TY - JOUR
T1 - Automated retrieval of sea surface salinity from satellite data and investigation of the spatio-temporal variation of ocean salinity across the West and Central African coasts
AU - Anejionu, O. C. D.
AU - Ebinne, E. S.
AU - Ajibola-James, O.
JO - The International Hydrographic Review
VL - 29(2)
SP - 122
EP - 136
PY - 2023
DA - 2023/11/30
UR - https://ihr.iho.int/articles/automated-retrieval-of-sea-surface-salinity-from-satellite-data-and-investigation-of-the-spatio-temporal-variation-of-ocean-salinity-across-the-west-and-central-african-coasts/
KW - Aquarius satellite
KW - data democratization
KW - ETL data pipeline
KW - ocean dynamics
KW - ocean remote sensing
KW - OISSS salinity data
KW - Python
KW - sea surface salinity
KW - SMAP satellite
AB - Sea Surface Salinity (SSS) is an important variable that affects the physical properties of the oceanic environment. Satellite sensors have been developed to routinely acquire SSS concentrations. However, many in the broader scientific community do not engage with this data, because it requires relevant remote sensing skills and tools to extract and process into formats that can be readily utilized by non-remote sensing experts. To address this issue, we used Python, PostgreSQL database, and Power BI visualization tool, to develop an automated tool, that facilitates the retrieval, processing, analysis, and visualisation of data from the Optimally Interpolated Sea Surface Salinity (OISSS) dataset, comprising data from the Aquarius/SAC-D, Soil Moisture Active Passive (SMAP), and Soil Moisture and Ocean Salinity (SMOS) satellite missions. Using this tool, we extracted long term series of SSS from 2011 to March 2022. In addition, an interactive reporting dashboard was developed in this research to enable users to visualize and interrogate the underlying dataset. Analysis of this data revealed detailed spatiotemporal patterns of SSS variations in the study area across varying spatial and temporal scales. We were able to identify the cold and hot spots of SSS, as well as longitudinal (interand intra-annual) variations in SSS concentrations. We anticipate that the outcome of this research will boost further research in this area, contribute to future climatic change studies and stimulate the democratization of remote sensing data. Climatic changes have been found to have profound effects on ocean salinity. The data obtained in this research could also feed into machine learning models for predicting future impacts of climate change on salinity concentrations across the ocean. In addition, the ability to monitor the salinity on a regular basis can be deployed alongside other relevant variables to forecast future climatic changes. Due to the fact that salinity is an excellent indicator for water cycle changes, the ability to routinely monitor salinity at relatively higher spatial and temporal resolutions is of great significance to the local community, as it could be used to understand current and future water cycle regimes in the area. The water cycle has profound effect on various socio-economic activities including agriculture and food security, ecology, hydrology (water quality), and drought.