Abstract
The successful launch of the Landsat 8 satellite provides important data for the monitoring of urban heat island effects. Since the Landsat 8 TIRS data has two thermal infrared bands, it is suitable for many algorithms to retrieve the land surface temperature (LST). However, the selection of algorithms for retrieving the LST, the acquisition of algorithm input parameters, and the verification of the results are problems without obvious solutions. Taking Changchun City as an example, this paper used the mono-window algorithm (MWA), the split window algorithm (SWA), and the single-channel (SC) method to extract the LST from the Landsat 8 image and compared the three algorithms in terms of input parameters, accuracy, and sensitivity. The results show that all three algorithms can achieve good results in retrieving the LST. The SWA is the least sensitive to the error of the input parameters. The MWA and the SC method are sensitive to the error of the input parameters, and compared with the error of the LSE, these two algorithms are more sensitive to the error of atmospheric water vapor content. In addition, the MWA is also very sensitive to the error of the effective mean atmospheric temperature.
Highlights
Land surface temperature (LST) is the direct driving factor for water heat exchange between the Earth’s surface and the atmosphere, and is the key parameter in many physical processes [1,2,3].Retrieving LST from thermal infrared remote sensing data at global, regional, and urban scales have unparalleled advantages, and this is the most common method for studying urban heat island effects.Since its launch on 11 February 2013, the Landsat 8 satellite has ingested and transmitted over 500 multispectral-image scenes to the ground every day; the revisit time of Landsat 8 is 16 days and together with Landsat 7 ETM+ constitutes an 8 day interval Landsat repeat observation cycle [4]
According the sensitivity analysis the mono-window algorithm (MWA), we found that the estimation of According to the to sensitivity analysis of theofMWA, we found that the estimation errorerror of atmospheric atmospheric water vapor content had the greatest influence on the error of the retrieved
The sensitivity analysis of the split window algorithm (SWA) to atmospheric water vapor content is to analyze the influence of the estimation error of the water vapor on the LST retrieved by the SWA under different air humidity conditions
Summary
Land surface temperature (LST) is the direct driving factor for water heat exchange between the Earth’s surface and the atmosphere, and is the key parameter in many physical processes [1,2,3]. Method, developed by Jiménez-Muñoz and Sobrino et al [12] These three algorithms were originally proposed for other thermal infrared remote sensing; when applied to Landsat 8 TIR data, they should be improved. There are other scholars who have improved these algorithms and have received good results [16,17] When these algorithms are applied for LST retrieval, some corresponding input parameters are needed; land surface emissivity (LSE) and atmospheric transmittance (τ) are essential for all of these three methods. In order to solve these three problems, this paper takes Changchun City, Jilin Province, China, as an example and extracts the LST from the Landsat 8 image by the improved mono-window algorithm (MWA), split window algorithm (SWA), and single-channel (SC) method. This paper uses the split window algorithm verify its accuracy, whichits is accuracy, meaningful judge whetherto the corrected
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