Remotely sensed big data for the oceans and polar regions

Abstract

The oceans, which account for 71% of the Earth’s area, and the polar regions, the largest cold source on Earth, jointly play crucial roles in energy exchange and circulation, and in climate change (e.g. McGuire, Chapin, Walsh, & Wirth, 2006). In particular, against the background of global climate change, both the Arctic and Antarctic are experiencing profound changes (Bracegirdle, Connolley, & Turner, 2008; Jeffries, Overland, & Perovich, 2013), and the interactions between the oceans, polar regions and the atmosphere are closer than ever. Remote sensing has become one of the main research tools in ocean and polar studies (Lubin, Ayres, & Hart, 2009). At the same time, the amount of acquired data is undergoing explosive growth (Ma et al., 2015), thus leading oceanography into the era of “big data”. Although there is no agreed definition of big data, the “5 V” characteristics of volume, velocity, variety, veracity and value are commonly used to distinguish big data from other types of data. Satellite and airborne remote sensing data can be considered representative of big data and, along with the development of modern information techniques such as machine learning and cloud computing, have been associated with many advances in Earth observation. Using remote sensing data, particularly big data, the past, present and future of the oceans and polar regions can be better understood. To support the development of remote sensing big data, this Special Issue, “Remotely Sensed Big Data for Ocean and Polar Regions”, contains relevant research, review and data articles aimed at highlighting the recent progress made in the field of remote sensing big data as applied to the ocean and polar regions. Sea surface wind and waves are two important parameters related to the air‒sea interface and play a crucial role in the interactions between sea ice and ocean dynamic processes in the Arctic Ocean. To provide high-resolution ocean wind and wave data that have wide coverage, Li, Wu, and Huang (2021) developed an ocean wind and wave dataset based on Sentinel-1 synthetic aperture radar (SAR) that covered the pan-Arctic Ocean. This dataset, which covers the regions above 60°N, has a spatial resolution of around 2 km and covers the period from January 2017 to May 2021. Based on comparisons with scatterometer data, the SAR-retrieved wind data were found to have an accuracy of 1.23 m s‒1 and the SAR-retrieved significant wave height was found to have an RMSE of 0.66 m from a comparison with altimeter data. The development of this dataset will support offshore construction as well as shipping safety and security in the Arctic and further contribute to studies of the changing Arctic. Sea ice research is an essential component of studies of climate change in the Arctic, and the sea ice concentration (SIC) is one of the basic parameters used to describe the distribution of sea ice. Chen, Zhao, Pang, and Ji (2021) proposed a daily SIC product for the Arctic based on FY-3D Microwave Radiation Imager (MWRI) brightness temperature (TB) data. This product was calculated by applying the Arctic Radiation and Turbulence Interaction Study Sea Ice (ASI) algorithm to data with a 12.5-km resolution that were BIG EARTH DATA 2022, VOL. 6, NO. 2, 141–143 https://doi.org/10.1080/20964471.2022.2075100