Estimation of land surface temperature from a Geostationary Operational Environmental Satellite (GOES‐8)

Abstract

[1] Two algorithms are developed and applied to observations from the Geostationary Operational Environmental Satellite (GOES) to enable frequent estimate of Land Surface Temperature (LST) representing the diurnal cycle. The derived LSTs are evaluated against a wide range of ground observations. Both algorithms are based on radiative transfer theory; one is similar to the classical split window approach used for deriving Sea Surface Temperature (SST), while the other is a three-channel algorithm. The three-channel LST algorithm aims to improve atmospheric correction by utilizing the characteristics of the middle-infrared (MIR) band. Effects of both the atmosphere and the surface emissivity are accounted for. The simulations from the proposed algorithms are compared with previously developed generalized split window algorithm, and a split window algorithm with water vapor correction. During daytime, the proposed new split window algorithm gives the best LST retrievals, while during nighttime, the proposed three-channel algorithm gives the best retrievals, both within a Root Mean Square (RMS) error of less than 1 K and without a significant bias. Evaluations against the Atmospheric Radiation Measurement (ARM) observations of radiometric surface temperatures and Surface Radiation Network (SURFRAD) observations of outgoing long wave (LW) radiation indicate that LST can be determined from the actual GOES-8 observations within an RMS accuracy of about 1-2 K, standard error of about 1 K, and bias of less than 1 K. When evaluated against the North Carolina Agricultural Research Service (NCARS) soil temperature as observed at depth of 8 in. and against air temperature observations, the amplitude of the retrieved LST is found to be significantly greater than that of the observed soil temperature, lower than the nighttime air temperature, and higher than the daytime air temperature. When the soil observations are ''corrected'' to account for the depth difference, they are in good agreement with the LST retrieved from the satellite observations. This indicates that observations of soil temperature, which are more readily available than measurements of ''skin'' temperatures, can be useful in evaluating satellitebased estimates. The LST retrieved from both of the proposed algorithms and from a NOAA/NESDIS algorithm, are generally very close to the converted skin temperature from the SURFRAD surface outgoing LW radiation. In most cases, the newly proposed algorithm shows better agreement with ground observations.

Algorithm Development for Land Surface Temperature Retrieval: Application to Chinese Gaofen-5 Data

“…(iii) Assuming ∆ε = 0, François and Ottlé presented different coefficients for different ε values [25]. (iv) Sun and Pinker addressed the SW coefficients according to different surface types to account for LSE effect [26]. It should be noted that the land surface is complex and that the LSE may be quite different from unity and depends on the channel [27].…”

Simulation of land surface temperatures: comparison of two climate models and satellite retrievals

“…The retrieval of land surface temperature from satellite observations is discussed by Sun and Pinker (2003) and Sun et al (2006b). The algorithm makes use of the 10.8 and 12.0 micron channels on the GOES-8 satellite.…”

“…Satellite data offer a number of potential advantages for this Correspondence to: J. M. Edwards (john.m.edwards@metoffice.gov.uk) purpose including homogeneous spatial coverage and good temporal resolution of the diurnal cycle. Sun and Pinker (2003) and Sun et al (2006b) produced retrievals of land surface temperatures from geostationary satellites over the contiguous United States under clear-sky conditions: they estimated that the bias of their retrievals was less than 1 K with RMS errors of about 1-2 K. Sun et al (2006b) used these retrievals to characterize the seasonal variation of the diurnal temperature range across the United States. Inamdar et al (2008) proposed an algorithm using data from geostationary and polar-orbiting satellites to retrieve land surface temperatures at a resolution of 1 km.…”

“…Sun and Pinker, 2003;Inamdar et al, 2008), particularly with a view to characterising the diurnal range of temperature. Satellite data offer a number of potential advantages for this Correspondence to: J. M. Edwards (john.m.edwards@metoffice.gov.uk) purpose including homogeneous spatial coverage and good temporal resolution of the diurnal cycle.…”

Evaluation of Land Surface Temperature Retrieval from FY-3B/VIRR Data in an Arid Area of Northwestern China

“…The classification-based emissivity method (CBEM) [53][54][55] can be used to estimate LSE for FY-3B/VIRR. This method assigns the emissivity from classification-based look-up tables to each class.…”