Development of a multiple linear regression model for meteorological drought index estimation based on landsat satellite imagery

Seon Woo Kim, Donghwi Jung, Yun Jae Choung

Research output: Contribution to journalArticlepeer-review

Abstract

Climate polarization due to global warming has increased the intensity of drought in some regions, and the need for drought estimation studies to help minimize damage is increasing. In this study, we constructed remote sensing and climate data for Boryeong, Chungcheongnam-do, Korea, and developed a model for drought index estimation by classifying data characteristics and applying multiple linear regression analysis. The drought indices estimated in this study include four types of standardized precipitation indices (SPI1, SPI3, SPI6, and SPI9) used as meteorological drought indices and calculated through cumulative precipitation. We then applied statistical analysis to the developed model and assessed its ability as a drought index estimation tool using remote sensing data. Our results showed that its adj.R2 value, achieved using cumulative precipitation for one month, was very low (approximately 0.003), while for the SPI3, SPI6, and SPI9 models, the adj.R2 values were significantly higher than the other models at 0.67, 0.64, and 0.56, respectively, when the same data were used.

Original languageEnglish
Article number3393
JournalWater (Switzerland)
Volume12
Issue number12
DOIs
Publication statusPublished - 2020 Dec

Keywords

  • Boryeong
  • Drought index
  • Landsat
  • Multiple linear regression model
  • Remote sensing data
  • SPI

ASJC Scopus subject areas

  • Biochemistry
  • Geography, Planning and Development
  • Aquatic Science
  • Water Science and Technology

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