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This repository contains code that analyzes remote sensing and in-situ water quality data, develops machine learning algorithms to infer in-situ concentrations from satellite reflectance, and tests the machine learning algorithms.

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ChesapeakeBayRemoteSensingML

This repository contains code that analyzes remote sensing and in-situ water quality data, develops machine learning algorithms to infer in-situ concentrations from satellite reflectance, and tests the machine learning algorithms. Note that the results and scripts are in the Publication folder.

The folder ./Publication contains the scripts, data, and plots created in this research project.

The folder ./CB_MC_1km_from_L2/ contains monthly climatology of Rrs_412, Rrs_443, etc., created from MODIS Aqua in the Chesapeake Bay. These were used to create monthly climatology predictions of chlorophyll, TSS from the machine learning algorithms.

The folder ./GIS contains the image used for monthly climatology and shapefiles for data cleaning.

The folder ./GSHHS contains the maps we used for the station plots of the Chesapeake Bay.

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This repository contains code that analyzes remote sensing and in-situ water quality data, develops machine learning algorithms to infer in-situ concentrations from satellite reflectance, and tests the machine learning algorithms.

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