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

Abstract

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.

Land Use/Land Cover Changes and the Relationship with Land Surface Temperature Using Landsat and MODIS Imageries in Cameron Highlands, Malaysia

“…This is done, due to the study area comprising of both vegetative and non-vegetative details. Similarly, this was done by Tavares et al [32], Hesslerová et al [34], Kumari et al [35], and Walawender et al [36].…”

“…In this study, we chose both Landsat thermal bands of Landsat 7 ETM+; namely the low gains band 6 (6L) and high gains band 6 (6H). Band 6L detects areas where surface brightness is high (non-vegetated areas), whereas band 6H detects areas where surface brightness is low (vegetated areas) [32]. A study by Nguemhe Fils et al [33] had used only band 6H as their project was based on a high vegetation area where surface brightness was lower.…”

Evaluating the impacts of land use/land cover changes across topography against land surface temperature in Cameron Highlands

“…For this study, Landsat 7 ETM+ dual thermal bands were used; specifically, the low gain band 6 (6L) and high gain band 6 (6H). Band 6L shows areas where the surface brightness is high (non-vegetated areas), whereas band 6H highlights areas where surface brightness is low (vegetated areas) [ 27 , 37 ]. In a study where the investigated area sat in a high vegetation area, Nguemhe Fils et al [ 38 ] used only band 6H because the surface brightness was lower.…”

“…To obtain an average, both values of 6H and 6L were added and then divided by '2'. Various studies, such as [37,[39][40][41], previously used this method.…”

The Impact of Climate Warming on Lake Surface Heat Exchange and Ice Phenology of Different Types of Lakes on the Tibetan Plateau

“…The MOD11A2 product includes two instantaneous observations each day (approximately 11:00 and 21:00 local time), with a spatial resolution of 1 km. Many studies have shown that the MOD11A2 product has good accuracy for individual regions, with mean biases ranging from 0.2 • C to 1.05 • C [12,54,55]. The water surface temperature from the MODIS observations of the skin layer is generally lower than the in situ observation of the mixed layer [56][57][58], because of the cool skin effect [12,59].…”