Key words: remote sensing, suspended sediment concentration, slope algorithm.

Abstract
Remote sensing in detecting offshore-suspended sediment is one of the important tasks of ocean research. Currently most achievements in this field focus on the low SSC (Suspended Sediment Concentration). However, in China the offshore environment is characterized turbid water with high SSC. This background makes the research in high SSC becomes a special and urgent task of China.

In marine remote sensing, how to remove atmosphere effect is a crucial and necessary step because only about 30% of the single detected by sensors is valid (Hovis and Leung, 1997). And the most difficult point is high SSC remote sensing is it too. Recently a new method, slope algorithm, is put forward by LiYan and Lijin (1998, 1999) to solve this problem. To put it into operational system and test this algorithm, we select the coast of East China Sea, including Chandjiang Estuary and Hangzhou Bay which are famous for their high SSC, as our study area, NOAA14/AVHRR CH1 and CH2 as data source and realize this algorithm through computer program. Preliminary long time series of SSC images have been acquired successfully. In this paper we will illustrate the following aspects in detail: general construction, solution to the difficult and key points, discussion about its feasibility, stability and application.

1 Fundamental about the slope algorithm
The water-leaving reflectance of sea surface changes greatly in the transmitting process. If consider two different bands, it can be proved that the change in the slope of their relation curve is linear in the transmitting process, namely the relation curve of water-leaving reflectance is similar with that of the reflectance detected by remote sensor. It is just through the relation between slopes, atmospheric effects, including high SSC areas, can be corrected. And the next, through the relation between water-leaving reflectance of sea surface and SSC to deduce the relation between slope and SSC, thus SSC can be calculated out (Li Yan, Li Jin, 1998; 1999).

2 Basis for programming algorithm design
The key step in carrying out this algorithm on computer is how to get the relationship curve of CH1/CH2 and corresponding slope from NOAA/AVHRR CH1 and CH2 images.

The following basic facts are the basis for the computer algorithm design:

1. Relation between CH1 and CH2: (see fig.1) the two have the same alteration trends. For example, when CH1 increase, CH2 increases too. And it is clear that the relation curve can be simulated with linear equation or quadratic equation.
2. Relation among CH1, CH2, K (Slope) and SSC: (see if.1) simply say, Ch1 µ K µ SSC and CH2 µ K µ SSC.
3. Sensitivity of CH1 and CH2 to SSC: (see fig. 1). For low SSC, CH1 is more sensitive than CH2; and for high SSC, to the contrary. This phenomenon is related with the fact that as SSC increases its peak value of reflectance moves to the long wave (Chen Tao et al. , 1994).
4. Special 'inversion': When SSC is very high, CH1 stop increasing and even decrease while Ch2 can maintain increasing to some degree, which makes the relation curve of CH1 and CH2 appears to reverse (see fig.2). It often happens at the shoals covered with very shallow water.
5. Asymmetry of atmosphere: the general relationship between CH1 and CH2 has been stated in fact 1. but notice that because of the asymmetry of atmosphere, its concrete form is fluctuant at different places and in different times (see fig.2).
6. Influence of cloud over sea: sometimes it is too difficult to distinguish them from water, especially the thin cloud or fog, which often lead to wrong results.

According to fact 5, different from the previous ways that build some equations to correct atmospheric effect in whole image, here we use local area, including spatial local area and gray level local area to carry out our calculation. The former area refers to a small adjacent area comprised by a central pixel and its surrounding pixels. The latter is the mapping of the former in the gray level coordinate system. Using the former is to adapt to the asymmetry of atmosphere and using the latter is try to insure the stability of calculation.

In practice, we take two methods, simulation method and max method.

Fig.1 CH1_CH2 relation curve (1997/10/16)



Fig. 2 the influence of atmospheric asymmetry.
3. Simulation

3.1 Basic steps of program
This method directly uses spatial local area and gray level local area to acquire slope K.

The basic steps are;

1. Constructing a rectangle block (spatial local area) around current pixel (center point).
2. Filtering initial gray data then get valid data through the predefined gray level local area.
3. Using valid data to simulate the relation curve of CH1_CH2 (according to fact 1).
4. Acquiring slope K and then SSC of current pixel from the simulation equation.
5. Moving block one pixel by one until every pixel of image has been done with these steps.