In the upcoming chapters we will work with remotely sensed imagery. We will mostly process Landsat 8 imagery of the Berlin region downloaded from the U.S. Geological Survey (USGS) EarthExplorer on October, 14 2022. First, you will be given a short introduction into Landsat 8 data products.

About Landsat¶

Landsat is a joint project of the U.S. Geological Survey and the NASA. The Landsat Earth Observation satellites have continuously acquired images of the Earth's land surface, providing uninterrupted data about natural resources and the environment (USGS 2022). Landsat 8 was launched on February 11, 2013 and comes with a repeat coverage of 16 days (NASA).

Timeline of Landsat Missions Timeline of Landsat Missions, Source: USGS 2016.

Landsat 8 Instruments¶

Landsat 8 carries two sensors. The Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS). Landsat OLI includes the follwoing nine spectral bands (USGS 2018):

Band Property Wavelength range (µm) Spatial Resolution (m)
1 Coastal Aerosol 0.43 - 0.45 30
2 Blue (VIS) 0.450 - 0.51 30
3 Green (VIS) 0.53 - 0.59 30
4 Red (VIS) 0.64 - 0.67 30
5 Near-Infrared (nIR) 0.85 - 0.88 30
6 SWIR 1 1.57 - 1.65 30
7 SWIR 2 2.11 - 2.29 30
8 Panchromatic (PAN) 0.50 - 0.68 15
9 Cirrus 1.36 - 1.38 30

Landsat TIRS comes with t wo spectral bands (USGS 2018):

Band Property Wavelength range (µm) Spatial Resolution (m)
10 TIRS 1 10.6 - 11.19 100
11 TIRS 2 11.5 - 12.51 100

Naming Convention of Landsat Data¶

When downloading and working with Landsat Data it is very helpful to understand the file naming convention an the meta data. It will contain information about the sensor, how the data was processed, the acquisition time and how the data is structured. A nice overview about these information is given by USGS or click here for a whole tutorial on Landsat data by Wasser 2021. Here, you will be given only a short overview.

``LXSS_LLLL_PPPRRR_YYYYMMDD_yyyymmdd_CC_TX`

where:

  • L = Landsat
  • X = Sensor ("C"=OLI/TIRS combined, "O"=OLI-only, "T"=TIRS-only, "E"=ETM+, "T"=TM, "M"=MSS)
  • SS = Satellite (”07”=Landsat 7, “08”=Landsat 8)
  • LLL = Processing correction level (L1TP/L1GT/L1GS)
  • PPP = WRS path
  • RRR = WRS row
  • YYYYMMDD = Acquisition year, month, day
  • yyyymmdd - Processing year, month, day
  • CC = Collection number (01, 02, …)
  • TX = Collection category ("RT"=Real-Time, "T1"=Tier 1, "T2"=Tier 2)

As an example we will explore our Berlin scene:

`LC08_L1TP_193023_20220803_20220806_02_T1`

Means:

  • LC08 = Landsat 8; OLI/TIRS combined
  • L1TP = processing correction level L1GT
  • 192 = path 19
  • 023 = row 023
  • 20220803 = acquired August 3, 2022
  • 20220806 = processed August 6, 2022
  • 02 = Collection 2
  • T1 = Tier 1

Citation

The E-Learning project SOGA-Py was developed at the Department of Earth Sciences by Annette Rudolph, Joachim Krois and Kai Hartmann. You can reach us via mail by soga[at]zedat.fu-berlin.de.

Creative Commons License
You may use this project freely under the Creative Commons Attribution-ShareAlike 4.0 International License.

Please cite as follow: Rudolph, A., Krois, J., Hartmann, K. (2023): Statistics and Geodata Analysis using Python (SOGA-Py). Department of Earth Sciences, Freie Universitaet Berlin.