Research article

How accurate is the remote sensing based estimate of water physico-chemical parameters in the Danube Delta (Romania)?

Marian Necula, Iris Maria Tușa, Manuela Elisabeta Sidoroff, Corina Ițcuș, Daniela Florea, Alexandru Amărioarei, Andrei Păun, Octavian Pacioglu, Mihaela Marinela Păun

Marian Necula
The Bucharest University of Economic Studies, Romania
Iris Maria Tușa
National Institute of Research and Development for Biological Sciences, Bucharest, Romania
Manuela Elisabeta Sidoroff
National Institute of Research and Development for Biological Sciences, Bucharest, Romania
Corina Ițcuș
National Institute of Research and Development for Biological Sciences, Bucharest, Romania
Daniela Florea
National Institute of Research and Development for Biological Sciences, Bucharest, Romania
Alexandru Amărioarei
National Institute of Research and Development for Biological Sciences, Bucharest, Romania University of Bucharest, Faculty of Mathematics and Computer Science, Bucharest, Romania
Andrei Păun
National Institute of Research and Development for Biological Sciences, Bucharest, Romania University of Bucharest, Faculty of Mathematics and Computer Science, Bucharest, Romania
Octavian Pacioglu
National Institute of Research and Development for Biological Sciences, Bucharest, Romania
Mihaela Marinela Păun
National Institute of Research and Development for Biological Sciences, Bucharest, Romania. Email: mihaela.paun@incdsb.ro

Online First: December 30, 2022
Necula, M., Tușa, I., Sidoroff, M., Ițcuș, C., Florea, D., Amărioarei, A., Păun, A., Pacioglu, O., Păun, M. 2022. How accurate is the remote sensing based estimate of water physico-chemical parameters in the Danube Delta (Romania)?. Annals of Forest Research DOI:10.15287/afr.2022.2682


The current paper estimated the physico-chemical properties of water in the Danube Delta (Romania), based on Sentinel 2 remote sensing data. Eleven sites from the Danube Delta were sampled in spring and autumn for three years (2018-2020) and 21 water physico-chemical parameters were measured in laboratory. Several families of machine learning algorithms, translated into hundreds of models with different parameterizations for each machine learning algorithm, based on remote sensing data input from Sentinel 2 spectral bands, were employed to find the best models that predicted the values measured in laboratory. This was a novel approach, reflected in the types of selected models that minimised the values of performance metrics for the tested parameters. For alkalinity, calcium, chloride, carbon dioxide, hardness, potassium, sodium, ammonium, dissolved oxygen, sulphates, and suspended matter the results were promising, with an overall percentage bias of the estimates of +/- 10% from the observed values. For copper, magnesium, nitrites, nitrates, turbidity and zinc the estimates were fairly accurate, with percentage biases in the interval +/- 10% - 20%, whereas for detergents, led, and phosphates the percentage bias was higher than 20%. Overall, the results of the current study showed fairly good estimates between remote sensing based estimates and laboratory measured values for most water physico-chemical parameters.

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