Working with MODIS Imagery

MODIS (Moderate Resolution Imaging Spectroradiometer) is an instrument aboard the Terra and Aqua satellites that provides daily global coverage at moderate spatial resolution.

Available MODIS Collections:

To find MODIS imagery for your area of interest, use the search endpoint:

POST

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

Request Parameters

Code Example

import requests
import json

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

payload = {
    "geometry_id": "123e4567-e89b-12d3-a456-426614174000",
    "start_date": "2023-01-01",
    "end_date": "2023-01-31",
    "product": "modis-13A1-061"
}

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

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

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

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

POST

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

Request Parameters

Code Example

import requests
import json

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

payload = {
    "geometry_id": "123e4567-e89b-12d3-a456-426614174000",
    "start_date": "2023-01-01",
    "end_date": "2023-03-31",
    "collection": "modis-13A1-061",
    "index": "500m_16_days_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: {result['mean']:.2f}, Min: {result['min']:.2f}, Max: {result['max']:.2f}")

You can retrieve MODIS imagery for visualization and analysis:

GET

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

Path Parameters

Query Parameters

Code Example

import requests
import os
from PIL import Image
from io import BytesIO

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

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

params = {
    "item_id": item_id,
    "collection": "modis-13A1-061",
    "index": "500m_16_days_NDVI",
    "image_type": "png",
    "colormap": "RdYlGn"
}

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

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

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

MODIS provides various indices and data layers across different collections:

modis-13A1-061 (500m)

modis-11A2-061 (1km)

modis-14A1-061 (Fire Detection)

MODIS's global coverage and long data record make it ideal for large-scale environmental monitoring:

Other Applications:

• Drought monitoring: Track vegetation stress over large regions
• Snow and ice cover: Monitor seasonal and long-term changes in snow and ice
• Land cover change: Detect large-scale land use changes
• Air quality: Track aerosols and atmospheric conditions
• Ocean color: Monitor phytoplankton and ocean productivity

Now that you understand how to work with MODIS imagery, you can:

• Learn about higher resolution Sentinel-2 imagery
• Explore Landsat's long-term archive
• Perform time series analysis