Exploring Spatial Aggregations and Temporal Windows for Water Quality Match-Up Analysis Using Sentinel-2 MSI and Sentinel-3 OLCI Data
<p>(<b>a</b>) Inland waterbodies investigated. (<b>b</b>) Exemplary illustration of the spatial aggregations extracted for MSI data.</p> "> Figure 2
<p>Overall performance of different spatial aggregations using the match-up dataset (same day) with MSI data processed by CyanoAlert (orange) and EOMAP-MIP (blue). Scatterplots are shown in log-log scale with selected error metrics and the number of observations (both processors combined) for the three target variables of chlorophyll-a (<b>top</b>), turbidity (<b>middle</b>), and Secchi depth (<b>bottom</b>). The grey dashed line refers to the 1:1 line.</p> "> Figure 3
<p>Overall performance of different spatial aggregations (left column, 1 pixel; middle column, 3 × 3 macropixels; right column, waterbody scale) using the match-up dataset (same day) with OLCI data processed by CyanoAlert (orange) and EOMAP-MIP (blue). Scatterplots are shown in log-log scale with selected error metrics and the number of observations (both processors combined) for the three target variables of chlorophyll-a (<b>top</b> row), turbidity (<b>middle</b> row), and Secchi depth (<b>bottom</b> row). The grey dashed line refers to the 1:1 line.</p> "> Figure 4
<p>Overall performance of different spatial aggregations using the match-up dataset (same day) with MSI and OLCI data processed by CyanoAlert (top) and EOMAP-MIP (bottom). The table shows selected error metrics, the number of observations (matches), and the number of waterbodies (N Lakes) for the three target variables of chlorophyll-a, turbidity, and Secchi depth. Dark grey shades indicate the poorer performance of the variant, while the lightest shade represents the best performance within each processing scheme.</p> "> Figure 5
<p>Overall performance of different temporal windows (left column, same day; middle column, ±1 day; right column, ±5 days) using the match-up dataset (3 × 3 macropixel) with MSI data processed by CyanoAlert (orange) and EOMAP-MIP (dark blue), and OLCI data processed by CyanoAlert (yellow) and EOMAP-MIP (light blue). Scatterplots are shown in log-log scale with selected error metrics and the number of observations for the three target variables of chlorophyll-a (<b>top</b> row), turbidity (<b>middle</b> row), and Secchi depth (<b>bottom</b> row). The grey dashed line refers to the 1:1 line.</p> "> Figure 6
<p>Overall performance of different temporal windows using the match-up dataset with MSI and OLCI data processed by CyanoAlert (top) and EOMAP-MIP (bottom). The table shows selected error metrics, the number of observations (N, “matches”), and the number of waterbodies (N Lakes) for the three target variables of chlorophyll-a, turbidity, and Secchi depth. Dark grey shades indicate poorer performance of the variant, while the lightest shades represent the best performance within each processing scheme.</p> "> Figure A1
<p>Scatterplots of chlorophyll-a (<b>left</b>), turbidity (<b>middle</b>), and Secchi depth (<b>right</b>) match-up dataset, log10 transformed (<b>bottom</b>) and untransformed (<b>top</b>).</p> "> Figure A2
<p>Overall performance of different spatial aggregations using the match-up dataset (same-day) with MSI and OLCI data separated, as in <a href="#remotesensing-16-02798-f004" class="html-fig">Figure 4</a>, but the two processors EOMAP-MIP and CynaoAlert combined. Table shows selected error metrics, number of observations (matches) and number of waterbodies (N Lakes) for the three target variables chlorophyll-a, turbidity and Secchi depth. Dark-grey shades indicate poorer performance of variant, while lightest shades represent the best performance.</p> "> Figure A3
<p>Overall performance of two different spatial aggregations (3 × 3 macropixels and waterbody-scale variants) using the match-up dataset (same day) with data from both sensors (S2-MSI, S3-OLCI) and processors (EOMAP-MIP, CyanoAlert) combined. The table shows selected error metrics, the number of observations (matches; N), and the number of waterbodies (N Lakes) for the three target variables of chlorophyll-a, turbidity, and Secchi depth.</p> "> Figure A4
<p>Overall performance of different temporal windows using the match-up dataset (3 × 3 macropixels) with data of both sensors (MSI, OLCI) and processors (EOMAP-MIP, CyanoAlert) combined. The table shows selected error metrics, the number of observations (matches), and the number of waterbodies (N Lakes) for the three target variables of chlorophyll-a, turbidity, and Secchi depth. Dark grey shades indicate poorer performance of the variant, while the lightest shades represent the best performance.</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. In Situ Data Observations of More than 100 Lakes and Reservoirs
2.2. Satellite-Based Detection of Water Quality with Sentinel-2 MSI and -3 OLCI
2.3. Statistical Analyses and Comparison of In Situ and Satellite-Based Observations
3. Results
3.1. Spatial Aggregations
3.2. Temporal Windows
4. Discussion
4.1. Spatial Aggregations
4.2. Temporal Windows
4.3. Interplay between Temporal and Spatial Scales
4.4. Systematic Differences among Variables of Water Quality, Satellites, or Processors
4.5. Error Metrics
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
Turbidity | S2-MSI | S3-OLCI | |||||
---|---|---|---|---|---|---|---|
3 × 3 | 5 × 5 | 15 × 15 | Waterbody Scale | 1 × 1 | 3 × 3 | Waterbody Scale | |
CyanoAlert | 14,639 | 16,250 | 13,534 | 17,403 | 10,803 | 14,301 | 13,114 |
EOMAP-MIP | 15,895 | 16,004 | 16,083 | 19,630 | 16,990 | 18,542 | 20,730 |
Secchi Depth | S2-MSI | S3-OLCI | |||||
---|---|---|---|---|---|---|---|
3 × 3 | 5 × 5 | 15 × 15 | Waterbody Scale | 1 × 1 | 3 × 3 | Waterbody Scale | |
CyanoAlert | 14,639 | 15,053 | 13,534 | 17,403 | 11,808 | 16,250 | 13,114 |
EOMAP-MIP | 15,890 | 15,997 | 16,077 | 19,623 | 16,890 | 18,440 | 20,618 |
Target Variable | Unit | Minimum | Maximum |
---|---|---|---|
Chlorophyll-a | µg/L | 0.01 | 400 |
Turbidity | FNU | 0.01 | 100 |
Secchi depth | m | 0.05 | 20 |
CV (CyanoAlert) | CV (EOMAP-MIP) | ||||||
---|---|---|---|---|---|---|---|
Sensor | Target Variable | Same Day | 1 Day | 5 Days | Same Day | 1 Day | 5 Days |
S2-MSI | Chlorophyll-a | 36.7 | 38.5 | 39.5 | 13.6 | 13.5 | 14.1 |
Turbidity | 18.3 | 19.4 | 20.7 | 18.2 | 15.2 | 14.4 | |
Secchi depth | 17.8 | 19.0 | 19.7 | 8.8 | 8.4 | 8.5 | |
S3-OLCI | Chlorophyll-a | 25.1 | 22.8 | 27.9 | 23.5 | 23.5 | 23.2 |
Turbidity | 53.0 | 60.5 | 64.2 | 15.2 | 16.1 | 17.4 | |
Secchi depth | 43.6 | 39.2 | 40.9 | 14.9 | 14.9 | 15.1 |
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In Situ Data | Chlorophyll-A | Turbidity | Secchi Depth |
---|---|---|---|
Unit | µg/L | FNU | m |
Minimum | 2 | 2 | 2 |
Maximum | 173 | 241 | 231 |
Average | 30 | 89 | 33 |
Total n | 2956 | 1151 | 3179 |
Chlorophyll-A | S2-MSI | S3-OLCI | |||||
---|---|---|---|---|---|---|---|
3 × 3 | 5 × 5 | 15 × 15 | Waterbody Scale | 1 × 1 | 3 × 3 | Waterbody Scale | |
CyanoAlert | 14,639 | 15,053 | 13,534 | 17,403 | 10,802 | 11,867 | 14,508 |
EOMAP-MIP | 15,895 | 16,004 | 16,083 | 19,630 | 16,990 | 18,542 | 20,730 |
Spatial Matching | ||||||
---|---|---|---|---|---|---|
Temporal Matching | 1 Pixel | 3 × 3 Pixels | 5 × 5 Pixels | 15 × 15 Pixels | Waterbody Scale | |
S2-MSI | Same day (0 d) | X | X | X | X | |
±1 day | X | |||||
±5 days | X | |||||
S3-OLCI | Same day (0 d) | X | X | X | ||
±1 day | X | |||||
±5 days | X |
CV (CyanoAlert) | CV (EOMAP-MIP) | ||||||||
---|---|---|---|---|---|---|---|---|---|
Sensor | Target Variable | 3 × 3 | 5 × 5 | 15 × 15 | All | 3 × 3 | 5 × 5 | 15 × 15 | All |
S2-MSI | Chlorophyll-a | 36.7 | 44.0 | 78.9 | 87.8 | 13.6 | 18.0 | 33.3 | 204.8 |
Turbidity | 18.3 | 56.7 | 26.3 | 32.8 | 18.2 | 24.4 | 51.3 | 188.2 | |
Secchi depth | 17.8 | 20.8 | 28.2 | 30.5 | 8.8 | 12.1 | 20.3 | 52.1 | |
S3-OLCI | Chlorophyll-a | 13.6 | - | - | 115.4 | 23.5 | - | - | 109.2 |
Turbidity | 18.2 | - | - | 63.2 | 15.2 | - | - | 24.3 | |
Secchi depth | 8.8 | - | - | 55.4 | 14.9 | - | - | 36.0 |
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Schröder, T.; Schmidt, S.I.; Kutzner, R.D.; Bernert, H.; Stelzer, K.; Friese, K.; Rinke, K. Exploring Spatial Aggregations and Temporal Windows for Water Quality Match-Up Analysis Using Sentinel-2 MSI and Sentinel-3 OLCI Data. Remote Sens. 2024, 16, 2798. https://doi.org/10.3390/rs16152798
Schröder T, Schmidt SI, Kutzner RD, Bernert H, Stelzer K, Friese K, Rinke K. Exploring Spatial Aggregations and Temporal Windows for Water Quality Match-Up Analysis Using Sentinel-2 MSI and Sentinel-3 OLCI Data. Remote Sensing. 2024; 16(15):2798. https://doi.org/10.3390/rs16152798
Chicago/Turabian StyleSchröder, Tanja, Susanne I. Schmidt, Rebecca D. Kutzner, Hendrik Bernert, Kerstin Stelzer, Kurt Friese, and Karsten Rinke. 2024. "Exploring Spatial Aggregations and Temporal Windows for Water Quality Match-Up Analysis Using Sentinel-2 MSI and Sentinel-3 OLCI Data" Remote Sensing 16, no. 15: 2798. https://doi.org/10.3390/rs16152798