Abstract

Land surface temperature (LST) acquired from remote sensing observations is essential to monitor surface energy and water exchange processes at the land-atmosphere interface. Most LST retrieval methodologies are developed focusing on Northern hemisphere. Consequently, Southern hemisphere has a great need for investigating the performance of LST retrieval algorithms already consolidated in the literature. In this paper, we compared a Splitwindow (SW) and a Single-channel (SC) method to retrieve LST from Landsat 8 OLI/TIRS images in a dune field, Southern Brazil. To validate the results, the Atmospheric Correction Parameter Calculator (ACPC) tool and Radiative Transfer Equation (RTE) were used. Results demonstrated that both methodologies are in accordance with the RTE, despite they overestimated the LST. Analysis of variance (ANOVA) indicated that the means are not statistically significant (0.05 level). The correlations between LST retrieved and RTE were strong, producing R² of 0.984 and 0.973 for the SW and SC, respectively, and RMSE values of 1.18 and 1.6. SW also exhibited the best values of MSD (±0.983) and Bias (0.773), thus reinforcing its superior performance. SW can be applied with an accuracy of 1.18 K in Southern Brazil, without needing complex modeling or specific radiosonde.

Highlights

  • Information about land surface temperature (LST) acquired from remote sensing satellite observations is very important to monitor surface energy and water exchange processes at the land-atmosphere interface (Zhao et al 2019)

  • The Radiative Transfer Equation - RTE (Eq 1.) was used as reference to validate the data from both algorithms SW and SC in this work because to solve it there is a need for information about the atmospheric state at the time of the satellite overpass (Sobrino et al 2004; Li et al 2013; Meng et al 2018)

  • The web-based Atmospheric Correction Parameter Calculator (ACPC) is a very useful tool to obtain this kind of information (Zhang et al 2016), since it allows to acquire the required atmospheric parameters in the RTE for each region, time and date

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Summary

Introduction

Information about land surface temperature (LST) acquired from remote sensing satellite observations is very important to monitor surface energy and water exchange processes at the land-atmosphere interface (Zhao et al 2019). Remote sensing in the TIR region provides an opportunity to obtain information about this variable. In this context, several efforts have been devoted to establishing methods to retrieve the LST from remote sensing data. Several efforts have been devoted to establishing methods to retrieve the LST from remote sensing data These algorithms can be roughly grouped into three categories according to Li et al (2013) and Du et al (2015): (i) single-channel (SC) (ii) multichannel, and (iii) multi-time methods. The split-window (SW) methods are included in the group (ii)

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