Land Surface Temperature Retrieval from Landsat 5, 7, and 8 over Rural Areas: Assessment of Different Retrieval Algorithms and Emissivity Models and Toolbox Implementation

Abstract

Land Surface Temperature (LST) is an important parameter for many scientific disciplines since it affects the interaction between the land and the atmosphere. Many LST retrieval algorithms based on remotely sensed images have been introduced so far, where the Land Surface Emissivity (LSE) is one of the main factors affecting the accuracy of the LST estimation. The aim of this study is to evaluate the performance of LST retrieval methods using different LSE models and data of old and current Landsat missions. Mono Window Algorithm (MWA), Radiative Transfer Equation (RTE) method, Single Channel Algorithm (SCA) and Split Window Algorithm (SWA) were assessed as LST retrieval methods processing data of Landsat missions (Landsat 5, 7 and 8) over rural pixels. Considering the LSE models introduced in the literature, different Normalized Difference Vegetation Index (NDVI)-based LSE models were investigated in this study. Specifically, three LSE models were considered for the LST estimation from Landsat 5 Thematic Mapper (TM) and seven Enhanced Thematic Mapper Plus (ETM+), and six for Landsat 8. For the accurate evaluation of the estimated LST, in-situ LST data were obtained from the Surface Radiation Budget Network (SURFRAD) stations. In total, forty-five daytime Landsat images; fifteen images for each Landsat mission, acquired in the Spring-Summer-Autumn period in the mid-latitude region in the Northern Hemisphere were acquired over five SURFRAD rural sites. After determining the best LSE model for the study case, firstly, the LST retrieval accuracy was evaluated considering the sensor type: when using Landsat 5 TM, 7 ETM+, and 8 Operational Land Imager (OLI), and Thermal Infrared Sensor (TIRS) data separately, RTE, MWA, and MWA presented the best results, respectively. Then, the performance was evaluated independently of the sensor types. In this case, all LST methods provided satisfying results, with MWA having a slightly better accuracy with a Root Mean Square Error (RMSE) equals to 2.39 K and a lower bias error. In addition, the spatio-temporal and seasonal analyses indicated that RTE and SCA presented similar results regardless of the season, while MWA differed from RTE and SCA for all seasons, especially in summer. To efficiently perform this work, an ArcGIS toolbox, including all the methods and models analyzed here, was implemented and provided as a user facility for the LST retrieval from Landsat data.

The Impact of Urban Renewal on Land Surface Temperature Changes: A Case Study in the Main City of Guangzhou, China

“…Given that the main coverage area of cloud was farmland and woodland, cloud coverage had little impact on this research. Specifically, the LST of the study area was retrieved from Band 6 of the Landsat TM imagery on 26 July 2008, and from Band 10 of Landsat TIRS imagery on 22 July 2018, using the generalized single-channel algorithm [43,44], which has been proven an effective method to retrieve LST from Landsat imagery [45][46][47]. This algorithm mainly calculates LST from a combination of the surface emissivity, at-sensor registered radiance, atmospheric functions, and parameters dependent on Planck's function.…”

Sensitivity Analysis and Validation of Daytime and Nighttime Land Surface Temperature Retrievals from Landsat 8 Using Different Algorithms and Emissivity Models

“…However, surface parameters (emissivity and geometry), sensor parameters (spectral range and viewing angle), and atmospheric effects are the major factors that influence the accuracy of the LST retrieval from TIR data of satellites [5,29,[31][32][33]. Thus, accurate estimation of Land Surface Emissivity (LSE) and atmospheric parameters is a crucial procedure to obtain LST from TIR data [34]. Concerning these parameters, various TIR-based multi-channel and single-channel LST retrieval methods have been proposed by the researchers for different sensor types.…”

Land Surface Temperature Retrieval for Agricultural Areas Using a Novel UAV Platform Equipped with a Thermal Infrared and Multispectral Sensor

“…Satellite-based LST retrieval methods have been reviewed and compared in several case studies [1,19,20], but depending on the retrieval algorithm, large discrepancies between the retrieved LST and ground truth data can occur. Even the same sensor, such as a high precision satellite instrument (i.e., the Spinning Enhanced Visible and Infrared Imager-SEVIRI-aboard Meteosat-9) using different LST algorithms has featured discrepancies of about 6 K [21].…”

“…Even the same sensor, such as a high precision satellite instrument (i.e., the Spinning Enhanced Visible and Infrared Imager-SEVIRI-aboard Meteosat-9) using different LST algorithms has featured discrepancies of about 6 K [21]. The reason for this is often an inaccurate or even erroneously estimated surface emissivity [1], which has a great impact on the accuracy of LST [19,20]. The lower the emissivity, the higher the impact of environmental thermal emissions on the LWIR radiation (Section 2.1), which needs to be considered to avoid errors in LST retrieval (Section 2.1.3).…”