Quantifying methane emissions from Laurentian Great Lakes estuaries using in situ measurements, remote sensing and machine learning

This dataset contains the data used to develop the model and figures for the manuscript in L&O Letters. In this study, CH4 fluxes were measured from three drowned river mouths (DRM) estuaries along the eastern shore of Lake Michigan using low-cost, autonomous floating samplers in the littoral zone and discrete samples in the pelagic zone from May – October 2024. Sentinel-3 OLCI, gridMET, and MODIS were used to calculate environmental variable proxies, which were used to estimate CH4 fluxes with machine learning.
, , # Quantifying methane emissions from Laurentian Great Lakes estuaries using in situ measurements, remote sensing and machine learning

## Description of the data and file structure

The ch4_rs_MLprep.zip file contains the R Project repository containing data and Rmd files to analyze the data going into the ML model in sentinel3_ch4_model.zip as well as Rmd files corresponding to figures in the L&O Letters manuscript. The Metadata-LO-Letters-data2.doc file contains detailed metadata for each data file.

The ch4_rs_MLprep.zip contains the following data files for data preparation for ML and figure creation:

* **2_PSO_RMS_mac_modis_cc_1d.out / 2_PSO_RMS_mkg_modis_cc_1d.out / 2_PSO_RMS_wht_modis_cc_1d.out** – output from the air2water algorithm simulating surface water temperature from known air temperatures
* *year* – year (NA)
* *month* – month (NA)
* *day* – day (NA)
* *observed air temp* – observed air temperature from airport stations (°C)
* *observed water temp* – observed ...,