Machine-Learning Based Retrieval of Soil Moisture at High Spatio-Temporal Scales Using CYGNSS and SMAP Observations

High spatio-temporal soil moisture is critical for the understanding of land-atmosphere interactions and affects meteorological, hydrological and agricultural applications. Currently, most satellite-based microwave sensors provide global soil moisture retrievals at ∼40 km spatial and 2-3 days temporal resolution. Using the forward scattered L-band Global Navigation Satellite System (GNSS) signals, surface soil moisture can be estimated at higher spatial and temporal scales. However, due to the complex land surface characteristics and bistatic nature of GNSS signals, the retrieval algorithms for deriving surface soil moisture from GNSS signals are still under development. In this work, a machine learning (ML) algorithm has been used for estimating soil moisture from Cyclone Global Navigation Satellite System (CYGNSS) measurements. The in-situ data from International Soil Moisture Network and global soil moisture data from Soil Moisture Active Passive (SMAP) have been deployed as the reference data in the ML algorithm. In particular, various remote sensing-based land surface parameters have been included and facilitate a robust soil moisture retrieving process. The proposed approach has achieved an ubRMSD of 0.0523 m
³
/m
³
between the retrieved soil moisture from CYGNSS and in-situ measurements in a 5-fold cross-validation over 129 ground-based soil moisture sites, suggesting a satisfactory performance of the ML-based approach. Moreover, the global median ubRMSD of 0.042 m
³
/m
³
is obtained between SMAP and CYGNSS ML predictions. Surface soil moisture can be retrieved at ∼9 km spatial and 1-2 days temporal scales through the presented framework.