Journal of Advanced Research in Geo Sciences & Remote Sensing

Remote Sensing in The Field of Technology

V Basil Hans — 2026-04-16

Remote sensing has become a revolutionary tool in today’s scientific and technological world. It lets us get important information about the Earth’s surface without having to touch it directly. Remote sensing has greatly improved the ability to monitor the environment, agriculture, urban planning, disaster management, and climate research by combining new satellite systems, sensor technology, data analytics, and artificial intelligence. This article talks about how remote sensing is changing in the world of technology. It talks about its methods, uses, and the growing importance of geospatial intelligence. It also looks at new technologies like high-resolution photography, real-time data processing, and machine learning integration that are changing the way data is collected, analysed, and used. Even though it has a lot of potential, issues like data accuracy, cost, and ethics are still quite important. Overall, remote sensing is a key part of technological progress. It gives us great tools for making smart decisions and supporting sustainable development in a world that is becoming more data-driven.

Geospatial Technologies in Flood Risk Management and Modeling: Applications and Innovations

Priyanka Tiwari — 2026-03-28

Floods rank among the most destructive natural hazards globally, leading to widespread socio-economic losses, infrastructure damage, environmental degradation, and threats to human life. The increasing frequency and intensity of flood events, driven by climate change, urbanization, and land use changes, have underscored the urgent need for accurate flood risk assessment and effective management strategies. Recent advances in geospatial technologies, including remote sensing (RS), geographic information systems (GIS), and hydrological and hydraulic modeling, have significantly improved our ability to monitor, predict, and mitigate flood impacts. This paper provides a comprehensive overview of the application of geospatial technologies in flood risk management and modeling. It explores the use of satellite-based RS data for flood extent mapping, surface water monitoring, and post-disaster damage assessment, as well as digital elevation models (DEMs) for delineating flood-prone areas and modeling hydrodynamic processes. The integration of geospatial datasets with hydrological and hydraulic models enables more accurate flood forecasting and scenario analysis, supporting early warning systems and emergency response planning. Emerging technologies, such as artificial intelligence (AI), machine learning, and geospatial big data platforms, have further enhanced predictive capabilities and automated hazard assessment at regional and global scales. Key applications discussed include flood hazard mapping, floodplain delineation, vulnerability and risk assessment, urban flood management, and disaster mitigation planning. Despite these advancements, several challenges persist, including data availability and resolution constraints, model uncertainties, and the need for real-time integration of multi-source datasets.

Hydrogeological Investigations Using Integrated Geophysical and Remote Sensing Methods

Shreya Dixit — 2026-02-20

Hydrogeological investigations are critical for understanding the occurrence, distribution, and sustainable management of groundwater resources, which are vital for domestic, agricultural, and industrial water supply. Traditional methods, such as drilling, pumping tests, and field surveys, while accurate, are often time-consuming, labor-intensive, and limited in spatial coverage, making large-scale groundwater assessment challenging. To overcome these limitations, the integration of geophysical and remote sensing techniques has emerged as an effective and cost-efficient approach for groundwater exploration and aquifer characterization. Geophysical methods—including electrical resistivity tomography (ERT), seismic surveys, electromagnetic (EM) methods, and gravity measurements—provide subsurface information regarding lithology, aquifer geometry, and hydrogeological properties. When combined with remote sensing tools, such as satellite imagery, digital elevation models (DEMs), multispectral and hyperspectral data, and geographic information systems (GIS), these methods enable the identification of potential groundwater zones, recharge areas, and structural controls at both regional and local scales. This paper provides a comprehensive overview of integrated methodologies, discussing the underlying principles, technical applications, and relative advantages and limitations of combining geophysical and remote sensing approaches. Emphasis is given to case studies illustrating improved groundwater exploration, aquifer mapping, contamination assessment, and environmental monitoring. Recent developments involving artificial intelligence (AI), machine learning, and multi-source data integration are highlighted, demonstrating the potential for automated interpretation and enhanced predictive accuracy.

Enhancing Environmental Monitoring Accuracy Using Advanced Data Fusion Techniques in Remote Sensing

Himanshi Pathak — 2026-03-30

Remote sensing has become an indispensable tool for environmental monitoring, offering the ability to observe large geographic areas repeatedly and non-invasively, thereby supporting the assessment of dynamic Earth system processes such as land cover change, vegetation health, water quality, and natural hazards. Despite its advantages, individual remote sensing sensors often face inherent limitations in spatial, spectral, and temporal resolutions, which can compromise the accuracy of environmental analyses. To address these challenges, data fusion techniques have emerged as a robust solution, enabling the integration of multi-source datasets—such as optical, radar, LiDAR, and hyperspectral imagery—to generate more comprehensive and reliable information. This paper provides a systematic review of key data fusion strategies in remote sensing, focusing on pixel-level, feature-level, and decision-level approaches. It also emphasizes recent advances in artificial intelligence (AI)-driven fusion methods, including deep learning and machine learning frameworks, which have significantly improved the efficiency and predictive capabilities of fused datasets. Key applications across environmental monitoring domains, such as ecosystem management, pollution assessment, climate change studies, and disaster monitoring, are critically examined. Furthermore, the paper discusses current challenges, including data heterogeneity, computational complexity, and real-time implementation constraints, while proposing potential future directions for enhancing fusion accuracy, scalability, and operational deployment. This review highlights the growing potential of data fusion techniques to transform remote sensing into a more precise, multi-dimensional, and real-time tool for environmental management and decision-making.