The recent developments in the research area of remote sensing and environmental monitoring have shown a significant shift towards integrating advanced machine learning techniques with geospatial data to enhance predictive capabilities and decision-making processes. The field is increasingly leveraging deep learning models, particularly transformers and their variants, to capture complex spatial and temporal dependencies in environmental data. These models are being fine-tuned for specific tasks such as species richness prediction, temporal change detection in remote sensing, and sea ice condition forecasting, demonstrating improved accuracy and efficiency over traditional methods. Notably, there is a growing emphasis on uncertainty quantification and deconfounding techniques to ensure robust and reliable predictions, especially in safety-critical applications like maritime safety and human-robot interaction. Additionally, the integration of domain-specific data preprocessing and augmentation strategies is becoming crucial for handling unique challenges posed by different environmental dynamics. The field is also witnessing a push towards more comprehensive and independent evaluations of machine learning-based Earth System Models to enhance their credibility and applicability. Overall, the advancements are paving the way for more accurate, efficient, and reliable environmental monitoring and forecasting tools, with significant implications for conservation, urban planning, and climate change mitigation.