3.2 Example and discussion
See fig.3. It is the SSC result image on 16^th, Oct., 1997.

Our results show that the stability of slope is under the control of both spatial local area and gray level local area. To spatial local area, the smaller the block is, the better the result is. Because in small block the state of atmosphere could be considered as symmetry, which better ensures the accuracy of slope. But to gray level local area, the larger the block, the better. Because to simulate curve need enough statistic data. The conflict between the two local areas is the key and difficult point of the simulation method.

Presently, the following measures are taken:

1. Changing the size of spatial local area according to the state of gray level local area in the block: to those having enough valid data in current gray level local area, keep the block size. To those having few valid data, enlarge the block size. In this way, not only get enough statistic data to simulate curve but also reduce the computing time.
2. Changing range of gray level local area according to fact 3: to the clear water, relax the valid range of CH1 and reduce relax of CH2. To the turbid water, to the country.
3. Independent variable alteration according to fact 3: choose CH1 as independent variable in the low SSC and choose CH2 in the high SSC.

General speaking, the simulation method is influenced by many factors, and its stability is the prominent problem. But it also has the advantage of directly perceived through the senses and easy-understanding. The results of dozens of images improve that the general tendency is stable, though SSC at every pixel appears some different with the changing of block size and gray level local area.

4 Maximum method

4.1 Basic steps of program
Maximum method (LiYan, Lijing, 1998; 1999 ) is an in directed method to get slope.
It can be simply proved:
The slope (K) of CH1~CH2 relation curve can be expressed as:

K=dCH2/dCH1
Namely

K-dCH2/dCH1=0

Because K is a constant, there has:

d(K*CH1-CH2)/dCH1=0

This equation is just the discriminate function of the K*CH1-CH2 extremum. It can also prove this extremum is a maximum. Thus to get slope K could convert to get the max of K*CH1-CH2. Namely the distribution of K is equivalent to that of the max of K*CH1-CH2.

What we do in the practice is similar to that in the simulation method only different at the way we get slope. In every block, through calculating the max of K*CH1-CH2 to build the relation among K~CH1, CH2. Here K is preset (e.g., K= 0.02, 0.04 …….5 a arismetic series with step of 0.02).

4.2 Example and discussion
See fig.4. It is the SSC result image using maximum method on 16^th , Oct., 1997.

Through the comparison of a number of result images, we find that:

1. the conflict between spatial and gray level local area decrease: in the small block, K~CH1, CH2 strictly conform to fact 1,2 and does not appear fluctuation. While in bigger block, K~CH1, CH2 tend to be unstable and the bigger the block is the stronger this tendency is. It can be explained by that in big block the asymmetry of atmosphere get notable and influence the relation between.
2. Limitation: the control of high side (e.g., in the above series refer to when K near to 5). It can be deemed that every gray point (determined by [CH1, CH2]) in block is a control condition of K~CH1, CH2. when K is at low side (e.g., when K = 0.02, then 0.04 ……). Because of many control point above it (e.g., when K= 0.02 then 0.04, 0.06 … 5 are all control point above it) , a gray point will not increase without limit. But when K is near to high side, control point decrease, and the last gray point are difficult to find its correct slope.
3. It can't find its slope directly. For example, the center point is [68,56], and we only find 0.02~ [66,54], 0.08~ [73,62] directly. At this time we had to use the point we know to deduce the slope of center point.
4. In maximum method, the influence of clouds is obvious.

At present, the follow measures are taken:

1. Getting rid of the highest point: namely delete the last section in the series of K~CH1, CH2 . Because of no control point above it, we consider that the last one is uncertain.
2. Simple trend plane interpretation according to fact 2: for those that have not directly get slope. The result is related to the series of K~ CH1, CH2 and from this view max method still influenced by the conflict between spatial and gray level local area.

5. Error Comparison
A resulting the Changjiang Estuary shows that the maximum method is better than simulation method (See Fig.5). and we can see that to high SSC, the error of simulation method is apparent mainly caused by the fact 4 that we have stated before. And it means that the difference of two methods will enlarge within certain area.

Fig 5. Comparison of calculating data with true data
6. Conclusion
Close to one hundred NOAA14/AVHRR CH1 and CH2 images, receiving 1997 and 1998, have been dealt with the Simulation Method and Maximum Method for the Slope algorithm. Compared with our accumulated historical data of the past experience (notice that it is difficult to get synchronous in situ data) most results are satisfying. Of course this new algorithm need much more in situ data to firmly confirm it.

We also notice the following problems: how to decrease the influence effectively when thin clouds or fog cover most of the research area; how to increase the sensitivity tot the very high SSC water.

References:

• Li Yan, Lijin, 1998, Maximum aR1-R2, the point for removal atmospheric effects from satellite imagery of the coast ocean, Proceeding of PORSC'98- Qingdao, Vol. 586-590.
• Li Yan and Li Jin, 1999, A suspended sediment Satellite sensing Algorithm based on Gradient Transiting from water-leaving to Satellite detected Reflectance Spectrum, Chinese Science Bulletin (Vol. 44, in press)
• Chen Tao , Li Wu, Wu Shuchu, The Relation between Suspended Sediment Concentration and the Peak Value of Light Spectrum Reflectance, ACTA OCEANOLOGICA SINICA, 1994 (Vol. 16, No. 1: 38-43)(Chinese edition).