A Method for Chlorophyll-a and Suspended Solids Prediction through Remote Sensing and Machine Learning.
Total Suspended Solids (TSS) and chlorophyll-a concentration are two critical parameters to monitor water quality. Since directly collecting samples for laboratory analysis can be expensive, this paper presents a methodology to estimate this information through remote sensing and Machine Learning (ML) techniques. TSS and chlorophyll-a are optically active components, therefore enabling measurement by remote sensing. Two study cases in distinct water bodies are performed, and those cases use different spatial resolution data from Sentinel-2 spectral images and unmanned aerial vehicles together with laboratory analysis data. In consonance with the methodology, supervised ML algorithms are trained to predict the concentration of TSS and chlorophyll-a. The predictions are evaluated separately in both study areas, where both TSS and chlorophyll-a models achieved R-squared values above 0.8.
['Silveira Kupssinskü|Lucas|L|0000-0003-2580-3996', 'Thomassim Guimarães|Tainá|T|0000-0002-6362-6591', 'Menezes de Souza|Eniuce|E|0000-0003-0265-7586', 'C Zanotta|Daniel|D|0000-0003-2959-6525', 'Roberto Veronez|Mauricio|M|0000-0002-5914-3546', 'Gonzaga|Luiz|L|0000-0002-7661-2447', 'Mauad|Frederico Fábio|FF|0000-0002-2477-2019']
D000465:Algorithms D000077194:Chlorophyll A D004784:Environmental Monitoring D040362:Geographic Information Systems D007091:Image Processing, Computer-Assisted D000069550:Machine Learning D058998:Remote Sensing Technology D060753:Water Quality
chlorophyll-a remote sensing machine learning total suspended solids water quality K nearest neighbors artificial neural networks random forest