Multivariable integration method for estimating sea surface salinity in coastal waters from in situ data and remotely sensed data using random forest algorithm
文献类型：期刊论文
| 作者 | Liu, Meiling1; Liu, Xiangnan1; Liu, Da1; Ding, Chao1; Jiang, Jiale1 |
| 刊名 | COMPUTERS & GEOSCIENCES
 |
| 出版日期 | 2015 |
| 卷号 | 75页码:235-249 |
| 关键词 | Random forest algorithm Remote sensing Multivariable integration Sea surface salinity |
| 通讯作者 | Liu, XN (reprint author), China Univ Geosci, Sch Informat Engn, 29 Xueyuan Rd, Beijing 100083, Peoples R China. |
| 英文摘要 | A random forest (RF) model was created to estimate sea surface salinity (SSS) in the Hong Kong Sea, China, by integrating in situ and remotely sensed data. Optical remotely sensed data from China's HJ-1 satellite and in situ data were collected. The prediction model of salinity was developed by in situ environmental variables in the ocean, namely sea surface temperature (SST), pH, total inorganic nitrogen (TIN) and Chl-a, which are strongly related to SSS according to Pearson's correlation analysis. The large-scale SSS was estimated using the established salinity model with the same input parameters. The ordinary kriging interpolation using in situ data and the retrieval model based on remotely sensed data were developed to obtain the large-scale input parameters of the model. The different number of trees in the forest (ntree) and the number of features at each node (mtry) were adjusted in the RF model. The results showed that an optimum RF model was obtained with mtry=32 and ntree=2000, and the most important variable of the model for SSS prediction was SST, followed by TIN, Chl-a and pH. Such an RF model was successful in evaluating the temporal-spatial distribution of SSS and had a relatively low estimation error. The root mean square error (RMSE) was less than 2.0 psu, the mean absolute error (MAE) was below 1.5 psu, and the absolute percent error (APE) was lower than 5%. The final RF salinity model was then compared with a multiple linear regression model (MLR), a back-propagation artificial neural network model, and a classification and regression trees (CART) model. The RF had a lower estimation error than the other three models. In addition, the RF model was used extensively under different periods and could be universal. This demonstrated that the RF algorithm has the capability to estimate SSS in coastal waters by integrating in situ and remotely sensed data. (C) 2014 Elsevier Ltd. All rights reserved. |
| 研究领域[WOS] | Computer Science, Interdisciplinary Applications ; Geosciences, Multidisciplinary |
| 收录类别 | SCI ; EI |
| 语种 | 英语 |
| WOS记录号 | WOS:000348556700006 |
| 源URL | [http://ir.ceode.ac.cn/handle/183411/38491]  |
| 专题 | 遥感与数字地球研究所_SCI/EI期刊论文_期刊论文 |
| 作者单位 | 1.[Liu, Meiling 2.Liu, Xiangnan 3.Ding, Chao 4.Jiang, Jiale] China Univ Geosci, Sch Informat Engn, Beijing 100083, Peoples R China 5.[Liu, Da] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Beijing 100094, Peoples R China 6.[Liu, Da] Univ Chinese Acad Sci, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Liu, Meiling,Liu, Xiangnan,Liu, Da,et al. Multivariable integration method for estimating sea surface salinity in coastal waters from in situ data and remotely sensed data using random forest algorithm[J]. COMPUTERS & GEOSCIENCES,2015,75:235-249. |
| APA | Liu, Meiling,Liu, Xiangnan,Liu, Da,Ding, Chao,&Jiang, Jiale.(2015).Multivariable integration method for estimating sea surface salinity in coastal waters from in situ data and remotely sensed data using random forest algorithm.COMPUTERS & GEOSCIENCES,75,235-249. |
| MLA | Liu, Meiling,et al."Multivariable integration method for estimating sea surface salinity in coastal waters from in situ data and remotely sensed data using random forest algorithm".COMPUTERS & GEOSCIENCES 75(2015):235-249. |
入库方式： OAI收割
来源：遥感与数字地球研究所
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