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Sentinel 1 interface #5

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Sentinel 1 interface #5

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jgomezdans
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Merging into master the SAR stuff. So far, what we have here is

  1. The class to read in @McWhity SAR data format in a way that's useful for KaFKA. Needs a good treatment of observational uncertainty (currently, it's a made up number).
  2. The observation operator for SAR (currently, a simple water cloud model). As an interesting note, we're not using an emulator for this one, as it's quite simple. Further, we haven't bothered with calculating the Hessian for this (it's left as an exercise for the reader), so I have had to ignore the bits in the code where the Hessian of the observation operator is called for

For a single image, the code works (in the sense that it is able to closely reproduce the observations by changing the model parameters, and these are within the expected values). I haven't yet checked the uncertainty calculations.

José Gómez-Dans and others added 19 commits January 9, 2018 17:48
The new observation class allows one to hopefully read in a single
"pixel" of BHR data over time to do some quick tests. In essence,
it just needs a set of dates, albedos and a gp (this bit is missing)
It then needs to pack the data into a BHR structure.
Added SAR forward model, as well as code that crates the linear
approximation to the SAR model. The SAR observations have yet to
be included in the library. Obviously, untested code.
Currently using NC, but might need changing to GeoTIFF. Might also
need reprojecting/resampling to fit state grid etc. Fill value is
currently set to 0, which isn't great as values in file span from
-ve to +ve. Also data in dB probably needs to be converted to
linear scale to work with RT model.
The class seems to work, but untested within KaFKA
Current version has had SAR coupled. One thing that I didn't
envisage was that we need the Hessian of the RT model to fully
calculate the non-linear terms in the uncertainty, and in this
case, we do not have it available, so currently, the system breaks
when it tries to calculate the Hessian correction using the GP's
Hessian method... Other than that...

1. Observations are read and warped to match the state grid
2. The observation operator results in a linearised problem
3. The linear problem is solved
4. The linear part of the uncertainty is solved
5. It then crashes, but we know why!

@McWhity This is where we left it
Fixed issue with gradient calculation. Inversion of SAR data for
one single time step has now been tested. The results appear
plausible (e.g. the observations are closely fitted and the LAI
and SM values are within the expected ranges). I haven't yet
tested whether the uncertainty is sensible, but want to merge this
back into master.
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