Combining CYGNSS and SMAP for Soil Moisture Estimation in East Asia

The increase in extreme weather events due to climate change has led to irregular patterns in the hydrological cycle. In East Asia, a region characterized by a monsoon climate, natural disasters such as droughts and floods have become increasingly prevalent. This trend has underscored the necessity for effective soil moisture monitoring, as it is a crucial element in the hydrological cycle. To this end, various machine learning techniques based on satellite data combined with in-situ soil moisture observations are being actively researched for precise soil moisture estimation. However, the existing satellite images have limitations in temporal resolution compared to in-situ observations, and there is a need to improve the temporal resolution. In this study, soil moisture was estimated by linear regression using Cyclone Global Navigation Satellite System (CYGNSS) reflectivity and Soil Moisture Active Passive Level 2 (SMAP L2) soil moisture, vegetation opacity, surface roughness, and soil surface temperature in East Asia. The CYGNSS-based soil moisture was validated alongside SMAP Level 4 (L4) and Advanced SCATterometer (ASCAT) L2 data using Extended Triple Collocation (ETC) analysis, which demonstrated the high accuracy of CYGNSS. The results of this study provide high temporal resolution soil moisture data for East Asia, which can contribute to efficient hydrological factor monitoring and management.

Acknowledgment

This research was supported by the BK21 FOUR (Fostering Outstanding Universities for Research) funded by the Ministry of Education (MOE, Korea) and National Research Foundation of Korea (NRF). This work is financially supported by Korea Ministry of Land, Infrastructure and Transport (MOLIT) as「Innovative Talent Education Program for Smart City」. This work was supported by Korea Environment Industry & Technology Institute (KEITI) through Research and Development on the Technology for Securing the Water Resources Stability in Response to Future Change Project, funded by Korea Ministry of Environment (MOE)(RS-2024-00332300). This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (RS-2024-00416443). This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2022R1A2C2010266).