A Method for Chlorophyll-a and Suspended Solids Prediction through Remote Sensing and Machine Learning
Lucas Silveira Kupssinskü, Tainá Thomassim Guimarães, Eniuce Menezes de Souza, Daniel Capella Zanotta, Maurício Roberto Veronez, Luiz Gonzaga, Frederico Fábio Mauad
Abstract
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.