This research evaluates the potential of thermal infrared (TIR) remote sensing to determine and continuously monitor the horizontal water temperature distribution of inland surface water bodies. Usually, monitoring temporal and spatial variability of surface water temperature takes place by measurement networks of in-situ gauges, but these networks are often limited by sparse sampling in both time and especially space. For these and other reasons (e.g. relatively cheap, easy, and fast) the use of remote sensing in water management studies and practices has increased. By remote sensing in the TIR spectrum the TIR radiation leaving from the top surface water layer (< 0.1 mm) is measured, which can be used to determine the radiant water temperature distribution in the horizontal plane. This horizontal radiant water temperature distribution can be used as supplement to in-situ kinetic temperature measurements. It is however necessary to first evaluate the accuracy and precision of the remotely sensed water temperatures. Therefore, the goal of this study is to determine how and with what accuracy and precision TIR radiant water temperature measurements (Tremote) can be used as an approximation for the horizontal distribution of the kinetic water temperature, based on comparisons of Tremote with in-situ kinetic water temperature measurements (Tin-situ). The criteria used in this study to determine the accuracy and precision of Tremote is by means of comparison with Tin-situ, which are usually taken at a certain depth in the water column. Tremote represents a pixel value, which is thus compared with a point measurement of Tin-situ. The bias (Tremote – Tin-situ) statistics are indicative for the obtained accuracy and precision. In this study Landsat 7 ETM+ images measured in the thermal infrared spectrum (? = 8-14 ?m) are used for water temperature determination of Tremote (by means of the inverse of Planck’s Law), together with Landsat 7 ETM+ images measured in the visual and near infrared spectrum for water detection of inland surface water bodies. The effects of emissivity; atmospheric absorption, emission and scattering; and surface effects and thermal stratification are evaluated and, if possible, corrected for. Uncertainty contained by the Landsat 7 ETM+ instrument has been taken into account by means of applying a 95% confidence interval over the obtained surface water body. Reduction of thermal pollution by land-originating TIR radiation of radiant water temperatures is well established by such a 95 % confidence interval water body. The correction for atmospheric circumstances took place by means of the web-based Atmospheric Correction Parameter Calculator, ACPC (see http://atmcorr.gsfc.nasa.gov/), which makes use of the MODTRAN radiative transfer model. Obtained results have been verified by means of a newly derived atmospheric correction algorithm for Landsat 7 ETM+ TIR images, developed with use of the MODIS In-Scene Split Window Method. For clear-sky images, on which this study focuses, the uncertainty contained by the atmospheric correction is up to ±0.8 °C inland, which can increase up to ±1.5 °C near the coast. Coastal uncertainty of the atmospheric correction is larger because of larger coastal atmospheric gradients (mainly of water vapour), which is difficult to correct for. The emissivity of water approaches that of a black body (? = 1), but is usually 1 to 2 % lower. This causes a reduction of up to 1 °C of the established radiant water temperature, but introduces an uncertainty of up to ± 0.5 °C. The surface effects and thermal stratification are influenced by many factors and processes which are difficult to address. The combined result of the surface effets and thermal stratification lead to an uncertainty in winter and summer of ±1.6 and ±3.2 °C, respectively. Overall, this study concludes the best procedure to approximate the horizontal kinetic water temperature distribution of inland surface waters with Landsat 7 ETM+ TIR images makes use of a 95% Confidence Interval Water Mask, and an Emissivity and Atmospheric Correction. The accuracy and precision levels of the horizontal water temperature distribution display an average bias of 1.5 °C with ? = 1.5 °C and SE = 0.1 °C. Tremote tends to nearly always over-predict Tin-situ. Using the 95% Confidence Water Mask to avoid thermal pollution of water pixels by land (sub-pixel heterogeneity), rivers with a width less than 120 m cannot be well resolved from the ETM+ images anymore. Furthermore, no physical relation could be derived between Tremote and Tin-situ. The numerous and complex processes that together affect the measurement of Tremote and its agreement with Tin-situ, combined with the issue of the scale difference between Tremote and Tin-situ, make it difficult to derive a (physical) relation or formula that connects Tremote to Tin-situ. The seasonal influence, expressed by a difference between winter and summer, could be captured by means of the statistical analysis. During winter, Tremote over-predicts Tin-situ on average by 0.8 ° with a bias spread of ? = 0.8 °C and SE = 0.2 °C. During summer, Tremote over-predicts Tin-situ on average by 1.8 ° with a bias spread of ? = 1.6 °C and SE = 0.2 °C. The bias statistics of the obtained horizontal Tremote distribution and the statistical seasonal relation between Tremote and Tin-situ can be used as an approximation for the horizontal kinetic water temperature distribution. Based on the results in this study and the difficulty to establish a more direct relation between Tremote and Tin-situ, the proposed systematic correction becomes: in winter Tremote - 0.8 and in summer Tremote – 1.8. The bias spread statistics (? and SE) form a first and reasonable quantification for the precision and uncertainty contained by the obtained approximation. It is recommended to attempt to reduce the uncertainties contained by approximations obtained in this study by further research. Research towards a better Atmospheric Correction, which especially accounts for local surface conditions and the spatial variations in atmospheric circumstances, could mean a major improvement for the remotely sensed water temperature approximations. Other research to improve the approximations of horizontal kinetic water temperature distributions by Tremote is to assess the thermal stratification and the surface effects. To better understand the thermal water body processes and the skin effect, improving our insight in the relation between kinetic water temperature in the water column, kinetic water temperature at the surface (water depth 0 m) and the TIR radiant surface water temperature measured locally would help. This improved insight would also help to reduce the uncertainty of remote sensing measurements. It is also recommended to investigate and improve the operational abilities of remotely sensed water temperatures. A main practical constraint is the time interval by which images of the same location are generated by the Landsat 7 satellite: 16 days. Therefore, to operationalize space-borne remote sensing of water temperatures for daily water management practises it is highly recommended to include more satellites with a TIR spectral channel. Another practical recommendation is to optimize the time of ETM+ image acquisition and processing. With NASA currently requiring 1-3 days to process the images to L1T process level, it must be carefully decided for what purposes the water temperature information can be used.