Full text: Proceedings, XXth congress (Part 7)

| In- 
se of 
n In- 
ed by 
, and 
ail al- 
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B7. Istanbul 2004 
3.1 Precise Geometric Correction 
Satellite data is often used for validation using ground based mea- 
surement. Therefore, it is needed to be precise position to pixel. 
The accuracy of geometric correction is dependent on the ac- 
curacy of the satellite position and attitude. The GLI precise 
geometric correction algorithm enables to determine the precise 
satellite position and attitude using ground control points (GCPs). 
This algorithm converts satellite position, velocity and attitude 
for one segment to navigation data. And then, precise satellite 
position and attitude are determined by utilizing GCPs. Conven- 
tionally, GCPs are obtained by human operator, but this algorithm 
is able to extract automatically by using each 1 path data. Suf- 
ficient numbers of GCPs are necessary for exterior orientation. 
Two image patches contain coastal lines. One is a template from 
the GSHHS fine coastal data. Another is a reference image made 
by the binarization of the original image. The template is selected 
as a GCP candidate from the GCP library which have to be pre- 
pared. In this work, the GCP collection is realized by the template 
matching. But it can get parameters to specify each scene. This 
algorithm is able to determine precise satellite position and atti- 
tude with methodology based on photogrammetry. The scene is 
rectified mapped image(Latitude/Longitude coordinates or Polar 
Stereo Projection). The rectification of original image is carried 
out using the results of the exterior orientation. GTOPO30 is 
the digital elevation data used for 250m geometric correction for 
terrain elevation. Output pixel value is radiance [W/m?/str/um] 
derived from LIB data. lkm precise geometric correction im- 
ages are generated from LIB data. However, in 250m precise 
geometric correction, the algorithm will start from L1A on nadir 
data, which is similar to MODIS 250m processing described in 
3.2 Composite 
After precise geometric correction processing, output value is 
TOA radiance derived from L1B. When land users use optical 
moderate resolution satellite data, composite is conducted for 
clouds removal. And land users often use composited AVHRR- 
NDVI products, which generated with the maximum value com- 
posite (MVC) technique. MVC selects the maximum NDVI value 
on a per pixel basis over a set compositing period. However, 
this algorithm has problems associated with satellite sensor con- 
ditions. In practice, composite data are complicated owing to the 
intrinsic behavior of the sensor, surface bi-directional reflectance 
factors with STSG (Sun-Target-Sensor Geometry), and contami- 
nation of the various spectral response. Therefore, the constraint 
view angle maximum value composite (CVMVC) technique (Cih- 
lar et al.,1994a) is applied to generate these composites in GLI 
higher level processing. This algorithm produces geometrically 
corrected 16-day surface composites, which may select the best 
value pixel over a composite period, based on cloudiness and at- 
mospheric contamination. Cloud detection and screening algo- 
rithm products cloud flags on a pixel basis. This is similar to a 
threshold technique to prevent the selection of extreme off-nadir 
pixels. The CVMVC technique works very much the same way as 
the classical MVC. In Ist day composite, the pixels are selected 
by satellite zenith angle, and save as work file 1. In 2nd day com- 
posite, the pixels are selected by satellite zenith angle, and save 
as work file 2. In 3rd day composite, the pixels are selected by 
satellite zenith angle, and save as temporary file, and then maxi- 
mum NDVI value is selected on each pixels in work file 1,2, and 
temporary file. Therefore, work file 1 is maximum NDVI pixels, 
and work file 2 is next large NDVI value pixels. After 4th day 
composite, it is same way as 3 day composite. Output value of 
this algorithm is TOA reflectance in VNIR and SWIR wavelength 
region (Ch.1-29), 
2e Alsat 
pega Fo cos(0,) 
where poss is GLI observed reflectance, Fo[W/m?/um] is irradi- 
ance based on Thuiller 2002, 
Lsac| W/m?/str/um] is GLI observed radiance, and 0,|rad] is so- 
lar zenith angle. MTIR region (Ch.30-36) is GLI observed TOA 
radiance. The GLI project adopt solar irradiance from Thuil- 
lier2002 (Thuillier et al.,2003). GLI calibration team calculates 
GLI solar irradiances. These are the weight-integrated Thuil- 
lier2002 irradiances over GLI spectral responses. The GLI spec- 
tral responses are running averaged +2 samples (data interval is 
about Inm) window to reduce a measurement noise, and both 
response and solar irradiance datasets are linearly interpolated 
to 0.1nm spectral resolution before the integration. Irradiance 
data sets are linearly interpolated to 0.1nm spectral resolution be- 
fore the integration. Thuillier2002 is new spectral irradiance data 
set included SOLSPEC observed data, which is the UV and visi- 
ble solar spectrum acquired by various spaceborne sensors flown 
during the ATLAS Space Shuttle missions (spectral resolution is 
Inm-5nm, range 200nm-2500nm). In case of longer wavelength 
than 2500nm, MODTRAN4.0 IR solar irradiance is used. And 
the compositing algorithm will operate on projected gridded data 
defined by the GLI tile scheme (total of 56 tiles). In this study, 
250m composite algorithm is same as 1km algorithm, CVMVC. 
But this paper shows the results of test running in central Japan 
in current status. 
3.3 Atmospheric Correction 
Correction for rayleigh scattering and ozone absorption is applied 
in GLI land algorithm. Correction for aerosol over land is not 
conducted. Much of the computation during atmospheric correc- 
tion requires intensive CPU time due to floating point process- 
ing. Therefore, this algorithm is adopted method using Look-Up 
Tables (LUTs). And this algorithm is applied after composite 
processing. GLI land atmospheric correction has some assump- 
tions. Ozone layers are above molecular layer, and all molecules 
are above aerosols. Moreover, aerosol layer + ground surface is 
assumed as Lambertian, and all layers are horizontally homoge- 
neous. Rayleigh scattering and ozone absorption are corrected 
with the assistance of ancillary data 
NOAA/TOVS data set and GTOPO30. GLI observed reflectance 
at Top-Of-Atmosphere is described as the following equation: 
Pos (Tos; TR, 0s, 0,; Qs-v) = 
Tos(T03:05,0/) X 
In, (TE; 0, )psTR1 (Tr, 0,) 
1 — Sp(TR)Ps 
(PR(TR, 05, Ov, Ps—v) + ) 
where pos, is GLI observed reflectance derived from compos- 
ite algorithm, To, is ozone transmittance, pg is path radiance, 
Tn, is downward transmittance, Tg, is upward transmittance, 
Sg is Spherical Albedo, and ps is Rayliegh/Ozone Corrected Re- 
flectance, which is output of this algorithm. The path radiance, 
upward and downward transmittances and spherical albedo were 
tabulated as functions of optical thickness of rayleigh scattering 
(Tr), view and illumination angles. Then, these four values will 
be retrieved based on the values of 7g for each pixel at each land 
channel based on the pixel elevation, BPF, and STSG. 77 relates 
to elevation through standard pressure and temperature (US62), 
which can be calculated with GTOPO30. And To, can be de- 
rived from NOAA/TOVS data. This algorithm for 250m data will 
be also same as 1km algorithm. However, this algorithm is under 

Note to user

Dear user,

In response to current developments in the web technology used by the Goobi viewer, the software no longer supports your browser.

Please use one of the following browsers to display this page correctly.

Thank you.