The geographic environment, shaped by climate, landforms, hydrological processes, and anthropogenic influences, demands integrated approaches to capture its spatiotemporal heterogeneity and interconnected dynamics. Recent innovations-spanning remote sensing, geospatial big data, sensor networks, and multidisciplinary modeling-now enable multidimensional, multiscale observations across extended temporal and spatial ranges. These technologies facilitate the integration of data from global to microscale levels, enhancing our understanding of geographic processes and their implications for sustainable development. The following Topic compiles 44 peer-reviewed articles that exemplify cutting-edge methodologies and applications in this field. Studies employ diverse techniques, from satellite- and drone-based remote sensing to hybrid data fusion and machine learning, demonstrating the interdisciplinary nature of geographic environmental research. Key findings highlight the value of multiscale data integration in unraveling complex patterns. By integrating Earth system science with societal needs, this research trajectory aims to strengthen resilience to environmental risks and advance the United Nations Sustainable Development Goals (SDGs). This Topic underscores the transformative potential of multi-scale monitoring in deciphering geographic complexity, offering a foundation for interdisciplinary innovation and holistic environmental stewardship in an era of rapid global change.