Atmospheric correction for satellite-derived bathymetry in the Caribbean waters: from a single image to multi-temporal approaches using Sentinel-2A/B

Confronting turbidity, the major challenge for satellite-derived coastal bathymetry

Monitoring the complex seafloor morphology that drives the functioning of shallow coastal ecosystems is vital for assessing marine activities. Satellite-derived bathymetry (SDB) can provide a crucial dataset for creating the bathymetry maps needed to understand hazards and impacts produced by climate change in vulnerable coastal zones. SDB is effective in clear water, but still has limitations in application to areas with some turbidity. Here, using the twin satellites Sentinel-2A/B, we integrate water quality information from the satellite with a multi-temporal compositing method to demonstrate a potential for comprehensively operational bathymetric mapping over a range of environments. The automated compositing method diminishes the turbidity impact in addition to inferring the maximum detectable depth and removing optically deep-water areas. Examining a wide range of conditions along the Caribbean and eastern coast of the U.S. shows detailed bathymetry as deep as 30 m at 10 m spatial resolution with median errors <1 m when compared to high-resolution lidar surveys. These results demonstrate that the model adopted can provide useful bathymetry in areas that do not have consistently clear water and can be extended across multiple geographic regions and optical conditions at local, regional, and national scales.

Use of the Sentinel-2 and Landsat-8 Satellites for Water Quality Monitoring: An Early Warning Tool in the Mar Menor Coastal Lagoon

During recent years, several eutrophication processes and subsequent environmental crises have occurred in Mar Menor, the largest hypersaline coastal lagoon in the Western Mediterranean Sea. In this study, the Landsat-8 and Sentinel-2 satellites are jointly used to examine the evolution of the main water quality descriptors during the latest ecological crisis in 2021, resulting in an important loss of benthic vegetation and unusual mortality events affecting different aquatic species. Several field campaigns were carried out in March, July, August, and November 2021 to measure water quality variables over 10 control points. The validation of satellite biogeochemical variables against on-site measurements indicates precise results of the water quality algorithms with median errors of 0.41 mg/m3 and 2.04 FNU for chlorophyll-a and turbidity, respectively. The satellite preprocessing scheme shows consistent performance for both satellites; therefore, using them in tandem can improve mapping strategies. The findings demonstrate the suitability of the methodology to capture the spatiotemporal distribution of turbidity and chlorophyll-a concentration at 10–30 m spatial resolution on a systematic basis and in a cost-effective way. The multitemporal products allow the identification of the main critical areas close to the mouth of the Albujon watercourse and the beginning of the eutrophication process with chlorophyll-a concentration above 3 mg/m3. These innovative tools can support decision makers in improving current monitoring strategies as early warning systems for timely assistance during these ecological disasters, thus preventing detrimental conditions in the lagoon.

Water quality monitoring with Sentinel-2 and Landsat-8 satellites during the 2021 volcanic eruption in La Palma (Canary Islands)

In this study, seawater quality was monitored with high-resolution satellite imagery during the 2021 volcanic eruption (September-December) on La Palma Island (Spain), the longest recorded in the history of the island, and the most destructive in the last century in Europe. The Sentinel-2A/B twin satellites and Landsat-8 satellite were jointly used as an optical constellation, which allowed us to successfully characterize the short- and medium-term evolution of the new lava delta and subsequent impact on the seawater. Robust atmospheric and sunglint correction approaches were applied to thoroughly quantify the environmental changes caused on the adjacent coastal waters. The cloud and volcanic ash coverage remained very high over the coast during the event, so restricted information with 14 images (45% of the total scenes) was retrieved from the multi-sensor approach. Nevertheless, the availability of pre-, syn-, and post-eruption satellite products allowed us to map and detect the main water quality variations in the marine environment. On the one hand, during the eruption, a change in the properties of the water quality was observed, with a markedly increased turbidity on the western side of the island near the new lava delta due to the deposition of volcanic ash and material. On the other hand, chlorophyll-a concentration did not significantly increase, algal blooms were not observed, and oligotrophic conditions were not swiftly altered towards eutrophic conditions. This information offered an excellent opportunity to characterize the emplacement of the new lava delta and its impact on the marine environment in La Palma. The present multi-sensor strategy is an excellent opportunity to highlight the potential of remote sensing technology as a relevant and powerful tool for future hazard monitoring and assessment during catastrophes and for a better interpretation of their impact on the marine environment.

Assessing the Ability to Quantify Bathymetric Change over Time Using Solely Satellite-Based Measurements

Coastal regions are undergoing rapid change, due to natural and anthropogenic forcings. A current constraint in understanding and modeling these changes is the lack of multi-temporal bathymetric data, or recursive observations. Often, it is difficult to obtain the repeat observations needed to quantify bathymetric change over time or events. However, the recent availability of ICESat-2 bathymetric lidar creates the option to map coastal bathymetry from solely space-based measurements via satellite-derived bathymetry with multispectral imagery (IS-2/SDB). This compositional space-based bathymetric mapping technique can assess temporal change along the coasts without other remote sensing or in situ data. However, questions exist as to the accuracy of the technique relative to both quantitative uncertainties and the ability to resolve the spatial patterns of erosion and deposition in the nearshore environment, indicative of geomorphologic change. This paper addresses the concept using data from the Florida panhandle (Northern Gulf of Mexico) collected by Sentinel-2 and ICESat-2 at two epochs to assess the feasibility of using IS-2/SDB for bathymetric change detection at scientifically relevant scales, spatial resolutions and accuracies. The comparison of the satellite-only result is compared to airborne data collected at similar epochs to reveal both quantitatively and qualitatively the utility of this technique.

Satellite-derived bathymetry using Landsat-8 and Sentinel-2A images: assessment of atmospheric correction algorithms and depth derivation models in shallow waters

Satellite-derived bathymetry (SDB) has an extensive prospect in nearshore bathymetry for its high efficiency and low costs. Atmospheric correction and bathymetric modeling are critical processes in SDB, and examining the performance of related algorithms and models will contribute to the formulation of reliable bathymetry strategies. This study explored the effectiveness of three general atmospheric correction algorithms, namely Second Simulation of a Satellite Signal in the Solar Spectrum (6S), Atmospheric correction for OLI 'lite' (ACOLITE), and QUick Atmospheric Correction (QUAC), in depth retrieval from Landsat-8 and Sentinel-2A images using different SDB models over Ganquan Island and Oahu Island. The bathymetric Light Detection and Ranging (LiDAR) data was used for SDB model training and accuracy verification. The results indicated that the three atmospheric correction algorithms could provide effective corrections for SDB. For the SDB models except log-transformed band ratio model (LBR) and support vector machine (SVM), the impact of different atmospheric corrections on bathymetry was basically the same. Furthermore, we assessed the performance of six different SDB models: Lyzenga's model (LM), generalized additive model (GAM), LBR, SVM, multilayer perceptron (MLP), and random forest (RF). The bathymetric accuracy, consistency of bathymetric maps and generalization ability were considered for the assessment. Given sufficient training data, the accuracy of the machine learning models (SVM, MLP, RF) was generally superior to that of the empirical inversion models (LM, GAM, LBR), with the root mean square error (RMSE) varied between 0.735 m to 1.177 m. MLP achieved the best accuracy and consistency. When the depth was deeper than 15 m, the bathymetry error of all the SDB models increased sharply, and LM, LBR and SVM reached the upper limit of depth retrieval capability at 20-25 m. In addition, LM and LBR were demonstrated to have better adaptability in heterogeneous environment without training data.

Monitoring cyanoHABs and water quality in Laguna Lake (Philippines) with Sentinel-2 satellites during the 2020 Pacific typhoon season

Laguna Lake, the largest freshwater lake in the Philippines, is permanently subject to nutrient-driven eutrophication and pollution and experiences harmful algal blooms (cyanoHABs) periodically with serious socio-economic implications. The aim of this study is to evaluate the suitability of the Sentinel-2 imagery of the European Commission's Copernicus Earth Observation programme for lake monitoring during the 2020 Pacific typhoon season (September-November 2020). The Case-2 Regional CoastColour processor is used to atmospherically correct Level 1 data and generate water quality parameters, such as chlorophyll-a (Chl-a) and total suspended matter (TSM) at 10 m. Results show that Super Typhoon Goni and Typhoon Vamco delivered high suspended sediment loads to the reservoir at concentrations above 170 g/m³ compared to pre-storm situations (0-35 g/m³). The typhoons also affect Chl-a, with a mean concentration of 10 mg/m³ and 30 mg/m³ for pre- and post-typhoons, respectively. In addition, the normalized difference chlorophyll index (NDCI) is used in the Google Earth Engine platform for near-real time monitoring of cyanoHABs at 20 m spatial resolution. Satellite maps are key for detecting the distribution of the blooms due to the patchiness of the green algae species, which usually form scum and elongated slicks in the lake. Maximum records of bloom detection during the study period occur in the Central Bay, one of the lake sections with major aquaculture and fisheries activities. The Sentinel-2 mission improves synoptic mapping of cyanoHABs and enables trends in their extent and severity to be documented. These available methods provide an essential tool for rapid detection after extreme events and for regular water quality monitoring, which will assist and benefit the cost-effective management of Laguna Lake.

Comparison of In Situ and Remote-Sensing Methods to Determine Turbidity and Concentration of Suspended Matter in the Estuary Zone of the Mzymta River, Black Sea

The paper presents the results of a comparison of water turbidity and suspended particulate matter concentration (SPM) obtained from quasi-synchronous in situ and satellite remote-sensing data. Field measurements from a small boat were performed in April and May 2019, in the northeastern part of the Black Sea, in the mouth area of the Mzymta River. The measuring instruments and methods included a turbidity sensor mounted on a CTD (Conductivity, Temperature, Depth), probe, a portable turbidimeter, water sampling for further laboratory analysis and collecting meteorological information from boat and ground-based weather stations. Remote-sensing methods included turbidity and SPM estimation using the C2RCC (Case 2 Regional Coast Color) and Atmospheric correction for OLI ‘lite’ (ACOLITE) ACOLITE processors that were run on Landsat-8 Operational Land Imager (OLI) and Sentinel-2A/2B Multispectral Instrument (MSI) satellite data. The highest correlation between the satellite SPM and the water sampling SPM for the study area in conditions of spring flooding was achieved using C2RCC, but only for measurements undertaken almost synchronously with satellite imaging because of the high mobility of the Mzymta plume. Within the few hours when all the stations were completed, its boundary could shift considerably. The ACOLITE algorithms overestimated by 1.5 times the water sampling SPM in the low value range up to 15 g/m3. For SPM over 20–25 g/m3, a high correlation was observed both with the in situ measurements and the C2RCC results. It was demonstrated that quantitative turbidity and SPM values retrieved from Landsat-8 OLI and Sentinel-2A/2B MSI data can adequately reflect the real situation even using standard retrieval algorithms, not regional ones, provided the best suited algorithm is selected for the study region.