Direct current resistivity and electromagnetic methods at low induction numbers are commonly used to characterize near‐surface structures. Although both methods are related to the same property, resistivity or conductivity, they have different sensitivities. The electromagnetic method at low induction numbers is more sensitive to conductive structures, but faces problems resolving resistive bodies, whereas the direct current method can image conductive and resistive variations. Additionally, the electromagnetic method at low induction numbers is less expensive and faster to collect the data, which seems a promising way to provide information at a low cost to enhance the model robustness. Aside from that, the exploration depth in both methods is not the same, but they can complement each other. In this work, a joint inversion algorithm based on a linear approximation was developed to incorporate both data. To achieve the goal, we rewrite the linear integral equations of electromagnetic data at low induction numbers in terms of resistivity logarithm to overcome the differences in magnitude and incorporate both data sets. We explore the potential of the joint inversion testing the algorithm with two synthetic models. In our tests, the joint model improvements are mainly in the conductive geometry and show favourable influence compared to the individual models. Finally, we tested the algorithm with field data collected in the coastal aquifer at Maneadero Valley. Despite the high anthropogenic electromagnetic noise in the area, the joint inversion results are coherent with the individual inversions of electromagnetic soundings at low induction numbers and direct current resistivity along with the geological setting.