High Spatiotemporal Rugged Land Surface Temperature Downscaling over Saihanba Forest Park, China
Satellite-derived rugged land surface temperature (LST) is an important parameter indicating the status of the Earth’s surface energy budget and its seasonal/temporal dynamic change. However, existing LST products from rugged areas are more prone to error when supporting applications in mountainous...
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MDPI AG
2022-05-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/11/2617 |
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| author | Xiaoying Ouyang Youjun Dou Jinxin Yang Xi Chen Jianguang Wen |
| author_facet | Xiaoying Ouyang Youjun Dou Jinxin Yang Xi Chen Jianguang Wen |
| author_sort | Xiaoying Ouyang |
| collection | DOAJ |
| description | Satellite-derived rugged land surface temperature (LST) is an important parameter indicating the status of the Earth’s surface energy budget and its seasonal/temporal dynamic change. However, existing LST products from rugged areas are more prone to error when supporting applications in mountainous areas and Earth surface processes that occur at high spatial and temporal resolutions. This research aimed to develop a method for generating rugged LST with a high temporal and spatial resolution by using an improved ensemble LST model combining three regressors, including a random forest, a ridge, and a support vector machine. Different combinations of high-resolution input parameters were also considered in this study. The input datasets included Moderate Resolution Imaging Spectroradiometer (MODIS) LST datasets (MxD11A1) for nighttime, temporal Sentinel-2 Multispectral Instrument (MSI) datasets, and digital elevation model (DEM) datasets. The 30 m rugged LST datasets derived were compared against an in situ LST dataset obtained at Saihanba Forest Park (SFP) sites and an ASTER-derived 90 m LST, respectively. The results with in situ measurements demonstrated significant LST details, with an R<sup>2</sup> higher than 0.95 and RMSE around 3.00 K for both Terra/MOD- and Aqua/MYD-based LST datasets, and with slightly better results being obtained from the Aqua/MYD-based LST than that from Terra/MOD. The inter-comparison results with ASTER LST showed that over 80% of the pixels of the difference image for the two datasets were within 2 K. In light of the complex topography and distinct atmospheric conditions, these comparison results are encouraging. The 30 m LST from the method proposed in this study also depicts the seasonality of rugged surfaces. |
| first_indexed | 2024-03-10T00:55:21Z |
| format | Article |
| id | doaj.art-fd48765efadc4ebab995e9ac5c5bc852 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T00:55:21Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-fd48765efadc4ebab995e9ac5c5bc8522023-11-23T14:44:34ZengMDPI AGRemote Sensing2072-42922022-05-011411261710.3390/rs14112617High Spatiotemporal Rugged Land Surface Temperature Downscaling over Saihanba Forest Park, ChinaXiaoying Ouyang0Youjun Dou1Jinxin Yang2Xi Chen3Jianguang Wen4State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaInstitute of Urban Meteorology, China Meteorological Administration, Beijing 100089, ChinaSchool of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, ChinaState Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaState Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaSatellite-derived rugged land surface temperature (LST) is an important parameter indicating the status of the Earth’s surface energy budget and its seasonal/temporal dynamic change. However, existing LST products from rugged areas are more prone to error when supporting applications in mountainous areas and Earth surface processes that occur at high spatial and temporal resolutions. This research aimed to develop a method for generating rugged LST with a high temporal and spatial resolution by using an improved ensemble LST model combining three regressors, including a random forest, a ridge, and a support vector machine. Different combinations of high-resolution input parameters were also considered in this study. The input datasets included Moderate Resolution Imaging Spectroradiometer (MODIS) LST datasets (MxD11A1) for nighttime, temporal Sentinel-2 Multispectral Instrument (MSI) datasets, and digital elevation model (DEM) datasets. The 30 m rugged LST datasets derived were compared against an in situ LST dataset obtained at Saihanba Forest Park (SFP) sites and an ASTER-derived 90 m LST, respectively. The results with in situ measurements demonstrated significant LST details, with an R<sup>2</sup> higher than 0.95 and RMSE around 3.00 K for both Terra/MOD- and Aqua/MYD-based LST datasets, and with slightly better results being obtained from the Aqua/MYD-based LST than that from Terra/MOD. The inter-comparison results with ASTER LST showed that over 80% of the pixels of the difference image for the two datasets were within 2 K. In light of the complex topography and distinct atmospheric conditions, these comparison results are encouraging. The 30 m LST from the method proposed in this study also depicts the seasonality of rugged surfaces.https://www.mdpi.com/2072-4292/14/11/2617land surface temperature (LST)Moderate Resolution Imaging Spectroradiometer (MODIS)Sentinel-2Saihanba Forest Park (SFP)rugged areadownscaling |
| spellingShingle | Xiaoying Ouyang Youjun Dou Jinxin Yang Xi Chen Jianguang Wen High Spatiotemporal Rugged Land Surface Temperature Downscaling over Saihanba Forest Park, China Remote Sensing land surface temperature (LST) Moderate Resolution Imaging Spectroradiometer (MODIS) Sentinel-2 Saihanba Forest Park (SFP) rugged area downscaling |
| title | High Spatiotemporal Rugged Land Surface Temperature Downscaling over Saihanba Forest Park, China |
| title_full | High Spatiotemporal Rugged Land Surface Temperature Downscaling over Saihanba Forest Park, China |
| title_fullStr | High Spatiotemporal Rugged Land Surface Temperature Downscaling over Saihanba Forest Park, China |
| title_full_unstemmed | High Spatiotemporal Rugged Land Surface Temperature Downscaling over Saihanba Forest Park, China |
| title_short | High Spatiotemporal Rugged Land Surface Temperature Downscaling over Saihanba Forest Park, China |
| title_sort | high spatiotemporal rugged land surface temperature downscaling over saihanba forest park china |
| topic | land surface temperature (LST) Moderate Resolution Imaging Spectroradiometer (MODIS) Sentinel-2 Saihanba Forest Park (SFP) rugged area downscaling |
| url | https://www.mdpi.com/2072-4292/14/11/2617 |
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