Exploring the Most Effective Information for Satellite-Derived Bathymetry Models in Different Water Qualities
Abstract
:1. Introduction
2. Study Areas and Dataset
2.1. Sentinel-2 Data
2.2. ICESat-2 Data
3. Methods
3.1. ICESat-2 Bottom Photon Detection
3.2. QAA for Sentinel-2
3.3. Dimensionality Reduction
3.4. SDB Machine Learning Models
3.5. Validation Metrics
4. Results
4.1. Dataset Dimension Reduction and QAA Algorithm Results
4.2. Results of Different SDB Models and Training Dataset
5. Discussion
5.1. Error Analysis
5.2. Variations in the Quantities of Information during Feature Selection Process
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Location Center Coordinates | (A) Wenchang | (B) Laizhou Bay | (C) Qilian Islands | |||||||
---|---|---|---|---|---|---|---|---|---|---|
19.7810°N, 111.0021°E | 37.2724°N, 119.8064°E | 16.9803°N, 112.2386°E | ||||||||
Inherent optical parameters, IOPs (m−1) | a488 | bb488 | Kd490 | a488 | bb488 | Kd490 | a488 | bb488 | Kd490 | |
0.09 | 0.011 | 0.21 | 0.23 | 0.032 | 0.58 | 0.03 | 0.006 | 0.04 | ||
Sentinel-2 image | 21 June 2020 | 9 September 2019 | 4 April 2023 | |||||||
ICESat-2 points (training and validation) | 28 January 2019 | 20 June 2020 | 26 October 2019 | 13 April 2021 | ||||||
29 April 2019 | 26 July 2020 | 21 March 2020 | 16 July 2021 | |||||||
22 June 2019 | 18 June 2021 | 19 September 2020 | 14 January 2022 | |||||||
29 July 2019 | 24 July 2021 | 24 December 2020 | 15 July 2022 | |||||||
21 September 2019 | 17 September 2021 | 17 September 2021 | 9 January 2023 |
Rrs | 490 nm | 560 nm | 665 nm | 865 nm |
a | 443 nm | 490 nm | 560 nm | 665 nm |
bbp | 443 nm | 490 nm | 560 nm | 665 nm |
Study Area | Training Algorithm | RMSE (m) (Training) | R2 (Training) | RMSE (m) (Validation) | R2 (Validation) |
---|---|---|---|---|---|
Wenchang | GPR with Rrs | 0.85 | 0.90 | 0.92 | 0.88 |
GPR with QAA-IOP | 0.79 | 0.92 | 0.80 | 0.91 | |
NN with Rrs | 0.88 | 0.90 | 0.91 | 0.88 | |
NN with QAA-IOP | 0.80 | 0.91 | 0.85 | 0.91 | |
RF with Rrs | 0.87 | 0.90 | 0.92 | 0.88 | |
RF with QAA-IOP | 0.83 | 0.91 | 0.87 | 0.89 | |
SVR with Rrs | 0.89 | 0.90 | 0.91 | 0.89 | |
SVR with QAA-IOP | 0.89 | 0.89 | 0.79 | 0.91 | |
Laizhou Bay | GPR with Rrs | 0.49 | 0.71 | 0.54 | 0.71 |
GPR with QAA-IOP | 0.44 | 0.77 | 0.49 | 0.74 | |
NN with Rrs | 0.51 | 0.68 | 0.55 | 0.70 | |
NN with QAA-IOP | 0.44 | 0.78 | 0.48 | 0.77 | |
RF with Rrs | 0.50 | 0.70 | 0.56 | 0.68 | |
RF with QAA-IOP | 0.45 | 0.75 | 0.52 | 0.73 | |
SVR with Rrs | 0.52 | 0.68 | 0.58 | 0.67 | |
SVR with QAA-IOP | 0.48 | 0.74 | 0.51 | 0.72 | |
Qilian Islands | GPR with Rrs | 0.72 | 0.96 | 0.75 | 0.97 |
NN with Rrs | 0.73 | 0.96 | 0.74 | 0.96 | |
RF with Rrs | 0.91 | 0.94 | 0.93 | 0.94 | |
SVR with Rrs | 0.98 | 0.93 | 0.97 | 0.94 |
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Liu, Z.; Liu, H.; Ma, Y.; Ma, X.; Yang, J.; Jiang, Y.; Li, S. Exploring the Most Effective Information for Satellite-Derived Bathymetry Models in Different Water Qualities. Remote Sens. 2024, 16, 2371. https://doi.org/10.3390/rs16132371
Liu Z, Liu H, Ma Y, Ma X, Yang J, Jiang Y, Li S. Exploring the Most Effective Information for Satellite-Derived Bathymetry Models in Different Water Qualities. Remote Sensing. 2024; 16(13):2371. https://doi.org/10.3390/rs16132371
Chicago/Turabian StyleLiu, Zhen, Hao Liu, Yue Ma, **n Ma, Jian Yang, Yang Jiang, and Shaohui Li. 2024. "Exploring the Most Effective Information for Satellite-Derived Bathymetry Models in Different Water Qualities" Remote Sensing 16, no. 13: 2371. https://doi.org/10.3390/rs16132371