Machine Learning Based Estimation of Coastal Bathymetry From ICESat-2 and Sentinel-2 Data

Xu, Nan; Wang, Lin; Zhang, Han-Su; Tang, Shilin; Mo, Fan; Ma, Xin

doi:10.1109/jstars.2023.3326238

Cited by 4 publications

(1 citation statement)

References 46 publications

(50 reference statements)

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“…The bathymetric dataset was used to correct light attenuation by the water column for resolving bottom reflectance [26,32]. The bathymetric data can be obtained from lidar and multispectral remote sensing data [57]. Adding bathymetry data for this region could improve the model performance.…”

Section: Discussionmentioning

confidence: 99%

Quantifying Seagrass Density Using Sentinel-2 Data and Machine Learning

Meister,

2024

Remote Sensing

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Seagrasses, rooted aquatic plants growing completely underwater, are extremely important for the coastal ecosystem. They are an important component of the total carbon burial in the ocean, they provide food, shelter, and nursery to many aquatic organisms in coastal ecosystems, and they improve water quality. Due to human activity, seagrass coverage has been rapidly declining, and there is an urgent need to monitor seagrasses consistently. Seagrass coverage has been closely monitored in the Chesapeake Bay since 1970 using air photos and ground samples. These efforts are costly and time-consuming. Many studies have used remote sensing data to identify seagrass bed outlines, but few have mapped seagrass bed density. This study used Sentinel-2 satellite data and machine learning in Google Earth Engine and the Chesapeake Bay Program field data to map seagrass density. We used seagrass density data from the Chincoteague and Sinepuxent Bay to train machine learning algorithms and evaluate their accuracies. Out of the four machine learning models tested (Naive Bayes (NB), Classification and Regression Trees (CART), Support Vector Machine (SVM), and Random Forest (RF)), the RF model outperformed the other three models with overall accuracies of 0.874 and Kappa coefficients of 0.777. The SVM and CART models performed similarly and NB performed the poorest. We tested two different approaches to assess the models’ accuracy. When we used all the available ground samples to train the models, whereby our analysis showed that model performance was associated with seagrass density class, and that higher seagrass density classes had better consumer accuracy, producer accuracy, and F1 scores. However, the association of model performance with seagrass density class disappeared when using the same training data size for each class. Very sparse and dense seagrass classes had replacedhigherbetter accuracies than the sparse and moderate seagrass density classes. This finding suggests that training data impacts machine learning model performance. The uneven training data size for different classes can result in biased assessment results. Selecting proper training data and machine learning models are equally important when using machine learning and remote sensing data to map seagrass density. In summary, this study demonstrates the potential to map seagrass density using satellite data.

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Section: Discussionmentioning

confidence: 99%

Quantifying Seagrass Density Using Sentinel-2 Data and Machine Learning

Meister,

2024

Remote Sensing

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MuSRFM: Multiple scale resolution fusion based precise and robust satellite derived bathymetry model for island nearshore shallow water regions using sentinel-2 multi-spectral imagery

Qin,

Wu,

Luo

et al. 2024

ISPRS Journal of Photogrammetry and Remote Sensing

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Exploring the Most Effective Information for Satellite-Derived Bathymetry Models in Different Water Qualities

Liu,

et al. 2024

Remote Sensing

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Satellite-derived bathymetry (SDB) is an effective means of obtaining global shallow water depths. However, the effect of inherent optical properties (IOPs) on the accuracy of SDB under different water quality conditions has not been clearly clarified. To enhance the accuracy of machine learning SDB models, this study aims to assess the performance improvement of integrating the quasi-analytical algorithm (QAA)-derived IOPs using the Sentinel-2 and ICESat-2 datasets. In different water quality experiments, the results indicate that four SDB models (the Gaussian process regression, neural networks, random forests, and support vector regression) incorporating QAA-IOP parameters equal to or outperform those solely based on the remote sensing reflectance (Rrs) datasets, especially in turbid waters. By analyzing information gains in SDB, the most effective inputs are identified and prioritized under different water qualities. The SDB method incorporating QAA-IOP can achieve an accuracy of 0.85 m, 0.48 m, and 0.74 m in three areas (Wenchang, Laizhou Bay, and the Qilian Islands) with different water quality. Also, we find that incorporating an excessive number of redundant bands into machine learning models not only increases the demand of computing resources but also leads to worse accuracy in SDB. In conclusion, the integration of QAA-IOPs offers promising improvements in obtaining bathymetry and the optimal feature selection should be carefully considered in diverse aquatic environments.

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Machine Learning Based Estimation of Coastal Bathymetry From ICESat-2 and Sentinel-2 Data

Cited by 4 publications

References 46 publications

Quantifying Seagrass Density Using Sentinel-2 Data and Machine Learning

Quantifying Seagrass Density Using Sentinel-2 Data and Machine Learning

MuSRFM: Multiple scale resolution fusion based precise and robust satellite derived bathymetry model for island nearshore shallow water regions using sentinel-2 multi-spectral imagery

Exploring the Most Effective Information for Satellite-Derived Bathymetry Models in Different Water Qualities

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