Global Land Surface Temperature From the Along‐Track Scanning Radiometers

D J Ghent,G K Corlett,F.‐M Göttsche,J J Remedios

doi:10.1002/2017jd027161

Abstract

AbstractThe Leicester Along‐Track Scanning Radiometer (ATSR) and Sea and Land Surface Temperature Radiometer (SLSTR) Processor for LAnd Surface Temperature (LASPLAST) provides global land surface temperature (LST) products from thermal infrared radiance data. In this paper, the state‐of‐the‐art version of LASPLAST, as deployed in the GlobTemperature project, is described and applied to data from the Advanced Along‐Track Scanning Radiometer (AATSR). The LASPLAST retrieval formulation for LST is a nadir‐only, two‐channel, split‐window algorithm, based on biome classification, fractional vegetation, and across‐track water vapor dependences. It incorporates globally robust retrieval coefficients derived using highly sampled atmosphere profiles. LASPLAST benefits from appropriate spatial resolution auxiliary information and a new probabilistic‐based cloud flagging algorithm. For the first time for a satellite‐derived LST product, pixel‐level uncertainties characterized in terms of random, locally correlated, and systematic components are provided. The new GlobTemperature GT_ATS_2P Version 1.0 product has been validated for 1 year of AATSR data (2009) against in situ measurements acquired from “gold standard reference” stations: Gobabeb, Namibia, and Evora, Portugal; seven Surface Radiation Budget stations, and the Atmospheric Radiation Measurement station at Southern Great Plains. These data show average absolute biases for the GT_ATS_2P Version 1.0 product of 1.00 K in the daytime and 1.08 K in the nighttime. The improvements in data provenance including better accuracy, fully traceable retrieval coefficients, quantified uncertainty, and more detailed information in the new harmonized format of the GT_ATS_2P product will allow for more significant exploitation of the historical LST data record from the ATSRs and a valuable near‐real‐time service from the Sea and Land Surface Temperature Radiometers (SLSTRs).

Highlights

Land surface temperature (LST) as measured by ground-based, airborne, and spaceborne remote sensing instruments is the mean radiative skin temperature of all objects comprising the surface and provides the best approximation to the thermodynamic temperature based on a measure of radiance (Norman & Becker, 1995)
The purpose of this paper is to present the current version of what is the Leicester Along-Track Scanning Radiometer (ATSR) and Sea and Land Surface Temperature Radiometers (SLSTRs) Processor for LAnd Surface Temperature (LASPLAST) and the latest global land surface temperature (LST) product at 1 km produced for Advanced Along-Track Scanning Radiometer (AATSR) (GT_ATS_2P Version 1.0)
This paper presents the features of LASPLAST as implemented in the GT_ATS_2P product, which is available from European Space Agency (ESA) in the framework of the GlobTemperature project under the Data User Element of ESA’s Fourth Earth Observation Envelope Programme (2013–2017)

Summary

Introduction

Land surface temperature (LST) as measured by ground-based, airborne, and spaceborne remote sensing instruments is the mean radiative skin temperature of all objects comprising the surface and provides the best approximation to the thermodynamic temperature based on a measure of radiance (Norman & Becker, 1995). Through the inversion of Planck’s law, LST can be estimated from satellite measurements of top-of-atmosphere (TOA) radiances as the radiative energy emitted by the surface is directly related its temperature, effects from surface emissivity and the atmosphere must be considered. The principles of LST radiative transfer and algorithms have been established by a number of studies, including those of McMillin (1975) and Li and Becker (1993). Recent years has seen considerable evolution as discussed in the review of Li et al (2013) and in particular drivers toward globally applicable algorithms for long-term (climate-quality) data sets, detailed uncertainty analysis and cloud detection through improved radiative transfer simulations (Merchant et al, 2013)

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