Comparison of Methods to Estimate Lake-Surface-Water Temperature Using Landsat 7 ETM+ and MODIS Imagery: Case Study of a Large Shallow Subtropical Lake in Southern Brazil

• Published in: Water (Basel) Vol. 11; no. 1; p. 168
• Main Authors: Tavares, Matheus, Cunha, Augusto, Motta-Marques, David, Ruhoff, Anderson, Cavalcanti, J., Fragoso, Carlos, Martín Bravo, Juan, Munar, Andrés, Fan, Fernando, Rodrigues, Lucia
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.01.2019
• Subjects: Algorithms; Atmospheric correction; Case studies; Climate change; Climatic changes; Earth resources technology satellites; Emissivity; Inland waters; Lakes; Landsat; Landsat satellites; Methods; MODIS; Oceans; Parameter sensitivity; Radiative transfer; Remote sensing; Satellite imagery; Sensitivity analysis; Sensors; Skin effect; Studies; Surface water; Temperature effects; thermal infrared; Time series; Water temperature; water-surface temperature; Brazil; Austria; Rio Grande do Sul Brazil
• ISSN: 2073-4441; 2073-4441
• DOI: 10.3390/w11010168

Water temperature regulates many processes in lakes; therefore, evaluating it is essential to understand its ecological status and functioning, and to comprehend the impact of climate change. Although few studies assessed the accuracy of individual sensors in estimating lake-surface-water temperature (LSWT), comparative analysis considering different sensors is still needed. This study evaluated the performance of two thermal sensors, MODIS and Landsat 7 ETM+, and used Landsat methods to estimate the SWT of a large subtropical lake. MODIS products MOD11 LST and MOD28 SST were used for comparison. For the Landsat images, the radiative transfer equation (RTE), using NASA’s Atmospheric Correction Parameter Calculator (AtmCorr) parameters, was compared with the single-channel algorithm in different approaches. Our results showed that MOD11 obtained the highest accuracy (RMSE of 1.05 ° C), and is the recommended product for LSWT studies. For Landsat-derived SWT, AtmCorr obtained the highest accuracy (RMSE of 1.07 ° C) and is the recommended method for small lakes. Sensitivity analysis showed that Landsat-derived LSWT using the RTE is very sensitive to atmospheric parameters and emissivity. A discussion of the main error sources was conducted. We recommend that similar tests be applied for Landsat imagery on different lakes, further studies on algorithms to correct the cool-skin effect in inland waters, and tests of different emissivity values to verify if it can compensate for this effect, in an effort to improve the accuracy of these estimates.