Remote Sensing Based Estimation Of Chlorophyll-a Concentration In DianShanHu Lake

Lakes are very important in freshwater supply. At present, most of the lakes in the world are suffering from eutrophication with industrialization, economic growth, and human activities. Frequent monitoring and integrated management of lakes are neeessary. Howeve, conventional methods for monitoring in lakes which are time& money-consumed can't provide water quality information of the whole lake. With spatial and temporal information, remote sensing technique is suitable to obtain such information quickly and frequently, and it'11 be widely applied in water quality monitoring of lakes in future because of its advantage of fastness, economy and long-term dynamic monitoring.Chlorophlly-a(chl-a) is one of the most important indexs for estimating inland water quality. Based on the analysis of former theory, techniques and methods in eastimating chl-a concentration, this paper chooses semiempirical model and artificial neural network, using in situ measurements of chl-a concentration on Augest 27th, September 7th and 8th, the synchronous, HJ-1 and MODIS imageries and then discusses remote sensing eastimation of chl-a eastimating chl-a concentration in Dianshanhu Lake. The major contents and conclusions are introduced as following:1) Study on semiempirical model for estimating chl-a concentration based on spectrum data:choosing three bands to make [Rrs-1663)—Rrs-1(689)]×Rrs(759) as independent variable and build lined and curve models, which R2 are 0.519 and 0.87.2) The stusy develops semiempirical models based on different satellites data. To hyper spectral data of HJ-1, R2 of lined and curve models with [Rrs-1(70)—Rrs-1(77)]×Rrs(88) as independent variable are 0.934 and 0.955. To multi-spectral data, such as HJ-1 and MODIS, there lies difficulty on the application of semiempirical models.3) Study on estimation model based on artificial neural network. To spectrum data, the maximam of R2 of artificial neural network model is 0.938, whose input is three bands, while that of hyperspectral data and multi-spectral data of HJ-1 and MODIS are 0.992,0.87 and 0.91. The artificial neural network models' estimation precision is higher than regression models'.4) To improve the estimation precision of artificial neural network model, adding DO, COD, TP and TN into input data. As a result R2 is elevated to 0.96. This paper develops models for estimating chl-a concentration in Dianshanhu Lake, acquires good estimation precision. Hyper spectral data is batter than multi-spectral data. It is proved that the artificial neural network model for estimating water quality of complex inland water body possesses advantage. Inputing DO, COD, TP and TN factors into the neural network provides a new way for estimating chl-a concentration.