Extraction of the sea - ice information from NOAA Satellite Imagery
The spectral characteristics of sea ice and the criterion of identification and classification
We selected a series of ice images respecting different phase of sea ice Then we sampled a certain number of pixels of sea ice and water randomly and made a statistical analysis on the reflective characteristics of ice and water.
Table 1 shows the albedo of channel 1 (A1) and the ratio of channel 2 ( R ) because of reasonableness of assumption of normal distribution on most remote sensing analysis [2] according to the data on table -1 we get the probability curves of albedo of seaice and water under the assumption of gauss distribution fig 1 shows that sea and water differ greatly in spectral characteristics we can distinguish between them by using data of channel 1 and 2 According to the Bayes classification regulation[2] we used the point of intersection of probability as the criterion That is R =2.25 with 10.1% error identification probability .
Table 1. Mean and root of mean square deviation of albedo of sea-ice and water.
| |
sea-ice(%) |
Water (%) |
| A1 |
1.7 ± 4.8 |
8.2 ±1.4 |
| R |
1.7 ± 0.26 |
3.0 ± 0.5 |
On the basis of identification of sea-ice and water we classified the sea ice according to the difference inits albedo both the theoretical model of optical property of sea ice [3] and experimental observation [4] have shown that the albedo of sea ice increases as its thickness increases SHIROZWA hasobserved the thin and oneyear formed sea ice in Hokkaido of Japan [4] According to his observation results we deduced the curveof the thickness of ice versus its albedo shown in fig 3 and fig 4 is an example of sea ice satellite remote sensing image its result of classification in the liaodong Bay on12of February 1990 respectively The sea ice were classified into three types the thickness less than 10 cm 10-20 cm and more.
Than 20 cm those correspond `approximately rind ice grey ice respectively cording to the sea ice observation regulations of China These quantitative data are valuable for monitoring and analysis of sea ice.
The concentration of sea-ice
Sea-ice concentration is defined as the fraction of sea ice in the field of view its equivalence used in the numerical predication model is the percentage in the grid area . In order to provide the initial field for numerical prediction model of sea ice the unit area is taken to be 6' X 6' there are 63 pixels in this grid every pixel is identified by using the criterion described in the last section the number of sea ice pixel divided by 63 is the sea ice concentration in this grid.
However the image must taken in to account when the sea ice concentration is calculated the resolution of sea ice remote sensing imagery is 1.5 X 1.5 square kilometer in the Bohai Mercator projection image. It is possible that there are both sea ice and water in this is large area the albedo of mixing pixel is a mixing contribution of sea ice and water. This is one of error sources in sea ice identification in order to partially overcome the effect of mixed pixel defined twice mean square deviation of probability distribution as distinguish interval (Ri, Rw) it corresponds that the error identification probability of sea ice and water are 2.2 % respectively and the total error identification probability is 4.4 % Referring to the equation for the calculation of cloud coverage of radiometer observation we can calculate the percentage of sea ice in a mixed pixel (p);
R = p Ri + (1 - p) Rw
Where R is the measured ratio of albedo of channel 1 to albedo of channel 2 similarly the albedo of mixed pixel (A) is given by.
A = p Ai + (l - p) Aw
Substituting the measured albedo of mixed pixel (A) and the lower limit of the albedo of water (aw) we can obtain the albedo of sea ice in the mixed pixel (Ai) According to the relationship between the thickness of sea ice its albedo the thickness of sea ice in this pixel was derived We averaged the concentration and the thickness of sea ice of the all pixels the gird average concentration and average thickness of sea ice of all pixels in very useful numerical predication model.
