Publications

Ariman, S (2021). Determination of inactive water quality variables by MODIS data: A case study in the Kizilirmak Delta-Balik Lake, Turkey. ESTUARINE COASTAL AND SHELF SCIENCE, 260, 107505.

Abstract
This study aims to apply Moderate Resolution Imaging Spectroradiometer (MODIS) data to monitor water quality parameters of total phosphorus (TP) and total nitrogen (TN) in Balik Lake since TP and TN are important factors for eutrophication as well as it is essential to monitor TP and TN concentrations accurately for the management of water environments. Water chemical variables, including TN and TP, are soluble and optically inactive. For such a purpose, remote sensing (RS) enables an alternative way to monitor the optical activity of water with some limitations. In this study, TN and TP concentrations of Balik Lake are examined by RS data. The artificial neural networking (ANN) model was used to reveal the relation between the reflectance bands of MODIS along with the water quality parameters of TP and TN concentrations in aquatic environments by utilizing ground truth water samples collected concurrently with the MODIS overpass. The dataset used in the study consists of MODIS Aqua bands from 1 to 7 and ground truth observations of TP and TN from the study area of Balik Lake during 2017-2019. The development and testing of a feedforward back-propagation multilayer perceptron artificial neural network were performed by using the Levenberg-Marquardt training algorithm to predict TP and TN. The accuracy of the ANN model was determined to be relatively higher in the testing stage of TP prediction with R = 0.59 for Model-1 (3-10-1) and R = 0.56 for Model-2 (7-10-1) while it is lower in the testing stage of TN prediction with R = 0.36 for Model-1 and Model-2. The findings of this study indicated that the reflectance bands of MODIS have a moderate potential to monitor the solubility variables of the water quality for the management of the lake. However, the ANN model introduced in this study is expected to ensure an improvement in the prediction accuracy of inactive water quality variables based on the RS data.

DOI:
10.1016/j.ecss.2021.107505

ISSN:
0272-7714