oh you helped me a lot thank you now it is working
and what is the problem of this
what coordinate reference system did you select?
Your coordinates look like UTM to me, at least no geographical coordinates. If you select the wrong reference system during the import SNAP will place the points at the wrong location. Depends on how you collected the points and in which coordinate system.
you are right they are in UTM
when i change it to UTM like you told me it is loaded and I want to extract backs catering coefficient and incidence angle for these points and make a correlation graph will you help me with that?
to be able to make a correlation, your table must include the soil moisture measurements already before the input. Once you have imported them, you can start the correlative plot view tool which extracts the raster values from the selected data and plots them against the column of your choice (moisture in your case).
It is probably better to continue here: Backscatter values extracted and make regression analysis
Hi ABraun I was trying to make a CSV file format to my ground soil moisture data based on SNAP as you told me but when i import it SNAP is rejecting it i have tried to make it look like yours but i don’t get the problem will you help me with this please
please upload it here and I will have a look what is wrong with it.
am sorry but what do you mean by " around each line:
sorry, I meant the quotation marks
not good:
good:
Hi sir！there is the same problem that I can got the mapping using S1-SLC data with snap like this :
but I can’t export the image with high pixl ,like this
,I don’t konw how to solve it and to get the mapping like this
,colud you help me ?
you don’t need to export the raster as a tif file, you can open the img file in the data folder of your DIMAP product in any other GIS.
It differs how you define the no-data value in a GIS: S1 tile in QGIS: Zero values
If you post external materials, such as this map, please include the source. This not only good practice regarding copyright regulations but also gives others the chance to understand how it was created. Simply writing “I want a map like this” is quite challenging 
Thanks your advices.
I would say the central question is how you derived the Sm band from the radar data. Masking by NDVI values is a good idea to limit the prediction to non-vegetated areas.
I implemented the proposed model into the image using band math operation
many approaches were suggested and discussed in this topic, could you pleas specify?
sounds good - sorry for asking. I just already saw in here that dubious formulas of other studies were simply copied and applied to Sentinel-1 images of other regions and wanted to give honest feedback.
So if the regression has an acceptable coefficient of determination, masking invalid areas with NDVI thresholds looks good to me.
Thank you…