Appendix B — Homework Exercise 2: Unit conversion

Your task in this notebook is to load some data from EODC and create weekly mosaics across the whole year (the area can be relatively small), plot one of the weekly mosaics, and save it to a file. To successfully finish the exercise, you need to perform the following tasks:

• Load the data
• Create weekly mosaics
• Plot one of the weekly mosaics
• Save it as a GeoTIFF

As a reminder, backscatter data is typically provided in ‘dB’, so make sure to perform the mean calculation for the weekly mosaics in the correct domain.

B.1 Load the data

import pystac_client
import odc.stac
from rasterio.crs import CRS
import rioxarray  # noqa

# Search for some data
crs = CRS.from_epsg(27704)  # WGS 84

# Set Spatial extent
latmin, latmax = (
    ...,
    ...,
)
# YOUR CODE HERE, you can choose any location that you like e.g. a city in
# Austria
lonmin, lonmax = ..., ...  # YOUR CODE HERE
bounds = (lonmin, latmin, lonmax, latmax)

# Set Temporal extent
time_range = ...  # YOUR CODE HERE

# Search for Sentinel-1 data
items = (
    pystac_client.Client.open("https://stac.eodc.eu/api/v1")
    .search(
        bbox=bounds,
        collections=["SENTINEL1_SIG0_20M"],
        datetime=time_range,
        limit=100,
    )
    .item_collection()
)

print(len(items), "scenes found")

# Load the data and lazily combine items
sig0_dc = odc.stac.stac_load(
    items,
    bbox=bounds,
    bands=["VV"],
    chunks={"time": 1, "x": 1000, "y": 1000},
)

B.2 Preprocess the Data

# Define the nodata value and scale factor
nodata = -9999  # Sentinel-1 nodata value as defined by EODC
scale = 10  # Sentinel-1 scale factor as defined by EODC

# Preprocess the data
sig0 = ...  # YOUR CODE HERE

B.3 Create Weekly Mosaics

sig0_weekly = ...  # YOUR CODE HERE

B.4 Plot one of the Weekly Mosaics

# YOUR CODE HERE

B.5 Save one of the Weekly Mosaics as Tif

# YOUR CODE HERE