Improving CYGNSS-Based Soil Moisture Coverage Through Autocorrelation and Machine Learning-Aided Method

• Published in: IEEE journal of selected topics in applied earth observations and remote sensing Vol. 17; pp. 12554 - 12566
• Main Authors: Jia, Yan, Xiao, Zhiyu, Jin, Shuanggen, Yan, Qingyun, Jin, Yan, Li, Wenmei, Savi, Patrizia
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Autocorrelation; Correlation coefficient; Correlation coefficients; Cyclone global navigation satellite system (CYGNSS); Earth; Earth movements; Earth observations (from space); Earth surface; Estimation; gap-filling method; Global navigation satellite system; GNSS reflectometry (GNSS-R); Interpolation; Machine learning; Moisture content; Navigation; Navigation systems; Navigational satellites; Precipitation; Reflection; Reflectivity; Remote observing; Remote sensing; Retrieval; Satellite constellations; Satellite observation; Satellites; Soil improvement; Soil moisture; soil moisture (SM); soil moisture active passive (SMAP); Spatial resolution; Temporal resolution; Transmitters
• ISSN: 1939-1404; 2151-1535
• DOI: 10.1109/JSTARS.2024.3419779

Global Navigation System Reflectometry (GNSS-R) is a microwave remote sensing technology that enables Earth observation by receiving GNSS signals reflected from the Earth's surface. The Cyclone Global Navigation Satellite System (CYGNSS) constellation is a satellite system that uses GNSS-R technology with high temporal resolution and has been a popular data source in soil moisture retrieval in recent years. However, the constant movement of GNSS transmitters and GNSS-R satellites results in potentially chaotic and random observations of the Earth's surface, with many unevenly distributed gaps in the observed data. In this paper, a gap-filling method based on spatial autocorrelation is proposed to interpolate the gaps within these observation datasets, with SM being estimated post-interpolation. The sample set for the model comprises points surrounding the interpolation target, with modeling conducted considering factors of spatial weighting to estimate values at the interpolation target. Different autocorrelation-based gap-filling methods using CYGNSS data can achieve good estimation accuracy, and the data coverage after interpolation is on average 1.8 times greater than before interpolation. The gap-filling method using XGBoost achieves the best performance and offers the highest accuracy in SM estimation, with an average correlation coefficient of 0.8445, and an average RMSE of 0.0457 m 3 /m 3 . The gap-filling approach can significantly enhance data coverage and facilitate the filling of daily gaps in CYGNSS data with all maintaining high SM estimation accuracy. The estimation of daily missing values using CYGNSS data can fully exploit the embedded surface features in the data's fine resolution and can provide high-resolution SM retrieval.