Abstract
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 needed
Highlights
Salinity is one of the important parameters in the ocean that is the most often measured in every single ocean research activity
The ocean currents/circulation that move from one place to another were influenced by the changes of horizontal pressure gradients that generated by the differences of the density of seawater, salt concentration, sea temperature and pressure [2, 3, 1]
We extended our validation into 3 regions (Figure 2): (1) in the north of the Cendrawasih Bay and head of bird of Papua waters using Argo float # 5904515 and in the Halmahera and Maluku Seas using Argo Float # 5904508, which apparently influenced by south Pacific water masses; (2) In the South of Java to East Nusa Tenggara (NTT) waters using Argo floats # 5905014; 5905017 and 5904994; and Triumph Cruise of Training/RV of Madidihang II in 2018 that conducted by Research Center for Deep Sea (P2LD LIPI) Ambon, Indonesia, First Institute of Oceanography
Summary
Salinity is one of the important parameters in the ocean that is the most often measured in every single ocean research activity. Sea surface salinity (SSS) has been used intensively for studying the global climate variability, the same as using conventional data of SST, such as the ENSO in the Pacific [8, 9, 10, 11, 12] and Indian Ocean Dipole (IOD) mode in the tropical Indian Ocean [13, 14]. Salinity plays an important role, conventional measurement of salinity (SSS) carried out by research vessels or using Argo floats are sparse and irregular, so large part of the global oceans have not been measured [2]. Salinity measurement from space lags far behind the SST, Chlorophyll-a (Chl-a) and many other ocean parameters that have been used to study the local and global ocean phenomena effectively and efficiently for over 3 decades through remote sensing techniques [15]
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