> ## Documentation Index
> Fetch the complete documentation index at: https://docs.tilebox.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Create a Sentinel-1 radar image

> Query a Sentinel-1 GRD scene and render a monochrome image from its public SAR measurement COG.

Tilebox indexes global Sentinel-1 Ground Range Detected (GRD) scenes and their public AWS assets in the `open_data.aws_earth.sentinel1` dataset. In this guide, you query a scene over Venice, read its VV polarization measurement, and create a north-up monochrome radar image.

## Prerequisites

* You have a [Tilebox API key](/authentication).
* You have Python 3.11 or newer.

```bash theme={"system"}
uv add tilebox shapely numpy pillow rasterio
```

## Select a Sentinel-1 scene

Define an area around Venice, then query a dual-polarization Sentinel-1C scene from October 4, 2025:

```python Python theme={"system"}
from shapely import box
from tilebox.datasets import Client

# west, south, east, north
area = box(12.3, 45.385, 12.49, 45.466)  # Venice

collection = Client().dataset("open_data.aws_earth.sentinel1").collection("GRD")
scenes = collection.query(
    temporal_extent=("2025-10-04", "2025-10-05"),
    spatial_extent=area,
)

datapoint = scenes.isel(time=0)
print(datapoint.stac_id.item())
```

## Resolve the VV measurement

Each datapoint provides measurement COGs for its available polarizations, alongside product, calibration, noise, manifest, and preview assets. Select the VV measurement and resolve its public HTTPS location:

```python Python theme={"system"}
from tilebox.datasets.assets import AssetCollection
from tilebox.storage.aio import AssetAccessPolicy, Client as StorageClient

assets = AssetCollection.from_datapoint(datapoint)
vv = assets["vv"]

storage = StorageClient(
    policy=AssetAccessPolicy(preferred_schemes=("https",)),
)
resolved = storage.resolve(vv)
```

No AWS credentials or requester-pays configuration is required for this asset.

## Read a north-up image window

Sentinel-1 GRD measurement COGs store geolocation as ground control points. Use a `WarpedVRT` to apply that geolocation, project the image, and read a bounded north-up window without downloading the complete scene:

```python Python theme={"system"}
import numpy as np
import rasterio
from rasterio.enums import Resampling
from rasterio.vrt import WarpedVRT
from rasterio.warp import transform_bounds
from rasterio.windows import from_bounds

with rasterio.open(resolved.href) as source:
    with WarpedVRT(source, crs="EPSG:3857") as geotiff:
        projected_bounds = transform_bounds(
            "EPSG:4326",
            geotiff.crs,
            *area.bounds,
        )
        window = from_bounds(*projected_bounds, transform=geotiff.transform)
        vv_pixels = geotiff.read(
            1,
            window=window,
            out_shape=(675, 1200),
            resampling=Resampling.bilinear,
        ).astype(np.float32)
```

## Render the radar image

Stretch the central 96% of valid pixel values across a gray display range, then save the result:

```python Python theme={"system"}
from PIL import Image

valid = vv_pixels[vv_pixels > 0]
low, high = np.percentile(valid, (2, 98))
grayscale = np.clip((vv_pixels - low) / (high - low), 0, 1)

image = Image.fromarray((grayscale * 255).astype(np.uint8), mode="L")
image.save("sentinel1-venice.png")
```

<Frame>
  <img src="https://mintcdn.com/tilebox/N5_QRKGl0199z-k5/assets/guides/datasets/sentinel1-vv-venice.webp?fit=max&auto=format&n=N5_QRKGl0199z-k5&q=85&s=e01b5584a3160b149aabb40af12a07ce" alt="Monochrome Sentinel-1 VV radar image of Venice and the surrounding lagoon" width="1200" height="675" data-path="assets/guides/datasets/sentinel1-vv-venice.webp" />
</Frame>

Smooth water appears dark because it reflects little radar energy back toward the sensor, while dense buildings appear bright because their geometry produces strong returns.

<Note>
  This percentile stretch creates a visual image from the stored measurement values. Calibrate the measurement and account for acquisition geometry before using pixel values in quantitative SAR analysis.
</Note>

## Next steps

<Columns cols={2}>
  <Card title="Create a Sentinel-2 RGB image" icon="satellite" href="/guides/datasets/access-sentinel2-data" horizontal>
    Build a cloud-free optical RGB image from three spectral bands.
  </Card>

  <Card title="Assets and storage" icon="boxes-stacked" href="/datasets/assets-and-storage/overview" horizontal>
    Understand how dataset metadata connects queries to files in object storage.
  </Card>
</Columns>