Water Information Extraction Based on Multi-Model RF Algorithm and Sentinel-2 Image Data
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Sources and Pre-Processing
Data Pre-Processing
3. Study Methods and Steps
3.1. Study Methods
3.1.1. Random Forest Algorithm
3.1.2. Accuracy Verification Algorithm
3.2. The Extraction of Characteristic Variables
3.3. Sample Selection
4. Results
4.1. Results of Classification and Accuracy Evaluation
4.2. The Evaluation of Characteristic Importance
4.3. Accuracy Evaluation
5. Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Sentinel-2 Bands | Center Wavelength (nm) | Spectral Width (nm) | Spatial Resolution (m) |
---|---|---|---|
Band 1-Coastal aerosol | 443 | 20 | 60 |
Band 2-Blue | 490 | 65 | 10 |
Band 3-Green | 560 | 35 | 10 |
Band 4-Red | 665 | 30 | 10 |
Band 5-Vegetation Red-Edge 1 | 705 | 15 | 20 |
Band 6-Vegetation Red-Edge 2 | 740 | 15 | 20 |
Band 7-Vegetation Red-Edge 3 | 783 | 20 | 20 |
Band 8-NIR | 842 | 115 | 10 |
Band 8a-Vegetation Red-Edge 4 | 865 | 20 | 20 |
Band 11-SWIR1 | 1610 | 90 | 20 |
Band 12-SWIR2 | 2190 | 180 | 20 |
Abbreviations | Full Names | Calculation Formulas | Types | Citations |
---|---|---|---|---|
NDVI | Normalized Difference Vegetation Indices | (B8 − B4)/(B8 + B4) | Traditional Spectral Indices Characteristics | [30] |
LSWI | Land Surface Water Indices | (B8 − B11)/(B8 + B11) | Traditional Spectral Indices Characteristics | [31] |
NDWI | Normalized Difference Water Indices | (B8 − B4)/(B8 + B4) | Traditional Spectral Indices Characteristics | [32] |
MSAVI | Modified Soil Adjusted Vegetation Indices | 0.5 × (2 × (B8+1) − sqrt((2 × B8 + 1)2 − 8 × (B8 − B4))) | Traditional Spectral Indices Characteristics | [33] |
MNDWI | Modified Normalized Difference Water Indices | (B3 − B11)/(B3 + B11) | Traditional Spectral Indices Characteristics | [34] |
NDI45 | Normalized Difference Indices | (B5 − B4)/(B5 + B4) | Red-Edge Spectral Indices Characteristics | [35] |
MCARI | Modified Chlorophyll Absorption Ratio Indices | [(B5 − B4) − 0.2 × (B5 − B3)] × (B5 − B4) | Red-Edge Spectral Indices Characteristics | [36] |
REIP | Red-Edge Inflection Point Indices | 700 + 40 × ((B4 + B7)/2 − B5)/(B6 − B5) | Red-Edge Spectral Indices Characteristics | [37] |
S2REP | The Sentinel-2 Red-Edge Position Indices | 705 + 35 × ((B4 + B7)/2 − B5)/(B6 − B5) | Red-Edge Spectral Indices Characteristics | [38] |
IRECI | Inverted Red-Edge Chlorophyll Indices | (B7 − B4)/(B5/B6) | Red-Edge Spectral Indices Characteristics | [39] |
PSSRa | Pigment Specific Simple Ratio(chlorophyll) Indices. | B7/B4 | Red-Edge Spectral Indices Characteristics | [40] |
Characteristic Class | Training Samples | Validation Samples | Total Number of Samples |
---|---|---|---|
Water | 927 | 463 | 1390 |
Cultivated land | 1233 | 616 | 1849 |
Forest land | 1996 | 991 | 2987 |
Grass land | 146 | 73 | 219 |
Wetland | 104 | 52 | 156 |
Construction land | 251 | 125 | 376 |
Other land | 166 | 83 | 249 |
Types | Model A | Model B | Model C | Model D | ||||
---|---|---|---|---|---|---|---|---|
Producer’s Accuracy | User’s Accuracy | Producer’s Accuracy | User’s Accuracy | Producer’s Accuracy | User’s Accuracy | Producer’s Accuracy | User’s Accuracy | |
(PA) | (UA) | (PA) | (UA) | (PA) | (UA) | (PA) | (UA) | |
Water | 80.63 | 83.22 | 82.16 | 86.22 | 91.39 | 94.12 | 92.16 | 95.22 |
Cultivation land | 85.43 | 79.26 | 88.52 | 81.53 | 89.21 | 82.46 | 91.25 | 84.53 |
Forest | 90.45 | 92.76 | 92.87 | 93.98 | 93.07 | 94.19 | 93.98 | 95.06 |
Grassland | 82.36 | 76.52 | 84.51 | 79.84 | 87.12 | 81.65 | 90.12 | 83.54 |
Wetland | 80.15 | 73.28 | 85.62 | 80.17 | 87.45 | 81.56 | 90.22 | 93.14 |
Building land | 82.03 | 73.49 | 80.41 | 71.29 | 83.46 | 79.02 | 86.52 | 81.39 |
Others | 85.87 | 81.37 | 88.41 | 81.61 | 91.67 | 84.62 | 94.67 | 88.62 |
The overall accuracy (OA)% | 81.24 | 86.39 | 89.71 | 92.37 | ||||
Kappa coefficient | 0.8016 | 0.8092 | 0.8114 | 0.8116 |
Types | RF_16 | Relief-16 | Model D | |||
---|---|---|---|---|---|---|
Producer’s Accuracy | User’s Accuracy | Producer’s Accuracy | User’s Accuracy | Producer’s Accuracy | User’s Accuracy | |
(PA) | (UA) | (PA) | (UA) | (PA) | (UA) | |
Water | 94.16 | 96.22 | 92.39 | 95.42 | 92.16 | 95.22 |
Cultivation land | 90.12 | 88.53 | 92.21 | 85.46 | 91.25 | 84.53 |
Forest | 93.87 | 94.98 | 93.17 | 94.19 | 93.98 | 95.06 |
Grassland | 91.51 | 88.34 | 91.12 | 84.65 | 90.12 | 83.54 |
Wetland | 93.62 | 90.17 | 92.45 | 91.56 | 90.22 | 93.14 |
Building land | 91.41 | 89.29 | 89.56 | 85.02 | 86.52 | 81.39 |
Others | 94.81 | 92.61 | 91.67 | 89.62 | 94.67 | 88.62 |
The overall accuracy (OA)% | 93.16 | 93.04 | 92.37 | |||
Kappa coefficient | 0.8224 | 0.8176 | 0.8116 |
Methods | Kappa Coefficient | Missed Extraction Rate/% | False Extraction Rate /% | The Overall Accuracy (OA)% |
---|---|---|---|---|
NDVI | 0.8046 | 2.86 | 13.72 | 89.22 |
NDWI | 0.8112 | 1.94 | 11.24 | 90.12 |
MNDWI | 0.8206 | 1.89 | 10.56 | 92.57 |
RF_16 | 0.8224 | 0.48 | 7.22 | 93.16 |
Methods | Extraction Effect | First Area | Second Area | Third Area |
---|---|---|---|---|
NDVI | ||||
NDWI | ||||
MNDWI | ||||
RF_16 |
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Jiang, Z.; Wen, Y.; Zhang, G.; Wu, X. Water Information Extraction Based on Multi-Model RF Algorithm and Sentinel-2 Image Data. Sustainability 2022, 14, 3797. https://doi.org/10.3390/su14073797
Jiang Z, Wen Y, Zhang G, Wu X. Water Information Extraction Based on Multi-Model RF Algorithm and Sentinel-2 Image Data. Sustainability. 2022; 14(7):3797. https://doi.org/10.3390/su14073797
Chicago/Turabian StyleJiang, Zhiqi, Yijun Wen, Gui Zhang, and Xin Wu. 2022. "Water Information Extraction Based on Multi-Model RF Algorithm and Sentinel-2 Image Data" Sustainability 14, no. 7: 3797. https://doi.org/10.3390/su14073797