Sea ice drift from Sentinel-1 SAR data

A computationally efficient, open source feature tracking algorithm, called ORB, is adopted and tuned for retrieval of the first guess sea ice drift from Sentinel-1 SAR images. Pattern matching algorithm based on MCC calculation is used further to retrieve sea ice drift on a regular grid.

References:

• Korosov A.A. and Rampal P., A Combination of Feature Tracking and Pattern Matching with Optimal Parametrization for Sea Ice Drift Retrieval from SAR Data, Remote Sens. 2017, 9(3), 258; doi:10.3390/rs9030258
• Muckenhuber S., Korosov A.A., and Sandven S., Open-source feature-tracking algorithm for sea ice drift retrieval from Sentinel-1 SAR imagery, The Cryosphere, 10, 913-925, doi:10.5194/tc-10-913-2016, 2016

Running with Docker

```

run ipython with SeaIceDrift

docker run --rm -it -v /path/to/data:/home/jovyan/work nansencenter/seaicedrift ipython

run jupyter notebook with SeaIceDrift

docker run --rm -p 8888:8888 -v /path/to/data/and/notebooks:/home/jovyan/work nansencenter/seaicedrift ```

Installation on Ubuntu

```

install some requirements with apt-get

apt-get install -y --no-install-recommends libgl1-mesa-glx gcc build-essential

install some requirements with conda

conda install -c conda-forge gdal cartopy opencv

install other requirements with pip

pip install netcdf4 nansat

clone code

git clone https://github.com/nansencenter/seaicedrift.git cd seaicedrift

install SeaIceDrift

python setup.py install ```

Usage example

```

download example datasets

wget https://github.com/nansencenter/seaicedrifttestfiles/raw/master/S1BEWGRDM1SDH20200123T120618.tif wget https://github.com/nansencenter/seaicedrifttestfiles/raw/master/S1BEWGRDM1SDH20200125T114955.tif

start Python and import relevant libraries

import numpy as np import matplotlib.pyplot as plt from nansat import Nansat from seaicedrift import SeaIceDrift

open pair of satellite images using Nansat and SeaIceDrift

filename1='S1BEWGRDM1SDH20200123T120618.tif' filename2='S1BEWGRDM1SDH20200125T114955.tif' sid = SeaIceDrift(filename1, filename2)

run ice drift retrieval using Feature Tracking

uft, vft, lon1ft, lat1ft, lon2ft, lat2ft = sid.getdriftFT()

plot

plt.quiver(lon1ft, lat1ft, uft, vft);plt.show()

define a grid (e.g. regular)

lon1pm, lat1pm = np.meshgrid(np.linspace(-33.5, -30.5, 50), np.linspace(83.6, 83.9, 50))

run ice drift retrieval for regular points using Pattern Matching

use results from the Feature Tracking as the first guess

upm, vpm, apm, rpm, hpm, lon2pm, lat2pm = sid.getdriftPM( lon1pm, lat1pm, lon1ft, lat1ft, lon2ft, lat2ft)

select high quality data only

gpi = rpm*hpm > 4

plot high quality data on a regular grid

plt.quiver(lon1pm[gpi], lat1pm[gpi], upm[gpi], vpm[gpi], rpm[gpi])

``` Full example here