Soil moisture from sentinel-1 (GRD Data)

I want to detect soil moisture content for agricultural area also forest area. I use sentinel-1 (GRD data). I have already downloaded images for two years. Also i have a network of sensors scattered around my agricultural area. These sensors measure soil moisture content.
I will apply machine learning algorithms in order to make predictions of the soil moisture content.

My question is:

What are the steps that should be followed for pre-processing satellite-1 images, knowing that the resulting images will be used for do training for machine learning algorithms?

I seen many steps but I’m use SNAP from few time for that i can’t detect the correct steps for pre-processing?

FAQ: What are the minimal requirements for SNAP?

It depends a bit on the spatial distribution of your sensors - how large is your agricultural area?
The easiest way is to plot measurements from the field against the calibrated backscatter values in a scatter plot and derive a regression equation. Please have a look at these topics:

Pre-Processing is comparably easy, the most important steps are radiometric calibration (and conversion to dB) and geocoding: Radiometric & Geometric Correction Workflow