Spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) has been increasingly used for inland water detection, but most reflection point based methods mainly exploit single polarization observables. Dual polarization observations may provide additional information on surface scattering conditions, especially over mixed water and land backgrounds. This study proposes a low-confidence local correction framework for Tianmu-1 dual polarization GNSS-R water detection over the Amazon Basin. A class-weighted logistic model based on left hand circular polarization (LHCP) reflectivity is first established as the baseline classifier. The distance between the LHCP-based probability and its operating threshold is used to identify low-confidence samples. For these samples, the logarithmic ratio of LHCP reflectivity to right hand circular polarization (RHCP) reflectivity, termed logRatio, is introduced as an additional feature to correct the baseline decision; samples outside the low-confidence interval retain the baseline result. The method was evaluated using Tianmu-1 Level 1 observations from June to August 2024. Reference labels were derived from neighborhood water fractions in the Global Surface Water (GSW) product. Model parameters, operating thresholds, and the low-confidence threshold were determined from June samples, and independent evaluations were performed using July and August samples. Relative to the GSW-derived reference labels, the proposed correction increased overall accuracy (OA) from 89.57% to 92.12% in July and from 90.89% to 92.89% in August. The reported accuracy values are relative to long-term GSW-derived labels rather than contemporaneous water truth. Confusion matrices indicate that the improvement mainly resulted from fewer non-water samples being misclassified as water. Spatial cases further show that corrected samples were concentrated in complex boundary and wetland backgrounds. These results demonstrate that Tianmu-1 dual polarization observations provide useful supplementary information for water detection at GNSS-R reflection points.
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liu et al. (2026) studied this question.
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