Problems with creation of accurate DEM with S1 images

Hello everyone,

I have a problem with the selection of S1 images (SLC) for the creation of a good DEM. I tried to download many pairs of different images with a short time difference and big baseline between there, but at the end of coregistration i saw that the coherence estimation was not good. The histogram obtained from coherence estimation tool hadn’t 70% of frequency values over 0,4 of coherence values. That isn’t a good result, if i understand well: infact the DEM that i obtained have a lot of errors and is “foggy” in many areas and elvetion values don’t correspond to SRTM DEM elevetions. Now i ask you if there is a quickly method of selection of the images. There is a method to understand if two images are coherent without necessarily having to carry out coregistration first and then the coherence estimation? In the end i want to understand why two images are coherence between there or not, what is the parameters that determinate it?

I have another question, what is the best method to use S1 datas for recognition forestry formations, or what is the best method to improve the forestry cover obtain with S2 datas with S1 products?

Thank you a lot.

First please take a look at this whole thread, the Creation DEM from S-1 is discussed there,

Source of the thread

In general it’s possible to get an idea not mathematical results about the coherence of the study area, in case the study area has less stable objects within the resolution of the SAR images two passes, and highly vegetated or smooth area in this case and expectation of low coherence is first prediction; vise versa, spread of stable objects such as buildings ,… could give an expectation of high coherent images.
But it’s not possible to get a mathematical estimation of the coherence without processing the pair.

It is possible to calculate the Radar Vegetation Index, RVI, this topic is quite discussed in here, and how could be modified the equation,

Radar Vegetation Index

Radar Forest Degradation

Also you could take a look at

Agricultural Applications with SAR Data

Crop type classification

Also it is possible to classify S-1 images (GRD)

LAND COVER MAPPING USING SENTINEL-1 SAR DATA

DEFORESTATION MONITORING WITH SENTINEL-1 Case Study: Paraguay, 2018

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