This conference proceedings feature a selection of research papers (extended abstracts) and a full paper submitted for the 15th Biennial Pan Ocean Remote Sensing Conference (PORSEC). This is the first time we are publishing pre-conference proceedings due to restrictions in organizing conferences since the global pandemic of COVID-19 outbreak. It continues the legacy of seven previous special IJRS issues of PORSEC conferences, held in Busan, Korea; Guangzhou, China; Keelung, Taiwan; Kochi, India; Bali, Indonesia; Fortaleza, Brazil and Jeju Island, the Republic of Korea, respectively (Levy and Gower 2008, 2010, 2012; Gower and Levy 2014; Levy, Vigundelli, Gower 2016, 2018; and Levy, Nimit, Vigundelli, Gower, 2020). Established in 1990 (the International Space Year), with the name of “Pacific Ocean Remote Sensing Conference”, PORSEC has gained equal popularity among leading and aspirant countries in this field as a platform for sharing ocean remote sensing research and applications. The now common component of capacity building in the form of a pre-conference tutorial is one of the most acclaimed aspects of PORSEC and has cultivated young brains to pursue works for the benefit of mankind.
A total of 14 full papers planned to be presented at PORSEC2020 (now postponed till further update) have been accepted for publication by the scientific committee after reviewing and necessary revisions. Ten of the papers address regional science problems, while two others address tropical seas (Arabian Sea, Persian Gulf), one addresses the science of sub-tropical sea (Sea of Azov), and one addresses polar (Arctic) regions. Half of these papers make use of altimeter data, underlining the importance of the sensor and making a strong case for a constellation with altimeter sensors to be maintained. Among the other papers, two each make use of wind or ocean colour data. Most of the papers from the Indo-Pacific region address topics of coastal zone management and spatial planning in one way or the other. Further, a significant number of papers make use of the Artificial Intelligence (AI) to process the datasets or derive conclusions, which is a welcome approach considering ‘massive data’ necessities being applied to the satellite remote sensing in terms of day-by-day and ever growing (spatial, temporal, spectral) resolutions and longer time-series. This highlights the need for robust mapping with the help of geospatial and machine learning tools, for achieving the goals of the UN Decade of Ocean Science for Sustainable Development (2021-2030).
