Predicting bathymetry in shallow regions using a machine learning model and a time series of PlanetScope images

The aim of this study conducted in Tavira - Portugal, is to show the ability to determine depths without relying on in-situ data. To achieve this goal, a model previously trained with depth data and multispectral images from 2018 was used. This model enables depth determination for any period, providing multispectral images.

For this study, Cube satellite images from the PlanetScope constellation with a spatial resolution of 3.0 m and four spectral bands (blue, green, red, and near-infrared) were used. Corrections due to tidal height were obtained through modeled data provided by the Portuguese Hydrographic Institute for the tide gauge of Faro – Olhão. In-situ depths were obtained through the Digital Elevation Model of Reference (MDER) from the Coastal Monitoring Program of Continental Portugal of the Portuguese Environmental Agency.

The model used to determine depths was previously obtained using the Random Forest (RF) algorithm, trained with a set of reflectances from 15 images acquired between August and October 2018 by the PlanetScope constellation, and a set of depths from the MDER, referring to October 2018.

This RF model allowed the depth determination for a set of 7 images from the same constellation, acquired between August and October 2019. The results were corrected for tidal height to obtain all values in relation to the Hydrographic Zero reference. The Savitzky-Golay filter was applied to smooth the results, and then the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm was applied to eliminate outliers. Finally, the median depth value was determined, resulting in a bathymetric surface morphologically similar to the MDER (2019).

This final surface was compared with the 2019 MDER through differences between the two surfaces (residuals) and the respective statistics were calculated (mean, median, standard deviation, and histogram). A vertical profile between 0.0 and 10.0 meters of depth was also generated. The statistical results of the differences reveal a median of 0.5 meters, a mean of 0.7 meters, and a standard deviation of 1.3 meters. The histogram of differences between the two surfaces follows a normal distribution, with its center located at the median value, which is offset from zero.

The results obtained in this study are promising for obtaining depths in coastal regions through multispectral images without the need for in-situ data. However, we are aware that improving the current model is important to reduce the median and standard deviation of the differences between the determined depth and the reference. Enhancing the model will lead to more accurate results, enabling the determination of seasonal variations and changes caused by extreme events or climate alterations without in-situ data.