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Use the function modisin to create a "quicklook" TeraScan TDF dataset from a pass on the pass disk. Process only channels 1, 3, and 4, which you will use to view as a true-color image. Accept the default response of bowtie_filter=yes to correct for the bowtie effect of the MODIS sensor. MODIS pixels become taller and wider as they approach the edge of the scan line, causing oversampling into pixels on adjacent scan lines. The bowtie correction subsamples the data so that there is only one measurement for each spot on the earth. (Compare this with data that has not been corrected.) Accept the defaults for all other prompts. We will adjust some of the other responses later in the exercise. (num_scans is the number of 1000m lines x 10, 500m lines x 20, and 250m lines x 40 to be processed.) The output data is uncalibrated and earth-located. |
scrum% modisin output file : char(255) ? t1.070202.1930.tdf on_pass_disk : char( 3) ? [yes] pass_number : int ? [2] channels : int ( 36) ? [3 7 10 14 18 21 25 28 32 36] 1 3 4 delta_500m : char( 1) ? [1] delta_250m : char( 1) ? [1] bowtie_filter : char( 3) ? [yes] use_master : char( 3) ? [no] start_time : char( 15) ? [00:00:00] num_scans : int ? [580] start_sample : int ? [1] num_samples : int ? [1354] Pass Start: 16253 70229.052 - 664 - Day *** wcnt 0 - dt 1.477 *** seqflag 01 - samples 1354 Packet count 1530 packet 39 >*> 4 Scan error first: seq 1 - 2.954 seconds missing <*< 5 Scan error last: seq 1 - 2 lines written >*> 6 Scan error first: seq 1 - 4.431 seconds missing <*< 8 Scan error last: seq 1 - 3 lines written *** wcnt 10 - dt 1.477 *** seqflag 01 - samples 1354 *** wcnt 20 - dt 1.477 *** seqflag 01 - samples 1354 *** wcnt 30 - dt 1.477 *** seqflag 01 - samples 1354 . . . *** wcnt 370 - dt 1.477 *** seqflag 01 - samples 1354 *** wcnt 380 - dt 1.477 *** seqflag 01 - samples 1354 *** wcnt 390 - dt 1.477 *** seqflag 02 - samples 723 pass-2: 1000M_line final length 3910 pass-2: 500M_line final length 7820 pass-2: 250M_line final length 15640 Attitude( 382): R/P/Y 0.0000/0.0000/0.1290 CADU -to- MODIS packet processing summary Total input CADU frames processed: 908600 Number of CADU containing MODIS data: 844885 Number of usable MODIS packets: 1155997 Packets excluded with bad lengths: 1704 Packets excluded with bad checksums: 5412 Packets retained with repaired times: 21 Packets excluded with bad types: 0 Packets excluded with bad times: 0 Packets excluded by bad time sequence: 0
scrum% contents t1.070202.1930.tdf printout : char( 3) ? [no] Contents of File: t1.070202.1930.tdf Page 1 Dimension Size Coord Scale Offset Variable 1000M_line 3910 y 1 0 1000M_sample 1354 x 1 0 500M_line 7820 y 0.5 -0.25 500M_sample 2708 x 0.5 -0.25 250M_line 15640 y 0.25 -0.375 250M_sample 5416 x 0.25 -0.375 Attribute Type Units Value projection_name string16 sensor_scan satellite string12 terra-1 sensor_name string12 modis pass_date long std_date 2002/07/02 start_time double std_time 19:30:29.052 orb_elem_date long std_date 2002/07/03 Variable Type Units modis_ch01b short modis_ch03b short modis_ch04b short Variable Dimension Size modis_ch01b 250M_line 15640 modis_ch01b 250M_sample 5416 modis_ch03b 500M_line 7820 modis_ch03b 500M_sample 2708 modis_ch04b 500M_line 7820 modis_ch04b 500M_sample 2708
| Run TeraVision in true color (tvis -true). Load all 3 channels. In the Image Combine panel, select RGB and select R=channel 1, G=channel 4, and B=channel 3, and press Render Imagery. Then, enhance the combined image. (Suggestion: use a logarithmic enhancement.) |
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Now, process the pass again, this time responding no to the bowtie_filter prompt. Then look at the contents of this dataset and compare the variable names to those in the first dataset. |
scrum% modisin output file : char(255) ? t1.070202.1930.tdf_nobt on_pass_disk : char( 3) ? [yes] pass_number : int ? [2] 2 channels : int ( 36) ? [3 7 10 14 18 21 25 28 32 36] 1 3 4 delta_500m : char( 1) ? [1] delta_250m : char( 1) ? [1] bowtie_filter : char( 3) ? [yes] n use_master : char( 3) ? [no] start_time : char( 15) ? [00:00:00] num_scans : int ? [580] start_sample : int ? [1] num_samples : int ? [1354]
scrum% contents t1.070202.1930.tdf_nobt printout : char( 3) ? [no] Contents of File: t1.070202.1930.tdf_nobt Page 1 . . . Variable Dimension Size modis_ch01 250M_line 15640 modis_ch01 250M_sample 5416 modis_ch03 500M_line 7820 modis_ch03 500M_sample 2708 modis_ch04 500M_line 7820 modis_ch04 500M_sample 2708
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In TeraVision, compare the results with the data that was bowtie corrected. Display the same channel from each dataset in the same window, zoom in closely on an area near the edge of the image, and note the difference. Click here for an example of an image before and after bowtie correction. |
To limit the size of the output dataset, cut out an area of interest from the data. [Alternative methods for limiting the size of the dataset processed by modisin are subsampling (delta_250 and delta_500), specifying a start time later than the start of the pass, or specifying the number of scan lines/samples to be processed. Reminder: If you specify a new start_time, a time of 18:52:00 is hh:mm:ss; a time of 18:52 is mm:ss and will cause an error in earth-location. (See formats for more on acceptable time formats.)] To do this, first create a master that you will use to limit the data processed to a specific area of interest. |
scrum% master output file : char(255) ? [Master] projection : char( 14) ? rect center_lat : char( 15) ? 47N center_lon : char( 15) ? 128W num_lines : int ? 1000 num_samples : int ? 1000 pixel_width : real ? [1.1132] pixel_height : real ? [1.1132] rotate_angle : real ? [0] move_center : char( 3) ? [no]
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Now, rerun modisin with the master you created. If you answer yes to the full_width prompt, the data will be the full scan width. If you answer no, the data will be just large enough to cover the master. |
scrum% modisin output file : char(255) ? t1.070202.1930.tdf_master on_pass_disk : char( 3) ? [yes] pass_number : int ? [2] channels : int ( 36) ? [3 7 10 14 18 21 25 28 32 36] 1 3 4 delta_500m : char( 1) ? [1] delta_250m : char( 1) ? [1] bowtie_filter : char( 3) ? [yes] use_master : char( 3) ? [no] y master_file : char(255) ? [Master] full_width : char( 3) ? [no] pass-2: Master subset 543 +1308 lines, 134 +1043 samples pass-2: New start time 19:31:49.105
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Now, look at the contents and see how the dimensions of the data have changed. Because the data is still in sensor scan projection, the number of lines and samples will not match the number of lines and samples of the master. |
scrum% contents t1.070202.1930.tdf_master printout : char( 3) ? [no] Contents of File: t1.070202.1930.tdf_master Page 1 Dimension Size Coord Scale Offset Variable 1000M_line 1308 y 1 0 1000M_sample 1043 x 1 133 500M_line 2616 y 0.5 -0.25 500M_sample 2086 x 0.5 132.75 250M_line 5232 y 0.25 -0.375 250M_sample 4172 x 0.25 132.62 Attribute Type Units Value projection_name string16 sensor_scan satellite string12 terra-1 sensor_name string12 modis pass_date long std_date 2002/07/02 start_time double std_time 19:31:50.295 Variable Dimension Size modis_ch01b 250M_line 5232 modis_ch01b 250M_sample 4172 modis_ch03b 500M_line 2616 modis_ch03b 500M_sample 2086 modis_ch04b 500M_line 2616 modis_ch04b 500M_sample 2086
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Use fastreg to remap the data to the master you created earlier. The image will be oriented exactly as the master, and will have exactly the same size as the master [see "Using a Master at Registration"]. Then look at the contents of the dataset, noting the dimensions as well as some of the dataset attributes. |
scrum% fastreg t1.070202.1930.tdf_master t1.070202.1930.r master_file : char(255) ? [Master] include_vars : char(255) ? [] poly_size : real ? [100]
t1.070202.1930.r: modis_ch01b: [ 1, 125] X [ 1, 500] t1.070202.1930.r: modis_ch01b: [ 1, 62] X [ 501, 1000] t1.070202.1930.r: modis_ch01b: [ 63, 125] X [ 501, 1000] t1.070202.1930.r: modis_ch01b: [ 126, 250] X [ 1, 500] t1.070202.1930.r: modis_ch01b: [ 126, 187] X [ 501, 1000] t1.070202.1930.r: modis_ch01b: [ 188, 250] X [ 501, 1000] t1.070202.1930.r: modis_ch01b: [ 251, 375] X [ 1, 500] t1.070202.1930.r: modis_ch01b: [ 251, 312] X [ 501, 1000] t1.070202.1930.r: modis_ch01b: [ 313, 375] X [ 501, 1000] t1.070202.1930.r: modis_ch01b: [ 376, 500] X [ 1, 500] t1.070202.1930.r: modis_ch01b: [ 376, 437] X [ 501, 1000] t1.070202.1930.r: modis_ch01b: [ 438, 500] X [ 501, 1000] t1.070202.1930.r: modis_ch01b: [ 501, 625] X [ 1, 500] t1.070202.1930.r: modis_ch01b: [ 501, 562] X [ 501, 1000] t1.070202.1930.r: modis_ch01b: [ 563, 625] X [ 501, 1000] t1.070202.1930.r: modis_ch01b: [ 626, 750] X [ 1, 500] t1.070202.1930.r: modis_ch01b: [ 626, 687] X [ 501, 1000] t1.070202.1930.r: modis_ch01b: [ 688, 750] X [ 501, 1000] t1.070202.1930.r: modis_ch01b: [ 751, 875] X [ 1, 500] t1.070202.1930.r: modis_ch01b: [ 751, 812] X [ 501, 1000] t1.070202.1930.r: modis_ch01b: [ 813, 875] X [ 501, 1000] t1.070202.1930.r: modis_ch01b: [ 876, 1000] X [ 1, 500] t1.070202.1930.r: modis_ch01b: [ 876, 937] X [ 501, 1000] t1.070202.1930.r: modis_ch01b: [ 938, 1000] X [ 501, 1000] t1.070202.1930.r: modis_ch03b: [ 1, 250] X [ 1, 1000] t1.070202.1930.r: modis_ch03b: [ 251, 500] X [ 1, 1000] t1.070202.1930.r: modis_ch03b: [ 501, 625] X [ 1, 1000] t1.070202.1930.r: modis_ch03b: [ 626, 750] X [ 1, 1000] t1.070202.1930.r: modis_ch03b: [ 751, 875] X [ 1, 1000] t1.070202.1930.r: modis_ch03b: [ 876, 1000] X [ 1, 1000] t1.070202.1930.r: modis_ch04b: [ 1, 250] X [ 1, 1000] t1.070202.1930.r: modis_ch04b: [ 251, 500] X [ 1, 1000] t1.070202.1930.r: modis_ch04b: [ 501, 625] X [ 1, 1000] t1.070202.1930.r: modis_ch04b: [ 626, 750] X [ 1, 1000] t1.070202.1930.r: modis_ch04b: [ 751, 875] X [ 1, 1000] t1.070202.1930.r: modis_ch04b: [ 876, 1000] X [ 1, 1000]
scrum% contents t1.070202.1930.r printout : char( 3) ? [no] Contents of File: t1.070202.1930.r Page 1 Dimension Size Coord Scale Offset Variable line 1000 y 1 0 sample 1000 x 1 0 Attribute Type Units Value projection_name string16 rectangular satellite string12 terra-1 sensor_name string12 modis pass_date long std_date 2002/07/02 start_time double std_time 19:31:50.295 center_lat double std_latitude 47 0.00 N center_lon double std_longitude 128 0.00 W Variable Dimension Size modis_ch01b line 1000 modis_ch01b sample 1000 modis_ch03b line 1000 modis_ch03b sample 1000 modis_ch04b line 1000 modis_ch04b sample 1000
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Note that the data is no longer in sensor scan projection. View the image and apply a lat/lon grid to help you see the difference. |
modisin, master, fastreg, MODIS_overview
Last Update: $Date: 2002/09/10 20:34:13 $