title: Remote sensing estimation of the total phosphorus concentration in a large lake using band combinations and regional multivariate statistical modeling techniques.
abstract: Remote sensing has been widely used for ater quality monitoring, but most of these monitoring studies have only focused on a few water quality variables, such as chlorophyll-a, turbidity, and total suspended solids, which have typically been considered optically active variables. Remote sensing presents a challenge in estimating the phosphorus concentration in water. The total phosphorus (TP) in lakes has been estimated from remotely sensed observations, primarily using the simple individual band ratio or their natural logarithm and the statistical regression method based on the field TP data and the spectral reflectance. In this study, we investigated the possibility of establishing a spatial modeling scheme to estimate the TP concentration of a large lake from multi-spectral satellite imagery using band combinations and regional multivariate statistical modeling techniques, and we tested the applicability of the spatial modeling scheme. The results showed that HJ-1A CCD multi-spectral satellite imagery can be used to estimate the TP concentration in a lake. The correlation and regression analysis showed a highly significant positive relationship between the TP concentration and certain remotely sensed combination variables. The proposed modeling scheme had a higher accuracy for the TP concentration estimation in the large lake compared with the traditional individual band ratio method and the whole-lake scale regression-modeling scheme. The TP concentration values showed a clear spatial variability and were high in western Lake Chaohu and relatively low in eastern Lake Chaohu. The northernmost portion, the northeastern coastal zone and the southeastern portion of western Lake Chaohu had the highest TP concentrations, and the other regions had the lowest TP concentration values, except for the coastal zone of eastern Lake Chaohu. These results strongly suggested that the proposed modeling scheme, i.e., the band combinations and the regional multivariate statistical modeling techniques, demonstrated advantages for estimating the TP concentration in a large lake and had a strong potential for universal application for the TP concentration estimation in large lake waters worldwide.
authors.affiliation: State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China. Electronic address: yngao@niglas.ac.cn.
authors.forename: Yongnian
authors.initials: Y
authors.lastname: Gao
authors.affiliation: State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China.
authors.forename: Junfeng
authors.initials: J
authors.lastname: Gao
authors.affiliation: State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China.
authors.forename: Hongbin
authors.initials: H
authors.lastname: Yin
authors.affiliation: Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China.
authors.forename: Chuansheng
authors.initials: C
authors.lastname: Liu
authors.affiliation: College of Environment, Nanjing University of Technology, Nanjing 210002, China.
authors.forename: Ting
authors.initials: T
authors.lastname: Xia
authors.affiliation: Jiangsu Institute of Land Surveying and Planning, Nanjing 210024, China.
authors.forename: Jing
authors.initials: J
authors.lastname: Wang
authors.affiliation: State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China.
authors.forename: Qi
authors.initials: Q
authors.lastname: Huang
mesh_terms: D002681:China; D004784:Environmental Monitoring / Q000379:methods*; D006801:Humans; D060106:Lakes; D015233:Models, Statistical; D010758:Phosphorus / Q000737:chemistry*; D012044:Regression Analysis; D058998:Remote Sensing Technology; D014874:Water Pollutants, Chemical / Q000737:chemistry*; D060753:Water Quality
chemical_list: D014874:Water Pollutants, Chemical; D010758:Phosphorus
keywords: Band combination; Lake Chaohu; Regional statistical modeling; Remote sensing; Total phosphorus