A flood monitoring method using FY-3 GNSS-R accounting for surface reflectivity uncertainty: a case study of the August 2 Guangdong rainstorm disaster

doi:10.11947/j.AGCS.2026.20250365

Abstract

Abstract:

From August 2 to 6, 2025, Guangdong province experienced the fifth most intense and the strongest August rainstorm of 21st century. The prolonged extreme precipitation triggered severe flooding, causing significant casualties and economic losses. Spaceborne global navigation satellite system reflectometry (GNSS-R) has shown great potential for flood monitoring due to its short revisit cycle and insensitivity to clouds and rainfall. This study presents the first evaluation of GNSS-R data from China's Fengyun-3 (FY-3) satellite series for emergency flood monitoring during the August 2 Guangdong extreme rainstorm event. The calculation of surface reflectivity (SR) and the multi-GNSS SR fusion model are introduced. To address the limitations of traditional “hard threshold” methods, which are often affected by high soil moisture, an improved flood detection approach considering the fuzzy transition of SR is proposed. The method applies a Sigmoid function to map continuous SR values into surface inundation probabilities. Based on the confidence interval of these probabilities, an uncertainty-driven dynamic threshold is introduced to classify the study area into high-confidence water, high-confidence non-water, and uncertain regions. The effectiveness of the proposed method was evaluated by comparison with the cyclone GNSS (CYGNSS) surface water product using a confusion matrix. Results show that, compared with the traditional “hard threshold” method, the proposed approach improved the overall detection accuracy of surface water by 10.38% and 10.96% before and after the flood, respectively, effectively reducing false positives caused by high soil moisture. Further comparison with surface water and ocean topography (SWOT) and global flood monitoring (GFM) products indicates that the detected flood extents are largely consistent among the three datasets. In summary, the results demonstrate the capability of FY-3 GNSS-R for emergency flood monitoring and provide a new methodological framework for flood detection using spaceborne GNSS-R observations.

Key words: FY-3, spaceborne GNSS-R, GNOS-Ⅱ, flood monitoring, August 2 Guangdong extreme rainstorm

CLC Number:

P228

Zhongmin MA, Shuangcheng ZHANG, Xin ZHOU, Qi LIU, Ning LIU, Hengli WANG. A flood monitoring method using FY-3 GNSS-R accounting for surface reflectivity uncertainty: a case study of the August 2 Guangdong rainstorm disaster[J]. Acta Geodaetica et Cartographica Sinica, 2026, 55(3): 451-464.

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校准系数	斜率	截距（b）
GPS	1.075	0.94
BDS	1	0
Galileo	1	0.34

硬阈值法	风云三号结果	CYGNSS参考地表水		总体精度
硬阈值法	风云三号结果	非淹没区	淹没区	总体精度
洪涝前	非淹没区	64.57	14.03	81.10
洪涝前	淹没区	4.87	16.53	81.10
洪涝后	非淹没区	57.85	13.84	79.63
洪涝后	淹没区	6.53	21.78	79.63

动态阈值法	风云三号结果	CYGNSS参考地表水		总体精度
动态阈值法	风云三号结果	非淹没区	淹没区	总体精度
洪涝前	高置信非水区	75.17	1.98	91.48
洪涝前	高置信水区	6.54	16.31	91.48
洪涝后	高置信非水区	68.15	1.93	90.59
洪涝后	高置信水区	7.48	22.44	90.59