MODIS

Introduction

• About MODIS

There are two MODIS Satellites, Aqua and Terra.

MODIS Aqua

Sea surface color, temperature, and derivative images

• http://oceancolor.gsfc.nasa.gov/PRODUCTS/

The following examples assume you want to import multiple images at once, and so use shell scripting loops.

SST (Level 3)

Get the data

• Get data in HDF4 format from http://oceancolor.gsfc.nasa.gov/PRODUCTS/L3_sst.html

File names look like: A20023532002360.L3m_8D_SST_4

key:

* A: Aqua
* 2002: Year at start
* 353: Julian day at start
* 2002: Year at end
* 360: Julian day at end
* L3m: Level 3 data, mapped
* 8D: 8 day ensemble
* SST: Sea Surface Temperature product
* 4: 4.6km pixel size (8640x4320 image, 2.5 minute resolution)

Decompress

bzip2 -d A20023532002360.L3m_8D_SST_4.bz2

Check

• Use GDAL's gdalinfo tool to view the HDF file's meta-data.

gdalinfo A20023532002360.L3m_8D_SST_4

Example of SST metadata from gdalinfo:

 Parameter=Sea Surface Temperature
 Measure=Mean
 Units=deg-C
 Scaling Equation=(Slope*l3m_data) + Intercept = Parameter value
 Slope=0.000717185
 Intercept=-2
 Scaled Data Minimum=-2
 Scaled Data Maximum=45
 Data Minimum=-1.999999
 Data Maximum=37.06
Subdatasets:
 SUBDATASET_1_NAME=HDF4_SDS:UNKNOWN:"A20023532002360.L3m_8D_SST_4":0
 SUBDATASET_1_DESC=[4320x8640] l3m_data (16-bit unsigned integer)
 SUBDATASET_2_NAME=HDF4_SDS:UNKNOWN:"A20023532002360.L3m_8D_SST_4":1
 SUBDATASET_2_DESC=[4320x8640] l3m_qual (8-bit unsigned integer)

Extract

Because the HDF4 data file contains multiple data arrays we need to extract the one we are interested in. In this example we extract the "data" layer but not the "quality" layer, and output a GeoTIFF file using GDAL's gdal_translate tool, using the appropriate SUBDATASET NAME:

for file in *SST_4 ; do
  gdal_translate HDF4_SDS:UNKNOWN:"$file":0 ${file}_unproj.tif
done

Import

• Create a simple XY GRASS location and mapset
• Import the imagery and set image bounds and resolution:

for file in *unproj.tif ; do
  BASE=`basename $file _unproj.tif`
  echo "map: $file"
  r.in.gdal in=$file out=$BASE
  r.region $BASE n=90 s=-90 w=-180 e=180
done

• Check:

g.list rast
r.info A20023532002360.L3m_8D_SST_4

• Convert the simple XY location into a Lat/Lon location

(work in progress: we badly need a cleaner method)

Integerized Sinusoidal (ISIN) projection, Plate carrée, or WGS84? -- Does it matter with data at the km scale?

Modify the location's projection settings with g.setproj? (run that from the PERMANENT mapset)

What I did: after import move the mapset dir into a world_ll location, then edit the mapset's WIND file and change the proj: line from 0 to 3. (0 is unprojected, 3 is LL) You also have to edit each file header in the $MAPSET/cellhd/ directory.

• Remove NULLs

for map in `g.mlist rast pat=*SST_4` ; do
  r.null $map setnull=65535
done

• Convert to temperature in degrees C. Note slope and intercept are taken from the image's metadata using gdalinfo.

for map in `g.mlist rast pat=*SST_4` ; do
  echo "$map"
  g.region rast=$map
  #r.info -r $map
  Slope=0.000717185
  Intercept=-2
  r.mapcalc "${map}.degC = ($Slope * $map) + $Intercept"
  r.support "${map}.degC" units="deg-C"
  #r.colors "${map}.degC" color=bcyr  # fairly nice
done

• Check:

r.info A20023532002360.L3m_8D_SST_4.degC
r.univar A20023532002360.L3m_8D_SST_4.degC

Set colors

Although the standard bcyr color map looks nice, for a very nice image we can use Goddard's OceanColor palettes from http://oceancolor.gsfc.nasa.gov/PRODUCTS/colorbars.html

Those are given in 0-255 range, the imported HDF4 data is 0-65535, and the converted temperatures are -2-45 deg C.

• Use the UNIX awk tool to convert each color rule with the same scaling function we used to convert the data, and save it to a rules file:

# scale 0-255 to 0-65535 and then convert to temperature values
echo "# Color rules for MODIS SST" > palette_sst.gcolors
Slope=0.000717185
Intercept=-2
cat palette_sst.txt | grep -v '^#' | \
  awk -v Slope=$Slope -v Intercept=$Intercept \
    '{ printf("%f %d:%d:%d\n", \
        (Slope * (($1 +1)^2 -1) + Intercept), $2, $3, $4)}' \
  >> palette_sst.gcolors
echo "nv black" >> palette_sst.gcolors
# better: edit last rule to be 45.000719 206:206:206

The processed color rules file is here: [1]

• Apply color rules to imported maps:

for map in `g.mlist rast pat=*SST_4` ; do
  r.colors "${map}.degC" rules=palette_sst.gcolors
done

Chlorophyll-a (Level 3)

Get the data

• Get data in HDF4 format from http://oceancolor.gsfc.nasa.gov/PRODUCTS/L3_chlo.html

File names look like: A20023352002365.L3m_MO_CHLO_4

key:

* A: Aqua
* 2002: Year at start
* 335: Julian day at start
* 2002: Year at end
* 365: Julian day at end
* L3m: Level 3 data, mapped
* MO: One month ensemble
* CHLO: Chlorophyll a concentration product
* 4: 4.6km pixel size (8640x4320 image, 2.5 minute resolution)

Decompress

bzip2 -d A20023352002365.L3m_MO_CHLO_4.bz2

Check

• Use GDAL's gdalinfo tool to view the HDF file's meta-data.

gdalinfo A20023352002365.L3m_MO_CHLO_4

Example of CHLO metadata from gdalinfo:

 Parameter=Chlorophyll a concentration
 Measure=Mean
 Units=mg m^-3
 Scaling=logarithmic
 Scaling Equation=Base**((Slope*l3m_data) + Intercept) = Parameter value
 Base=10
 Slope=5.813776e-05
 Intercept=-2
 Scaled Data Minimum=0.01
 Scaled Data Maximum=64.5654
 Data Minimum=0.002637
 Data Maximum=99.99774

Import

• Create a simple XY GRASS location and mapset
• Import the imagery and set image bounds and resolution:

for file in A*_MO_CHLO_4 ; do
  echo "map: $file"
  r.in.gdal in=$file out=$file
  r.region $file n=90 s=-90 w=-180 e=180
done

• Check:

g.list rast
r.info A20023352002365.L3m_MO_CHLO_4

• Convert the simple XY location into a Lat/Lon location

(work in progress: we badly need a cleaner method)

Integerized Sinusoidal (ISIN) projection, Plate carrée, or WGS84? -- Does it matter with data at the km scale?

Modify the location's projection settings with g.setproj? (run that from the PERMANENT mapset)

What I did: after import move the mapset dir into a world_ll location, then edit the mapset's WIND file and change the proj: line from 0 to 3. (0 is unprojected, 3 is LL) You also have to edit each file header in the $MAPSET/cellhd/ directory.

• Remove NULLs

for map in `g.mlist rast pat=*MO_CHLO_4` ; do
  r.null $map setnull=65535
done

• Convert to chlorophyll a concentration. Note slope and intercept are taken from the image's metadata using gdalinfo.

for map in `g.mlist rast pat=*MO_CHLO_4` ; do
  echo "$map"
  g.region rast=$map
  #r.info -r $map
  Slope=5.813776e-05
  Intercept=-2
  r.mapcalc "${map}.chlor_A = 10^(($Slope * $map) + $Intercept)"
  r.support "${map}.chlor_A" units="mg m^-3"
  #r.colors -e "${map}.chlor_A" color=bcyr  # :-(
done

• Check:

r.info A20023352002365.L3m_MO_CHLO_4.chlor_A
r.univar A20023352002365.L3m_MO_CHLO_4.chlor_A

Set colors

The chlorophyll maps are logarithmic which poses some challenges to rendering nice colors. (the unconverted import image displays nicely with a linear color map) We can use Goddard's OceanColor palettes from http://oceancolor.gsfc.nasa.gov/PRODUCTS/colorbars.html

Those are given in 0-255 range, the imported HDF4 data is 0-65535, and the converted chlorophyll a concentration is 0-65 mg/m^3.

• Use the UNIX awk tool to convert each color rule with the same exponential function we used to convert the data, and save it to a rules file:

# scale 0-255 to 0-65535 and then convert to chlor-a values
echo "# Color rules for MODIS Chloropyll-a" > palette_chl_etc.gcolors
Slope=5.813776e-05
Intercept=-2
cat palette_chl_etc.txt | grep -v '^#' | \
  awk -v Slope=$Slope -v Intercept=$Intercept \
    '{ printf("%f %d:%d:%d\n", \
        10^((Slope * (($1 +1)^2 -1)) + Intercept), $2, $3, $4)}' \
  >> palette_chl_etc.gcolors
echo "nv black" >> palette_chl_etc.gcolors
# better: edit last rule to be 64.574061 100:0:0

The processed color rules file is here: [2]

• Apply color rules to imported maps:

for map in `g.mlist rast pat=*MO_CHLO_4` ; do
  r.colors "${map}.chlor_A" rules=palette_chl_etc.gcolors
done

Removing holes

Cloud cover or technical problems often causes there to be some gaps in the data. GRASS has a r.fillnulls module which uses spline interpolation to fill in these small gaps. These holes are left in the data for a reason, so check if it is appropriate for your analysis to remove them.

The first step is to create a mask of the sea, i.e. the area which it is valid to interpolate over. To do this we overlay a time series of satellite images and see which areas are permanently no data.

• Overlay all maps; the data will be mixed up and bogus but the spatial coverage will be informative

r.patch in=`g.mlist -r rast pat='A200.*4$' sep=,` out=all_sat_images_stacked

• Create a 0/1 mask layer showing areas of sea, and label categories

r.mapcalc "sea_mask = if( isnull(all_sat_images_stacked), 0, 1)"
r.category sea_mask rules=- << EOF
  0:Land
  1:Sea
EOF

g.remove all_sat_images_stacked

The next step is to perform the filling operation. To speed up processing, we first temporarily fill known land areas with a value as there is no point interpolating over those gaps. To avoid interpolation overshoots, smear coastal values slightly inland before interpolation. After the holes are filled we reapply the mask to again remove those areas. As the r.fillnulls step can take some time, it is advisable to use g.region to zoom into an area of interest first.

for map in `g.mlist rast pat=*SST_4.degC` ; do
   r.grow in=$map out=${map}.grown radius=5
   r.mapcalc "tmp_landfill = if( isnull($map) && sea_mask == 1, \
     null(), if( isnull(${map}.grown), 0, ${map}.grown) )"
   r.fillnulls in=tmp_landfill out=${map}.filled_tmp
   g.copy sea_mask,MASK
   r.mapcalc "${map}.filled = ${map}.filled_tmp"
   g.remove tmp_landfill,${map}.grown,${map}.filled_tmp,MASK
done