07 — Sentinel-1 SAR¶
Sentinel-1 Ground Range Detected (GRD) radar backscatter (vv, vh) extracted on the Major TOM grid, using the same built-ins as the optical examples: search_stac + read_odc_stac + reproject_odc + write_geotiff. No new plugin needed — SAR is just another STAC collection.
Data comes from Microsoft Planetary Computer's sentinel-1-grd collection (Cloud-Optimized GeoTIFFs), so no credentials are required.
# Install AerEO and any required plugins for this notebook (Google Colab)
!pip install -q "aereo[viz,pc]"
# Download config files and AOIs from the GitHub repository so this
# notebook can run outside the repo (e.g. Google Colab).
import os
import urllib.request
GITHUB_RAW = "https://raw.githubusercontent.com/frandorr/aereo/main"
os.makedirs("config/aoi", exist_ok=True)
# Config files
urllib.request.urlretrieve(
f"{GITHUB_RAW}/examples/config/job_sentinel1.yaml",
"config/job_sentinel1.yaml",
)
# AOI files
urllib.request.urlretrieve(
f"{GITHUB_RAW}/examples/config/aoi/chocon.geojson",
"config/aoi/chocon.geojson",
)
Config used in this notebook¶
config/job_sentinel1.yaml queries Planetary Computer for sentinel-1-grd (vv/vh) over the Chocón reservoir AOI. Two SAR-specific details live in the read section:
crs: "EPSG:32719"andresolution: 10— PC's Sentinel-1 items carry noprojmetadata, soodc-staccannot auto-guess the native grid (UTM 19S, 10 m) and we declare it explicitly.patch_url: planetary_computer.sign— signs asset URLs at load time, same as the Sentinel-2 PC quickstart.
from aereo.cache import TaskResultCache
from aereo.executors import LocalExecutor
from aereo.pipeline import ExtractionJob
# Load the job from the Hydra config package.
job = ExtractionJob.load_from_config(
config_dir="config",
config_name="job_sentinel1",
)
assets = job.search() # Use the search method from the job object to get the assets.
tasks = job.build_tasks(assets)
len(tasks)
# now we create an Executor, in this case a LocalExecutor to run
# each ExtractionTask using Threads
local_exec = LocalExecutor(workers=-1, use_threads=True, cache=TaskResultCache())
# Extract!
print("Extracting...")
artifacts = job.execute(tasks, executor=local_exec)
print(f"✓ Extracted {len(artifacts)} artifacts")
Plot the extracted patches¶
vv backscatter in linear units, rendered with a grayscale percentile stretch. Dark open water in the reservoir contrasts with brighter land — the classic SAR signature, available day or night and through clouds.
from aereo.viz import plot_artifact_patches
plot_artifact_patches(
artifacts,
ds_factor=1,
cmap="gray",
stretch="percentile",
aoi=job.target_aoi,
aoi_edgecolor="blue",
)
