LoRa is being widely adopted by industrial communities for its long range, robustness and low power wireless communication capabilities. In fact LoRa is gaining more popularity even amongst the common people as it is an affordable solution and operates in the unlicensed radio spectrum. However, LoRa provides a widely heterogeneous coverage; it can reach hundreds of meters or up to tens of kilometers, depending on the surrounding environment. Determining the coverage of LoRa stations is key to provide a good quality of service. On one hand, the traditional method of expensive measurement campaigns can be employed to estimate LoRa's coverage; but this is impractical due to the large geographical areas involved. On the other hand, popular channel models can be adopted; but many of them are yet to be explored for LoRa or rely entirely on the user predening the type of environment to estimate coverage. Neither of those approach are suitable for thousands of non-expert citizens and organizations around the world looking forward to understanding the coverage of their LoRa stations. The aim of the thesis is to automatically estimate the coverage of LoRa, before the deployment of the gateway and without relying on on-site measurements or the user's perception of the environment. Moreover, the estimation must be carried out in a simple, low cost and low eort approach. Considering that the surrounding environment determines in a fundamental manner, the coverage of wireless technologies including LoRa, we use readily available remote sensing information coming from satellites to estimate the characteristics of an area. In this manner, we free up the user from providing any type of data. Based on this remote sensing approach, the thesis provides two main contributions: First we analyze a group of parametric models (ITU-R 1812 and Okumura Hata model) and determine that the Okumura Hata model is better suited for LoRa. Second we improve the performance of using the basic Okumura Hata model by proposing an automated approach that explores remotely sensed height models and land cover maps to automatically congure channel model parameters. The performance is evaluated based on a relative comparison due to some unknown transmitter setting parameters and assess which algorithm accurately tracks the changes in the real path loss. A validation using a relative comparison approach on 18000+ samples of real LoRa data shows that the modied algorithm gives an improved performance compared to the novel approach in path loss prediction and the ITU model. The modied algorithm could improve the coverage up to a factor of 5 compared to the novel approach in free space ranges. Moreover, in an urban built-up city the modied algorithm could improve the coverage by up to 1.5 km compared to the novel approach.