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Machine Learning Regression Approaches for Colored Dissolved Organic Matter (CDOM) Retrieval with S2-MSI and S3-OLCI Simulated Data

Ana Ruescas, Martin Hieronymi, Gonzalo Mateo-Garcia, Sampsa Koponen, Kari Kallio, Gustau Camps-Valls
2018 Remote Sensing  
Two different datasets of radiative transfer simulations are used for the development and training of the machine learning regression approaches.  ...  Instrument (S2-MSI) and Sentinel-3 Ocean and Land Colour Instrument (S3-OLCI).  ...  In addition, an operational Matlab toolbox implementing all machine learning methods is available at  ... 
doi:10.3390/rs10050786 fatcat:bkdjc5s4nzflvcfteulsebqtgm

Potential for High Fidelity Global Mapping of Common Inland Water Quality Products at High Spatial and Temporal Resolutions Based on a Synthetic Data and Machine Learning Approach

Jeremy Kravitz, Mark Matthews, Lisl Lain, Sarah Fawcett, Stewart Bernard
2021 Frontiers in Environmental Science  
This dataset was used to calculate typical surviving water-leaving signal at top-of-atmosphere, and used to train and test four state-of-the-art machine learning architectures for multi-parameter retrieval  ...  There is currently a scarcity of paired in-situ aquatic optical and biogeophysical data for productive inland waters, which critically hinders our capacity to develop and validate robust retrieval models  ...  extremely absorbing and scattering cases due to global instances of elevated colored dissolved organic matter (CDOM) and non-algal particles (NAP), the phytoplankton component of these models is not optimized  ... 
doi:10.3389/fenvs.2021.587660 fatcat:usszcouwxraqvbp6kyl3ykp2ki