Soil erosion leading to land degradation is a significant geo-environmental challenge that may adversely impact agricultural productivity and hence threaten food security, underscoring the need for implementation of sustainable management policies. Rapid population growth and intensified rainfall patterns driven by climate change have accelerated soil erosion, exposing the fertile topsoil layer to transport. The eroded sediments are then deposited downstream, contributing to sedimentation in reservoirs. Addressing such challenges requires systematic investment in monitoring and modeling of soil erosion and sediment transport.
This session will emphasize recent advancements in process-based modeling, the application of remote sensing, and AI/ML techniques for sustainable soil and water management across different temporal and spatial scales. Satellite platforms such as Landsat, Sentinel, MODIS, and other high-resolution sensors offer valuable opportunities to monitor LULC changes, map reservoir surface areas, and assess land degradation. To quantify soil erosion and sediment delivery, both process-based and empirical models -- including RUSLE, SWAT, and InVEST -- remain essential tools for evaluating hydrological and geomorphological processes. In recent years, the integration of machine learning techniques has further enhanced these approaches by improving predictive accuracy, supporting robust classification, and enabling comprehensive uncertainty assessments of model outcomes.
We would like to invite abstract submissions in the following sub-domains:
• Field observations related to soil erosion and sediment transport.
• Remote sensing applications for soil and water management.
• Advances in modelling of soil erosion and sediment transport.
• Advances in cloud computing, including platforms such as Google Earth Engine (GEE), high-performance computing, and big-data platforms for large-scale analysis.
• AI/ML and geospatial techniques for soil erosion and sediment transport modeling.
• Policy-level interventions for soil and water management.
This session aims to bring together researchers, engineers, scientists, and policymakers to share innovative methodologies and interdisciplinary perspectives through regional to global case studies. The primary goal is to foster an integrated approach to sustainable soil and water management by combining geospatial technologies, advanced modeling frameworks, and machine learning techniques.