基于资源一号卫星的小兴凯湖水体浊度反演

Water turbidity inversion of Xiaoxingkai Lake based on ZY-1 satellite

为更好地反演小兴凯湖水体浊度,在普通回归模型的基础上,引入哑变量和空间自回归理论,优化小兴凯湖水体浊度遥感反演模型,以期提高反演模型的精度。结果表明:哑变量空间自回归模型能够有效提高浊度反演精度,相较普通回归模型、哑变量模型和空间自回归模型,其R^(2)分别提高了12.39%、6.55%、1.97%,e_(RMSE)分别减少了39.59%、30.21%、1.45%;小兴凯湖水体浊度呈北高南低的趋势,符合水体浊度分布近岸高、远岸低的规律,5月水体浊度分布较为均衡,浊度高值积聚在承紫河河口、兴凯湖农场等区域,8月水体浊度整体高于5月。

This paper aims to efficiently invert the water turbidity of Xiaoxingkai Lake.The study is focused on the efforts to introduce dummy variables and spatial autoregression theory to optimize the remote sensing inversion model of water turbidity of Xiaoxingkai Lake for its higher accuracy on the basis of the ordinary regression model.The results show that the spatial autoregressive model with dummy variables can effectively improve the turbidity inversion accuracy.Compared with the ordinary regression model,the dummy variable model and spatial autoregressive model,the R^(2) increases by 12.39%、6.55% and 1.97%,respectively,and the e_(RMSE) is reduced by 39.59%、30.21% and 1.45%,respectively.The water turbidity distribution of Xiaoxingkai Lake presents a trend of high in the north and low in the south,which is consistent with the distribution law of water turbidity,that is higher near bank and lower far from bank.In May,the water turbidity distribution is more balanced,with the high turbidity value accumulated in Chengzi River estuary,Xingkai Lake farm and other areas,and in August,the water turbidity is higher than that in May.