A Weighted Algorithm Based on Normalized Mutual Information for Estimating the Chlorophyll-a Concentration in Inland Waters Using Geostationary Ocean Color Imager (GOCI) Data
Abstract
:1. Introduction
2. Materials
2.1. Study Area
2.2. In Situ Data
2.2.1. Samples Collection and Datasets Used
2.2.2. Reflectance Measurements
2.2.3. Laboratory Analysis
2.3. Satellite Data
Band | Central Wavelength (nm) | Band Width (nm) | SNR | Type |
---|---|---|---|---|
1 | 412 | 20 | 1000 | Visible |
2 | 443 | 20 | 1090 | Visible |
3 | 490 | 20 | 1170 | Visible |
4 | 555 | 20 | 1070 | Visible |
5 | 660 | 20 | 1010 | Visible |
6 | 680 | 10 | 870 | Visible |
7 | 745 | 20 | 860 | NIR |
8 | 865 | 40 | 750 | NIR |
3. Methods
3.1. Pre-Treatment of Data
3.1.1. Pre-Treatment of the In Situ Spectral Data
3.1.2. Pre-Treatment of the GOCI Data
3.2. Spectra Classification and Matching Based on the NMI Algorithm
3.2.1. Normalized Mutual Information Theory
3.2.2. Classification and Matching Algorithms
3.3. NMI Weighted Algorithms for Chl-a Estimation
3.4. Accuracy Assessment
4. Results
4.1. Variations in the Optical Properties of Each Water Type
Water Type | Parameter | Min | Max | Mean | S.D. |
---|---|---|---|---|---|
Class 1 | Chl-a(mg/m3) TSM(mg/L) ISM(mg/L) aCDOM(440)(m−1) Chl-a:TSM | 21.31 10.50 5.99 0.13 0.46 | 201.30 86.76 67.31 1.45 6.35 | 81.63 42.72 19.59 0.61 1.95 | 42.81 21.09 15.91 0.44 1.66 |
Class 2 | Chl-a(mg/m3) TSM(mg/L) ISM(mg/L) aCDOM(440)(m−1) Chl-a:TSM | 4.99 12.15 9.00 0.17 0.07 | 82.65 116.12 106.12 1.89 1.86 | 22.61 36.31 29.64 0.51 0.86 | 13.60 18.90 17.29 0.34 0.41 |
Class 3 | Chl-a(mg/m3) TSM(mg/L) ISM(mg/L) aCDOM(440)(m−1) Chl-a:TSM | 5.29 4.63 2.13 0.16 0.22 | 36.45 40.34 30.46 1.54 2.57 | 12.22 8.54 4.67 0.46 1.22 | 4.71 5.86 3.42 0.38 0.62 |
4.2. Estimation of Chl-a Using the NMI Weighted Algorithm
4.3. Validation of the NMI Weighted Algorithm
GOCI Type | Index | Non-Classification Algorithm | Hard-Classification Algorithm | NMI Weighted Algorithm |
---|---|---|---|---|
Class 1 | MAE RMSE(mg/m3) | 33.4% 28.83 | 30.06% 25.40 | 27.28% 22.52 |
Class 2 | MAE RMSE(mg/m3) | 28.40% 8.15 | 25.77% 7.34 | 21.56% 6.64 |
Class 3 | MAE RMSE(mg/m3) | 30.89% 6.67 | 22.45% 5.98 | 21.69% 4.37 |
All validation data | MAE RMSE(mg/m3) | 31.09% 12.76 | 26.23% 11.02 | 22.63% 9.41 |
5. Discussion
5.1. Assessment of Water Classification
5.2. Application of the NMI Weighted Algorithm to GOCI Images
5.2.1. Assessment of Atmospheric Correction
5.2.2. Effect of Fuzzy Boundaries of Water Types in a GOCI Image
5.2.3. Spatial-Diurnal Distribution of Chl-a in Taihu Lake
6. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Bao, Y.; Tian, Q.; Chen, M. A Weighted Algorithm Based on Normalized Mutual Information for Estimating the Chlorophyll-a Concentration in Inland Waters Using Geostationary Ocean Color Imager (GOCI) Data. Remote Sens. 2015, 7, 11731-11752. https://doi.org/10.3390/rs70911731
Bao Y, Tian Q, Chen M. A Weighted Algorithm Based on Normalized Mutual Information for Estimating the Chlorophyll-a Concentration in Inland Waters Using Geostationary Ocean Color Imager (GOCI) Data. Remote Sensing. 2015; 7(9):11731-11752. https://doi.org/10.3390/rs70911731
Chicago/Turabian StyleBao, Ying, Qingjiu Tian, and Min Chen. 2015. "A Weighted Algorithm Based on Normalized Mutual Information for Estimating the Chlorophyll-a Concentration in Inland Waters Using Geostationary Ocean Color Imager (GOCI) Data" Remote Sensing 7, no. 9: 11731-11752. https://doi.org/10.3390/rs70911731