Application of Artificial Neural Network Technology in Water Color Remote Sensing Inversion of Inland Water Body Using Tm Data

For water color remote sensing study of inland water body, terrestrial satellite is often a good data source, because it has high spatial resolution. However, the precision of water color remote sensing inversion limits its application to water environmental monitoring and pollution analysis. This paper firstly studied traditional regression arithmetic and found that it was difficult to extract a good combination to construct the regression model. In order to acquire good inversion results, the paper introduced an advanced nonlinear science, artificial neural network technology. On the basis of satellite synchronous monitoring experiment, a BP neural network model was constructed to inverse SS, CODMn, DO, T-N, T-P and chl-a from Landsat TM data. The accuracy was acceptable and the relative error could be controlled below 25%. Moreover, the reasons of simulating error, ways of improving model and applications of the model were also analyzed in details. The result of this research showed that based on a small-scale of satellite synchronous experiment, the model could be applied successfully in investigation, analysis and estimation of water quality. * Corresponding author. It comes through long time that water quality data collection often depends on traditional monitoring, which needs much time and labor. So it is impossible to realize real-time and quick data acquiring. Along with continuous development of environmental information technology, water color remote sensing is applied more and more widely in the water quality monitoring of oceanic, coastal and inland water body because it has many advantages, such as wide range, synchronization and low cost of data collecting (Campbell, 1988; Claudia, 2001; Zhao, 2000). However, in order to utilize water color remote sensing technology more deeply and widely, there are many aspects to be improved: water color remote sensor technology, atmospheric correction and inversing model, which are in accordance with many scientific fields, and this paper paid more attention to inversing arithmetic study. 1. OVERVIEW Researches of water color remote sensing began at 1920s, which had given correct explanation of sea color and begun to study optical field of water body (Shuleikin, 1933). But only after spatial technology occurred, water color remote sensing developed truly and quickly. Morel & Prieur classified water body as two types: Case I water body, which is Open Ocean; Case II water body, which is coastal, estuary and inland water body (Morel, 1977). Now the inversion research of Case I water body is correspondingly mature and the accuracy of inversing model is relatively good, since which component is mainly chlorophyll and has little suspended solids (SS). And that of Case II water body is very difficult due to the interaction of many water components, such as SS, chlorophyll and yellow substance. Inversion research of Case II water body is a hot issue currently. Aiming at Case II water body inversion model, both theoretical model (Bricaud, 1986; Cheng, 2002) and empirical model (Chen, 1996; Ekstrand, 1992; Kuang, 2002) is getting along in recent years. And TM or ETM data was used by most researches (Chen, 1996; Cheng, 2002; Zhan, 2000). At present, the theoretical research still cannot be applied to practical inversion, but it can provide useful information to direct and modify empirical inversion arithmetic. Empirical model is often the main choice of quantitative calculation. How to improve its precision has become a key issue. Water color remote sensing satellites, such as SeaWiFS and OCTS, have high spectral resolution and good optical attributes of water body, which are ideal data sources for water color remote sensing research, but in practice, the small spatial resolution often limits their application in inland water body. Terrestrial remote sensing satellites, such as landsat7, which has good spatial resolution (about 30meters), is often adopted as data sources, but spectral resolution of terrestrial satellite is a little low and can’t reflect optical attributes of water body well. All these reasons make it more difficult to identify suspended solids, chlorophyll and yellow substances, and also limit the application of water color remote sensing in inland water bodies. Artificial neural network (ANN) technology is a kind of nonlinear science developed from 1980’s, which tries to simulate some basic attributes of people, such as self-adapting, self-organizing and fault tolerance. ANN has been used in many fields, such as mode identification and system simulation. Integrating water color remote sensing and characteristics of ANN, the paper hoped that artificial neural network model could perform the research of water color remote sensing inversion well. For all these, the paper drew the following research plan (Figure 1).