Emerging Trends in the Integrating Remote Sensing and Machine Learning for Groundwater Resources Management: A Bibliometric Study

Sustainable management of groundwater resources, as a multifaceted challenge in water sciences, necessitates the adoption of novel computational approaches for monitoring, modeling, and predicting dynamics. This research investigates the status and outlines future research directions in advanced remote sensing applications for groundwater management from 2000 to 2024. Data extracted from the Web of Science Core Collection database (n=2356) were analyzed using the Bibliometrix package in the R environment. The results reveal a significant growth in the research field, with GRACE satellite data recognized as a key indicator in assessing groundwater storage changes. Furthermore, the emergence of "land-surface model" and "risk assessment" concepts reflects a trend towards integrated modeling and comprehensive risk evaluation. The integration of multi-source remote sensing data (including data from new-generation satellites and aerial platforms) with advanced artificial intelligence algorithms (such as deep neural networks, physics-informed models, and reinforcement learning methods) is expected to become a central focus for leading researchers in hydrology, water resources, and machine learning engineering. This convergence will pave the way for developing more reliable prediction models, accurate uncertainty analysis, and the provision of optimal groundwater resources management solutions under various climatic and land-use scenarios. This bibliometric analysis, by systematically reviewing the research published in reputable scientific databases, provides an overview of the status, emerging trends, and future priorities in this vital field for the scientific community.