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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.12729 |
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| _version_ | 1866918203866742784 |
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| author | Hassanzadeh, Amirhossein Mancini, Robert Gerace, Aaron Eon, Rehman Montanaro, Matthew |
| author_facet | Hassanzadeh, Amirhossein Mancini, Robert Gerace, Aaron Eon, Rehman Montanaro, Matthew |
| contents | Current Landsat Level 2 surface temperature products are derived using a single-channel (SC) methodology to estimate per-pixel surface temperature (ST) maps from Level~1 radiance data. A known issue with the Level 2 uncertainty, however, is its susceptibility to overestimation of uncertainty due to its dependence on Landsat's cloud mask, which is prone to false-positives. Beginning with Collection 3, the split window (SW) approach will serve as the surface temperature algorithm for the level-2 product, reflecting its adaptability across conditions which necessitates the development of a dedicated uncertainty workflow. We introduce an improved uncertainty workflow, based on a physical parameter called total precipitable water (TPW), that more adequately estimates the uncertainty associated with surface temperature estimates. We leveraged a SW algorithm for estimating surface temperature to drive the uncertainty methodology discussed here. First, considering Landsat is not equipped with the optical channels necessary for deriving TPW, we create an XGBoost-based machine learning pipeline that relates TIRS bands 10 & 11 image radiance to TPW using the MODIS product as reference. The resulting modeling approach achieves a mean absolute error in estimating TPW of 0.54 [cm] and a coefficient of determination (R2) as high as 0.89. Secondly, we propose an improved (SW-based) uncertainty workflow that also uses standard error propagation but incorporates uncertainty as a function of TPW. Our work fills the gap in the operational surface temperature algorithms and their corresponding uncertainty workflow tailored for Landsat 8 and 9, and machine learning based models for predicting atmospheric water vapor using thermal infrared sensor bands on board Landsat 8 and 9. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_12729 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Development of an Uncertainty Workflow to Support Landsat TIRS Split Window-Derived Surface Temperature Products Hassanzadeh, Amirhossein Mancini, Robert Gerace, Aaron Eon, Rehman Montanaro, Matthew Geophysics Current Landsat Level 2 surface temperature products are derived using a single-channel (SC) methodology to estimate per-pixel surface temperature (ST) maps from Level~1 radiance data. A known issue with the Level 2 uncertainty, however, is its susceptibility to overestimation of uncertainty due to its dependence on Landsat's cloud mask, which is prone to false-positives. Beginning with Collection 3, the split window (SW) approach will serve as the surface temperature algorithm for the level-2 product, reflecting its adaptability across conditions which necessitates the development of a dedicated uncertainty workflow. We introduce an improved uncertainty workflow, based on a physical parameter called total precipitable water (TPW), that more adequately estimates the uncertainty associated with surface temperature estimates. We leveraged a SW algorithm for estimating surface temperature to drive the uncertainty methodology discussed here. First, considering Landsat is not equipped with the optical channels necessary for deriving TPW, we create an XGBoost-based machine learning pipeline that relates TIRS bands 10 & 11 image radiance to TPW using the MODIS product as reference. The resulting modeling approach achieves a mean absolute error in estimating TPW of 0.54 [cm] and a coefficient of determination (R2) as high as 0.89. Secondly, we propose an improved (SW-based) uncertainty workflow that also uses standard error propagation but incorporates uncertainty as a function of TPW. Our work fills the gap in the operational surface temperature algorithms and their corresponding uncertainty workflow tailored for Landsat 8 and 9, and machine learning based models for predicting atmospheric water vapor using thermal infrared sensor bands on board Landsat 8 and 9. |
| title | Development of an Uncertainty Workflow to Support Landsat TIRS Split Window-Derived Surface Temperature Products |
| topic | Geophysics |
| url | https://arxiv.org/abs/2511.12729 |