Version 1
: Received: 6 September 2023 / Approved: 7 September 2023 / Online: 7 September 2023 (10:32:43 CEST)
How to cite:
Batina, A.; Krtalić, A. A Review of Remote Sensing Applications for Determining Lake Water Quality. Preprints2023, 2023090489. https://doi.org/10.20944/preprints202309.0489.v1
Batina, A.; Krtalić, A. A Review of Remote Sensing Applications for Determining Lake Water Quality. Preprints 2023, 2023090489. https://doi.org/10.20944/preprints202309.0489.v1
Batina, A.; Krtalić, A. A Review of Remote Sensing Applications for Determining Lake Water Quality. Preprints2023, 2023090489. https://doi.org/10.20944/preprints202309.0489.v1
APA Style
Batina, A., & Krtalić, A. (2023). A Review of Remote Sensing Applications for Determining Lake Water Quality. Preprints. https://doi.org/10.20944/preprints202309.0489.v1
Chicago/Turabian Style
Batina, A. and Andrija Krtalić. 2023 "A Review of Remote Sensing Applications for Determining Lake Water Quality" Preprints. https://doi.org/10.20944/preprints202309.0489.v1
Abstract
Remote sensing methods have the potential to improve lake water quality monitoring and deci-sion-making in water management. This reviews introduces novel findings in the field of opti-cally active water quality parameters using remote sensing. It summarizes existing retrieval methods (analytical, semi-analytical, empirical, semi-empirical, and artificial intelli-gence/machine learning (AI/ML)), examines measurement methods used to determine concen-tration of specific water quality parameters, summarizes satellite systems that enable temporal data for understanding the state of the lake with focus on water quality parameters, and pro-poses enhancements for future research of lake water quality using remote sensing. As part of this review, eight optically active biological and physical water quality parameters were ana-lyzed, including chlorophyll-α (chl-α), transparency (Secchi disk depth (SDD)), colored dis-solved organic matters (CDOM), turbidity (TUR), electrical conductivity (EC), surface salinity (SS), total suspended matter (TSM), and water temperature (WT). The research proposes a shift from point-based data representation to a more reliable raster representation and encourages optimizing grid selection for in situ measurements by combining hydrodynamic model with re-mote sensing methods. This review presents a comprehensive summary of the bands, band combinations, and band equations per sensor for eight optically active water quality parameters as listed in Tables A1-A8. The review’s findings indicate that use of remotely sensed data is an effective method for estimating water quality parameters in lakes, with a significant increase in global utilization. The review highlights potential solutions and limitations to the challenges of remote sensing water quality determination in lakes.
Keywords
water management; remote sensing; lake; water quality; optically active; water quality parame-ters
Subject
Environmental and Earth Sciences, Remote Sensing
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
