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.
- "Robust algorithm for estimating total suspended solids (TSS) in inland and nearshore coastal waters". S.V. Balasubramanian, et al. (2020). Remote Sensing of Environment. 111768. 10.1016/j.rse.2020.111768. Code.
- "Hyperspectral retrievals of phytoplankton absorption and chlorophyll-a in inland and nearshore coastal waters". N. Pahlevan, et al. (2021). Remote Sensing of Environment. 112200. 10.1016/j.rse.2020.112200.
- "A Chlorophyll-a Algorithm for Landsat-8 Based on Mixture Density Networks". B. Smith, et al. (2021). Frontiers in Remote Sensing. 623678. 10.3389/frsen.2020.623678.
The package can be cloned into a directory with:
git clone https://github.com/BrandonSmithJ/MDN
Alternatively, you may use pip to install:
pip install git+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_data, get_sensor_bands
sensor = "<OLI, MSI, OLCI, or HICO>"
# Tile should be the output of an atmospheric correction program e.g. SeaDAS
bands, Rrs = get_tile_data("path/to/my/tile.nc", sensor, allow_neg=False)
chla, idxs = image_estimates(Rrs, sensor=sensor)
# Or, with just random data:
import numpy as np
random_data = np.random.rand(3, 3, len(get_sensor_bands(sensor)))
chla, idxs = image_estimates(random_data, sensor=sensor)
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 <OLI, MSI, OLCI, or HICO> path/to/my/Rrs.csv
Note: The user-supplied input values should correspond to Rrs (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.
