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.

An adequate estimate of the radiation components on the earth’s surface may help reveal many important interactions between the earth’s surface and the atmosphere. In-situ measurements of radiation components are sparse, and remote sensing is one way to overcome this limitation. The clouds and the earth’s radiant energy system (CERES) provides a long-term estimate of shortwave and longwave radiation over the entire globe. This article compared and evaluated all components of the surface radiation, estimated using CERES SYN1deg Ed3A and SYN1deg Ed4A data (shortwave up and down, longwave up and down, and photosynthetically active radiation) against measurements for 15 sites located in Brazil. Our results indicated that CERES SYN1deg estimates are accurate for all variables evaluated, with the SYN1deg Ed4A version increasing the $R^{2}$ and decreasing the RMSE from the SYN1deg Ed3A version. We also evaluated the main driving factors controlling the variability of the surface radiation components, using cluster analysis and multiple linear regression. The results showed that surface temperature and total precipitable water vapor are the main driving factors affecting the variability of the different radiation components. The results also highlighted the influence of climate conditions and biome features on the estimates of surface radiation components by CERES. The radiation data provided by CERES SYN1deg Ed4A proved to be a promising alternative for large regions where meteorological information is unavailable or sparse.