Using Python Interpret Landsat File Naming Convention

 If you have ever worked with Landsat images, you would have noticed that the image files usually have a long name made-up of letters and numbers that probably looks like this: LC08_L2SP_188054_20221120_20221129_02_T1.

In this article, we will use python to extract the meaning of the file name.

After downloading a Landsat file from the USGS Earth Explorer, there’s surprisingly a lot of information you can get from just the file name. The following web pages provide detailed information on the file naming convention:-

• https://www.usgs.gov/faqs/what-naming-convention-landsat-collections-level-1-scenes
• https://gisgeography.com/landsat-file-naming-convention/

The Landsat satellite series is a long-running satellite program that provides high-resolution imagery of the Earth's surface. The images taken by the Landsat satellites are named using a standardized naming convention that includes information about the satellite, the path and row of the image, the date the image was taken, and the band of the image.

Here is an example of a Landsat image file name: LC08_L1TP_012030_20170604_20170615_01_T1

This file name can be interpreted as follows:

LC08: The satellite that took the image (Landsat 8)

L1TP: The processing level of the image (Level 1 Terrain Corrected)

012030: The path and row of the image (path 01, row 20)

20170604: The date the image was taken (June 4, 2017)

20170615: The date the image was processed (June 15, 2017)

01: The band of the image (band 1)

T1: The product type of the image (Type 1)

You can use Python to parse and interpret Landsat image file names by splitting the file name string using the split() method and extracting the relevant pieces of information using string slicing and indexing. For example:

filename = 'LC08_L1TP_012030_20170604_20170615_01_T1'

satellite = filename[:4]  # 'LC8'
processing_level = filename[5:9]  # 'L1TP'
path_row = filename[10:16]  # '012030'
date_taken = filename[17:25]  # '20170604'
date_processed = filename[26:34]  # '20170615'
band = filename[35:37]  # '01'
product_type = filename[38:]  # 'T1'


print(f'''
      The satellite name is: {satellite}, 
      The processing level of the image is: {processing_level}, 
      The path and row of the image is: {path_row}, 
      The date the image was taken on: {date_taken}, 
      The date the image was processed is: {date_processed}, 
      The band of the image is: {band}, 
      The product type of the image is: {product_type}
      ''')

The code use string slicing to get the required information from the file names.