Estimating Satellite-Derived Bathymetry (SDB) with the Google Earth Engine and Sentinel-2

Abstract

Bathymetry mapping forms the basis of understanding physical, economic, and ecological processes in the vastly biodiverse coastal fringes of our planet which are subjected to constant anthropogenic pressure. Here, we pair recent advances in cloud computing using the geospatial platform of the Google Earth Engine (GEE) with optical remote sensing technology using the open Sentinel-2 archive, obtaining low-cost in situ collected data to develop an empirical preprocessing workflow for estimating satellite-derived bathymetry (SDB). The workflow implements widely used and well-established algorithms, including cloud, atmospheric, and sun glint corrections, image composition and radiometric normalisation to address intra-and inter-image interferences before training, and validation of four SDB algorithms in three sites of the Aegean Sea in the Eastern Mediterranean. Best accuracy values for training and validation were R 2 = 0.79, RMSE = 1.39 m, and R 2 = 0.9, RMSE = 1.67 m, respectively. The increased accuracy highlights the importance of the radiometric normalisation given spatially independent calibration and validation datasets. Spatial error maps reveal over-prediction over low-reflectance and very shallow seabeds, and under-prediction over high-reflectance (<6 m) and optically deep bottoms (>17 m). We provide access to the developed code, allowing users to map bathymetry by customising the time range based on the field data acquisition dates and the optical conditions of their study area.

Towards Routine Mapping of Shallow Bathymetry in Environments with Variable Turbidity: Contribution of Sentinel-2A/B Satellites Mission

“…Whereas some researchers suggested there is still work to be performed regarding the identification of the optimal period throughout the year where bathymetric errors are minimized [18,29], others asked for novel strategies to allow seabed mapping without the laborious analysis per image and the visual inspection of the "clearest scene" [19]. Recent studies have already indicated the potential of multi-scene approaches in order to select the optimal scene or eliminate noise over clear waters [16,17,19,20]. However, our temporal compositing strategy successfully reduced the turbidity impact without requirement of visual inspection, thereby enhancing SDB performance in an easy way.…”

“…In accordance with our findings, the portability and reliability application of the proposed approach using minimum in-situ measurements is a distinct advantage in effectiveness and may especially benefit developing countries. In addition, using the semi-automated solution and a computer cloud-based system may allow exploitation of the Sentinel-2 data for regional to global scale coastal SDB [16]. The average time required for processing a Sentinel-2 image for bathymetry estimation is 1 hour.…”

“…There have been previous studies inspecting water quality issues on SDB [11][12][13][14][15]. Recent studies have already indicated the potential of multi-scene approaches in order to eliminate noise over clear waters [16][17][18][19][20]. The impact of turbidity on SDB is not random and often appears as a false shoal [21,22].…”

Mapping Winter Crops in China with Multi-Source Satellite Imagery and Phenology-Based Algorithm

“…All images were the top of atmosphere (TOA) reflectance. Landsat and Sentinel-2 TOA data have been widely used for remote sensing monitoring in previous studies [38,60,61]; for example, Dong et al [41] mapped paddy rice planting areas in northeastern Asia with Landsat-8 TOA images. Additionally, the Google Earth Engine has cloud-mask model codes for Landsat and Sentinel-2 TOA data production and, thus, we can easily remove the pixels which were covered with clouds.…”

Impact of Various Atmospheric Corrections on Sentinel-2 Land Cover Classification Accuracy Using Machine Learning Classifiers

“…The first step in atmospheric correction procedure is the division of satellite image values with quantification value of 1000 to obtain TOA reflectance [16]. TOA reflectance is the amount of reflectance reflected off the surface which can be collected using a satellite sensor.…”