The Thermal InfraRed Sensor (TIRS) is a new instrument carried onboard the Landsat 8 satellite that is dedicated to capturing temperature-specific information. Using radiation information from the two electromagnetic spectral bands covered by this sensor, it is possible to estimate the temperature at the Earth’s surface (albeit at a 100m resolution, compared to the 30m resolution of the other instrument, the Operational Land Imager).
I used data from the TIRS to estimate the surface temperature in the city-state of Delhi, India as of the 29th of May, 2013. The relevant tarball file containing the data was downloaded using the United States’ Geological Survey’s (USGS) EarthExplorer tool; the area of interest was encompassed by [scene identifier: path 146 row 040] in the WRS-2 scheme. I think I’ll leave the specific explanations describing WRS-2, path/row values and the other miscellaneous small data-management operations for a later post. For now, I’ll let it be understood that these are important things to know when in the process of actually obtaining this data. When the tarball is unpacked fully, the bands from the TIRS instrument are bands 10 and 11; the relevant .tif files are [“identifier”_B10.tif] and [“identifier”_B11.tif], and these were clipped to the administrative boundary of Delhi. There’s also a text file containing metadata: [“identifier”_MTL.txt] is essential for the math we’re going to do on these two bands.
This will be a relatively short post; I’ve been working with Landsat data for a few years now, and I find it absolutely fascinating. The new Landsat satellite, initially named the Landsat Data Continuity Mission and now known as Landsat 8, is actually the 7th in the series; Landsat 6 never made it to orbit. When Landsat 8 was launched on the 11th of February 2013, I was really anxious and excited and when it made it to orbit successfully, I was ecstatic. I downloaded my first set of Landsat data (Path146/Row040, covering the Indian city of Delhi) off the USGS EarthExplorer website last week, and have been tinkering with it ever since.
I was employed as a spatial data and cartographic consultant on a project to analyse specific agricultural commodities and Agricultural Produce Marketing Committees (APMCs) in the Indian states of Karnataka and Madhya Pradesh. The final product was a set of maps for various publications, as well as the clean datasets themselves.
Agricultural market datasets for the states of Karnataka and Madhya Pradesh were obtained for the purposes of spatial visualisation; these contained information on wheat procurement in Madhya Pradesh (2008 – 2012), tuar production in Karnataka (2007 – 2009) and the locations and categories of APMCs in both these states. Some of the data was linked to district names, while the rest was geocoded using a free online geocoding service. I used Quantum GIS, TextEdit and Microsoft Excel extensively for this project; Excel and TextEdit are invaluable when processing CSV files, and QGIS is where all the actual mapping itself takes place.
The actual process itself involved lots of data-cleaning and a little bit of mapping. First, for the geocoding, I ran the column containing the village names through the geocoder thrice; at each repetition, I tweaked the names a little more to get more accurate coordinate results. I then had to similarly tweak the district names to get them to match up with my source shapefiles; fixing bad spellings can be a LOT of work. In its entirity, this was a tedious process that involved organising, cleaning and validating four distinct datasets with both automated and manual operations. However, the final products were datasets that were clean, had accurate spatial locations and could easily be used to produce analytically valuable maps.