Nowadays, Earth is being affected by the greenhouse effect. A first step to improve the current situation is to track the involved variables under temporal and spatial sampling. In this paper, we propose an integrated system for monitoring the related greenhouse effect gasses and several environmental variables. The system has been designed to be mounted on an unmanned aerial vehicle to overcome the spatial constraints and it also has been designed to work under the Internet of Things paradigm to overcome the temporal constraints. Unlike previous approaches, we have integrated an optimal filtering step with Kalman Filter, improving the reliability and precision of the measurements. Our experiments show that the proposed system can provide the information to the final user in near real-time. In addition, the use of the Kalman filter decreases the mean square error of our system with respect to a reference sensor.