Mohammadmehdi Saberioon, Vahid Khosravi, Jakub Brom, Asa Gholizadeh, Karl Segl

The Environmental Mapping and analysis program (EnMAP) is a new Earth observation satellite which will use imaging spectroscopy to obtain a diagnostic characterisation of the Earth's surface and record changes. Since we hypothesis that imaging spectroscopy can significantly improve the accuracy of predicting and assessing water quality traits of small in-land waters, our study investigates the capability of the simulated EnMAP data to predict chlorophyll-a (Chl-a) and total suspended solids (TSS) as two of the most crucial water quality indicators. Three machine learning (ML) approaches(i.e., The methods used were Principal Component Regression(PCR), Partial Least Square Regression (PLSR) and Random Forest (RF)) were employed to establish links between the simulated image spectra and the above-mentioned water attributes of the samples collected from several in-land water reservoirs within the southern part of the Czech Republic.
Additionally, an airborne hyperspectral image likewise was used for developing a model to compare its performance with models developed based on simulated EnMAP data. According to the results, adequate prediction accuracy was obtained for Chl-a (RF: R\(^2\) = 0.89, RMSE = 43.06, and LCCC = 0.91 ) and TSS (RF: R\(^2\) = 0.91, RMSE = 17.53, and LCCC = 0.94), which was close enough to those obtained for the airborne hyperspectral image. Chl-a and TSS showed a correlation with around 550 and 650 nm and from 700 nm to 800 nm of the red and near-infrared (NIR) regions.
The spatial distribution maps derived from the simulated EnMAP were comparable to those obtained by the source image, particularly in water bodies with relatively high and low contents of the water attributes. Overall, it can be concluded that the simulated EnMAP image was successful and reliable in the prediction and spatial mapping of the selected biophysical properties of the small in-land water bodies.