A Physically Constrained Downscaling Framework for Hourly, All-Sky Land Surface Temperature in Mountainous Regions
High-resolution, all-weather land surface temperature (LST) data on a global scale are pivotal for accurately reflecting the thermal feedback from the underlying surface. We developed a novel physically constrained land surface model (LSM) simulated LST downscaling and polar-orbiting satellite LST f...
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/10884019/ |
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| author | Yongkang Li Qing He Yongqiang Liu Yang Yan Hailiang Zhang Jiao Tan |
| author_facet | Yongkang Li Qing He Yongqiang Liu Yang Yan Hailiang Zhang Jiao Tan |
| author_sort | Yongkang Li |
| collection | DOAJ |
| description | High-resolution, all-weather land surface temperature (LST) data on a global scale are pivotal for accurately reflecting the thermal feedback from the underlying surface. We developed a novel physically constrained land surface model (LSM) simulated LST downscaling and polar-orbiting satellite LST for reconstructing a time-series framework that is particularly effective in addressing the challenges associated with frequent cloud cover and precipitation in mountainous regions. The framework employs observations from 11 stations and MODIS data, integrating the Noah-MP and the ensemble Kalman filter to construct the data assimilation downscaling framework (DADF). This approach provides a comprehensive solution for acquiring high-resolution, all-weather LST, with both temporal (hourly) and spatial (1 km) precision. To improve the accuracy of LSMs and DADF, we corrected the momentum roughness lengths for each of the six surface categories and soil emissivity based on site observations. Six underlying surface parameters derived from measured data serve as a valuable reference for investigating land surface processes in this region. In a validation of MODIS day and night LST with data from 11 measurements, we observed high precision in high-altitude alpine terrains (Kalasai and Arou) and undulating desert terrains (Tazhong sites A–E). The sand–air admixture in desert terrain, caused by wind, introduces errors in site-based observations. The average RMSE of DADF-LST is 3.85 K (compared to the RMSE of 4.72 K for MODIS LST), and the R<sup>2</sup> > 0.82 across six categories. The DADF incorporates data assimilation algorithms for LST downscaling, enabling accurate capture of actual LST under cloudy conditions. It provides a physically constrained solution for obtaining LST data with high spatial and temporal resolution globally. |
| format | Article |
| id | doaj-art-c1f48a70fc064d788adbb863f03bb702 |
| institution | Kabale University |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
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| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-c1f48a70fc064d788adbb863f03bb7022025-08-20T03:42:19ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-01188151817410.1109/JSTARS.2025.354137410884019A Physically Constrained Downscaling Framework for Hourly, All-Sky Land Surface Temperature in Mountainous RegionsYongkang Li0https://orcid.org/0000-0001-7059-368XQing He1https://orcid.org/0000-0001-9375-1417Yongqiang Liu2Yang Yan3Hailiang Zhang4Jiao Tan5College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi, ChinaInstitute of Desert Meteorology, China Meteorological Administration, Urumqi, ChinaCollege of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi, ChinaCollege of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi, ChinaInstitute of Desert Meteorology, China Meteorological Administration, Dabancheng National Special Test Field for Comprehensive Meteorological Observation, Urumqi, ChinaCollege of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi, ChinaHigh-resolution, all-weather land surface temperature (LST) data on a global scale are pivotal for accurately reflecting the thermal feedback from the underlying surface. We developed a novel physically constrained land surface model (LSM) simulated LST downscaling and polar-orbiting satellite LST for reconstructing a time-series framework that is particularly effective in addressing the challenges associated with frequent cloud cover and precipitation in mountainous regions. The framework employs observations from 11 stations and MODIS data, integrating the Noah-MP and the ensemble Kalman filter to construct the data assimilation downscaling framework (DADF). This approach provides a comprehensive solution for acquiring high-resolution, all-weather LST, with both temporal (hourly) and spatial (1 km) precision. To improve the accuracy of LSMs and DADF, we corrected the momentum roughness lengths for each of the six surface categories and soil emissivity based on site observations. Six underlying surface parameters derived from measured data serve as a valuable reference for investigating land surface processes in this region. In a validation of MODIS day and night LST with data from 11 measurements, we observed high precision in high-altitude alpine terrains (Kalasai and Arou) and undulating desert terrains (Tazhong sites A–E). The sand–air admixture in desert terrain, caused by wind, introduces errors in site-based observations. The average RMSE of DADF-LST is 3.85 K (compared to the RMSE of 4.72 K for MODIS LST), and the R<sup>2</sup> > 0.82 across six categories. The DADF incorporates data assimilation algorithms for LST downscaling, enabling accurate capture of actual LST under cloudy conditions. It provides a physically constrained solution for obtaining LST data with high spatial and temporal resolution globally.https://ieeexplore.ieee.org/document/10884019/Data assimilation (DA)data assimilation downscaling framework (DADF)downscalingKunlun mountainland surface temperature (LST) |
| spellingShingle | Yongkang Li Qing He Yongqiang Liu Yang Yan Hailiang Zhang Jiao Tan A Physically Constrained Downscaling Framework for Hourly, All-Sky Land Surface Temperature in Mountainous Regions IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Data assimilation (DA) data assimilation downscaling framework (DADF) downscaling Kunlun mountain land surface temperature (LST) |
| title | A Physically Constrained Downscaling Framework for Hourly, All-Sky Land Surface Temperature in Mountainous Regions |
| title_full | A Physically Constrained Downscaling Framework for Hourly, All-Sky Land Surface Temperature in Mountainous Regions |
| title_fullStr | A Physically Constrained Downscaling Framework for Hourly, All-Sky Land Surface Temperature in Mountainous Regions |
| title_full_unstemmed | A Physically Constrained Downscaling Framework for Hourly, All-Sky Land Surface Temperature in Mountainous Regions |
| title_short | A Physically Constrained Downscaling Framework for Hourly, All-Sky Land Surface Temperature in Mountainous Regions |
| title_sort | physically constrained downscaling framework for hourly all sky land surface temperature in mountainous regions |
| topic | Data assimilation (DA) data assimilation downscaling framework (DADF) downscaling Kunlun mountain land surface temperature (LST) |
| url | https://ieeexplore.ieee.org/document/10884019/ |
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