Name
Title | ||
|---|---|---|
Detection of Suspended-Matter Concentrations in the Shallow Subtropical Lake Taihu, China, Using the SVR Model Based on DSFs |
Abstract | ||
|---|---|---|
Accurate detection of suspended-matter concentrations in water columns is an important task in remotely sensing water color. This letter aims to identify an optimal model for estimating suspended-matter concentration in the optically complex Lake Taihu of China. Remote sensing reflectance Rrs(λ), inherent optical properties, and constituent concentrations of the Lake water were synchronously measured in November of 2007. After the effects of water constituents on Rrs(λ) were analyzed, the definitive spectral factors were determined, which are indicative primarily of total suspended matter (TSM). Several methods were compared in modeling the relationship between Rrs(λ) and TSM. Results show that the support vector regression (SVR) model performs best with a root-mean-square error of 4.7 mg · l-1 (R2 = 0.968). Its predictive errors in four seasons were also assessed with the mean absolute percentage errors varying in the range of 22.0%-60.0%. Thus, the SVR model can be used to reliably retrieve TSM concentrations in Lake Taihu. This finding offers new insights into the optical signals of in-water constituents in optically complex lakes. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/LGRS.2010.2048299 | IEEE Geosci. Remote Sensing Lett. |
Keywords | Field | DocType |
total suspended matter,sediments,remote sensing,water quality,lake water remote sensing reflectance,lake water constituent concentrations,lakes,lake taihu,lake water definitive spectral factors,support vector regression,regression analysis,china,lake water optical properties,dsf based svr model,suspended matter detection,hydrological techniques,underwater optics,support vector regression (svr),suspended matter concentration estimation,geophysical signal processing,shallow subtropical taihu lake,water color remote sensing,suspended matter,definitive spectral factors (dsfs),support vector machines,ad 2007 11,atmospheric modeling,data models,absorption,seasonality,prediction error,artificial neural networks,spectral factorization,mean absolute percentage error | Sediment,Regression analysis,Remote sensing,Total suspended matter,Subtropics,Atmospheric model,Reflectivity,Mathematics,Water quality,Water column | Journal |
Volume | Issue | ISSN |
7 | 4 | 1545-598X |
Citations | PageRank | References |
1 | 0.44 | 4 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Deyong Sun | 1 | 46 | 10.06 |
| Yun Mei Li | 2 | 13 | 2.20 |
| Qiao Wang | 3 | 97 | 21.94 |
| Heng Lv | 4 | 3 | 1.87 |
| Chengfeng Le | 5 | 23 | 6.30 |
| Changchun Huang | 6 | 28 | 5.68 |
| Shaoqi Gong | 7 | 4 | 1.38 |