Semi-supervised domain adaptation (SSDA) aims to transfer knowledge from a labeled source domain to a scarcely labeled target domain, despite distribution shifts. The challenge becomes greater when source and target data differ in acquisition modality, as in remote sensing where variations in sensor type (e.g., optical versus radar), spectral properties [e.g., RGB versus multispectral (MS)], or spatial resolution are common. This challenging scenario, known as semi-supervised heterogeneous domain adaptation (SSHDA), requires learning across modalities with limited target labels. In this work, we propose semi-supervised adaptation in heterogeneous domains via conditional adversarial representation disentanglement and adaptive pseudo-labeling (SAHARA), a new method for SSHDA that combines conditional adversarial feature adaptation with dynamic pseudo-labeling to learn domain-invariant features and handle extremely scarce target annotations. Experiments on two heterogeneous remote sensing benchmarks for scene classification, conducted with both convolutional and transformer-based backbones, demonstrate that SAHARA consistently outperforms existing SSHDA and semi-supervised methods.