Using TES to atmospherically correct THEMIS

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{{DavinciWiki_Procedure|This procedure illustrates the method to produce a set of atmospherically corrected [[TES]] emissivity spectra to atmospherically correct [[THEMIS]] data using [[rematm2|rematm2()]]. The TES data must overlap the THEMIS training area and should be downloaded from JMARS as a .txt file. When possible, select pixels from a single ock and from the same season and similar atmospheric opacity as your [[THEMIS]] image.
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{{DavinciWiki_Procedure|This procedure illustrates the method to produce a set of atmospherically corrected [[TES]] emissivity spectra to atmospherically correct [[THEMIS]] data using [[rematm2|rematm2()]].  
 
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The TES data must overlap the THEMIS training region (a compositionally homogenous region, best indicated by THEMIS DCS) and should be downloaded from JMARS as a .txt file. When possible, select TES pixels with the following constraints:
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# Found using Warm surface, High Quality, Clear Atmosphere JMARS TES predefined template
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# From a single ock, preference to ocks less than 7000 (see note below)
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# From the same season as your [[THEMIS]] image (JMARS TES keyword heliocentric_lon +/- 30 degrees Ls)
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# Or, alternatively, from a different season but with similar (+/- 25%) atmospheric opacity (JMARS TES parameters tot_dust/tot_ice) as your [[THEMIS]] image (JMARS TES parameters tau_ice_avg/tau_dust_avg)
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Note: For ocks above 7000, the TES detectors sometimes exhibit an instrumental error that results in a reversal of the dust absorption near 1100 wavenumbers. This could dramatically effect the extracted atmospheric spectrum, so avoid high ocks in general, and check for this absorption if high ocks are all that are available.
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Load your spectral library (downloaded from the ASU [[Spectral Library Tool]] output) with tes 73-point spectra as the output.
 
Load your spectral library (downloaded from the ASU [[Spectral Library Tool]] output) with tes 73-point spectra as the output.
 
  dv> lib = read("your_spec_lib.hdf")
 
  dv> lib = read("your_spec_lib.hdf")
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Use [[sma|sma()]] to model TES atmospherically corrected surface emissivity spectra.
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Use [[sma|sma()]] to model TES atmospherically corrected surface emissivity spectra. #### should be replaced with your ock of interest, located inside the TES structure.
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  dv> spec = sma(tes.####avgcat, lib, atm, wave1 = 200, wave2 = 1305, surface = 1, nn = 1)
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  dv> spec = sma(tes.ock####avgcat, lib, atm, wave1 = 200, wave2 = 1305, surface = 1, nn = 1)
  
  
  
 
Resample the [[TES]] atmospherically corrected data to the same spectral sampling as [[THEMIS]].
 
Resample the [[TES]] atmospherically corrected data to the same spectral sampling as [[THEMIS]].
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  dv> tes73x = make_band(tes73 = 1)
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  dv> surf_em = i2i(spec.rematm, from = 'tes73', to = 'themis')
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dv> surf_em = i2i(spec, from = tes73x, to = 'themis')
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Load [[Standard THEMIS IR Processing with ISIS3|processed THEMIS IR cube]]. Note: Do not use cube with [[rrmnoise_pca|white noise removal]].
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  dv> image = load_pds("I10418002.cub")
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Determine [[rematm2|training region]] pixels for [[Standard THEMIS IR Processing with ISIS3|THEMIS IR cube]] that correspond to TES samples.
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Try using [[display|display()]], cropping the THEMIS training region, then typing 'i' to view pixel range of cropped area.
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  dv> display(do_dcs(image.cube, ignore=-32768))
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[[rematm2|Atmospherically correct]] your [[Standard THEMIS IR Processing with ISIS3|THEMIS IR cube]] emissivity spectra with atmospherically corrected [[TES]] spectra. x and y are your training region bounds (pixels).
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  dv> thm_spec = rematm2(image.cube,surf_em,image.cube[x1:x2,y1:y2])
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thm_spec.surf is your atmospherically corrected THEMIS IR surface emissivity.
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  dv> write(thm_spec.surf,"I10418002.atmcorr.cub",isis3,force=1)
  
  
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Add a discussion of [[rematm2]]
 
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*[[sma]]
 
*[[sma]]
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*[[split_ock]]
 
*[[split_ock]]
 
*[[read]]
 
*[[read]]
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*[[make_band]]
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*[[i2i]]
 
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*[[Standard THEMIS IR Processing with ISIS3]]
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*[[THEMIS deconvolution]]
 
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}}
 
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[[Category:Procedures]]

Latest revision as of 14:31, 13 June 2012

Description

This procedure illustrates the method to produce a set of atmospherically corrected TES emissivity spectra to atmospherically correct THEMIS data using rematm2().

Procedure

The TES data must overlap the THEMIS training region (a compositionally homogenous region, best indicated by THEMIS DCS) and should be downloaded from JMARS as a .txt file. When possible, select TES pixels with the following constraints:

  1. Found using Warm surface, High Quality, Clear Atmosphere JMARS TES predefined template
  2. From a single ock, preference to ocks less than 7000 (see note below)
  3. From the same season as your THEMIS image (JMARS TES keyword heliocentric_lon +/- 30 degrees Ls)
  4. Or, alternatively, from a different season but with similar (+/- 25%) atmospheric opacity (JMARS TES parameters tot_dust/tot_ice) as your THEMIS image (JMARS TES parameters tau_ice_avg/tau_dust_avg)


Note: For ocks above 7000, the TES detectors sometimes exhibit an instrumental error that results in a reversal of the dust absorption near 1100 wavenumbers. This could dramatically effect the extracted atmospheric spectrum, so avoid high ocks in general, and check for this absorption if high ocks are all that are available.


Load your spectral library (downloaded from the ASU Spectral Library Tool output) with tes 73-point spectra as the output. 

dv> lib = read("your_spec_lib.hdf")


Load the 73 channel atmospheric library, ether from the Spectral Library Tool or the example file below. 

dv> atm = read($DV_EX+"/TES_atmlib_73channel.hdf")


Load your TES emissivity data and split the data by orbit using split_ock() so that an assessment can be made of the atmospheric conditions from each orbit. 

dv> tes = split_ock("tesfile.txt")


Use sma() to model TES atmospherically corrected surface emissivity spectra. #### should be replaced with your ock of interest, located inside the TES structure. 

dv> spec = sma(tes.ock####avgcat, lib, atm, wave1 = 200, wave2 = 1305, surface = 1, nn = 1)


Resample the TES atmospherically corrected data to the same spectral sampling as THEMIS. 

dv> surf_em = i2i(spec.rematm, from = 'tes73', to = 'themis')


Load processed THEMIS IR cube. Note: Do not use cube with white noise removal. 

 dv> image = load_pds("I10418002.cub")


Determine training region pixels for THEMIS IR cube that correspond to TES samples. Try using display(), cropping the THEMIS training region, then typing 'i' to view pixel range of cropped area. 

 dv> display(do_dcs(image.cube, ignore=-32768))


Atmospherically correct your THEMIS IR cube emissivity spectra with atmospherically corrected TES spectra. x and y are your training region bounds (pixels). 

 dv> thm_spec = rematm2(image.cube,surf_em,image.cube[x1:x2,y1:y2])


thm_spec.surf is your atmospherically corrected THEMIS IR surface emissivity. 

 dv> write(thm_spec.surf,"I10418002.atmcorr.cub",isis3,force=1)


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