Open AccessWater Turbidity Determination by a Satellite Imagery-Based Mathematical Equation for the Chao Phraya River
Author(s) -
Wilaiporn Pimwiset,
K. Tungkananuruk,
Thitima Rungratanaubon,
Pratin Kullavanijaya,
Chalisa Veesommai Sillberg
Publication year - 2022
Publication title -
environment and natural resources journal/warasan singwaetlom lae sappayakon tammachat
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.202
H-Index - 5
eISSN - 2408-2384
pISSN - 1686-5456
DOI - 10.32526/ennrj/20/202100237
Subject(s) - turbidity , environmental science , remote sensing , water quality , satellite , satellite imagery , hydrology (agriculture) , geology , physics , geotechnical engineering , ecology , oceanography , astronomy , biology
Turbidity is a standard water quality parameter that indicates its optical property in scattering light along the column containing suspended particles. The satellite imagery information of Sentinel-2 and the Chao Phraya River turbidity data from December 2016 to February 2021 was applied to develop a mathematical equation for turbidity determination. This practical and straightforward approach eliminates some constraints of traditional laboratory analysis, which is labour-intensive and time-consuming in monitoring the entire river. Four studied steps were implemented: data pre-processing, correlating analysis of numerical turbidity and satellite image reflectance, developing the mathematic equations for turbidity estimation, and its validation of use. Four different bands (B2, B3, B4, and B8) and three selection methods were investigated; single-band, combination band, and ratio band. The obtained results depicted that the reflectance of B4 in the single-band process promoted the highest correlation with turbidity compared to the others. The reflectance in visible wavelengths increased when the turbidity of river water increased, particularly B4. The mathematical power equation was a more suitable function for evaluating turbidity than linear regression, quadratic, and exponential functions. A similar concentration was obtained for measured and estimated turbidity in the validation. This finding demonstrated the potential application of remotely sensed data to estimate river water turbidity with high capability and accuracy that adequately supports spatial data continuity acquisition.