Abstract
<p> <p id="x-x-sp0005"> Using reflectance data and water sample data from 75 stations in several eutrophic lakes of East China (Lake Taihu, Lake Gehu, Lake Dongjiu) between 23 April and 3 May 2010, we evaluated several recently proposed <a title="Learn more about Remote Sensing from ScienceDirect's AI-generated Topic Pages"> remote sensing </a> algorithms to estimate chlorophyll-a concentrations (Chla, 1.0–42 μg/L) and phycocyanin pigment concentrations (PC, 0.1–7.7 μg/L). These lakes experience <a title="Learn more about Phytoplankton from ScienceDirect's AI-generated Topic Pages"> phytoplankton </a> blooms of the cyanobacteria <em> <a title="Learn more about Microcystis from ScienceDirect's AI-generated Topic Pages"> Microcystis </a> </em> <em> aeruginosa </em> every year due to eutrophication. It was found that after local tuning of the algorithm parameterizations, most algorithms yielded acceptable results for Chla retrievals while accurate PC retrievals were more challenging due to changing species composition (PC:Chla ratios) and low PC concentrations. For the data ranges in the study region, the best Chla algorithm yielded RMSE <sub> rel </sub> (Relative Root Mean Square Error) of ~ 46% (R <sup> 2 </sup> = 0.92, <em> n </em> = 75) and the best PC algorithm yielded RMSE <sub> rel </sub> of ~ 83% (R <sup> 2 </sup> = 0.88, <em> n </em> = 75). Based on these observations, it is recommended that local tuning of algorithm parameters should be performed for <a title="Learn more about Remote Sensing Application from ScienceDirect's AI-generated Topic Pages"> remote sensing applications </a> , and future efforts should emphasize on application of the algorithms to satellite data. </p></p>
Original language | American English |
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Journal | Remote Sensing of Environment |
Volume | 126 |
DOIs | |
State | Published - Jan 1 2012 |
Keywords
- Remote sensing
- Chlorophyll-a
- Phycocyanin
- Microcystis aeruginosa
- MERIS
- Lake Taihu
Disciplines
- Life Sciences