Any thoughts on these methods to monitor crop health ?

I’m trying to develop a method that can help in crop monitoring (grain crops like wheat and rice) using SAR. So far, I’ve narrowed it down to two methods and I don’t know if which path can lead to success.

  1. SAR dataset training: In this method, I’m planning to get an NDVI data set and train VH/VV data (from NRPB- normalized ratio procedure between bands) sentinel1. With regressions, a model is developed using a neural network. The true data set would be NDVI and the model will match this result taking SAR data input.

  2. Water cloud model: When I looked at the equation, it requires in-situ measurements. I don’t have the ground soil moisture data for the WCM equation. However, I can still computer the LAI from the sentinel 2 dataset. I have the ground precipitation and temperature data but those are not enough to get soil moisture.

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as you don’t have moisture data from the field the first approach seems more robust.
The exact choice depends on what you exactly want to monitor: the type of crop or its condition.