there are many ways to do it, but I am no expert in either of them, sorry.
My advice is to look at some studies and try to find the approach which fits your needs best.
Did you get a linear relationship between backscatter and moisture?
The easiest way then would be to apply the regression on an image and mask out all invalid areas (vegetation, urban, water).
The approach described by johngan here is also quite straightforward and promising, but it is based on images from the rainy and dry season: Soil moisture identification -Sentinel
But you could still use your measurements for refinement or validation in this approach.
I think the most important thing for you is to decide for one strategy and see what is needed to accomplish it over the entire workflow, instead of proceeding by single steps unknowing what comes next.
Hope this helps you a bit.
Some interesting approaches:
- Soil moisture mapping using Sentinel - 1 images: Algorithm and preliminary validation
- Synergetic use of Sentinel - 1 and Sentinel-2 data for soil moisture mapping at 100 m resolution
- SMOSAR algorithm for soil moisture retrieval using Sentinel - 1 data
- Synergic use of Sentinel - 1 and Sentinel-2 images for operational soil moisture mapping at high spatial resolution over agricultural areas
- Soil moisture content estimation based on Sentinel - 1 and auxiliary earth observation products. A hydrological approach
- Joint Sentinel ‐ 1 and SMAP data assimilation to improve soil moisture estimates