Abstract

Southern Ocean Islands are globally significant conservation areas. Predicting how their terrestrial ecosystems will respond to current and forecast climate change is essential for their management and requires high-quality temperature data at fine spatial resolutions. Existing datasets are inadequate for this purpose. Remote-sensed land surface temperature (LST) observations, such as those collected by satellite-mounted spectroradiometers, can provide high-resolution, spatially-continuous data for isolated locations. These methods require a clear sightline to measure surface conditions, however, which can leave large data-gaps in temperature time series. Using a spatio-temporal gap-filling method applied to high-resolution (~1 km) LST observations for 20 Southern Ocean Islands, we compiled a complete monthly temperature dataset for a 15-year period (2001–2015). We validated results using in situ measurements of microclimate temperature. Gap-filled temperature observations described the thermal heterogeneity of the region better than existing climatology datasets, particularly for islands with steep elevational gradients and strong prevailing winds. This dataset will be especially useful for terrestrial ecologists, conservation biologists, and for developing island-specific management and mitigation strategies for environmental change.

Highlights

  • Background & SummaryThe Southern Ocean Islands (SOIs) are among the most remote islands on Earth

  • High-resolution (~1 km) remote-sensed land surface temperature (LST) data were extracted from the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature and Emissivity dataset (MOD11A2 Terra37)

  • The MODIS sensor is mounted on the Terra satellite and has been observing every point on Earth once every 1 to 2 days in a near-polar, sun-synchronous orbit, since its launch in December 1999

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Summary

Introduction

Background & SummaryThe Southern Ocean Islands (SOIs) are among the most remote islands on Earth. A second set of gap-fill validation scenarios was applied to the un-filled MODIS data of the same 13 islands, where the original LST observations were deleted in random spatial clusters (3 × 3 grids), instead of random points, to mimic observed patterns of spatial autocorrelation in the distribution of missing observations (see Supplementary File 1).

Results
Conclusion
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