Appendix C — Homework Exercise 3: Backscattering Coefficients

To successfully finish this notebook, you have to answer/solve the following three questions.

import xarray as xr

import matplotlib.pyplot as plt  # noqa
import hvplot.xarray  # noqa
import intake

from pathlib import Path
from functools import partial
from mrs.catalog import get_intake_url

C.1 Question 1

Execute the lines below to create the plot. You now see a SAR image with unknown processing level. Based on what you see, which pre-processing steps have been applied to the image?

Cloud masking
Geometric terrain correction
Conversion to dB
Radiometric terrain correction (terrain flattening)

Select the answers you think are correct by editing the markdown text and putting an ‘x’ in the brackets. Note: One, several, or all answers are possible.

ex3_dc.hvplot.image(
    x="x",
    y="y",
    robust=True,
    data_aspect=1,
    cmap="Greys_r",
    groupby="band",
    rasterize=True,
).opts(frame_height=600, framewise=False, aspect="equal")

C.2 Question 2

Name all types of geometric image distortions in a SAR image. How are they caused, and how can we get rid of them?

Your answer here

C.3 Question 3

Use the figure below question 1 to identify areas that are affected by foreshortening and radar shadows. Use the mouseover to retrieve their x/y coordinates. Then use these coordinates to annotate the following matplotlib figure with labels by putting them in the lines indicated with # Your coordinates here.

zoom = ex3_dc.sel(x=slice(11.25, 11.5), y=slice(47.75, 47.5)).band_data
fig, ax = plt.subplots(figsize=(10, 8))

bbox = dict(boxstyle="round", fc="0.8")

ax.annotate(
    "foreshortening",
    xy=(..., ...),  # Your foreshortening coordinates here
    xytext=(0.3, 0.25),
    textcoords="subfigure fraction",
    bbox=bbox,
    arrowprops=dict(facecolor="white", shrink=0.05),
)
ax.annotate(
    "shadows",
    xy=(..., ...),  # Your radar shadow coordinates here
    xytext=(0.47, 0.8),
    textcoords="subfigure fraction",
    bbox=bbox,
    arrowprops=dict(facecolor="white", shrink=0.05),
)

zoom.sel(band="unknown1").plot(robust=True, cmap="Greys_r")