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作 者:LI Guosheng WANG Fang LIAO Heping
机构地区:[1]Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China [2]University of Calgary, Calgary T2N 1N4, Alberta, Canada [3]School of Geographic Science, Southwest University, Chongqing 400715, China
出 处:《Journal of Geographical Sciences》2008年第4期443-454,共12页地理学报(英文版)
基 金:National Natural Science Foundation of China, No.40771030; No.40571020
摘 要:This paper brought out a new idea on the retrieval of suspended sediment concentration, which uses both the water-leaving radiance from remote sensing data and the grain size of the suspended sediment. A principal component model and a neural network model based on those two parameters were constructed. The analyzing results indicate that testing errors of the models using the two parameters are 0.256 and 0.244, while the errors using only water-leaving radiance are 0,384 and 0.390. The stability of the models with grain size parameter is also better than the one without grain size. This research proved that it is necessary to introduce the grain size parameter into suspended sediment concentration retrieval models in order to improve the retrieval precision of these models.This paper brought out a new idea on the retrieval of suspended sediment concentration, which uses both the water-leaving radiance from remote sensing data and the grain size of the suspended sediment. A principal component model and a neural network model based on those two parameters were constructed. The analyzing results indicate that testing errors of the models using the two parameters are 0.256 and 0.244, while the errors using only water-leaving radiance are 0,384 and 0.390. The stability of the models with grain size parameter is also better than the one without grain size. This research proved that it is necessary to introduce the grain size parameter into suspended sediment concentration retrieval models in order to improve the retrieval precision of these models.
关 键 词:Bohai Sea suspended sediment concentration remote sensing binary-parameter model
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