Applications of Remote Sensing and GIS in Earth and Environmental Sciences: A Comprehensive Review

Remote Sensing (RS) and Geographic Information Systems (GIS) have become indispensable pillars in Earth and environmental systems sciences, offering unparalleled capabilities for large-scale, multi-temporal monitoring, modeling, and analysis of complex environmental phenomena. Their integration has revolutionized how researchers, policymakers, and practitioners approach challenges such as climate change, land use/land cover (LULC) dynamics, disaster management, hydrology, and biodiversity conservation. By combining synoptic, repetitive observations from RS with the spatial integration and analytical power of GIS, these technologies enable holistic, data-driven decision-making across diverse environmental domains. This paper examines the synergistic applications of RS and GIS, synthesizing key technologies—including multispectral and hyperspectral satellite imagery, LiDAR, and advanced GIS platforms—and demonstrating their transformative role in environmental sciences. Remote sensing provides consistent, high-resolution datasets that capture temporal changes in vegetation, water bodies, and atmospheric conditions. GIS complements this by facilitating spatial overlay analysis, modeling, and visualization, thereby enabling researchers to derive actionable insights from raw geospatial data. Together, they form a powerful toolkit for addressing pressing global issues. Case studies highlight the effectiveness of integrated approaches in detecting deforestation, mapping flood risk zones, and assessing ecosystem health. For instance, machine learning algorithms applied to RS datasets have achieved accuracies exceeding 85–95% in LULC classification, while GIS-based hydrological models have improved flood prediction and mitigation strategies. These successes underscore the potential of RS-GIS integration in advancing sustainable environmental management. However, notable gaps remain, particularly in real-time data processing, multi-scale data fusion, and accessibility in resource-constrained regions. Bridging these gaps requires not only technological innovation but also interdisciplinary collaboration and policy support. Looking ahead, the integration of Artificial Intelligence (AI) and cloud-based GIS platforms promises to further enhance the efficiency, scalability, and accessibility of geospatial analyses. AI-driven models can automate feature extraction, anomaly detection, and predictive modeling, while cloud infrastructures democratize access to computational resources and datasets. The paper concludes by emphasizing the need for collaborative frameworks that unite scientists, technologists, and policymakers to harness RS and GIS for sustainable environmental stewardship in the face of global change.