The effective flux incident upon the detectors of the sensor, after is has been corrected for atmospheric effects, is a nonlinear function of the emissivity of the target for that channel and the temperature of the target. The sensor system cannot separate the contribution from the emissivity and the temperature that constitute the flux value. In this paper, we describe a method that estimates the water temperature from thermal data. This method is then tested with remotely sensed data obtained from NASA's Thermal Infrared Multispectral Scanner (TIMS)--a 6 channel thermal sensor. Since this is an under-determined set of equations i.e. there are 7 unknowns (6 emissivities and 1 temperature) and 6 equations (corresponding to the 6 channel fluxes), there exist theoretically an infinite combination of values of emissivities and temperature that can satisfy these equations. However using some realistic bounds on the emissivities, bounds on the temperature are calculated. These bounds on the temperature are refined to estimate a tighter bound on the emissivity of the source. An error analysis is also carried out to quantitatively determine the extent of uncertainty introduced in the estimate of these parameters. This method is useful only when a realistic set of bounds can be obtained for the emissivities of the data. In the case of water the lower and upper bounds were set at 0.97 and 1.00 respectively. A set of images obtained with the TIMS are then used as real imagery data. The data was acquired over Utah Lake, Utah, a large freshwater lake near Salt Lake City, in early April 1991. It will be used to identify water temperatures for detection of underwater thermal, saline, and fresh water springs. An image entirely consisting of water is analyzed. The temperatures of the pixels are calculated to an accuracy of less than 1 deg. K. The error histograms of the temperature estimates are also calculated.