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‎_toc.yml‎

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- file: notebooks/courses/microwave-remote-sensing/09_in_class_exercise
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- caption: Templates
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chapters:
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- file: notebooks/templates/classification
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- file: notebooks/templates/prereqs-templates
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sections:
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- file: notebooks/templates/classification
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- caption: Tutorials
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chapters:
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- file: notebooks/tutorials/floodmapping
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- file: notebooks/tutorials/prereqs-tutorials
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sections:
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- file: notebooks/tutorials/floodmapping
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- caption: References
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chapters:
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- file: notebooks/references

‎notebooks/courses/microwave-remote-sensing.ipynb‎

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"# Microwave Remote Sensing\n",
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"\n",
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"\n",
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"This course at the TU Wien teaches students to read, visualize and analyze Synthetic Aperture Radar (SAR) data. This will aid interpretation of SAR data based upon a physical understanding of sensing principles and the interaction of microwaves with natural objects.\n",
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"This course at the TU Wien teaches students to read, visualize and analyze\n",
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"Synthetic Aperture Radar (SAR) data. This will aid interpretation of SAR data\n",
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"based upon a physical understanding of sensing principles and the interaction of\n",
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"microwaves with natural objects.\n",
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"\n",
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"| Concepts | Importance | Notes |\n",
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"|---|---|---|\n",
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"| [Intro to xarray](https://foundations.projectpythia.org/core/xarray/xarray-intro.html) | Necessary | |\n",
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"| [Dask Arrays](https://foundations.projectpythia.org/core/xarray/dask-arrays-xarray.html)| Necessary| |\n",
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"| [Intake](https://projectpythia.org/intake-cookbook/README.html)|Helpful| |\n",
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"| [Matplotlib](https://foundations.projectpythia.org/core/matplotlib.html)|Helpful|Ploting in Python|\n",
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"| [Documentation hvPlot](https://hvplot.holoviz.org/)|Helpful|Interactive plotting|\n",
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"| [Documentation odc-stac](https://odc-stac.readthedocs.io/en/latest/)|Helpful|Data access|\n",
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"\n",
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"- **Time to learn**: 90 min\n",
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"\n",
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":::{note}\n",
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"These notebooks contain interactive elements. The full interactive elements can only be viewed on Binder by clicking on the Binder badge or 🚀 button.\n",
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"These notebooks contain interactive elements. The full interactive elements can\n",
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"only be viewed on Binder by clicking on the Binder badge or 🚀 button.\n",
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":::\n"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "microwave-remote-sensing",
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "microwave-remote-sensing"
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"name": "python3",
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"path": "/opt/hostedtoolcache/Python/3.11.11/x64/share/jupyter/kernels/python3"
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},
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"language_info": {
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"codemirror_mode": {

‎notebooks/courses/microwave-remote-sensing/01_in_class_exercise.ipynb‎

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"id": "8",
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"metadata": {},
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"source": [
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"Each collection has multiple items. An item is one spatio-temporal instance in the collection, for instance a satellite image. If items are needed for a specific timeframe or for a specific region of interest, we can define this as a query."
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"Each collection has multiple items. An item is one spatio-temporal instance in the collection, for instance a satellite image. If items are needed for a specific timeframe or for a specific region of interest, we can define this as a query.\n"
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]
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},
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{
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"id": "14",
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"metadata": {},
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"source": [
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"Using our previously loaded STAC catalog, we can now search for items fulfilling our query. In this example we are using the bounding box. If we want to use an area of interest specified in the geojson format - one has to use the intersects parameter as documented in the comment below."
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"Using our previously loaded STAC catalog, we can now search for items fullfilling our query. In this example we are using the bounding box. If we want to use an area of interest specified in the geojson format - one has to use the intersects parameter as documented in the comment below.\n"
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{
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"display_name": "microwave-remote-sensing",
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"language": "python",
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"name": "python3"
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"name": "microwave-remote-sensing"
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},
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‎notebooks/courses/microwave-remote-sensing/02_in_class_exercise.ipynb‎

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"cell_type": "code",
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"execution_count": null,
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"id": "8",
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"metadata": {},
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"metadata": {
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"jupyter": {
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"source_hidden": true
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}
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},
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"outputs": [],
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"source": [
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"aoi = subset.isel(time=0, x=slice(0, 500), y=slice(0, 500))\n",
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"id": "9",
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"source": [
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"*Figure 1: Visually comparing $\\sigma^0$ on a logarithmic and linear scale (left column). In addition, the benefit of using the robust plotting method is shown (middle column). The robust argument uses the 2<sup>nd</sup> and 98<sup>th</sup> percentiles of the data to compute the color limits to eliminate washing out the plot due to data outliers.*\n",
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"*Figure 1: Visually comparing $\\sigma^0$ on a logarithmic and linear scale (left column). In addition, the benefit of using the robust plotting method is shown (middle column). The robust argument uses the 2^nd^ and 98^th^ percentiles of the data to compute the color limits to eliminate washing out the plot due to data outliers.*\n",
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"\n",
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"In the plot above you can see the difference between the two scales. The values in dB are more evenly distributed and are therefore easier to plot. The values in linear scale are more spread out and are therefore harder to interpret.\n",
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"This is why we use the dB scale for plotting/visualization.\n",
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"metadata": {
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"kernelspec": {
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"display_name": "microwave-remote-sensing",
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"name": "microwave-remote-sensing"
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"language_info": {
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‎notebooks/courses/microwave-remote-sensing/03_in_class_exercise.ipynb‎

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"display_name": "microwave-remote-sensing",
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"name": "microwave-remote-sensing"
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‎notebooks/courses/microwave-remote-sensing/04_in_class_exercise.ipynb‎

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"name": "microwave-remote-sensing"
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‎notebooks/courses/microwave-remote-sensing/05_in_class_exercise.ipynb‎

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"metadata": {
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‎notebooks/courses/microwave-remote-sensing/06_in_class_exercise.ipynb‎

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"name": "microwave-remote-sensing"
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‎notebooks/courses/microwave-remote-sensing/08_in_class_exercise.ipynb‎

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‎notebooks/templates/classification.ipynb‎

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"display_name": "classification",
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"name": "classification"
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