Primary Convener:
Sushree Swagatika Swain, Scripps Institution of Oceanography

Convener:
Akash Koppa, University of Maryland College Park
Somnath Mondal, Northeastern University
Ashutosh Sharma, Indian Institute of Technology Roorkee

Early Career Convener:
Sudhanshu Kumar, Auburn University

Chair:
Akash Koppa, University of Maryland College Park
Sudhanshu Kumar, Auburn University
Sushree Swagatika Swain, Scripps Institution of Oceanography

Monitoring agricultural water use and soil moisture is essential for ensuring sustainable food production, improving irrigation efficiency, and responding to climate-driven water challenges. Recent advancements in Earth observations (EO) and Machine Learning (ML) are enabling more precise, timely, and scalable assessments of these critical parameters.This session invites contributions that leverage EO data such as satellite imagery, airborne sensing, and in situ networks, alongside ML techniques to enhance monitoring, modeling, and forecasting of agricultural water use and soil moisture dynamics. Key topics include: 1) Satellite-based soil moisture retrieval and validation (e.g., SMAP, Sentinel, Landsat), 2) Hybrid physics-ML modeling approaches for soil moisture estimation, 3) Monitoring evapotranspiration, irrigation, and agricultural water use, 4) Integration of EO, meteorological, and ground data using ML techniques, 5) Real-time and near-real-time monitoring, and Sub-Seasonal to Seasonal (S2S) forecasting systems for agricultural water management, 6) Case studies on applied monitoring for climate resilience and food security

Index Terms
0402 Agricultural systems
1807 Climate impacts
1842 Irrigation
1866 Soil moisture

Suggested Itineraries:
Disasters‚ Calamities and Extreme Events
National Climate Assessment
Climate Change and Global Policy
Machine Learning and AI
Global Impacts‚ Solutions‚ & Policies

Cross-Listed:
IN - Informatics

Co-Organized Sessions:
Global Environmental Change

Neighborhoods:
3. Earth Covering