TY  - GEN 
AU  - Chowdhury, Masuma
AU  - de la Calle, Ignacio
AU  - Laiz Alonso, Irene María
AU  - Ruescas, Ana B.
A4  - Física Aplicada
PY  - 2025
SN  - 2072-4292
UR  - http://hdl.handle.net/10498/39532
AB  - Highlights: What are the main findings? We have developed a machine-learning algorithm able to quantify high-turbid environments. We use open-source and free databases to train the model and open-source and free tools to develop it, which makes it...
LA  - eng
PB  - MDPI
KW  - water quality
KW  - spectral convolution
KW  - data harmonization
KW  - machine-learning
KW  - gradient boosting
KW  - SHAP
KW  - optical water types
KW  - uncertainty analysis
KW  - operational monitoring
KW  - automation and scalability
TI  - Near-Real-Time Turbidity Monitoring at Global Scale Using Sentinel-2 Data and Machine Learning Techniques
DO  - 10.3390/RS17223716
ER  -