# Estimating Chlorophyll a Concentrations of Several Inland Waters with Hyperspectral Data and Machine Learning Models

## TL;DR

This study demonstrates that machine learning models, especially Random Forest with spectral derivatives, can accurately estimate chlorophyll a concentrations in inland waters using hyperspectral remote sensing data, offering a cost-effective monitoring method.

## Contribution

The paper evaluates the performance of three machine learning models on hyperspectral data for inland water chlorophyll a estimation, including the impact of spectral resolution and derivatives.

## Key findings

- Random Forest achieved 80-90% R2 in estimation accuracy.
- Spectral derivatives improved model performance, especially for Random Forest.
- Lower spectral resolution still maintained high estimation accuracy.

## Abstract

Water is a key component of life, the natural environment and human health. For monitoring the conditions of a water body, the chlorophyll a concentration can serve as a proxy for nutrients and oxygen supply. In situ measurements of water quality parameters are often time-consuming, expensive and limited in areal validity. Therefore, we apply remote sensing techniques. During field campaigns, we collected hyperspectral data with a spectrometer and in situ measured chlorophyll a concentrations of 13 inland water bodies with different spectral characteristics. One objective of this study is to estimate chlorophyll a concentrations of these inland waters by applying three machine learning regression models: Random Forest, Support Vector Machine and an Artificial Neural Network. Additionally, we simulate four different hyperspectral resolutions of the spectrometer data to investigate the effects on the estimation performance. Furthermore, the application of first order derivatives of the spectra is evaluated in turn to the regression performance. This study reveals the potential of combining machine learning approaches and remote sensing data for inland waters. Each machine learning model achieves an R2-score between 80 % to 90 % for the regression on chlorophyll a concentrations. The random forest model benefits clearly from the applied derivatives of the spectra. In further studies, we will focus on the application of machine learning models on spectral satellite data to enhance the area-wide estimation of chlorophyll a concentration for inland waters.