Technological Advances, Challenges and Future Prospects for Remote Sensing-Driven Climate Change Monitoring

Background Climate change poses existential threats to environmental sustainability, resource management, and global ecosystem stability. Remote sensing technologies have become indispensable for monitoring these climate-related processes, providing continuous, large-scale and high-resolution observations of the Earth surface and atmosphere. Purpose This review examines recent advancements in remote sensing technologies and their expanding applications in climate change monitoring, environmental sustainability and the study of trace elements and minerals. Furthermore, it evaluates the integration of artificial intelligence (AI) to enhance environmental assessment and strategic decision-making. Methods A comprehensive literature review was conducted, focusing on satellite platforms, unmanned aerial vehicles (UAVs), LiDAR, hyperspectral imaging, Synthetic Aperture Radar (SAR), and AI-driven analytics. We critically analyzed their applications across climate indicators, disaster management, agriculture, water resources, and geochemical risk assessments. Results Significant progress has been made in tracking vital climate variables, including temperature anomalies, vegetation dynamics, land-cover shifts, and atmospheric composition. Advanced AI techniques such as machine learning, large language models (LLMs), vision-language models (VLMs) and geospatial foundation models (GFMs) have revolutionized data processing, prediction accuracy, and automated decision-support. Remote sensing also demonstrates profound potential in mapping soil geochemistry, heavy-metal contamination, mineral exploration and dust transport. However, challenges such as cloud interference, massive data processing requirements and limited ground-truthing remain significant constraints. Conclusions Remote sensing continues to transform Earth system sciences by enabling timely, synoptic observations. The seamless integration of AI and advanced geospatial analytics further amplifies our capacity to build climate resilience and manage natural resources sustainably. Continued sensor innovation and rigorous validation frameworks are critical to maximizing the future utility of these technologies.