Spatio-temporal water quality mapping from satellite images using geographically and temporally weighted regression

Hone Jay Chu, Shish Jeng Kong, Chih Hua Chang

Research output: Contribution to journalArticlepeer-review

19 Citations (Scopus)

Abstract

The turbidity (TB) of a water body varies with time and space. Water quality is traditionally estimated via linear regression based on satellite images. However, estimating and mapping water quality require a spatio-temporal nonstationary model, while TB mapping necessitates the use of geographically and temporally weighted regression (GTWR) and geographically weighted regression (GWR) models, both of which are more precise than linear regression. Given the temporal nonstationary models for mapping water quality, GTWR offers the best option for estimating regional water quality. Compared with GWR, GTWR provides highly reliable information for water quality mapping, boasts a relatively high goodness of fit, improves the explanation of variance from 44% to 87%, and shows a sufficient space–time explanatory power. The seasonal patterns of TB and the main spatial patterns of TB variability can be identified using the estimated TB maps from GTWR and by conducting an empirical orthogonal function (EOF) analysis.

Original languageEnglish
Pages (from-to)1-11
Number of pages11
JournalInternational Journal of Applied Earth Observation and Geoinformation
Volume65
DOIs
Publication statusPublished - 2018

All Science Journal Classification (ASJC) codes

  • Global and Planetary Change
  • Earth-Surface Processes
  • Computers in Earth Sciences
  • Management, Monitoring, Policy and Law

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