Geospatial Methods for SDG-Aligned Land and Water Resource Management: A Bibliometric and Systematic Review of Research Trends (2021–2026)

Abstract Geospatial methods have become indispensable for sustainable land and water resource management under growing pressures from climate change, urbanization, and water scarcity. This study maps global research trends in geospatial methods for land and water resource management, evaluates the comparative performance of dominant analytical frameworks, and identifies critical methodological gaps and future research priorities. A bibliometric analysis of 335 peer-reviewed articles (2021–2026, Dimensions API) was combined with a PRISMA-based systematic review of 59 studies drawn from Scopus, PubMed, and Dimensions AI. Publication output grew by 59.2% between 2021 and 2025, peaking at 1,735 articles in 2025, driven by post-COVID remote monitoring demand and the mainstreaming of cloud-based geospatial platforms. China and the United States dominated output and collaboration, while Kenya, Ecuador, and South Africa achieved disproportionately high citation impact, reflecting the research value of studies addressing acute resource stress. Groundwater potential mapping was the most studied application (39% of the corpus), with the Analytical Hierarchy Process (AHP) being the most widely applied method despite machine learning consistently outperforming it in within-study comparisons (mean ROC-AUC: 0.860 vs. 0.748). Meta-regression revealed that model accuracy is poorly predicted by the number of thematic input layers (R²=0.094), challenging the assumption that more input data produces better models. Furthermore, 92% of comparative studies confirmed that urban and agricultural land expansion measurably reduces groundwater levels or water body extent, providing strong directional evidence for SDG 6 and SDG 15 monitoring. Despite these advances, many studies rely on static datasets and single-site case studies, with limited attention paid to real-time monitoring, policy integration, and large-scale validation. The review identifies real-time monitoring, Explainable AI, and climate-resilient geospatial frameworks as the three most critical priorities for advancing policy-relevant and reproducible geospatial science.