High Resolution Estimation of GNSS-IR Soil Moisture Using the Genetic Algorithm Back Propagation

Yajie Shi

This paper proposes a multi-data fusion soil moisture inversion algorithm based on machine learning. The method uses the Genetic Algorithm Back-Propagation (GA-BP) neural network model, by combining GNSS-IR site data with other surface environmental parameters around the site. In turn, soil moisture is obtained by inversion, and finally obtains a soil moisture product with a high spatial and temporal resolution of 500 m per day.




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