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Convert Landsat 8 top of atmosphere thermal band data to land surface temperature

These are scripts that calculate land surface temperature, as we did in our paper:

Alber M, O’Connell JL. 2019. Elevation drives gradients in surface soil temperature within salt marshes. Geophysical Research Letters 46:5313–22.

You can use these scripts to map spatiotemporal variation in land surface temperture as shown below for tidal marsh areas on Sapelo Island, GA for high vs low tide conditions. Landsurface temperature

These methods begin by retrieving atmospheric correction parameters from Barsi et al.'s (2003, 2005) NASA webtool and calculating land surface temperature from top of atmosphere Landsat 8 thermal band data. Currently, one should only use Landsat 8's band 10 for thermal data, because noise from stray light reduces the utility of Landsat 8's band 11 (see Cook et al. 2014).

Cook, M., Schott, J. R., Mandel, J., & Raqueno, N. (2014). Development of an operational calibration methodology for the Landsat thermal data archive and initial testing of the atmospheric compensation component of a Land Surface Temperature (LST) product from the archive. Remote Sensing, 6(11), 11244–11266. https://doi.org/10.3390/rs61111244

Step 1: download Landsat 8 surface reflectance data, which also includes top of atmosphere brightness temperature bands 10 and 11, for points of interest, from Google Earth Engine with the script available at https://github.com/jloconnell/Google_Earth_Engine/blob/master/landsat8_to_points.js

Step 2: Preprocess the landsat 8 data with the script:

https://github.com/jloconnell/convert_top_of_atmosphere_thermal_to_land_surface_temperature/blob/master/preprocess_landsat_8.r

This script loads the data, filters to high quality cloud-free pixels and creates date and location fields from standard Google Earth Engine output

Step 3: Cacluate standard landsat 8 spectral indices (NDVI, etc). If desired, use the function available at:

https://github.com/jloconnell/remote_sensing_with_R/blob/master/landsat8_vegetation_indices.r

Step 4: Uset the atmosphereic correction parameter retrival script to automate interaction with Julia Barsi's NASA web tool. This script is available at https://github.com/jloconnell/remote_sensing_with_R/blob/master/atmospheric_correction_landsat_suface_temp.r

This script will create a .csv file with for each landsat observation date and coarse lat/long location with the needed atmospheric parameters for calculate_lst_landsat8.r function in step 5.

For more about Barsi's webtool see:

Barsi, J. A., Barker, J. L., & Schott, J. R. (2003). An Atmospheric Correction Parameter Calculator for a single thermal band earth-sensing instrument. In IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477) (Vol. 5, pp. 3014–3016 vol.5). https://doi.org/10.1109/IGARSS.2003.1294665

Barsi, J. A., Schott, J. R., Palluconi, F. D., & Hook, S. J. (2005). Validation of a web-based atmospheric correction tool for single thermal band instruments. In Earth Observing Systems X (Vol. 5882, p. 58820E). International Society for Optics and Photonics. https://doi.org/10.1117/12.619990

Step 5: Merge the landsat data with the atmospheric parameters retrieved in step 4. Then apply the function for calculating land surface temperature available at: https://github.com/jloconnell/convert_top_of_atmosphere_thermal_to_land_surface_temperature/blob/master/calculate_lst_landsat8.r

This function requires the atmospheric transmission parameters from step 4, NDVI, an estimate of land cover based on NDVI cut-offs, and an estimate of emissivity for each land cover type. NDVI cut-offs that distinguish among land cover types need to estimated for the region of interest. For example, in coastal wetlands, NDVI values will be depressed by perennially moist soils in areas that are not densely vegetated (water is a good absorber of light, and thus water and moist soils will have lower spectral reflectance the dry soils and vegetation). Thus the cut-off that distinguishes water, soil, and vegetation will be different in wetlands than in upland areas. Once the land cover type is estimated from NDVI, a series of ifelse statements that essentially create a land cover look-up table are used to associate the land cover with emissivity. Emissivity for land cover types can be calculated either from ground-truth data, or by averaging emissivity information for the land cover type from ASTER's band 13 (the closest wavelength to Landsat 8's band 10) (https://lpdaac.usgs.gov/products/ag100v003/). ASTER emissivity data are also freely available on Google Earth Engine with the image id "NASA/ASTER_GED/AG100_003".

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Functions for retrieving atmospheric correction parameters from Barsi et al 's NASA webtool and calculating land surface temperature from Landsat thermal bands

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