Name
Title | ||
|---|---|---|
Algorithms and Schemes for Chlorophyll a Estimation by Remote Sensing and Optical Classification for Turbid Lake Taihu, China |
Abstract | ||
|---|---|---|
Monitoring chlorophyll a (CHLA) by remote sensing is particularly challenging for turbid productive waters. Although several empirical and semianalytical algorithms have been developed for such waters, their accuracy varies significantly due to variability in optical properties. In this paper, we evaluated the performance of six CHLA concentration (Cchla) estimation algorithms [e.g., two-band ratio algorithm (TBR), normalized difference chlorophyll index (NDCI), synthetic chlorophyll index (SCI), three-band algorithm (TBS), four-band algorithm (FBS), and improved four-band algorithm (IOC3M)] for a highly turbid lake based on remote sensing reflectance classification. Remote sensing reflectance was classified using the iterative k-mean clustering method. We also developed four estimation schemes (S1-S4) for the six algorithms to assess the effect of the estimation scheme on the accuracy of the algorithms. The estimation schemes were developed based on classification methods (no, soft, or hard classification) and the optimization bands used. The six algorithms performed differently for different remote sensing reflectance classes and different estimation schemes. The optimal algorithms for Classes 1, 2, and 3 were TBS, NDCI, and TBR, respectively. For the four estimation schemes, TBS and NDCI outperformed the other four algorithms. The accuracy of TBS and NDCI was higher than FBS, IOC3M, TBR, and SCI. The accuracy of all six algorithms was improved by remote sensing reflectance classification, particularly for Classes 2 and 3. Soft classification with recalibration of the bands for each class outperformed hard classification for all the three classes. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/JSTARS.2014.2333540 | Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of |
Keywords | Field | DocType |
hydrological techniques,iterative methods,lakes,parameter estimation,remote sensing,water pollution measurement,china,chlorophyll a estimation,four-band algorithm,improved four-band algorithm,iterative k-mean clustering method,normalized difference chlorophyll index,optical classification,remote sensing reflectance classification,synthetic chlorophyll index,three-band algorithm,turbid lake taihu,two-band ratio algorithm,chlorophyll a (chla),chlorophyll textit{a} (chla),lake taihu,estimation algorithm,estimation,water,absorption,clustering algorithms | Normalization (statistics),Chlorophyll a,Remote sensing,Algorithm,Reflectivity,Cluster analysis,Mathematics | Journal |
Volume | Issue | ISSN |
8 | 1 | 1939-1404 |
Citations | PageRank | References |
8 | 0.79 | 6 |
Authors | ||
9 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| fangfang zhang | 1 | 77 | 15.29 |
| Junsheng Li | 2 | 16 | 10.98 |
| Qian Shen | 3 | 19 | 11.63 |
| bing zhang | 4 | 8 | 1.13 |
| Chuanqing Wu | 5 | 24 | 5.26 |
| Yuanfeng Wu | 6 | 20 | 5.61 |
| ganlin wang | 7 | 8 | 0.79 |
| ShengLei Wang | 8 | 8 | 1.46 |
| zhaoyi lu | 9 | 11 | 1.62 |