Working with Sentinel-1 Radar Imagery

Sentinel-1 is a radar satellite mission that provides continuous all-weather, day-and-night imagery for land and ocean monitoring. Unlike optical satellites like Sentinel-2, Sentinel-1 uses Synthetic Aperture Radar (SAR) technology.

Common Applications:

• Flood monitoring
• Oil spill detection
• Sea ice mapping
• Ground deformation monitoring
• Crop monitoring
• Forest monitoring

Radar imagery works differently from optical imagery. Instead of capturing reflected sunlight, radar satellites emit microwave signals and measure how they bounce back from Earth's surface.

Key Concepts:

• Backscatter: The amount of radar signal that returns to the satellite after hitting objects on Earth
• Polarization: The orientation of radar waves (VV, VH, HH, HV)
• Orbit direction: Ascending (south to north) or descending (north to south) passes

To find Sentinel-1 imagery for your area of interest, use the search endpoint:

POST

https://observearth.com/api/s1/search/

Request Parameters

Code Example

import requests
import json

api_key = "your_api_key_here"
url = "https://observearth.com/api/s1/search/"

payload = {
    "geometry_id": "123e4567-e89b-12d3-a456-426614174000",
    "start_date": "2023-01-01",
    "end_date": "2023-01-31"
}

headers = {
    "Content-Type": "application/json",
    "X-API-Key": api_key
}

response = requests.post(url, json=payload, headers=headers)
data = response.json()

# Print the results
print(f"Found {len(data['results'])} images")
for image in data['results']:
    print(f"ID: {image['id']}, Date: {image['date']}, Platform: {image['platform']}")ers = {
    "X-API-Key": api_key,
    "Content-Type": "application/json"
}

response = requests.post(url, headers=headers, data=json.dumps(payload))
data = response.json()

for image in data['results']:
    print(f"ID: {image['id']}, Date: {image['date']}")

Once you've found available images, you can calculate statistics for radar indices over your area of interest.

POST

https://observearth.com/api/s1/stats/

Request Parameters

Code Example

import requests
import json

api_key = "your_api_key_here"
url = "https://observearth.com/api/s1/stats/"

payload = {
    "geometry_id": "123e4567-e89b-12d3-a456-426614174000",
    "start_date": "2023-01-01",
    "end_date": "2023-01-31",
    "index": "rvi"
}

headers = {
    "X-API-Key": api_key,
    "Content-Type": "application/json"
}

response = requests.post(url, headers=headers, data=json.dumps(payload))
data = response.json()

for result in data:
    print(f"Date: {result['date']}, Mean RVI: {result['mean']:.2f}")

Once you've identified the images you want, you can retrieve them for visualization or analysis.

GET

https://observearth.com/api/s1/image/{geometry_id}/

Path Parameters

Query Parameters

Code Example

import requests

api_key = "your_api_key_here"
geometry_id = "123e4567-e89b-12d3-a456-426614174000"
item_id = "S1A_IW_GRDH_1SDV_20230115T123456_20230115T123521_046880_059F1A_5D8B"
url = f"https://observearth.com/api/s1/image/{geometry_id}/"

params = {
    "item_id": item_id,
    "image_type": "png",
    "index": "rvi"
}

headers = {
    "X-API-Key": api_key
}

response = requests.get(url, params=params, headers=headers)

# Save the image
if response.status_code == 200:
    with open("sentinel1_rvi.png", "wb") as f:
        f.write(response.content)
    print("Image saved successfully")
else:
    print(f"Error: {response.status_code}, {response.text}")

Interpreting Radar Imagery

In radar imagery, brightness corresponds to the strength of the radar signal that returns to the satellite:

• Bright areas: Strong radar returns from rough surfaces, buildings, or structures aligned with the radar beam
• Dark areas: Weak radar returns from smooth surfaces like calm water or roads

Now that you understand how to work with Sentinel-1 radar imagery, you can:

• Learn about Sentinel-2 optical imagery
• Explore Landsat imagery
• Perform time series analysis