Remote sensing methods have the potential to improve lake water quality monitoring and decision-making in water management. This review discusses the use of remote sensing methods for monitoring and assessing water quality in lakes. It explains the principles of remote sensing and the different methods used for retrieving water quality parameters in complex waterbodies. The review highlights the importance of considering the variability of optically active parameters and the need for comprehensive studies that encompass different seasons and time frames. The paper addresses the specific physical and biological parameters that can be effectively estimated using remote sensing, such as chlorophyll-α, turbidity, water transparency (Secchi disk depth), electrical conductivity, surface salinity, and water temperature. It further provides a comprehensive summary of the bands, band combinations, and band equations commonly used for remote sensing of these parameters per satellite sensor. It also discusses the limitations of remote sensing methods and the challenges associated with satellite systems. The review recommends integrating remote sensing methods using in situ measurements and computer modelling to improve the understanding of water quality. It suggests future research directions, including the importance of optimizing grid selection and time frame for in situ measurements by combining hydrodynamic models with remote sensing retrieval methods, considering variability in water quality parameters when analysing satellite imagery, the development of advanced technologies, and the integration of machine learning algorithms for effective water quality problem-solving. The review concludes with a proposed workflow for monitoring and assessing water quality parameters in lakes using remote sensing methods.

The comprehensive analysis of spatiotemporal variations in water quality is crucial for ecosystem management. This study analyses and maps spatiotemporal variations in water quality at Vrana Lake, a coastal shallow lake in Dalmatia, Croatia. We established a monitoring grid of 20 stations and conducted monthly in situ measurements of seven water quality parameters from July 2023 to June 2024 using multiparameter probe. We measured electrical conductivity, turbidity, salinity, water temperature, dissolved oxygen, oxygen saturation, and chlorophyll-a. We analysed the correlation between these parameters, water level, and meteorological factors over a year and the impact of climate change over the 34 years. Additionally, we evaluated 15 geographic information system (GIS) spatial interpolation methods for mapping the distribution of water quality parameters, using root mean square error (RMSE) and mean error (ME) metrics. The vertical stratification analysis revealed that the lake’s shallow nature allows effective assessment through median values. Key findings highlighted that air temperature, precipitation, and wind significantly affect water quality dynamics. The Simple Kriging – Trend emerged as the best GIS spatial interpolation method for modelling water quality parameters. Overall, this study enhances the understanding of water quality variations and their implications for ecosystem health in coastal shallow lakes.

Coastal lakes require monitoring approaches that capture spatial and temporal variability beyond the limits of conventional in situ measurements. In this study, a SIGMaL framework (Satellite–In situ–GIS-multicriteria decision analysis (MCDA)–Machine Learning (ML)) was developed, a unified methodology that integrates in situ monitoring, GIS MCDA-derived water quality index (WQI), satellite imagery, and ML models for comprehensive coastal lake water quality assessment. A WQI, derived from a 12-month series of in situ measurements and environmental parameters, was used alongside four physicochemical parameters measured by a multiparameter probe. First, satellite reflectance from each sensor was used to train a set of nine regression models for modelling electrical conductivity (EC), turbidity, water temperature (WT), and dissolved oxygen (DO). Second, convolutional neural networks (CNNs) with spectral and temporal inputs were trained to classify WQI classes, enabling a cross-sensor evaluation of their suitability for lake water quality monitoring. Third, the trained CNNs were applied to generate WQI maps for a subsequent 12-month period without in situ data. Across all analyses, WQI-based models provided more stable and accurate models than those trained on raw parameters. Sentinel-2 achieved the most consistent WQI performance (AUC ≈ 1.00, R² ≈ 0.84), PlanetScope captured fine-scale spatial detail (R² ≈ 0.77), while Landsat 8–9 was most effective for WT but less reliable for multi-class WQI discrimination. Sentinel-2 is recommended as the primary satellite sensor for WQI mapping within the SIGMaL framework. These findings demonstrate the advantages of WQI-based modelling and highlight the potential of ML–remote sensing integration to support coastal lake water quality monitoring.

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.

This study evaluates the effectiveness of Sentinel-2 MSI imagery, corrected using the Case-2 Regional Coast Colour (C2RCC) algorithm in SNAP software (Sentinel Application Platform), for estimating chlorophyll-a (Chl-a) and total suspended solids (TSS) in Deran Lake, a karstic lake with minimal anthropogenic pressure within Hutovo Blato Nature Park, Bosnia and Herzegovina. In situ measurements were collected in March 2025 using a YSI EXO2s multiparameter probe at ten monitoring stations under the SMART-Water project. TSS was used as a proxy for total suspended matter (TSM). A regression analysis between satellite-derived and measured values showed a strong correlation for TSS/TSM (R² = 0.76) and a moderate correlation for Chl-a (R² = 0.61). Spatial averaging over 5×5-pixel windows improved estimation accuracy, yielding R² values of 0.85 for TSS/TSM and 0.69 for Chl-a. Thematics maps of SNAP-derived Chl-a and TSS/TSM distribution was performed using QGIS 3.40. Chl-a concentrations confirmed oligotrophic conditions, while TSS patterns aligned with known hydrodynamic features such as inflow zones and sediment resuspension areas. These findings align with other regional studies demonstrating Sentinel-2’s potential for monitoring small and optically complex waterbodies when paired with appropriate atmospheric correction, statistical estimators (e.g., mean), and spatial windowing strategies. This work reinforces the value of remote sensing for cost-effective, high-resolution monitoring of inland waters, improving water resource management across diverse ecosystems.