An approach to guide cooperative wind field mapping for autonomous soaring is presented. The environment is discretized into a grid and a Kalman filter is used to estimate vertical wind speed in each cell. Exploration is driven by uncertainty in the vertical wind speed estimate and by the relative likelihood that a thermal will occur in a given cell. This relative likelihood is computed based on solar incidence, computed from a digital elevation map of terrain (obtained from US Geological Survey) and the position of the sun. An exploration priority function is computed for each cell, and mapping is guided by this exploration priority. The effectiveness of this approach is evaluated by using Monte Carlo simulation with different flock sizes, topographic elevation models and different level of altitude floors that triggers immediate energy exploitation mode. Simulation result indicates an overall improvement in endurance while priori information is precise. With high uncertainty in thermal distribution, a high altitude floor prevents performance loss in guided exploration. Final simulation result obtained using a commercially available high fidelity simulator demonstrates feasibility of the cooperative mapping and guided exploration approach.