Climate patterns in Europe: A focus on ten countries through remote sensing

Abstract

Leveraging high-temporal resolution remote sensing data enables the investigation of the impacts of climate change with unprecedented detail and accuracy. This approach provides consistent observations, allowing for tracking of short-term fluctuations and long-term trends in climate patterns. The majority of existing studies focus on local impacts, overlooking broader national-scale implications. This research addresses this gap, examining the effects of climate change on European countries, i.e., Türkiye, Germany, Belgium, the United Kingdom (UK), France, Spain, Switzerland, Italy, Ukraine and Poland from 2001 to 2023, emphasizing the interconnected nature of climate change and the need for comprehensive strategies on a national scale. This research involved a comprehensive examination of essential environmental variables, such as precipitation (PCP), land surface temperature (LST), evapotranspiration (ET), potential evapotranspiration (PET), normalized difference vegetation index (NDVI), vegetation condition index (VCI), temperature condition index (TCI), vegetation health index (VHI) and forest area loss (FAL) through an extensive time-series analysis. The primary aim was to reveal temporal patterns within these datasets. Subsequently, pair-wise correlations among the datasets were computed, offering valuable insights into the complex interconnections among the factors used. The experiments revealed that the UK experienced a significant decline in PCP, while Ukraine and Poland exhibited higher rates of LST increase. Switzerland, France and Italy showed higher ET rates; and Belgium, France and Italy exhibited the highest rate of PET increase. Türkiye, Poland and Italy had a more pronounced rise in vegetation health. The study found strong positive correlations (average 0.72) between LST and PET. Additionally, LST showed a notable correlation with NDVI (average 0.55) and VCI (average 0.42). PCP generally exhibited negative correlations with other factors and ET was generally correlated with both NDVI (average 0.55) and VCI (average 0.56). This study is expected to contribute to the understanding of the impacts of climate change on national scale.

Keywords

• Climate Change
• Remote Sensing
• Time-series Analysis
• Land Surface Temperature
• Precipitation

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