A combination of statistical models and multisource data were used to map above-ground forest biomass for National Forest lands in California. To do this, data from the Moderate Resolution Imaging Spectoradiometer were used in combination with precipitation, temperature, and elevation data. The results show that coarse resolution remotely sensed data in combination with relevant topographic and climate data can be used to map above-ground biomass with good accuracy over large areas. For the data sets considered, empirical models based on a 2 percent sample explained 73 percent of the variance in biomass in the remaining 98 percent of the data with a root mean square error of 44.4 tons/ha. These results suggest that it should be feasible to improve estimates of above-ground carbon stocks at regional to continental scales in the near future.