Plotting: McIDAS and sort of natural colour images

I use a range of RGB products – most of these are pretty standard  in the meteorology with satellite community.  The information in red / green and blue is chosen to maximise the geophysical information avaliable.

But sometimes what we want is a nice image that looks similar to what a human eye might see from space. On most meteorological satellites we don’t have red, green and blue. The most common combination I use is red = 1.6 micron, green = 0.8 micron and blue = 0.6 micron. Which gives brownish desserts, dark seas, green vegetation, white low clouds and cyan ice clouds.

Simon Proud @simon_rp84  suggests  using: red =0.6 micron x 2.2 + 0.8 micron x 2.5 + 1.6 micron x 1.3,  green =0.8 micron and , blue = 0.6 micron.  This gives whiter shades for clouds. Like this:

test

In McIdas other RGBs can be made. Here is how I implemented Simon’s recipe.  Here is my Jython code (Tools -> Formulas – Jython library – add to local library). Here is the jython:

# Simon's more natural colour RGB
#r=0.6u*2.2+0.8u*2.5+1.6u*1.3, 
#g=0.8u, 
#b=0.6u 
# Natural colours, linear enhancement btw minrefl-maxrefllower-upper limit
# units: % Refl
# minrefl	lower limit of reflectivity range
# maxrefl	upper limit of reflectivity range
def SP_NCOL_RGB(ch06,ch08,ch16):
 min=0
 max1=95*2.2+100*2.5+100*1.3
 max2=95
 max3=95
 red = rescale((ch06*2.2)+(ch08*2.5)+(ch16*1.3),min,max1,0,255)
 grn = rescale(ch08,min,max2,0,255)
 blu = rescale(ch06,min,max3,0,255)
 return combineRGB(red,grn,blu)

Note that spaces matter to python

Clipboard01

and corresponding formula (add as a formula)

SP_NCOL_RGB(D1[label=VIS0.6,pattern=0\.6],D2[label=VIS0.8,pattern=0\.8],D3[label=NIR1.6,pattern=1\.6])

I have the formula set to only display an RGB composite.

Clipboard01

Thanks Simon for the recipe.

 

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