Bibliometric Analysis of Synthetic Aperture Radar (SAR) Remote Sensing Satellite Data Utilization for Rice Paddy Mapping

This study conducts a comprehensive bibliometric analysis to evaluate the application of Synthetic Aperture Radar (SAR) remote sensing data in rice paddy mapping. SAR technology offer solutions for mapping rice paddy, especially in areas with frequent cloud cover. This analysis draws from 168 relevant articles published between 1997 and 2024, sourced from the Web of Science (WoS) database. Various bibliometric methods were applied, including co-citation, co-authorship, and keyword analysis. The research highlights leading authors, journals, and institutions, while also focusing on collaborations at country and institutional levels. The results revealed the increase of publication started in 2017 as there is a lot of open-access SAR data availability. The three countries with the largest number of publications were the China (47.0%), the United States (19.0%), and India (12.5%). Based on the number of publications, Chinese Academy Science was the institution with the most publications. This study showed that Sentinel-1 data and C-band SAR data were the most used data for mapping rice paddy. Keyword co-occurrence analysis revealed emerging trends such as the integration of SAR data and other remote sensing sensor as well as machine learning algorithm utilization such as Support Vector Machine (SVM), Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (BiLSTM), Gated Recurrent Unit (GRU), and Convolutional Neural Network (CNN) to handle big data remote sensing analysis. This study contributes to the field by offering a holistic understanding of SAR applications in agriculture, providing valuable insights for researchers, policymakers, and practitioners aiming to address challenges in rice production.