Title :
Study of retrieving models for chlorophyll-a concentration based on classification of above-water remote sensing reflectance
Author :
Xu, Xin ; Li, Yunmei ; Lv, Heng ; Tan, Jing ; Guo, Yulong ; Huang, Jiazhu
Author_Institution :
Key Lab. of Virtual Geographic Environ. of Educ. Minist., Nanjing Normal Univ., Nanjing, China
Abstract :
In this study, according to the characteristics of above-water remote sensing reflectance (Rrs) curves, we put our samples into different classes, and for each class, find a best retrieving model among the first-derivative model, band ratio model and three-band model to show the correlation between Rrs data and the concentration of chlorophyll-a. As a result, it is proved that Rrs curves can be classified into 6 classes by differences in peaks around the wavelength of 560nm or 705nm, as well as valleys around the wavelength of 630nm or 680nm. Compared with non-classified retrieving models based on the whole data set, classified models reduce the absolute value of average relative error (RE) range from 7.39% to 39.85%, effectively improve the retrieving precision.
Keywords :
hydrological techniques; ocean chemistry; oceanographic techniques; organic compounds; reflectivity; remote sensing; underwater optics; water quality; above water remote sensing reflectance classification; band ratio model; chlorophyll-a concentration retrieval models; first derivative model; remote sensing reflectance curves; three band model; wavelength 560 nm; wavelength 630 nm; wavelength 680 nm; wavelength 705 nm; Data models; Estimation; Hyperspectral sensors; Lakes; Reflectivity; Variable speed drives; Lake Taihu; chlorophyll-a concentration; classification; remote sensing reflectance; retrieving models;
Conference_Titel :
Remote Sensing, Environment and Transportation Engineering (RSETE), 2011 International Conference on
Conference_Location :
Nanjing
Print_ISBN :
978-1-4244-9172-8
DOI :
10.1109/RSETE.2011.5964596
