Improvements of a COMS Land Surface Temperature Retrieval Algorithm Based on the Temperature Lapse Rate and Water Vapor/Aerosol Effect

A-Ra Cho,Youn-Young Choi,Myoung-Seok Suh

doi:10.3390/rs70201777

Abstract

The National Meteorological Satellite Center in Korea retrieves land surface temperature (LST) by applying the split-window LST algorithm (CSW_v1.0) to Communication, Ocean, and Meteorological Satellite (COMS) data. Considerable errors were detected under conditions of high water vapor content or temperature lapse rates during validation with Moderate Resolution Imaging Spectroradiometer (MODIS) LST because of the too simplified LST algorithm. In this study, six types of LST retrieval equations (CSW_v2.0) were developed to upgrade the CSW_v1.0. These methods were developed by classifying “dry,” “normal,” and “wet” cases for day and night and considering the relative sizes of brightness temperature difference (BTD) values. Similar to CSW_v1.0, the LST retrieved by CSW_v2.0 had a correlation coefficient of 0.99 with the prescribed LST and a slightly larger bias of −0.03 K from 0.00K; the root mean square error (RMSE) improved from 1.41 K to 1.39 K. In general, CSW_v2.0 improved the retrieval accuracy compared to CSW_v1.0, especially when the lapse rate was high (mid-day and dawn) and the water vapor content was high. The spatial distributions of LST retrieved by CSW_v2.0 were found to be similar to the MODIS LST independently of the season, day/night, and geographic locations. The validation using one year’s MODIS LST data showed that CSW_v2.0 improved the retrieval accuracy of LST in terms of correlations (from 0.988 to 0.989), bias (from −1.009 K to 0.292 K), and RMSEs (from 2.613 K to 2.237 K).

Highlights

Land surface temperature (LST) is an important factor that controls the sensible heat flux and latent heat flux [1,2,3], and it has been used as a biophysical indicator of the land surface in many studies where estimates of the radiation balance, temperature, and evapotranspiration rates were determined.LST is used often in studies of urban heat islands [4,5,6,7,8,9]
To minimize the drawbacks of CSW_v1.0, which was developed by Cho and Suh [25], we developed an improved algorithm (CSW_v2.0) that considers the lapse rate and water vapor/aerosol effects, which are different between day and night and in space and time
Because the Solar Zenith Angle (SoZA) that is required to decide between the day and night modes cannot be calculated from the data retrieved by radiative transfer simulations, they were substituted with the differences ∆T between the temperature and LSTs provided in the simulation and corresponding atmospheric temperatures

Summary

Introduction

LST is used often in studies of urban heat islands [4,5,6,7,8,9] In all of these studies, LSTs observed at high temporal and spatial resolutions are required to ensure the usability of the data [10,11,12,13]. Since the land surfaces are composed of various elements and specific heat is small, LST, which is more prone to temporal/spatial variations than other meteorological elements, is not sufficiently accurate compared to other field observation data [13,14]. Considering the large spatial/temporal variations of LST, recent attempts at and on-site applications of LST retrieval from geostationary meteorological satellite data have been very promising [27,28,29,30,31]

Methods

Results

Conclusion