMohammad BasirMDPI AGarticleland surface temperatureall-weatherfusionrandom forestAMSR-2Tibetan PlateauScienceQENRemote Sensing, Vol 13, Iss 4574, p 4574 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	land surface temperature all-weather fusion random forest AMSR-2 Tibetan Plateau Science Q
spellingShingle	land surface temperature all-weather fusion random forest AMSR-2 Tibetan Plateau Science Q Yanmei Zhong Lingkui Meng Zushuai Wei Jian Yang Weiwei Song Mohammad Basir Retrieval of All-Weather 1 km Land Surface Temperature from Combined MODIS and AMSR2 Data over the Tibetan Plateau
description	Land surface temperature (LST) is one of the most valuable variables for applications relating to hydrological processes, drought monitoring and climate change. LST from satellite data provides consistent estimates over large scales but is only available for cloud-free pixels, greatly limiting applications over frequently cloud-covered regions. With this study, we propose a method for estimating all-weather 1 km LST by combining passive microwave and thermal infrared data. The product is based on clear-sky LST retrieved from Moderate-resolution Imaging Spectroradiometer (MODIS) thermal infrared measurements complemented by LST estimated from the Advanced Microwave Scanning Radiometer Version 2 (AMSR2) brightness temperature to fill gaps caused by clouds. Terrain, vegetation conditions, and AMSR2 multiband information were selected as the auxiliary variables. The random forest algorithm was used to establish the non-linear relationship between the auxiliary variables and LST over the Tibetan Plateau. To assess the error of this method, we performed a validation experiment using clear-sky MODIS LST and in situ measurements. The estimated all-weather LST approximated MODIS LST with an acceptable error, with a coefficient of correlation (r) between 0.87 and 0.99 and a root mean square error (RMSE) between 2.24 K and 5.35 K during the day. At night-time, r was between 0.89 and 0.99 and the RMSE was between 1.02 K and 3.39 K. The error between the estimated LST and in situ LST was also found to be acceptable, with the RMSE for cloudy pixels between 5.15 K and 6.99 K. This method reveals a significant potential to derive all-weather 1 km LST using AMSR2 and MODIS data at a regional and global scale, which will be explored in the future.
format	article
author	Yanmei Zhong Lingkui Meng Zushuai Wei Jian Yang Weiwei Song Mohammad Basir
author_facet	Yanmei Zhong Lingkui Meng Zushuai Wei Jian Yang Weiwei Song Mohammad Basir
author_sort	Yanmei Zhong
title	Retrieval of All-Weather 1 km Land Surface Temperature from Combined MODIS and AMSR2 Data over the Tibetan Plateau
title_short	Retrieval of All-Weather 1 km Land Surface Temperature from Combined MODIS and AMSR2 Data over the Tibetan Plateau
title_full	Retrieval of All-Weather 1 km Land Surface Temperature from Combined MODIS and AMSR2 Data over the Tibetan Plateau
title_fullStr	Retrieval of All-Weather 1 km Land Surface Temperature from Combined MODIS and AMSR2 Data over the Tibetan Plateau
title_full_unstemmed	Retrieval of All-Weather 1 km Land Surface Temperature from Combined MODIS and AMSR2 Data over the Tibetan Plateau
title_sort	retrieval of all-weather 1 km land surface temperature from combined modis and amsr2 data over the tibetan plateau
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/df63c917d5994317ae9723f4e9cd8504
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Retrieval of All-Weather 1 km Land Surface Temperature from Combined MODIS and AMSR2 Data over the Tibetan Plateau

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