There is an inadequate network of in situ stations for monitoring the global distribution of land surface temperature anomalies. Therefore, we are using satellite observations to help identify this global distribution. The Special Sensor Microwave Imager (SSMI), flown by the Defense Meteorological Satellite Program, is used to derive near surface temperatures over land, as it flies 14 times around the globe each day. The over flights correspond with 6 A.M. and 6 P.M. local equatorial crossing times, and the period of record for this suite of satellite instruments is 1987 - present. We have developed a series of algorithms, using the SSMI seven channel measurements, to calculate the near surface temperature for various surface conditions. The algorithm uses the unique signal of each surface type to make dynamic surface emissivity adjustments under conditions such as vegetation, wet ground, snow cover and deserts. The in situ data from the time of over flight serves as ground truth for the calibration these equations. This presentation will show the accuracy of the dynamic adjustments for various surfaces types. In order to calculate precise temperature anomalies over the period of record, it was necessary to derive inter-satellite calibrations between the three SSMI instruments used in the analysis. We will demonstrate how satellite values were inter-calibrated, and its improvement on the anomaly fields. After the above procedures were finalized, we independently validated the accuracy of the final algorithm, using the best high density network in the world. Additional filters were developed at this stage to remove observations that are not representative of the mean monthly anomaly. The data that passes through all the filters has less than a 1O standard error in a one degree grid box. The final step is to blend satellite and in situ anomalies into a single global land surface temperature product