Would you like to share, do you have any results obtained?
didn’t work on it since then due to other priorities. But it is planned for the upcoming months.
Thank for an answer! Please let me know when you will recieve any results.
I would like to know that still, it’s not possible to calculate Radar Vegetation Index using Sentinel-1 data?
a dual-pol alternative is suggested here: 267020154_Radar_Vegetation_Index_as_an_Alternative_to_NDVI_for_Monitoring_of_Soyabean_and_Cotton
Thank you for your prompt reply.
According to the above-mentioned paper where it assumed that ϭHH ≈ ϭVV and since Sentinel-1 has VH and VV polarizations so can we modify the equation as follows?
Yes, this is an option. Different scatterers might be targeted by VV but in general, the index is about the contribution of volume scattering which is indicated by cross-polarized response.
Thank you so much.
I’m trying to create a RVI from S1 data by using Charbonneau’s equation from the paper above. But instead of getting a value between 0-1 I get one between 0-4
I could just divide the result with 4 to get a band with values between 0-1, but I’m curious if I’m meant to do that or if I’m doing something wrong.
I replaced HV with VH and HH with VV in the equation
I’ve split this thread into two. Because it went into a different direction as the initial question.
Can I see your research results?Thank you！
Sorry but I can’t find the splitteds posts, could you link these?
That’s the other one:
I’ m trying to get RVI values by HV and VV polarization GRDH data of a two years (2017-2018) time lapse on meddle Portugal. Now, that’s the result of RVI=4*VH/VV+VH formula
Values range is min=1,44 max=1,7, is it possible?
Starting values are the means dB values of the whole parcel pixels, ones of them are showed below
this parcel is an Eucalyptal forestry, and we would to detect the cut date, which has supose to be in 2017, using this data. Datas were getted with SNAP and S1B_IW_GRDH kind products by this process Calibration with sigma0band.
So, could I use RVI in this way?
Where I can find _Charbonneau, F., Trudel, M., and Fernandes, R. 2005. Use of Dual Polarization and MultiIncidence SAR for soil permeability mapping. In: Advanced Synthetic Aperture Radar (ASAR) 2005, St-Hubert, Canada. _
and _Kim, Y., and van Zyl, J. 2004. Vegetation effects on soil moisture estimation. In Geoscience and Remote Sensing Symposium, 2004. IGARSS ‘04. Proceedings. 2004 IEEE International, Vol. 2, pp. 800-802. _ used here to justify RVI?
Thanks for your attention
one thing to note is that if you convert to dB scale, your data is in logarithmic scale. That means, you cannot divide as initially given in the formula. I tried to translate it back to power scale (here), but I am not 100% sure if it is mathematically correct.
I recommend skipping the LinearToFromdB step instead and then apply the formula as you mentioned it: RVI=4VH/VV+VH
I have done the RADAR VEGETATION INDEX process by using the formula 4VH/(VV+VH), after that I need to extract RVI value for my different fields,can you please help me to extract RVI values for my fields, and also I have done RVI for the time series images, now I need to plot RVI Vs time series images, I’m requesting you to please suggest me the process.
like this I got some time series images, please help me to do the process…
many thanks in advance.
you can either digitize polygons for your fields or place pins. In a second step, you use the time-series plugin to extract and display temporal measures as described here: Time Series (Temporal) of NDVI
I’m trying to do RVI for time series images, by using RVI=4* VH/VV+VH this formula, I’m getting some outputs, but I don’t know whether it is right or wrong. Sir kindly please help me, how we will calrify that.
is the RVI will come same as NDVI??
thanks in advance.
It will not completely replace NDVI because it is based on surface characteristics (mainly the ratio between cross-polarization and co-polarization) instead of the chlorophyll concentration.
But similar to the NDVI higher values indicate higher vegetation presence. It is quite a relative measure with no distinct minimum or maximum.