A Novel Inland Water Body Detection Model Using Swin-ResUNet Hybrid Architecture with CYGNSS

Abstract

Highlights: What are the main findings? A deep learning framework is proposed for the first time to reconstruct the missing values in gridded short-term CYGNSS surface reflectance data, thereby significantly enhancing data completeness and usability for high-resolution monitoring. A novel weakly supervised training paradigm driven by pseudo-labels is proposed, which effectively reduces dependence on costly pixel-level annotations while maintaining high detection accuracy. What are the implications of the main findings? The missing value reconstruction method overcomes a key limitation of sparse CYGNSS data, unlocking its potential for reliable, high spatiotemporal resolution inland water body mapping. The pseudo-label-based weakly supervised framework establishes a practical and efficient technical pathway, which can significantly reduce data labeling costs and can be extended to other remote sensing tasks with limited ground truth. Cyclone Global Navigation Satellite System (CYGNSS) has emerged as an effective technique for inland water body detection due to its high sensitivity to inland waters. However, existing methods for inland water body detection using CYGNSS are limited by the difficulty in balancing high spatiotemporal resolution with strong generalization capability. Moreover, the limited spatial redundancy in short-term CYGNSS data restricts its capacity for high-precision inland water detection on its own. To address these issues, this study proposed a novel dual-branch model, termed STRUE. The model integrated a Swin Transformer and ResNet within a U-Net-enhanced student-teacher framework. This framework was developed through the fusion of multi-source data, including CYGNSS, SMAP, FABDEM, MODIS, and GSWE. The results showed that, for inland water body detection, the model attained a spatial resolution of 0.01° and a temporal resolution of 7 days. In terms of performance, it achieved an F1-score (F1) of 0.914, a mean Intersection over Union (mIoU) of 0.880, a Matthews Correlation Coefficient (MCC) of 0.873, and a Recall (R) of 0.963. Additionally, compared with traditional methods and models, the proposed model demonstrated a better performance in spatial continuity, structural integrity, and detail recovery, while mitigating common limitations such as cloud obscuration, spatial incoherence, and overestimation artifacts. These results further enhance the capacity of spaceborne GNSS-R for inland water body detection.