Dense Time Series Generation of Surface Water Extents through Optical–SAR Sensor Fusion and Gap Filling
<p>Workflow overview for our method, which shows optical processing in green with SAR processing in blue. The data processing steps, which include both optical and SAR data, are in light gray.</p> "> Figure 2
<p>Study area map displaying the regions used to validate the methods for this study. The inset locations are shown on the global map with numbers corresponding to specific insets. The surface water occurrence derived from the JRC Global Surface Water Mapping Layers is shown to illustrate the permanent and seasonal water extents for each region.</p> "> Figure 3
<p>Results from the water mapping and gap filling for SAR (<b>top row</b>) and optical (<b>bottom row</b>) for an area in the Cambodia region. The left column shows the raw observation, the middle column shows the water probability from the respective logistic regression results, and the right column shows the final water map, where gaps in the optical data were filled using information from the SAR images.</p> "> Figure 4
<p>Same as <a href="#remotesensing-16-01262-f002" class="html-fig">Figure 2</a> but for the Gabon region, illustrating a more extreme case of gap filling, where more of the optical image was cloud occluded, relying more heavily on SAR data to fill the gaps.</p> "> Figure 5
<p>Confusion matrix for all the samples normalized by true values.</p> "> Figure 6
<p>Accuracy of water mapping results for all sites for months with available data in 2019 and monthly average cloud cover (black line). Months with no reported accuracy are time periods with no available validation data.</p> "> Figure 7
<p>Histograms of the number of valid observations for each pixel at 30 m resolution covering the different regions. Optical-only observations are blue, SAR-only observations are orange, and all observations are green.</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Data
2.2. Study Area
2.3. Model Training
2.4. Water Prediction and Sensor Fusion
2.5. Validation/Statistical Analysis
3. Results
3.1. Logistic Regression Model Fitting
3.2. Water Prediction
3.3. Dense Time Series of Surface Water
3.4. Caveats and Limitations
3.5. Future Work
4. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Sensor Type | Variable Name | Description |
---|---|---|
Optical | Blue | Blue band |
Optical | Green | Green band |
Optical | Red | Red band |
Optical | NIR | Near-infrared band |
Optical | SWIR1 | Short-wave infrared 1 band |
Optical | SWIR2 | Short-wave infrared 2 band |
Optical | MNDWI | Calculated MNDWI (Green − SWIR1)/(Green + SWIR1) |
SAR | VV | Vertical transmit vertical receive polarization |
SAR | VH | Vertical transmit horizontal receive polarization |
SAR | VV/VH | Calculated ratio of VV/VH |
SAR | VVmean | Calculated mean value of VV polarization for 9 × 9 window |
SAR | VVstd | Calculated standard deviation value of VV polarization for 9 × 9 window |
SAR | VHmean | Calculated mean value of VH polarization for 9 × 9 window |
SAR | VHstd | Calculated standard deviation value of VH polarization for 9 × 9 window |
Region | Accuracy | FAR | POD | CSI | n Records |
---|---|---|---|---|---|
Colombia | 0.8843 | 0.0034 | 0.4353 | 0.6369 | 1470 |
Gabon | 0.8813 | 0.0020 | 0.8261 | 0.8501 | 1500 |
Mexico | 0.9120 | 0.0000 | 0.7956 | 0.8303 | 1500 |
Zambia | 0.8656 | 0.0322 | 0.5815 | 0.6822 | 1600 |
Cambodia | 0.9110 | 0.0349 | 0.7612 | 0.7790 | 1012 |
Myanmar | 0.9265 | 0.0140 | 0.7430 | 0.78434 | 1892 |
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Markert, K.N.; Williams, G.P.; Nelson, E.J.; Ames, D.P.; Lee, H.; Griffin, R.E. Dense Time Series Generation of Surface Water Extents through Optical–SAR Sensor Fusion and Gap Filling. Remote Sens. 2024, 16, 1262. https://doi.org/10.3390/rs16071262
Markert KN, Williams GP, Nelson EJ, Ames DP, Lee H, Griffin RE. Dense Time Series Generation of Surface Water Extents through Optical–SAR Sensor Fusion and Gap Filling. Remote Sensing. 2024; 16(7):1262. https://doi.org/10.3390/rs16071262
Chicago/Turabian StyleMarkert, Kel N., Gustavious P. Williams, E. James Nelson, Daniel P. Ames, Hyongki Lee, and Robert E. Griffin. 2024. "Dense Time Series Generation of Surface Water Extents through Optical–SAR Sensor Fusion and Gap Filling" Remote Sensing 16, no. 7: 1262. https://doi.org/10.3390/rs16071262