Surface Temperatures at the Continental Scale: Tracking Changes with Remote Sensing at Unprecedented Detail

Markus Metz,Markus Neteler,Duccio Rocchini

doi:10.3390/rs6053822

Abstract

Temperature is a main driver for most ecological processes, and temperature time series provide key environmental indicators for various applications and research fields. High spatial and temporal resolutions are crucial for detailed analyses in various fields of research. A disadvantage of temperature data obtained by satellites is the occurrence of gaps that must be reconstructed. Here, we present a new method to reconstruct high-resolution land surface temperature (LST) time series at the continental scale gaining 250-m spatial resolution and four daily values per pixel. Our method constitutes a unique new combination of weighted temporal averaging with statistical modeling and spatial interpolation. This newly developed reconstruction method has been applied to greater Europe, resulting in complete daily coverage for eleven years. To our knowledge, this new reconstructed LST time series exceeds the level of detail of comparable reconstructed LST datasets by several orders of magnitude. Studies on emerging diseases, parasite risk assessment and temperature anomalies can now be performed on the continental scale, maintaining high spatial and temporal detail. We illustrate a series of applications in this paper. Our dataset is available online for download as time aggregated derivatives for direct usage in GIS-based applications.

Highlights

High spatial and temporal resolution datasets of environmental indicators are crucial requirements for detailed analyses in various fields of research [1,2,3,4,5,6]
Examples are the commonly used land surface temperature and emissivity products (LST/E; [9]) from the Moderate Resolution Imaging Sensor (MODIS) instruments onboard the Terra and Aqua satellites, which contain spatialized surfaces with global coverage accompanied by a quality assessment layer
Since we are working on the continental scale, other factors considering the continental scale need to be taken into account in order to achieve small-scale spatial enhancement using elevation

Summary

Introduction

High spatial and temporal resolution datasets of environmental indicators are crucial requirements for detailed analyses in various fields of research [1,2,3,4,5,6]. Temperature data can be acquired with satellites carrying thermal infrared sensors, which provide continuous coverage of large areas. Seamless gridded surfaces allow for spatially explicit and detailed analyses, whereas sparse points, such as station readings, are only valid for a single location and its immediate surroundings, leaving, at times, large gaps between stations. Gridded surfaces of environmental indicators are often created by interpolating meteorological point data recorded at ground stations. Examples are the commonly used land surface temperature and emissivity products (LST/E; [9]) from the Moderate Resolution Imaging Sensor (MODIS) instruments onboard the Terra and Aqua satellites, which contain spatialized surfaces with global coverage accompanied by a quality assessment layer.

Methods

Results

Discussion

Conclusion