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Mapping without the sun
Zhang, Jixian

completed. But, the velocity of water flow becomes slower and
the capability of purification decreased, which increases the
possibility of sand sediment, organic matter deterioration and
algae growth. Therefore, how to monitor the pollution of the
Three Gorges Dam and protect the water quality effectively
will be an important question.
2.1 Data Information
The CHRIS/PROBA data in this study is acquired on April 1 st ;
2006.The quality of images is good. In order to retrieve the
chlorophyll concentration with images using semi- experiential
method, one campaign was carried out for 18 samples on 6-7 th ,
April, 2006.The samplings were distributed along the
reservoir where is far away from the dam above 5 kilometres.
The geography coordinates of the samplings were recorded by
GPS device. Simultaneously, remote sensing reflectance was
measured with Field Spec HandHeld2500. Water samples were
collected from the surface above 30 cm below in the vertical
direction with a standard 21 polyethylene water-fetching
instrument immediately after measuring spectra. Then they
were held on the deepfreeze half with ice bags to reserve for
approximately 4h every afternoon, then returned to the
laboratory for concentration measure. Additionally,
transparency was measured with a secchi disk at the shade side
of the boat.
2.2 Image Processing
2.2.1 Destriping: Push-broom images equipped with a matrix
detector, like CHRIS sensor, are heavily affected by coherent
disturbance spatially and spectrally due to sensitivity changes
between the detector elements (Dong Guang-xiang, 2006).So
noises affected imaging are more complicated and there are
horizontal and vertical strips noises on images.
More scholars have done researches to remove the destrpings.
Sandra Mannheim (2004), Alessandro Barducci (2005), Dong
Guang-xiang (2006) deal with the images with emendation
factor, ratio and iterative methods respectively. The values of
the resultant images that they obtained are consistent with the
original values nearly, and most noises have been removed.
However, those methods, which are applicable for professional
peoples, are difficult for curbstone persons. Taking one thing
with another, we use HDFclean of ESA to remove the strips
(See http://www.earth.esa.int/proba/HDFcleanv2.html).This
method is easier than those methods and the result is not worse
either. The same result has been educed in other researches
(Ted Miltion, 2006)
Fig 2. The contrast of the original and the resultant images
Fig 2 shows the contrast between the original and the resultant
images. The value does not change obviously, while the noises
have removed, not only the wide strips (shown as red rectangle
mark) but also the slender ones (shown as blue rectangle mark).
2.2.2 Radiation Correction: In order to compare and analyze
quantificationally, the data that satellite sensor transmits needs
to be transformed to radiation lightness or reflectivity (Wu
Yun-zhao, 2004). As the data that the CHRIS sensor accepts is
atmospheric radiation lightness, we only need to transform the
atmospheric radiation lightness to reflectivity.
The reflectivity (r ) is calculated by equation (1).
r= 7tLid ]-< (!)
E> eosift)
Where L*= the atmospheric radiation lightness;
d= the distance between the sun and the earth
(astronomical unit);
Ex= Mean solar atmospheric irradiances;
9s= solar zenith angle.
2.2.3 Atmospheric Correction: The process of imaging is
very complex and the reflected electromagnetic signals
transmitting form the sun to the sensor is affected by
atmosphere inevitably. The main atmospheric components
affecting the electromagnetic radiation in the visible and NIR
spectral region are ozone, aerosols, and water vapour. Those
cause the loss or the corruption of part of the carried
information above the observed target. When the radiation
transmits in the atmosphere, it is absorbed by gas molecules
and scattered by aerosol particles, and at the same time, the
scattered signals are accepts directly or reflected into sensor. In
those visible and NIR wavelengths, the atmospheric influence
is strong enough to modify the reflected electromagnetic signal.
As a result, the contrast of images is reduced and the
readability is debased, which increases the difficulties to
interpretation (Wang Zhao, 2006). Thus, any set of
CHRIS/PROBA data needs for a cautious removal of the
atmospheric effects in the initial processing steps, to assure a
maximal accuracy and reliability in the results inferred by the
latter exploitation of the data.
Fig3. Histogram of reflectivity distributing
At present, the best method to correct the atmosphere effect is
radiation transfer model, which is based on the radiation
transfer theory and yields satisfactory results. The methods that
applied widely are 6S model, LOWTRAN model, MORTRAN