Monitoring of Chlorophyll-a Levels in Lakes

This study aimed to examine the possibility of introducing a more sustainable system for Chlorophyll-a monitoring in Lakes Palic and Ludas. The approach was based on identifying relationships between Chlorophyll-a concentrations and environmental indices to establish equations for predicting future estimates of Chlorophyll-a concentrations. Consequently, we employed remote sensing techniques, utilizing Sentinel-2 LC1 products and the SNAP software for data processing, to extract and manipulate satellite images for this purpose. The atmospheric corrections were performed using the C2RCC algorithm. The obtained images were used to extract water, resulting in normalized reflectance values that were then used to calculate a set of environmental indices. Evaluating the correlation between Chlorophyll-a concentrations and calculated indices helped select the indices for the models. Lastly, we assessed the developed models through statistical tests and via standard verification procedures. The results varied from good and reliable to unreliable, depending on the specific location. It was noted that this method of forecasting Chlorophyll-a concentrations is not suitable for lakes under significant influence of artificial factors. Further refining of the modeling procedure could make the models more dependable.

Authors

Institutions

Publication Details

Journal
Sustainability
Published
2025-08-11
DOI
https://doi.org/10.3390/su17167256
Primary Topic
Remote Sensing in Agriculture
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Monitoring of Chlorophyll-a Levels in Lakes

Mirjana Horvat, Zoltan Horvat, Emese Kutassy
Sustainability
Remote Sensing in Agriculture
article

Monitoring of Chlorophyll-a Levels in Lakes

Mirjana Horvat, Zoltan Horvat, Emese Kutassy
article en

Abstract

This study aimed to examine the possibility of introducing a more sustainable system for Chlorophyll-a monitoring in Lakes Palic and Ludas. The approach was based on identifying relationships between Chlorophyll-a concentrations and environmental indices to establish equations for predicting future estimates of Chlorophyll-a concentrations. Consequently, we employed remote sensing techniques, utilizing Sentinel-2 LC1 products and the SNAP software for data processing, to extract and manipulate satellite images for this purpose. The atmospheric corrections were performed using the C2RCC algorithm. The obtained images were used to extract water, resulting in normalized reflectance values that were then used to calculate a set of environmental indices. Evaluating the correlation between Chlorophyll-a concentrations and calculated indices helped select the indices for the models. Lastly, we assessed the developed models through statistical tests and via standard verification procedures. The results varied from good and reliable to unreliable, depending on the specific location. It was noted that this method of forecasting Chlorophyll-a concentrations is not suitable for lakes under significant influence of artificial factors. Further refining of the modeling procedure could make the models more dependable.

SustainabilityVol. 17(16)
University of Novi Sad (RS), Bajcsy-Zsilinszky Kórház és Rendelőintézet (HU)
Life in Land
Openalex Percentile: Top 80%
Remote Sensing in Agriculture
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.