On improved nearshore bathymetry estimates from satellites using ensemble and machine learning approaches
| Autor: | V. V. Arun Kumar Surisetty, Rashmi Sharma, K. V. S. R. Prasad, Ch. Venkateswarlu, B. Gireesh |
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| Rok vydání: | 2021 |
| Předmět: |
Atmospheric Science
business.industry Empirical modelling Aerospace Engineering Astronomy and Astrophysics Machine learning computer.software_genre Pearson product-moment correlation coefficient Support vector machine Set (abstract data type) symbols.namesake Geophysics Data point Space and Planetary Science symbols Range (statistics) General Earth and Planetary Sciences Bathymetry Artificial intelligence Test phase business computer Mathematics |
| Zdroj: | Advances in Space Research. 68:3342-3364 |
| ISSN: | 0273-1177 |
| DOI: | 10.1016/j.asr.2021.06.034 |
| Popis: | In this article, two popular linear empirical methods viz., log-ratio model (LRM) and log-linear model (LLM) are used to derive water depths in shallow nearshore waters from Sentinel-2 multi-spectral images. Based on these empirical models, a multi-scene ensemble and a non-linear Support Vector Regression (SVR) machine learning approaches are applied to improve the accuracies from these traditional methods. In this analysis, firstly the best scene is selected from a set of six Sentinel-2 A&B images using noise-equivalent reflectance NEΔRrs (sr−1), optimal bands for LRM and LLM are selected using Optimal band ratio analysis (OBRA) and by computing Pearson Correlation Coefficient (R) between each band respectively. A total of 80% depth data points obtained from JetSki based echo-sounding measurements are used for training and the remaining 20% are used for testing each approach. The overall errors during the test phase for the range of depth 0–12 m are 0.79 m (0.67 m), 0.94 m (0.66 m) and 0.57 m (0.39 m) using traditional empirical LRM (LLM) methods from the best image, empirical-based ensemble and SVR approaches respectively. Irrespective of the approach, the LLM produced smoother and relatively accurate bathymetry as compared to the LRM. The LLM based SVR ML approach provides the best performance over the entire depth range as compared to all the methods considered in this study. Therefore, this method can be used for efficiently estimating nearshore depths and produce updated high-resolution bathymetry maps for many coastal applications. |
| Databáze: | OpenAIRE |
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