How to apply vegetation correction for soil moisture estimation using Cumulative Density Function Transformation in SAR Imagery (Sentinel-1)?
@ABraun Can you help?
Never heard of that, sorry. What is your aim and what steps did you already undertake?
My aim is to apply Cumulative Density Function Transformation for soil estimation using Multi-Temporal SAR data. For that, I need to know how the vegetation errors can be eliminated because there are two factors that have a major role in soil moisture estimation, i.e Soil Roughness and Vegetation. Is water cloud model is the only way to do it? if yes, then please help with the algorithm.
I have pre-processed 7 Sentinel-1 Dataset using SNAP to find out backscatterer coffiecient (sigma0).
P.S: I’m new to this topic.
if you are new to SAR analyses, soil moisture estimation is probably one of the most difficult tasks to achieve, especially with the cumulative density function transformation. Just like learning to ride a bike and starting with a back-flip
To make it short: There is no standard way to do this in SNAP, it probably requires a lot of mathematical, physical knowledge and advanced coding to achieve your aim. Maybe I’m wrong and someone can suggest an easier way to do it but I don’t think this can be done from scratch by pressing some buttons, sorry.
Thanks @ABraun. I agreed to your point. I have changed my mind of applying cumulative density function transformation as it seems an extensive task. After realizing, I started working on water cloud model. For the same, I need two things:
- Leaf Area Index: MODIS already have a product for LAI.
- Normalized Plant Water Content: It is related to NDWI.
Is there a way to estimate NDWI from microwave data? If yes, please address the procedure and requirements.
Hello sir, I hope you are well.
please I would like to know how to find the parameters A and B to apply the model ‘Water Cloud Model’.