Interferometric Synthetic Aperture Radar (InSAR) is a remote sensing tool that provides comprehensive information about the Earth's surface. However, InSAR parameters are highly corrupted by speckle, which limits their utility. Deep learning methods have recently achieved promising results in improving the reliability of InSAR parameters. Most of the proposed methods are fully supervised. These methods are usually trained on synthetic data, which are not able to fully take into account all the properties of real images. In this paper, we address this issue by extending the self-supervised denoising approach Noise2Noise, previously proposed by Lehtinen et al. in 2018, for the joint estimation of InSAR parameters. Additionally, the proposed method uses a loss function that is adapted to the InSAR noise model, making it well-suited for the problem we are addressing.