Ocean color as a proxy to predict sea surface salinity in the Banda Sea

Salinity is an important ocean parameter that greatly influences physical, chemical, and biological ocean properties and processes. Salinity combines with sea temperature and chlorophyll-a (Chl-a) that mostly sourced from remote sensing-based measurements can reveal ocean quality and supports fisheries. However, the satellite-derived Sea Surface Salinity (SSS) dataset (∼ 9 years) is not as temporally adequate as SST and Chl-a datasets (∼3 decades) and thus, preventing a comprehensively spatio-temporal analysis of this water quality aspect. Since (SSS) can be approximated using satellite-derived ocean color products having the similar temporal length of datasets to the available SST and Chl-a datasets, predicted SSS can be produced from these ocean color products to fill the gap of the existing SSS dataset. This study aims to estimate the SSS from ocean color products of Aqua-MODIS satellite with a spatial and temporal resolution of 4 km and 8-daily by developing an empirical model. The ocean color data used were remote sensing reflectance (Rrs) of blue, green and red wavelengths (412, 433, 469, 488, 531, 547, 555, 645, 667 and 678 nm). The absorption coefficients due to detritus material non-algae, Gelbstof and CDOM (ADG) at 443 nm and the absorption coefficient due to phytoplankton (APH) at 443 nm data were also used. The Banda Sea was chosen due to its large-scale upwelling system (∼300 km × 300 km) that providing an important ocean process related to fishery and the availability of in-situ salinity measurements (i.e. CTD casts from series of Research Vessel (R/V) Baruna Jaya III, VII and VIII cruises and Argo floats), which a part of these datasets will be used to validate predicted SSS. Results showed that of all ocean color parameters tested, ADG at 443 nm was strongly correlated with in-situ SSS through the polynomial order 5 regression equation with a high R2 of 0.94 and a low RMES value of 0.101 PSU. Although this empirical model has high accuracy, but based on RMSE analysis results from various locations within and outside the Banda Sea that influenced by the Pacific and the Indian ocean water masses indicates that this model actually good to predict in-situ SSS only for a narrow range SSS of 33.4-34.5 PSU. Nevertheless, this model has a limitation, it is still can be used for predicting and mapping the SSS for Banda Sea as well as for most of the Indonesian waters. The long-term meteorological SSS map (2003-2017) derived by this model together with the SST and Chl-a maps can show clearly the upwelling phenomena of the Banda Sea, which occurred during the southeast monsoon (June-July-August, JJA). This study proves that ocean color data from Aqua-MODIS satellite can be applied to estimate and to map the SSS for most of the Indonesian waters, but validations for this model is still neede

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Ocean salinity affects ocean circulation on a global scale and low salinity water around coastal areas often has an impact on aquaculture and fisheries. Microwave satellite sensors (e.g., Soil Moisture Active Passive [SMAP]) have provided sea surface salinity (SSS) based on the dielectric characteristics of water associated with SSS and sea surface temperature (SST). In this study, a Light Gradient Boosting Machine (LGBM)-based model for generating high resolution SSS from Geostationary Ocean Color Imager (GOCI) data was proposed, having machine learning-based improved SMAP SSS by Jang et al. (2022) as reference data (SMAP SSS (Jang)). Three schemes with different input variables were tested, and scheme 3 with all variables including Multi-scale Ultra-high Resolution SST yielded the best performance (coefficient of determination = 0.60, root mean square error = 0.91 psu). The proposed LGBM-based GOCI SSS had a similar spatiotemporal pattern with SMAP SSS (Jang), with much higher spatial resolution even in coastal areas, where SMAP SSS (Jang) was not available. In addition, when tested for the great flood occurred in Southern China in August 2020, GOCI SSS well simulated the spatial and temporal change of Changjiang Diluted Water. This research provided a potential that optical satellite data can be used to generate high resolution SSS associated with the improved microwave-based SSS especially in coastal areas

Influence of suspended sediment front on nutrients and phytoplankton dynamics off the Changjiang Estuary: A FVCOM-ERSEM coupled model experiment

Under embargo until: 2021-12-27High-turbidity water is a common feature in the estuary and inner shelf. Sediment suspension functions as a modulator that directly influences the interactions among nutrients, phytoplankton and other related ecosystem variables. A physical-biological coupling model system was applied to examine the impact of sediment front on interactions among on suspended sediment, vertical mixing, nutrients and phytoplankton over the inner shelf off the high-turbidity, phosphate-limited Changjiang Estuary. The physical model was the Finite-Volume Community Ocean Model (FVCOM) and the biological model was the European Regional Seas Ecosystem Model (ERSEM). Results revealed that in the nearshore region the growth of phytoplankton over the spring-summer seasons was limited by suspended sediments and intensified vertical mixing during the autumn-winter seasons extended the sediment-induced suppression extended offshore to restrict the phytoplankton growth over the shelf. Nutrients were diluted by spreading of freshwater discharge and significantly decreased off the suspended sediment front due to the depletion by the offshore phytoplankton growth. The simulation results showed that although the diatom phytoplankton dominated the Chlorophyll a (Chl-a) concentration, the non-diatom group had a more contribution to the biomass. The relatively high phytoplankton biomass was found over the offshore deep underwater valley area as results of remote advection by the Taiwan Warm Current and weak turbulent mixing.acceptedVersio

Detection and Monitoring of Marine Pollution Using Remote Sensing Technologies

Recently, the marine habitat has been under pollution threat, which impacts many human activities as well as human life. Increasing concerns about pollution levels in the oceans and coastal regions have led to multiple approaches for measuring and mitigating marine pollution, in order to achieve sustainable marine water quality. Satellite remote sensing, covering large and remote areas, is considered useful for detecting and monitoring marine pollution. Recent developments in sensor technologies have transformed remote sensing into an effective means of monitoring marine areas. Different remote sensing platforms and sensors have their own capabilities for mapping and monitoring water pollution of different types, characteristics, and concentrations. This chapter will discuss and elaborate the merits and limitations of these remote sensing techniques for mapping oil pollutants, suspended solid concentrations, algal blooms, and floating plastic waste in marine waters

Estimation of Fugacity of Carbon Dioxide in the East Sea Using In Situ Measurements and Geostationary Ocean Color Imager Satellite Data

The ocean is closely related to global warming and on-going climate change by regulating amounts of carbon dioxide through its interaction with the atmosphere. The monitoring of ocean carbon dioxide is important for a better understanding of the role of the ocean as a carbon sink, and regional and global carbon cycles. This study estimated the fugacity of carbon dioxide (fCO(2)) over the East Sea located between Korea and Japan. In situ measurements, satellite data and products from the Geostationary Ocean Color Imager (GOCI) and the Hybrid Coordinate Ocean Model (HYCOM) reanalysis data were used through stepwise multi-variate nonlinear regression (MNR) and two machine learning approaches (i.e., support vector regression (SVR) and random forest (RF)). We used five ocean parameters-colored dissolved organic matter (CDOM; <0.3 m(-1)), chlorophyll-a concentration (Chl-a; <21 mg/m(3)), mixed layer depth (MLD; <160 m), sea surface salinity (SSS; 32-35), and sea surface temperature (SST; 8-28 degrees C)-and four band reflectance (Rrs) data (400 nm-565 nm) and their ratios as input parameters to estimate surface seawater fCO(2) (270-430 mu atm). Results show that RF generally performed better than stepwise MNR and SVR. The root mean square error (RMSE) of validation results by RF was 5.49 mu atm (1.7%), while those of stepwise MNR and SVR were 10.59 mu atm (3.2%) and 6.82 mu atm (2.1%), respectively. Ocean parameters (i.e., sea surface salinity (SSS), sea surface temperature (SST), and mixed layer depth (MLD)) appeared to contribute more than the individual bands or band ratios from the satellite data. Spatial and seasonal distributions of monthly fCO(2) produced from the RF model and sea-air CO2 flux were also examined

Spatial-Temporal Data Mining for Ocean Science: Data, Methodologies, and Opportunities

With the increasing amount of spatial-temporal~(ST) ocean data, numerous spatial-temporal data mining (STDM) studies have been conducted to address various oceanic issues, e.g., climate forecasting and disaster warning. Compared with typical ST data (e.g., traffic data), ST ocean data is more complicated with some unique characteristics, e.g., diverse regionality and high sparsity. These characteristics make it difficult to design and train STDM models. Unfortunately, an overview of these studies is still missing, hindering computer scientists to identify the research issues in ocean while discouraging researchers in ocean science from applying advanced STDM techniques. To remedy this situation, we provide a comprehensive survey to summarize existing STDM studies in ocean. Concretely, we first summarize the widely-used ST ocean datasets and identify their unique characteristics. Then, typical ST ocean data quality enhancement techniques are discussed. Next, we classify existing STDM studies for ocean into four types of tasks, i.e., prediction, event detection, pattern mining, and anomaly detection, and elaborate the techniques for these tasks. Finally, promising research opportunities are highlighted. This survey will help scientists from the fields of both computer science and ocean science have a better understanding of the fundamental concepts, key techniques, and open challenges of STDM in ocean

Exploring Himawari-8 geostationary observations for the advanced coastal monitoring of the Great Barrier Reef

Larissa developed an algorithm to enable water-quality assessment within the Great Barrier Reef (GBR) using weather satellite observations collected every 10 minutes. This unprecedented temporal resolution records the dynamic nature of water quality fluctuations for the entire GBR, with applications for improved monitoring and management

Remote Sensing of the Aquatic Environments

The book highlights recent research efforts in the monitoring of aquatic districts with remote sensing observations and proximal sensing technology integrated with laboratory measurements. Optical satellite imagery gathered at spatial resolutions down to few meters has been used for quantitative estimations of harmful algal bloom extent and Chl-a mapping, as well as winds and currents from SAR acquisitions. The knowledge and understanding gained from this book can be used for the sustainable management of bodies of water across our planet