Ocean-surface Heterogeneity Mapping (OHMA) to identify regions of change

R.G. Scarrott*, Fiona Cawkwell, Mark Jessopp, Caroline Cusack, Eleanor O’Rourke, C.A.J.M. de Bie

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Mapping heterogeneity of the ocean’s surface waters is important for understanding biogeographical distributions, ocean surface habitat mapping, and ocean surface stability. This article describes the Ocean-surface Heterogeneity MApping (OHMA) algorithm—an objective, replicable approach that uses hypertemporal, satellite-derived datasets to map the spatio-temporal heterogeneity of ocean surface waters. The OHMA produces a suite of complementary datasets—a surface spatio-temporal heterogeneity dataset, and an optimised spatio-temporal classification of the ocean surface. It was demonstrated here using a hypertemporal Sea Surface Temperature image dataset of the North Atlantic. Validation with Underway-derived temperature data showed higher heterogeneity areas were associated with stronger surface temperature gradients, or an increased presence of locally extreme temperature values. Using four exploratory case studies, spatio-temporal heterogeneity values were related to a range of region-specific surface and sub-surface characteristics including fronts, currents and bathymetry. The values conveyed the interactions between these parameters as a single metric. Such over-arching heterogeneity information is virtually impossible to map from in-situ instruments, or less temporally dense satellite datasets. This study demonstrated the OHMA approach is a useful and robust tool to explore, examine, and describe the ocean’s surface. It advances our capability to map biologically relevant measures of ocean surface heterogeneity. It can support ongoing efforts in Ocean Surface Partitioning, and attempts to understand marine species distributions. The study highlighted the need to establish dedicated spatio-temporal ocean validation sites, specifically measured using surface transits, to support advances in hypertemporal ocean data use, and exploitation. A number of future research avenues are also highlighted.
Original languageEnglish
Article number1283
Pages (from-to)1-33
Number of pages33
JournalRemote sensing
Volume13
Issue number7
DOIs
Publication statusPublished - 27 Mar 2021

Keywords

  • hypertemporal satellite imagery
  • SST
  • North Atlantic
  • heterogeneity
  • surface waters
  • ocean surface partitioning
  • water masses
  • spatio-temporal
  • ISODATA
  • Generalised Linear Models
  • ITC-ISI-JOURNAL-ARTICLE
  • ITC-GOLD

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