An exploratory framework integrating scientometric analysis and SAR-Oriented geospatial modelling for NISAR research prioritization

The rapid advancement of Synthetic Aperture Radar (SAR) technology, particularly through the NASA – ISRO Synthetic Aperture Radar (NISAR) mission, has created significant opportunities for large-scale Earth observation and environmental monitoring. However, current research remains fragmented, with limited integration between scientometric insights and application-oriented geospatial modelling. This study addresses this gap by developing a hybrid framework integrating bibliometric analysis, machine learning, and spatial statistics to identify and prioritize emerging research domains in L-band SAR applications. A curated dataset of 164 peer-reviewed publications (2014–2026) was analysed using the bibliometrix R package and VOSviewer to extract thematic structures, collaboration networks, and citation patterns. Two quantitative indicators, namely the Gap Index (GI) and Research Priority Score (RPS), were introduced to identify underexplored yet high-impact research themes. Results reveal a non-linear publication growth trajectory, with annual output exceeding 35 publications and a compound annual growth rate of approximately 18–22%. Thematic analysis identified four major research clusters, highlighting a dual structure of technological innovation and application-driven science, with machine learning and InSAR functioning as important bridging domains. Country-level analysis demonstrates a core – periphery collaboration structure, with the United States (N = 69) and India (N = 24) emerging as dominant research hubs, supported by high collaboration indices (0.82–0.88) and strong L-band research focus (~78–85%). The proposed framework establishes a preliminary conceptual linkage between scientometric research gaps and SAR-based geospatial application pathways. The geospatial modelling component employed Sentinel-1 C-band SAR data as a simulated dataset for workflow demonstration, while recognizing that future validation using operational NISAR L-band observations remains necessary.Overall, the integration of bibliometric intelligence with geospatial modelling provides a scalable and data-driven framework for research prioritization and enhancing the scientific utility of next-generation SAR missions.