Assessing CYGNSS Satellite Soil Moisture Data for Drought Monitoring with Multiple Datasets and Indicators - Zhaolu Hou; Zhaoxia Pu (Remote Sensing, 2023)
Drought monitoring is crucial for various sectors, and soil moisture data play a pivotal role, especially in agricultural contexts. This study focuses on the recent CYGNSS Level 3 soil moisture data derived from the NASA Cyclone Global Navigation Satellite System (CYGNSS), notable for its wide coverage and rapid revisit times, yet underexplored in drought research. Spanning from 1 January 2018 to 31 December 2022, this research analyzed daily CYGNSS soil moisture data, comparing them with the ERA5, SMAP, and GLDAS-NOAH datasets. It was found that the average and standard deviation (std) of CYGNSS soil moisture exhibited spatial patterns largely similar to other datasets, although some regions showed discrepancies (std differences reached up to 0.05 in some regions). The correlation coefficients and RMSE values between CYGNSS and other datasets depended on climate and land cover types. Four drought indicators from different soil moisture datasets were compared with the improved monthly Standardized Precipitation Evapotranspiration Index (SPEI). The drought indicators based on CYGNSS data demonstrate the capacity to describe drought extent and intensity. The correlation coefficients between certain drought indicators obtained from CYGNSS and SPEI reached 0.27 for drought percentage and 0.16 for drought intensity. Further investigations with selected extreme drought cases revealed that the indicator from CYGNSS data is relatively weak, influenced by the selected regions, times, and drought indicators. The results of this study provide insights into the potential application of CYGNSS soil moisture data in drought monitoring, offering a foundation for future research and practical implementation with current and future improved products.