Exercises with Level-2 satellite data

Transcription

1 Exercises with Level-2 satellite data Mati Kahru WimSoft, & Scripps Institution of Oceanography UCSD, La Jolla, CA , USA 24-Jan-15 1

2 Processing Level: Level 0 - raw radiance counts from sensor, spacecraft and instrument telemetry data Level 1A -Level-0 data with appended calibration and navigation data, instrument and spacecraft telemetry Level 1B calibrated radiances at sensor level (TOA) Level 2 - geophysical values, derived from Level-1A by applying sensor calibration, atmospheric corrections, and bio-optical algorithms, downloadable from NASA Level 3 - Binned (time and space), SMI (Standard Mapped Image) mapped to Equidistant Cylindrical projection Level 4 After application of models (e.g. Net Primary Production, Export Flux of Carbon) to L3 data 2

3 Advantages in using L2 data: Full (unmapped) spatial resolution a major advantage! All variables included or can be computed from Rrs Disadvantages in using L2 data: Unmapped images are hard to compare with anything but point data (match-ups) Contain data with various quality; some lowquality data must be removed Large data volume 3

4 Evaluate sample OC L2 files Find C:\JES\2015\tmp\Good, sort files by size, load any 2 large HDF4 files (A*=MODISA, T* = MODIST, V*=VIIRS) Load datasets chlor_a, convert to Log-Chl; stretch colors to Explain the different shape and size of features! Apply Geo-Grid! 4

5 Evaluate sample OC L2 files Find C:\Images\MODISA\L2 and 2 MODISA *OC.x.hdf files Load datasets chlor_a and l2_flags (convert chlor_a to Log-Chl) Explain the missing data in the center, check l2_flags! 5

6 Before ordering data, decide your area boundaries! Order data (in web browser) Download data (cd tmp, dload.bat) Define your standard map (projection) Remap to standard projection Merge between multiple sensors Composite over 1-day, 5-day, 15-day, monthly, annual time periods 6

7 NASA Ocean Color: Data Access L2: use Level-1&2 browser to order and download individual L2 datasets (granules or full swaths) After order is fulfilled, use wget to download ordered L2 data. L3: Use ftp or wget to download ALL global mapped L3 (SMI) data; No need to browse! 7

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9 51.5 for JES 125 for JES 145 for JES 32 for JES 9

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12 Contents of L2 OC files MODISA L2: SeaWiFS L2: (different Rrs, etc) VIIRS L2: (different Rrs, etc) Open sample files, load SDS, explain meaning and units using attributes! 12

13 Open sample L2 OC files MODISA L2: C:\Sat\JES\2015\tmp\A2015*.L2_LAC_OC.x.hdf - load chlor_a, read units and other attributes! VIIRS L2: C:\Sat\JES\2015\tmp\V2015*.L2_NPP_OC.x.hdf - load chlor_a, read units and other attributes! MODIST L2: C:\Sat\JES\2015\tmp\T2015*.L2_LAC_OC.x.hdf - load chlor_a, read units and other attributes! 13

14 VIIRS OC L2 Contents of L2 IOP files VIIRS IOP L2 Open sample files, load SDS, explain meaning and units using attributes! 14

15 Contents of L2 SST files MODISA and MODIST SST L2 file: VIIRS SST L2 file: missing Open sample files, load SDS, explain meaning and units using attributes! 15

16 L2 data needs to be remapped to standard map,, composited, annotated (e.g. color scale). Options: 1. Manually with WIM 2. With wam_series 3. With WAM command line programs In all cases you need to define your standard map projection, then remap and composite L2 datasets to the standard projection, overlay coastlines, color scale, Lat-Lon grid, etc. 16

17 1.Manually with WIM See exercise 3.17 Using MODIS-Aqua Level-2 images in Exercises_WIM_WAM.pdf. Install Google Earth. Follow exercise 3.17 for your area of interest! Download some recent Aqua L2 granules for your area of interest. 2. With wam_series See exercise 4.1 wam_series in Exercises_WIM_WAM.pdf. Automate remapping and annotating of L2 data with wam_series. First generate a standard map projection of your area in WIM. Choose a Linear projection with File-New then remap all L2 chl-a datasets to that projection. 3. With WAM command line programs See Merging Level-2 Chl-a and SST data in Exercises_Merging_L2_Chl_and_SST.pdf (needs update!) 17

18 Make a standard map image (projection) Different options: use File-New in WIM Create coastlines (Geo-Get Map Overlay, ) with pixel value 1, i.e. black) using coast_full.b Fill land with pixel value 255 (white) (may need to manually fill gap between coastline and edge) with Edit-Draw 18

19 Make a standard map image (projection) Create color scale: Set the scaling to Log-Chl, stretch color range to (= 0.05 to 10 mg Chla m -3 ) add color bar in WIM with View-Annotate Pick Custom ticmarks for Chl: 0.05,0.1,0.2,0.5,1,2,5,10 For SST: 15,16,17,18,19,20,21,11,23,24 Note the place of image annotation, above, left of the color scale, set X=411, set Y=28 in bat files 19

20 Follow the exact directory structure for L2 processing scripts to work! C:\Sat\%Area%, e.g. C:\Sat\JES Can include standard maps here, e.g. for Chl and SST C:\Sat\%Area%\%Year%, e.g. C:\Sat\JES\2015 Script (batch) files here, e.g. sort_2015.bat, sst_2015.bat, chl_2015.bat C:\Sat\%Area%\%Year%\tmp, e.g. C:\Sat\JES\2015\tmp Download files here! extract scripts here, e.g. dload.bat, extract.bat Sequence of commands: 1. dload 2. extract 3. cd..; sort_ sst_ chl_ others 20

21 0. Download necessary data files, e.g. Chl and/or SST Either download from or copy/move to C:\Sat\JES\2015\tmp Assume that our area of interest is JES (Japan East Sea) or replace with your area of interest Have a directory structure like that: C:\Sat\JES 2013\ 2014\ 2015\ \tmp This is where you download L2 files! Have the MAP in directory tree ABOVE the year, e.g. in C:\Sat\JES 21

22 Start in the tmp folder: cd C:\Sat\JES\2015\tmp dload.bat; extract.bat;..\ ; sort_2015.bat After processing with sort_2015.bat end with: A2015\ A2015_SST\ T2015\ T2015_SST\ V2015\ A2015_chl_day A2015_sst_day T2015_chl_day T2015_sst_day V2015_chl_day After processing with chl_2015.bat ; after sst_2015.bat C2015_chl_day M2015_sst_day C2015_chl_5day M2015_sst_5day C2015_chl_15day M2015_sst_15day C2015_chl_month M2015_sst_month 22

23 1. Open command prompt and cd to the folder with http_manifest.txt cd C:\Sat\JES\2015\tmp 2. Download ordered files with dload.bat command (using wget) 3. Untar/Uncompress downloaded files extract.bat command 4. cd to the folder above (with sort_2015.bat) cd.. 5. Process L2 data (screen, remap, daily composite) sort_ Process Chl data (merge 3 sensors, composite 5, 15 days, month) chl_ Process SST data (merge 2 sensors, composite 5, 15 days, month) sst_

24 Can also use wam_series for simple remapping, overlaying, etc not as powerful 24

25 Batch processing using WAM commands: sort_2015.bat wam_screen_mask tmp\*.nc %MAPSEA% wam_l2_map A%YEAR%\4\Good\A*L2* %MAP% %MAPCHL% %X4% %Y4% lut=%lut% Processing Chl using WAM commands: chl_2015.bat Merge sensors, e.g. VIIRS + MODIST => U; MODISA + U => N; MERIS? wam_composite_2sensors T%YEAR%_chl_day\T%YEAR%%DAY%*mapped.hdf V%YEAR%_chl_day\V%YEAR%%DAY%*mapped.hdf overlay=%map4% xpos=%x4% ypos=%y4% lut=%lut% Make 5-day, 15-day composites wam_composite_2x C%YEAR%_chl_day\?%YEAR%%DAY%*mapped.hdf 5 overlay=%map4% xpos=%x4% ypos=%y4% lut=%lut% cmin=%start% cmax=%end% Make monthly composites: wam_composite_month C%YEAR%_chl_day\*mapped.hdf overlay=%map4% xpos=%x4% ypos=%y4% lut=%lut% cmin=%start% cmax=%end% 25

26 Results for 3-November-2015: MODISA: A2015_chl_day Which one is right? VIIRS: V2015_chl_day 26

27 Batch processing SST using WAM commands: sst_2015.bat Merge MODISA + MODIST => M wam_composite_2sensors A%YEAR%_sst_day\A%YEAR%*mapped.hdf T%YEAR%_sst_day\T%YEAR%*mapped.hdf overlay=%map4% xpos=%x4% ypos=%y4% lut=%lut% cmin=%start% cmax=%end% Make 5-day, 15-day composites wam_composite_2x M%YEAR%_sst_day\M%YEAR%*mapped.hdf 5 overlay=%map4% xpos=%x4% ypos=%y4% lut=%lut% cmin=%start% cmax=%end% Make Monthly composites wam_composite_month M%YEAR%_sst_day\M%YEAR%*mapped.hdf overlay=%map4% xpos=%x4% ypos=%y4% lut=%lut% cmin=%start% cmax=%end% 27

28 Other compositing programs: wam_composite_2x (make composites of N days) wam_composite_last (make composites of LAST N days) wam_composite_list (make composites using a LIST of images) wam_composite_running (make running composites of N days) 28

29 Results in: C2015_chl_month M2015_sst_month 29