Development of an Uncertainty Workflow to Support Landsat TIRS Split Window-Derived Surface Temperature Products

TL;DR

This paper develops an improved uncertainty workflow for Landsat surface temperature products using a split window approach and machine learning to estimate atmospheric water vapor, enhancing accuracy over previous methods.

Contribution

It introduces a novel uncertainty estimation method based on total precipitable water, utilizing machine learning to relate Landsat thermal bands to atmospheric water vapor.

Findings

Achieved a mean absolute error of 0.54 cm in TPW estimation.

High correlation (R2=0.89) between predicted and reference TPW.

Enhanced uncertainty quantification for Landsat surface temperature products.

Abstract

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.