A statistical method for analysing temperature increase from remote sensing data with application to Spitsbergen Island

• Main Authors: Cendana Fitrahanjani, -, Tofan Agung Eka Prasetya, -, Rachmah Indawati, -
• Format: Article PeerReviewed
• Language: English; English; English
• Published: Springer Nature logo 2021
• Subjects: QC981 Climate
• Online Access: https://repository.unair.ac.id/113896/1/15%20turnitin.pdf; https://repository.unair.ac.id/113896/2/15%20gabungan.pdf; https://repository.unair.ac.id/113896/5/1a%20statistical%20method.pdf; https://repository.unair.ac.id/113896/; https://www.springer.com/journal/40808; https://doi.org/10.1007/s40808-020-00907-6
• Institution: Universitas Airlangga

Arctic plays as a key climatic region, it is highly affected by climate change. Climate change has long been considered as an effect of global warming, it is derived from complex linkages and changes in climate variables. Land surface temperature (LST) is known as one of the essential climate variables (ECVs). Recent study founds that LST has risen in the Arctic. Due to the rising temperatures, there has been a massive decrease in basic Arctic features, which elevated the percentage of heat trapped in the surface. LST is an ECV which needs to be further investigated in key regions. This study aims to investigate LST changes over February 2000 to November 2019 in Spitsbergen. We used autoregression and multivariate regression with cubic spline used to investigate LST changes over this period in Spitsbergen. Four knots and seven knots cubic spline were applied, respectively, to detect acceleration and 7-year cycle. Research founds that LST in Spitsbergen rise by 1.039 °C per decade (CI 0.576–1.501; z: 4.403). Gustav Adolf Land, Nordaustlandet has the highest temperature rise, location of the well-known Vegafonna ice-caps. A notable increase has shown during winter days.