<p>Remote sensing foundation models, pretrained on massive remote sensing data, have shown impressive performance in several Earth observation (EO) tasks. These models usually use single-modal temporal data for pretraining, which is insufficient for multi-modal applications. Moreover, these models require a considerable number of samples for fine-tuning in downstream tasks, posing challenges in time-sensitive scenarios, such as rapid flood mapping. We present SkySense++, a multi-modal remote sensing foundation model for diverse EO tasks. SkySense++ has a factorized architecture to accommodate multi-modal images acquired by diverse sensors. We adopt progressive pretraining, which involves two stages, on meticulously curated datasets of 27 million multi-modal remote sensing images. The first representation-enhanced pretraining stage uses multi-granularity contrastive learning to obtain general representations. The second semantic-enhanced pretraining stage leverages masked semantic learning to learn semantically enriched representations, enabling few-shot capabilities. This ability allows the model to handle unseen tasks with minimal labelled data, alleviating the need for fine-tuning on extensive annotated data. SkySense++ demonstrates consistent improvements in classification, detection and segmentation over previous state-of-the-art models across 12 EO tasks in 7 domains: agriculture, forestry, oceanography, atmosphere, biology, land surveying and disaster management. This generalizability may lead to a new chapter of remote sensing foundation model applications for EO tasks at scale.</p>