Assessing the performance of GNSS-R observations in drought monitoring: a case study in Jiangxi and Hunan, China

Abstract

AbstractDrought is a disaster that seriously constrains economic development and endangers human life. This paper explores the potential of Global Navigation Satellite System Reflectometry (GNSS-R) for drought monitoring, using Cyclone Global Navigation Satellite System (CYGNSS) data to monitor drought in Jiangxi and Hunan Provinces, China, in 2022. This study applies the Random Under-sampling Boosting (RUSBoost) algorithm to detect waterbodies and linear regression to retrieve soil moisture (SM). Result shows that drought in September was heaviest, with the area of Poyang Lake in Jiangxi and Dongting Lake in Hunan decreasing by 70.2% and 76.9%, respectively, compared to that in June. The variation in retrieved SM shows that the Poyang Lake Plain and Jitai Basin in Jiangxi and the Dongting Lake, Yuanjiang River, and Xiangjiang River basins in Hunan suffered from the most serious drought. The variation in retrievals shows high consistency with various reference datasets, including Soil Moisture Active Passive (SMAP) SM data and vegetation condition index (VCI). The correlation coefficient between retrieved SM and VCI is 0.93 in Jiangxi and 0.94 in Hunan.

Keywords

• Cyclone global navigation satellite system (CYGNSS)
• drought monitoring
• global navigation satellite system reflectometry (GNSS-R)
• water detection
• soil moisture (SM)