Fusion of Electrical Resistivity Tomography and Satellite Imagery For Precision Agriculture

Earth observation involves the analysis of satellite and aerial imagery, social media data, in-situ observations, mathematical models, and other sources. Among the variety of data sources, geophysical methods have been employed to understand the underground soil characteristics. Particularly, in precision agriculture, electrical resistivity tomography (ERT) is one of the most popular methods due to its advantages in terms of non-destructiveness, data acquisition, processing facilities, and multiscale measurements. On the other hand, satellite imagery is a very popular tool to survey the use of soil across the agriculture territories, with the advantage of its wide field of view, relatively large temporal resolution, and ease to obtain data of surface observation. In this manuscript, a conceptual structure is presented with the general description of the most relevant building blocks of a machine learning system that combine earth observation data. Similarly, a proposal based on a mixture of experts strategy is described for ERT and satellite imagery combination in agriculture.