A Physics-Based Algorithm for the Simultaneous Retrieval of Land Surface Temperature and Emissivity From VIIRS Thermal Infrared Data

Land surface temperature (LST) is a key climate variable for studying the energy and water balance of the earth surface and monitoring the effects of climate change. This paper presents a physics-based temperature emissivity separation (TES) algorithm for the simultaneous retrieval of LST and emissivity (LST&E) from the thermal infrared bands of the Suomi National Polar-Orbiting Partnership's Visible Infrared Imaging Radiometer Suite (VIIRS) payload. The new VIIRS LST&E product (VNP21) was developed to provide continuity with the Moderate-Resolution Imaging Spectroradiometer (MODIS) equivalent LST&E product (MxD21) product, which is available in Collection 6, and to address inconsistencies between the current MODIS and VIIRS split-window LST products. The TES algorithm uses full radiative transfer simulations to isolate the surface emitted radiance, and an emissivity calibration curve based on the variability in the surface radiance data to dynamically retrieve both LST and spectral emissivity. Furthermore, an improved water vapor scaling model was implemented to improve the accuracy and stability of the atmospheric correction for conditions with high atmospheric water vapor content. An independent assessment of the VIIRS LST retrievals was performed against in situ LST measurements over two dedicated validation sites at Lake Tahoe and Salton Sea in the Southwestern USA, while the VIIRS emissivity retrievals were evaluated with the latest ASTER Global Emissivity Dataset Version 3 (GEDv3). The bias and root-mean-square error (RMSE) in retrieved VIIRS LST were 0.50 and 1.40 K, respectively for the two sites combined, while mean emissivity differences between VIIRS and ASTER GEDv3 were 0.2%, 0.1%, and 0.3% for bands M14 ($8.55~\mu \text{m}$ ), M15 ($10.76~\mu \text{m}$ ), and M16 ($12.01~\mu \text{m}$ ), respectively, with an RMSE of 1%. We further demonstrate close agreement between the MODIS and VIIRS TES algorithm LST products to within ~0.3 K difference, as opposed to the current MODIS and VIIRS split window products, which had an average difference of 3 K.