Hey Quinten,
We are working on a project to map SPM in coastal waters with very high SPM values. We have noticed that there was a lot of masking in the SPM Nechad product (based on Sentinel-2 imagery) that was not present in other layers. We have figured out that the masking is on line 361 of acolite_l2.py
"cur_mask = np.where(cur_data >= (setu['nechad_max_rhow_C_factor'] * C_Nechad))"
Is this masking step always necessary? Is it a limitation of the Nechad algorithm?
For reference I am noticing this on tiles T20TMR and T20TLR on pretty much every image date
Masking of High Rhos for Nechad SPM
Masking of High Rhos for Nechad SPM
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Re: Masking of High Rhos for Nechad SPM
Hi Kristen
I added the nechad_max_rhow_C_factor as the Nechad algorithm is best applied in the linear rho_w:Turbidity regime, and that is assumed to be up to 0.5*C. For reflectances close to the C asymptote larger errors occur, and you should switch to a longer wavelength for retrieving Turbidity. This limitation is not present in the original Nechad paper, so feel free to set nechad_max_rhow_C_factor=1.0, in effect disabling this masking. (At least having this new masking makes you think about the algorithm design!
)
I hope this is clear!
Quinten
I added the nechad_max_rhow_C_factor as the Nechad algorithm is best applied in the linear rho_w:Turbidity regime, and that is assumed to be up to 0.5*C. For reflectances close to the C asymptote larger errors occur, and you should switch to a longer wavelength for retrieving Turbidity. This limitation is not present in the original Nechad paper, so feel free to set nechad_max_rhow_C_factor=1.0, in effect disabling this masking. (At least having this new masking makes you think about the algorithm design!
)I hope this is clear!
Quinten