Working with Sentinel-2 Imagery

Sentinel-2 is a constellation of satellites operated by the European Space Agency (ESA) that provides high-resolution optical imagery. Key features include:

• Spatial resolution: 10m, 20m, and 60m (depending on the band)
• Temporal resolution: 5-day revisit time (with two satellites)
• Spectral resolution: 13 bands from visible to shortwave infrared
• Coverage: Global land and coastal areas

Sentinel-2 imagery is particularly useful for:

• Vegetation monitoring
• Land cover classification
• Agricultural applications
• Forest management
• Water resource monitoring

The first step in working with Sentinel-2 imagery is to search for available images that cover your area of interest within a specific date range.

POST

https://observearth.com/api/s2/search/

Request Parameters

Code Example

import requests
import json

api_key = "your_api_key_here"
url = "https://observearth.com/api/s2/search/"

payload = {
    "geometry_id": "123e4567-e89b-12d3-a456-426614174000",
    "start_date": "2023-01-01",
    "end_date": "2023-01-31",
    "cloud_cover": 10
}

headers = {
    "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']}, Cloud Cover: {image['cloud_cover']}%")

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

POST

https://observearth.com/api/s2/stats/

Request Parameters

Code Example

import requests
import json

api_key = "your_api_key_here"
url = "https://observearth.com/api/s2/stats/"

payload = {
    "geometry_id": "123e4567-e89b-12d3-a456-426614174000",
    "start_date": "2023-01-01",
    "end_date": "2023-01-31",
    "cloud_cover": 10,
    "index": "ndvi"
}

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 NDVI: {result['mean']:.2f}")

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

GET

https://observearth.com/api/s2/image/{geometry_id}/

Path Parameters

Query Parameters

Code Example

import requests
import os

api_key = "your_api_key_here"
geometry_id = "123e4567-e89b-12d3-a456-426614174000"
item_id = "S2A_MSIL2A_20230105T123456_N0509_R123_T18TXM_20230105T150000"

url = f"https://observearth.com/api/s2/image/{geometry_id}/"

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

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

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

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

You can also retrieve raw Sentinel-2 imagery without any processing or index calculation.

GET

https://observearth.com/api/s2/raw/{geometry_id}/

Path Parameters

Query Parameters

Code Example

import requests
import os

api_key = "your_api_key_here"
geometry_id = "123e4567-e89b-12d3-a456-426614174000"
item_id = "S2A_MSIL2A_20230105T123456_N0509_R123_T18TXM_20230105T150000"

url = f"https://observearth.com/api/s2/raw/{geometry_id}/"

params = {
    "item_id": item_id,
    "bands": "B04,B03,B02",  # RGB natural color
    "image_type": "png"
}

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

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

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

Sentinel-2 imagery can be used to calculate various vegetation indices. Here are the most common ones:

Now that you understand how to work with Sentinel-2 imagery, you can:

• Learn about Sentinel-1 radar imagery
• Explore Landsat imagery
• Dive deeper into vegetation indices
• Perform time series analysis