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This page first made public: Aug 17, 2010

Calculating surface temperature using Landsat thermal imagery

Abduwasit Ghulam, Department of Earth and Atmospheric Sciences and Center for Environmental Sciences, Saint Louis University, St Louis, MO 63103

Summary

In this lab, students are walked through a hands-on exercise converting digital numbers to at sensor brightness temperatures using Level 1B Landsat ETM+ thermal imagery acquired over Tallahassee, Florida, USA on November 06, 1999.

Context

Type and level of course
GIS 4XX, Geospatial Methods, is an upper level course designed for college level geosciences students, and introduces integrated remote sensing, GIS, GPS techniques in coastal zone management and/or environmental studies.

Geoscience background assumed in this assignment
Introductory level geology knowledge is preferred, but not required.

GIS/remote sensing skills/background assumed in this assignment
An introductory course in remote sensing, GIS is preferred, but not required.

Software required for this assignment/activity:
ENVI; ArcGIS (ArcView or ArcInfo with Spatial Analyst Extension)

Time required for students to complete the assignment:
2 hours

Goals

GIS/remote sensing techniques students learn in this assignment

  1. Familiarize students with concepts of digital numbers, radiance, brightness and surface temperature
  2. Practice spectral sampling
  3. Familiarize with radiometric calibration

Other content/concepts goals for this activity
Familiarize students with the basics of groundwater hydrology, and structural controls of submarine springs in coastal areas

Higher order thinking skills goals for this activity
Groundwater discharge in coastal zones may be detected using thermal remote sensing data based on the temperature anomalies between sea water and ground water discharge - the so-called submarine springs. Sustainable harness of submarine groundwater discharge, exploiting them before flow into the sea is of paramount importance for arid regions located in sea shorelines.

Description of the activity/assignment

Students are walked through downloading Landsat ETM+ data, and converting digital numbers to radiance, and then to temperature. Students are required to apply what they have learned in the lecture on thermal remote sensing to a practical example. Students are expected to discuss identified thermal anomalies in Florida's coastal zone, and its possible association with submarine springs by consulting geologic maps.

Determining whether students have met the goals

The thermal anomaly maps and an extended abstract that illustrates the connection between submarine springs and thermal anomalies can be used for assessment. Evidences referenced from the other sources, e.g., geologic maps, to confirm the association of thermal anomalies with groundwater aquifers to prove/disprove their interpretation is a plus.
More information about assessment tools and techniques.

URLs and References

Go to USGS website http://glovis.usgs.gov, and download the data following the lab handout attached to this manual.

JIMÉNEZ-MUÑOZ, J.C., SOBRINO, J.A. 2003. A generalized single-channel method for retrieving land surface temperature from remote sensing data. Journal of Geophysical Research, 108, doi: 10.1029/2003JD003480
QIN, Z., KARNIELI, A., BERLINER, P. 2001. A mono-window algorithm for retrieving land surface temperature from Landsat TM data and its application to the Israel-Egypt border region. International Journal of Remote Sensing, 22, pp.3719-3746.
http://landsathandbook.gsfc.nasa.gov/data_prod/
http://www.dep.state.fl.us/

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Calculating surface temperature using Landsat thermal imagery --Discussion  

We have two thermal bands in L7.How can we use the two bands(Lmax,Lmin) in a single equation.

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As described in the lab manual, Band 61 and 62 use exactly the same detectors, and use the same wavelength and bandwidth, but the gain is set differently, i.e., 61 is set to 'low' gain, and 62 is set to 'high' gain to maximize the instrument's 8 bit radiometric resolution without saturating the detectors. It makes sense, therefore, to use the band 61 (low gain mode) when surface brightness is high (e.g., desert, or less vegetated areas), and band 62 (high gain mode) when surface brightness is lower (e.g., vegetated areas).

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How to calculate LST using ERDAS IMAGINE? Since in Student handout you have used ENVI software.

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