Effect of the One-to-Many Relationship between the Depth and Spectral Profile on Shallow Water Depth Inversion Based on Sentinel-2 Data
Abstract
:1. Introduction
2. Study Area and Data
2.1. Study Area
2.2. Data
3. Methods
3.1. Same Spectral Profile but Different Depth (SSPBDD)
3.2. Parameters
3.2.1. Depth Invariant Index (DII)
3.2.2. Spatial Neighbourhood Parameters
3.3. Depth Inversion Models
3.3.1. Stumpf Model
3.3.2. Random Forest (RF) Model
3.3.3. Support Vector Machine (SVM) Model
3.3.4. Mixture Density Network (MDN) Model
3.4. Training and Evaluation of the Models
4. Results
4.1. RMSEs of Different Models
4.2. Inversion Results of the Four-Band MDNhood Model
4.3. Influence of the SSPBDD Phenomenon on the Depth Inversion via the MDNhood Model
5. Discussion
6. Conclusions
- (1)
- The SSPBDD phenomenon is observed in a relatively high percentage of the pixels. However, the more bands used, the fewer pixels there are with the SSPBDD. The percentage of the SSPBDD pixels is lower in shallower water (0–5 m) and deeper water (≥15 m), while it is higher in intermediate depths (5 m–15 m), where the satellite spectral information is dominated by the water body information, and the bottom substrate information plays a secondary role. These results suggest that these areas are prone to the SSPBDD phenomenon.
- (2)
- The RF model and the SVM model show considerable changes in RMSE under the influence of different spatial distributions in the training datasets, while the Stumpf model and the MDN model are the least affected. However, the Stumpf model typically achieves the lowest inversion accuracy.
- (3)
- The SSPBDD phenomenon can reduce the accuracy of water depth inversion models, the number and the maximum depth difference of the SSPBDD pixels in a group are the main influencing factors. Adding optical bands not only effectively decreases the percentage of SSPBDD pixels but also improves the inversion accuracy of the machine learning models. After incorporating the dual-band logarithmic ratio, DII, and spatial neighbourhood information, the MDN models show a more significant improvement in water depth inversion accuracy. Compared to the other models, the MDN model better handles the uncertainty caused by the SSPBDD phenomenon and the spatial distribution of the training dataset.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Satellite | Region | Acquisition Time (UTC) | Tide Height (m) | Product Level | Band and Spectral Range (nm) | Spatial Resolution (m) |
---|---|---|---|---|---|---|
Sentinel-2B | Oahu Island | 2020-12-01 21:09 | 0.29 | Level 2A | B1: 433–453 | 60 |
B2: 458–523 B3: 543–578 B4: 650–680 | 10 | |||||
Sentinel-2A | Buck Island | 2022-01-29 14:57 | 0.71 |
Band Combination | Percentage of Pixels with the SSPBDD/All Water Pixels within 0–20 m on Oahu Island | Percentage of Pixels with the SSPBDD/All Water Pixels within 0–20 m on Buck Island |
---|---|---|
B2, B3 | 124,662/328,514 ≈ 37.9% | 233,398/251,867 ≈ 92.7% |
B2, B3, B4 | 86,513/328,514 ≈ 26.3% | 141,093/251,867 ≈ 56.0% |
B1, B2, B3, B4 | 12,188/328,514 ≈ 3.7% | 1878/251,867 ≈ 0.7% |
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© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
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Huang, E.; Chen, B.; Luo, K.; Chen, S. Effect of the One-to-Many Relationship between the Depth and Spectral Profile on Shallow Water Depth Inversion Based on Sentinel-2 Data. Remote Sens. 2024, 16, 1759. https://doi.org/10.3390/rs16101759
Huang E, Chen B, Luo K, Chen S. Effect of the One-to-Many Relationship between the Depth and Spectral Profile on Shallow Water Depth Inversion Based on Sentinel-2 Data. Remote Sensing. 2024; 16(10):1759. https://doi.org/10.3390/rs16101759
Chicago/Turabian StyleHuang, Erhui, Benqing Chen, Kai Luo, and Shuhan Chen. 2024. "Effect of the One-to-Many Relationship between the Depth and Spectral Profile on Shallow Water Depth Inversion Based on Sentinel-2 Data" Remote Sensing 16, no. 10: 1759. https://doi.org/10.3390/rs16101759