mdn's Introduction

Product Estimation

About

This repository contains source code for the following papers:

"Seamless retrievals of chlorophyll-a from Sentinel-2 (MSI) and Sentinel-3 (OLCI) in inland and coastal waters: A machine-learning approach". N. Pahlevan, et al. (2020). Remote Sensing of Environment. 111604. 10.1016/j.rse.2019.111604.

Usage

The package can be cloned into a directory with:

git clone https://github.com/BrandonSmithJ/MDN

The code may then either be used as a library, such as with the following:

from MDN import image_estimates, get_tile_Rrs
sensor = "<S2A, S2B, or OLCI>"

# Tile should be the output of an atmospheric correction program e.g. SeaDAS
bands, Rrs = get_tile_Rrs("path/to/my/tile.nc", sensor, allow_neg=False) 

estimates = image_estimates(*Rrs, sensor=sensor)
chlor_a = estimates[0]

Or, a .csv file may be given as input, with each row as a single sample. The .csv contents should be only the Rrs values to be estimated on (i.e. no header row or index column).

python3 -m MDN --sensor <S2A, S2B, or OLCI> path/to/my/Rrs.csv

Note: The user-supplied input values should correspond to R_rs (units of 1/sr).

Current performance is shown in the following scatter plots, with 50% of the data used for training and 50% for testing. Note that the models supplied in this repository are trained using 100% of the in situ data, and so observed performance may differ slightly.

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