Ice Detection with Sentinel-1 SAR Backscatter Threshold in Long Sections of Temperate Climate Rivers
<p>The Lithuanian part of the Nemunas and Neris Rivers, the location of hydrological stations (black dots) with available ground observation data and river sections (color stripes) covered by the matching Sentinel-1 SAR and Sentinel-2 MSI observations used for the development and validation of the river ice detection models.</p> "> Figure 2
<p>The distribution of backscatter coefficient Sigma0 from ice (6 March 2018) and open water pixels (data from 10 and 13 May, 3 and 6 November 2018), including and excluding the zone within 30 m away from riverbanks.</p> "> Figure 3
<p>The distribution of logistic model coefficients estimated using 100 training dataset subsets, each consisting of 7500 pixels. Points were randomly jittered in horizontal direction to avoid overlapping.</p> "> Figure 4
<p>Optimal ice and open water classification thresholds of the logistic, VH and VV models determined using 100 training dataset subsets. The threshold was considered optimal, and the true prediction rates for water (specificity) and ice (sensitivity) were equal.</p> "> Figure 5
<p>Interdependency between logistic model parameters β<sub>0,</sub> β<sub>VV</sub>, β<sub>VH</sub> and ice/water classification threshold value estimated using 100 training dataset subsets.</p> "> Figure 6
<p>Distribution of observations in the VV<sub>Sigma0</sub> and VH<sub>Sigma0</sub> plain on different dates in the testing and training datasets. Red lines represent the ice and water classification thresholds used in models based on VV and VH polarization backscatter.</p> "> Figure 7
<p>The agreement of SAR-based logistic, VH and VV model classification with the Sen2Cor ice and water classes on different days from the testing dataset.</p> "> Figure 8
<p>Nemunas River section near the Smalininkai HS affected by the translucent clouds and their shadows on 18 January 2017. The effect of clouds and their shadows for ice detection is visible in Sentinel-2 natural color composite (<b>a</b>) and the match of Sen2Cor classes with the VV model prediction (<b>b</b>).</p> "> Figure 9
<p>Nemunas River section between the Nemajunai HS and Kaunas HPS Reservoir (north from shown section) on 25 January 2017 was covered by ice. Sen2Cor predicted water class in the river valley shadow, while the VH model misclassified many pixels as water upstream from the HPS Reservoir (<b>a</b>). The VH model classified more pixels as water than the VV model (<b>b</b>).</p> "> Figure 10
<p>VV model prediction compared to the Sen2Cor class on 14 February 2017.</p> "> Figure 11
<p>Sentinel-2 natural color composition (<b>a</b>), VV model prediction compared to Sen2Cor class (<b>b</b>), VV polarization backscatter coefficient (<b>c</b>) and VV and VH classification mismatch (<b>d</b>) on 14 February 2017 in the Nemunas River upstream from the Panemunes HS.</p> "> Figure 12
<p>Sentinel-1 SAR backscatter in VV and VH polarizations in Nemunas River near the Lazdenai HS on a day with sparse frazil ice (7 February 2018) and without frazil ice (21 February 2018).</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Model Development and Validation
2.3. Satellite Data
3. Results
3.1. Backscatter from Ice and Open Water
3.2. Logistic Classification Model
3.3. Classification Threshold
3.4. Model Validation
3.4.1. Translucent Clouds and Cloud Shadow
3.4.2. Effect of Reservoir and River Valley Shadows
3.4.3. Water on Ice
3.4.4. Frazil and Consolidated Ice
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Sentinel-1 SAR IW GRD High Resolution | Sentinel-2 MSI L1C | Dataset | ||||
---|---|---|---|---|---|---|
Acquisition Date | Acquisition Time | Satellite | Orbit Direction | Relative Orbit | Acquisition Time | |
18 January 2017 | 16:19 | S1B | Ascending | 29 | 09:53 | Testing |
25 January 2017 | 16:10 | S1B | Ascending | 131 | 09:42 | |
14 February 2017 | 04:43 | S1A | Descending | 153 | 09:41 | |
7 February 2018 | 16:11 | S1A | Ascending | 131 | 09:51 | |
21 February 2018 | 04:43 | S1A | Descending | 153 | 09:30 | |
09:50 (22 February 2018) | ||||||
24 February 2018 | 16:19 | S1A | Ascending | 29 | 09:40 | |
26 February 2018 | 04:51 | S1A | Descending | 51 | 09:30 | |
16:03 | S1A | Ascending | 58 | 09:50 (27 February 2018) | ||
6 March 2018 | 04:34 | S1B | Descending | 80 | 09:40:19 | Training |
10 May 2018 | 04:42 | S1B | Descending | 153 | ||
13 May 2018 | 16:19 | S1B | Ascending | 29 | ||
3 November 2018 | 16:20 | S1A | Ascending | 29 | ||
6 November 2018 | 04:42 | S1B | Descending | 153 |
Model | Equation |
---|---|
VV | water 0 = VVSigma0 < −13.7 dB ice = VVSigma0 ≥ −13.7 dB |
VH | water = VHSigma0 < −21.2 dB ice = VHSigma0 ≥ −21.2 dB |
Logistic | ice = 7.8 + 0.76 × VVSigma0 − 0.07 × VHSigma0 < 0.24 water = 7.8 + 0.76 × VVSigma0 − 0.07 × VHSigma0 ≥ 0.24 |
Date | VV with Sen2Cor | VH with Sen2Cor | Logistic with Sen2Cor | Logistic with VV | Logistic with VH | VH with VV |
---|---|---|---|---|---|---|
18 January 2017 | 80.5 | 79.1 | 80.5 | 99.6 | 93.3 | 93.6 |
25 January 2017 | 81.7 | 73.8 | 82.2 | 99.0 | 84.8 | 85.8 |
14 February 2017 | 69.9 | 76.7 | 68.4 | 97.8 | 80.8 | 83.1 |
7 February 2018 | 91.2 | 91.5 | 91.0 | 99.6 | 94.1 | 94.5 |
21 February 2018 | 93.6 | 93.1 | 93.5 | 99.7 | 96.1 | 96.4 |
24 February 2018 | 46.7 | 47.6 | 46.6 | 98.6 | 83.0 | 84.4 |
26 February 2018 | 80.9 | 80.4 | 80.8 | 99.4 | 90.0 | 90.6 |
Training data | 95.2 | 93.7 | 95.4 | 99.7 | 96.7 | 97.0 |
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Stonevicius, E.; Uselis, G.; Grendaite, D. Ice Detection with Sentinel-1 SAR Backscatter Threshold in Long Sections of Temperate Climate Rivers. Remote Sens. 2022, 14, 1627. https://doi.org/10.3390/rs14071627
Stonevicius E, Uselis G, Grendaite D. Ice Detection with Sentinel-1 SAR Backscatter Threshold in Long Sections of Temperate Climate Rivers. Remote Sensing. 2022; 14(7):1627. https://doi.org/10.3390/rs14071627
Chicago/Turabian StyleStonevicius, Edvinas, Giedrius Uselis, and Dalia Grendaite. 2022. "Ice Detection with Sentinel-1 SAR Backscatter Threshold in Long Sections of Temperate Climate Rivers" Remote Sensing 14, no. 7: 1627. https://doi.org/10.3390/rs14071627