i.atcorr manual, imageryintro: clarify TOAR part; cross links added; keywords added

git-svn-id: https://svn.osgeo.org/grass/grass/trunk@68456 15284696-431f-4ddb-bdfa-cd5b030d7da7

imagery/i.albedo/main.c

@@ -95,6 +95,11 @@ int main(int argc, char *argv[])
     G_add_keyword(_("imagery"));
     G_add_keyword(_("albedo"));
     G_add_keyword(_("reflectance"));
+    G_add_keyword(_("satellite"));
+    G_add_keyword(_("Landsat"));
+    G_add_keyword(_("ASTER"));
+    G_add_keyword(_("AVHRR"));
+    G_add_keyword(_("MODIS"));
     module->description = _("Computes broad band albedo from surface reflectance.");
 
     /* Define the different options */

imagery/i.aster.toar/i.aster.toar.html

@@ -1,9 +1,9 @@
 <h2>DESCRIPTION</h2>
 
 <em>i.aster.toar</em> calculates the Top Of Atmosphere (TOA) reflectance
-for Terra-Aster L1B in the visible, NIR and SWIR bands (9+1 bands) and
+for Terra-ASTER L1B in the visible, NIR and SWIR bands (9+1 bands) and
 brigthness temperature for the TIR bands (5 bands), all from L1B DN values. 
-It is useful after importing your Aster imagery from storage format that
+It is useful to apply after import of original ASTER imagery that
 is generally in standard DN values range.
 
 <p>
@@ -35,7 +35,11 @@ The function is defined in gain_aster.c file.
 <a href="r.in.aster.html">r.in.aster</a>
 </em>
 
-<h2>AUTHORS</h2>
+<p>
+ASTER sensor data download:
+<a href="http://asterweb.jpl.nasa.gov/">ASTER: Advanced Spaceborne Thermal Emission and Reflection Radiometer</a>
+
+<h2>AUTHOR</h2>
 
 Yann Chemin, CSU, Australia
 

imagery/i.aster.toar/main.c

@@ -96,10 +96,11 @@ int main(int argc, char *argv[])
     module = G_define_module();
     G_add_keyword(_("imagery"));
     G_add_keyword(_("radiometric conversion"));
-    G_add_keyword(_("Terra-ASTER"));
     G_add_keyword(_("radiance"));
     G_add_keyword(_("reflectance"));
     G_add_keyword(_("brightness temperature"));
+    G_add_keyword(_("satellite"));
+    G_add_keyword(_("ASTER"));
     module->description =
 	_("Calculates Top of Atmosphere Radiance/Reflectance/Brightness Temperature from ASTER DN.\n");
 

imagery/i.atcorr/README

@@ -38,7 +38,7 @@ See for example ./sensors_csv/ikonos.csv.
 Usage 
   create_iwave.py <csv file>
 
-If generates the filter function as IWave.cpp template from csv file.
+It generates the filter function as an insert for IWave.cpp from the given file.
 
 Note: If the spectral response is null for a frequency, leave it empty
 in csv file. Ex.:

imagery/i.atcorr/i.atcorr.html

@@ -628,9 +628,11 @@ g.region raster=lsat7_2002_40 -p
 
 It is important to verify the available metadata for the sun position which
 has to be defined for the atmospheric correction. An option is to check the
-satellite overpass time with sun position as reported in
-<a href="ftp://ftp.glcf.umiacs.umd.edu/glcf/Landsat/WRS2/p016/r035/p016r035_7x20020524.ETM-EarthSat-Orthorectified/p016r035_7x20020524.met">metadata</a>. For the North Carolina sample dataset, they have also been
-stored for each channel and can be retrieved like this:
+satellite overpass time with sun position as reported in the
+<a href="ftp://ftp.glcf.umd.edu/glcf/Landsat/WRS2/p016/r035/p016r035_7x20020524.ETM-EarthSat-Orthorectified/p016r035_7x20020524.met">metadata</a>
+file (<a href="http://www.grassbook.org/wp-content/uploads/ncexternal/landsat/2002/p016r035_7x20020524.met">file copy</a>; North Carolina
+sample dataset). In case of the North Carolina sample dataset, values
+have been stored for each channel and can be retrieved like this:
 
 <div class="code"><pre>
 r.info lsat7_2002_40
@@ -647,13 +649,23 @@ r.sunmask -s elev=elevation out=dummy year=2002 month=5 day=24 hour=10 min=42 se
 # .. reports: sun azimuth: 121.342461, sun angle above horz.(refraction corrected): 65.396652
 </pre></div>
 
-If the overpass time is unknown, use the <a href="http://www-air.larc.nasa.gov/tools/predict.htm">Satellite Overpass Predictor</a>.
-<p>Convert DN (digital number = pixel values) to Radiance at top-of-atmosphere (TOA), using the
-formula
+If the overpass time is unknown, use the
+<a href="http://cloudsgate2.larc.nasa.gov/cgi-bin/predict/predict.cgi">NASA LaRC Satellite Overpass Predictor</a>.
+
+<h4>Conversion of digital number (DN) to radiance at top-of-atmosphere (TOA)</h4>
+
+For Landsat and ASTER, the conversion can be conveniently done with
+<a href="i.landsat.toar.html">i.landsat.toar</a> or <a href="i.aster.toar.html">i.aster.toar</a>,
+respectively.
+
+<p>
+In case of different satellites, the conversion of DN (digital number = pixel values) to
+radiance at top-of-atmosphere (TOA) can also be done manually, using e.g. the formula
 <div class="code"><pre>
-   L&lambda; = ((LMAX&lambda; - LMIN&lambda;)/(QCALMAX-QCALMIN)) * (QCAL-QCALMIN) + LMIN&lambda;
+# formula depends on satellite sensor, see respective metadata
+L&lambda; = ((LMAX&lambda; - LMIN&lambda;)/(QCALMAX-QCALMIN)) * (QCAL-QCALMIN) + LMIN&lambda;
 </pre></div>
-Where:
+where:
 <ul>
 <li> L&lambda; = Spectral Radiance at the sensor's aperture in Watt/(meter squared * ster * &micro;m), the
       apparent radiance as seen by the satellite sensor;</li>
@@ -664,9 +676,10 @@ Where:
 <li> QCALMAX = the maximum quantized calibrated pixel value (corresponding to LMAX&lambda;) in DN=255.</li>
 </ul>
 
-LMIN&lambda; and LMAX&lambda; are the radiances related to the minimal and maximal DN value, and are reported
-in the metadata file for each image, or in the table 1. High gain or low gain is also reported
-in the metadata file of each Landsat image. The minimal DN value (QCALMIN) is 1 for Landsat ETM+
+LMIN&lambda; and LMAX&lambda; are the radiances related to the minimal and
+maximal DN value, and are reported in the metadata file for each image, or in
+the table 1. High gain or low gain is also reported in the metadata file of each
+satellite image. For Landsat, the minimal DN value (QCALMIN) is 1 for Landsat ETM+
 images (see
 <a href="http://landsathandbook.gsfc.nasa.gov/pdfs/Landsat7_Handbook.pdf">Landsat handbook</a>, see chapter 11),
 and the maximal DN value (QCALMAX) is 255. QCAL is the DN value for every
@@ -688,7 +701,16 @@ need to be replaced with their related values):
 r.mapcalc "lsat7_2002_40_rad = ((241.1 - (-5.1)) / (255.0 - 1.0)) * (lsat7_2002_40 - 1.0) + (-5.1)"
 </pre></div>
 
+Again, the <em>r.mapcalc</em> calculation is only needed when working with satellite data
+other than Landsat or ASTER.
 
+<h4>Creation of parameter file for i.atcorr</h4>
+
+The underlying 6S model is parametrized through a control file, indicated with the
+<em>parameter</em> option. This is a text file defining geometrical and atmospherical
+conditions of the satellite overpass. Below some details:
+
+<p>
 <div class="code"><pre>
 # find mean elevation (target above sea level, used as initialization value in control file)
 r.univar elevation
@@ -730,7 +752,10 @@ optical depth at 550nm.
 
 GRASS Wiki page about
   <a href="http://grasswiki.osgeo.org/wiki/Atmospheric_correction">Atmospheric correction</a>
-<p><em>
+<p>
+<em>
+<a href="i.aster.toar.html">i.aster.toar</a>,
+<a href="i.landsat.toar.html">i.landsat.toar</a>,
 <a href="r.info.html">r.info</a>,
 <a href="r.mapcalc.html">r.mapcalc</a>,
 <a href="r.univar.html">r.univar</a>

imagery/i.atcorr/main.cpp

@@ -433,6 +433,10 @@ static void define_module(void)
 	_("6S - Second Simulation of Satellite Signal in the Solar Spectrum.");
     G_add_keyword(_("imagery"));
     G_add_keyword(_("atmospheric correction"));
+    G_add_keyword(_("radiometric conversion"));
+    G_add_keyword(_("radiance"));
+    G_add_keyword(_("reflectance"));
+    G_add_keyword(_("satellite"));
 
     /* 
        " Incorporated into Grass by Christo A. Zietsman, January 2003.\n"

imagery/i.biomass/i.biomass.html

@@ -8,14 +8,17 @@ Input:
  <li>Light Use Efficiency [0.0-1.0], in Uzbekistan cotton is at 1.9 most of the time.
  <li>Latitude [0.0-90.0], from <em>r.latlong</em>.
  <li>DOY [1-366].
- <li>Transmissivity of the Atmosphere single-way [0.0-1.0], mostly around 0.7+ in clear sky.
+ <li>Transmissivity of the atmosphere single-way [0.0-1.0], mostly around 0.7+ in clear sky.
  <li>Water availability [0.0-1.0], possibly using direct output from <em>i.eb.evapfr</em>.
 </ul>
 
 <h2>NOTES</h2>
-It can use the output of i.eb.evapfr directly as water availability input.
+
+<em>i.biomass</em> can use the output of <em>i.eb.evapfr</em> directly
+as water availability input.
 
 <h2>TODO</h2>
+
 Remove Latitude, DOY and Tsw from input and replace with a raster 
 input compatible with <em>r.sun</em> output.
 
@@ -23,7 +26,8 @@ input compatible with <em>r.sun</em> output.
 
 <em>
 <a href="i.eb.evapfr.html">i.eb.evapfr</a>,
-<a href="r.latlong.html">r.latlong</a>
+<a href="r.latlong.html">r.latlong</a>,
+<a href="r.sun.html">r.sun</a>
 </em>
 
 <h2>REFERENCES</h2>
@@ -38,7 +42,7 @@ Supplementing farm level water productivity assessment by remote
 sensing in transition economies. Water International. 30(4):513-521.
 
 
-<h2>AUTHORS</h2>
+<h2>AUTHOR</h2>
 
 Yann Chemin, Bec de Mortagne, France
 

imagery/i.landsat.acca/i.landsat.acca.html

@@ -51,6 +51,7 @@ i.landsat.acca -f band_prefix=226_62.toar output=226_62.acca
 <h2>SEE ALSO</h2>
 
 <em>
+<a href="i.atcorr.html">i.atcorr</a>,
 <a href="i.landsat.toar.html">i.landsat.toar</a>
 </em>
 

imagery/i.landsat.acca/main.c

@@ -103,9 +103,10 @@ int main(int argc, char *argv[])
     module->description =
 	_("Performs Landsat TM/ETM+ Automatic Cloud Cover Assessment (ACCA).");
     G_add_keyword(_("imagery"));
-    G_add_keyword(_("Landsat"));
     G_add_keyword("ACCA");
     G_add_keyword(_("cloud detection"));
+    G_add_keyword(_("satellite"));
+    G_add_keyword(_("Landsat"));
     
     band_prefix = G_define_standard_option(G_OPT_R_BASENAME_INPUT);
     band_prefix->label = _("Base name of input raster bands");

imagery/i.landsat.toar/main.c

@@ -72,8 +72,9 @@ int main(int argc, char *argv[])
     G_add_keyword(_("radiance"));
     G_add_keyword(_("reflectance"));
     G_add_keyword(_("brightness temperature"));
-    G_add_keyword(_("Landsat"));
     G_add_keyword(_("atmospheric correction"));
+    G_add_keyword(_("satellite"));
+    G_add_keyword(_("Landsat"));
     module->overwrite = TRUE;
 
     /* It defines the different parameters */

imagery/imageryintro.html

@@ -20,8 +20,9 @@ in 8 to 16 bits. DNs can be turned back into physical values by
 applying the reverse formula <tt>(x = (y - b) / a)</tt>.
 <p>
 The GRASS GIS module <a href="i.landsat.toar.html">i.landsat.toar</a>
-easily transforms Landsat DN to radiance-at-sensor. The equivalent 
-module for ASTER data is <a href="i.aster.toar.html">i.aster.toar</a>.
+easily transforms Landsat DN to radiance-at-sensor (top of atmosphere,
+TOA). The equivalent module for ASTER data is
+<a href="i.aster.toar.html">i.aster.toar</a>.
 For other satellites, <a href="r.mapcalc.html">r.mapcalc</a> can 
 be employed.
 <p>
@@ -30,11 +31,18 @@ be employed.
 When radiance-at-sensor has been obtained, still the atmosphere 
 influences the signal as recorded at the sensor. This atmospheric 
 interaction with the sun energy reflected back into space by 
-ground/vegetation/soil needs to be corrected. There are two ways to 
-apply atmospheric correction for satellite imagery. The simple way 
-for Landsat is with <a href="i.landsat.toar.html">i.landsat.toar</a>,
+ground/vegetation/soil needs to be corrected. The need of
+removing atmospheric artifacts stems from the fact that the
+atmosphericic conditions are changing over time. Hence, to gain
+comparability between Earth surface images taken at different
+times, atmospheric need to be removed converting at-sensor values
+which are top of atmosphere to surface reflectance values.
+<p>
+In GRASS GIS, there are two ways to apply atmospheric correction for
+satellite imagery. A simple, less accurate way for Landsat is with
+<a href="i.landsat.toar.html">i.landsat.toar</a>,
 using the DOS correction method. The more accurate way is using 
-<a href="i.atcorr.html">i.atcorr</a> (which works for many satellite 
+<a href="i.atcorr.html">i.atcorr</a> (which supports many satellite 
 sensors). The atmospherically corrected sensor data represent 
 surface <a href="http://en.wikipedia.org/wiki/reflectance">reflectance</a>,
 which ranges theoretically from 0% to 100%. Note that this level of