Inland water bodies are vital to the Earth’s ecosystem, global water cycles, and climate regulation. Global Navigation Satellite System Reflectometry (GNSS-R) has emerged as a powerful tool for water detection, particularly with the deployment of the Fengyun-3 (FY-3) E, F, and G satellites. This study proposes an inland water body detection method by integrating the Z-score algorithm with specular point land surface reflectivity (SRsp) derived from FY-3 Level-1 GNSS-R data. Using 2024 observations, the method was validated in the Amazon and Congo basins against optical water body products. The results demonstrate high detection performance, achieving overall accuracies of 95.39% and 97.38% in the two regions, respectively. Analysis of reflectivity expressed in decibels (dB) reveals that while dB-units enhance the detection of small tributaries, they are more susceptible to noise-induced misclassification compared to linear units. Furthermore, a comparative assessment of GNSS constellations shows that multi-system combination significantly reduces noise compared to single-system approaches. Notably, the Galileo system exhibited limited sensitivity to small tributaries due to lower observational density. Sensitivity analyses further reveal that interpolation methods and Z-score threshold selection are important factors influencing detection accuracy. As the first systematic evaluation of FY-3 GNSS-R data for inland water detection, this research provides a critical benchmark for future multi-platform and multi-constellation land surface retrieval studies.
Yang et al. (Tue,) studied this question.