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Proceedings Paper

Production, monitoring and evaluation of GOES-R series land surface temperature data (Conference Presentation)
Author(s): Yunyue Yu; Peng Yu; Jaime Daniels

Paper Abstract

Satellite Land surface temperature (LST) is defined as the radiative skin temperature of the land. It has been widely used in many aspects of the geosciences, e.g., studies of net radiation budget at the Earth surface, monitoring state of crops and vegetation. It is an important indicator of both the greenhouse effect and the physics of land-surface processes at local through global scales. Thus, LST has been listed as an Essential Climate Variable (ECV) in the Global Climate Observation System (GCOS). LST is one of the baseline products for the GOES-R series satellites measured from the Advanced Baseline Imager (ABI). The algorithm derivation was developed at NOAA/NESDIS center for SaTellite Applications and Research (STAR), based on a traditional split-window technique. It is primarily estimated from the top-of-atmosphere (TOA) brightness temperature (BT) at one ABI thermal infrared channel and corrected by the BT difference to the near-by thermal infrared channel. Quality of the LST estimation may vary depending on cloud fraction, water vapor, view zenith angle, etc. Such quality information, recorded as quality flags and metadata, is provided with the LST product for user reference, product monitoring and evaluation analysis. Comprehensive evaluation of the GOES-R LST product has been conducted using radiative transfer simulation datasets and proxy ABI data, before the launch of the first GOES-R satellite (i.e. GOES-16). Since then we have performed its evaluation using over one year of the on-orbit ABI SDR and LST dataset, towards its beta and provisional validated maturity levels. Quality flags and metadata of the LST product are tested and verified with local independent computation. LST retrievals were compared to in-situ LST data derived from the SURFRAD station measurements. This presentation shows our evaluation results, as well as the ABI LST derivation details, which are helpful in users’ product applications

Paper Details

Date Published: 18 October 2019
Proc. SPIE 11149, Remote Sensing for Agriculture, Ecosystems, and Hydrology XXI, 111491A (18 October 2019); doi: 10.1117/12.2532555
Show Author Affiliations
Yunyue Yu, National Oceanic and Atmospheric Administration (United States)
Peng Yu, National Oceanic and Atmospheric Administration (United States)
Jaime Daniels, National Oceanic and Atmospheric Administration (United States)

Published in SPIE Proceedings Vol. 11149:
Remote Sensing for Agriculture, Ecosystems, and Hydrology XXI
Christopher M. U. Neale; Antonino Maltese, Editor(s)

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