i.landsat8.swlst is a GRASS GIS add-on, implementing a practical Split-Window (SW) algorithm, estimating land surface temperature (LST), from the Thermal Infra-Red Sensor (TIRS) aboard Landsat 8 with an accuracy of better than 1.0 K.
After installation (see section Installation below), from within a GRASS-GIS
session, retrieve usage details via i.landsat8.swlst --help
The shortest call for processing a complete Landsat8 scene normally is:
i.landsat8.swlst mtl=MTL prefix=B landcover=FROM_GLCwhere:
mtl= the name of the MTL metadata file (normally with a .txt extension)prefix= the prefix of the band names imported in GRASS GIS' data baselandcover= the name of a FROM-GLC map product that covers the extent of the
Landsat8 scene under processing. FORM-GLC products are available at
http://data.ess.tsinghua.edu.cn/.A faster call is to use existing maps for all in-between processing steps: at-satellite temperatures, cloud and emissivity maps.
At-satellite temperature maps (options t10, t11) may be derived via
the i.landsat.toar module. Note that i.landsat.toar does not
process single bands selectively.
The clouds option can be any user-defined map. Essentialy, it applies
the given map as an inverted mask.
The emissivity maps, derived by the module itself, can be saved once
via the emissivity_out and delta_emissivity_out options and used
afterwards via the emissivity and delta_emissivity options. Expert
users, however, may use emissivity maps from other sources directly.
An example command may be:
i.landsat8.swlst t10=T10 t11=T11 clouds=Cloud_Map emissivity=Average_Emissivity_Map delta_emissivity=Delta_Emissivity_Map landcover=FROM_GLC -k -c For details and more examples, read the manual.
The algorithm removes the atmospheric effect through differential atmospheric absorption in the two adjacent thermal infrared channels centered at about 11 and 12 μm.
The components of the algorithm estimating LST values are at-satellite brightness temperature (BT); land surface emissivities (LSEs); and the coefficients of the main Split-Window equation (SWCs).
LSEs are derived from an established look-up table linking the FROM-GLC classification scheme to average emissivities. The NDVI and the FVC are not computed each time an LST estimation is requested. Read [0] for details.
The SWCs depend on each pixel's column water vapor (CWV). CWV values are retrieved based on a modified Split-Window Covariance-Variance Matrix Ratio method (MSWCVMR) [1, 2]. Note, the spatial discontinuity found in the images of the retrieved CWV, is attributed to the data gap in the images caused by stray light outside of the FOV of the TIRS instrument [2]. In addition, the size of the spatial window querying for CWV values in adjacent pixels, is a key parameter of the MSWCVMR method. It influences accuracy and performance. In [2] it is stated:
A small window size n (N = n * n, see equation (1a)) cannot ensure a high correlation between two bands' temperatures due to the instrument noise. In contrast, the size cannot be too large because the variations in the surface and atmospheric conditions become larger as the size increases.
The combination of the brightness temperatures to estimate the LST bases upon the equation:
LST = b0 +
Note, however, the last quadratic term of the Split-Window equation is applied only over barren land. [Reference Required!]
BTs are derived from Landsat 8's TIRS channels 10 and 11. Prior to any processing, the raw digital numbers are filtered for clouds.
To produce an LST map, the algorithm requires at minimum:
see GRASS Addons SVN repository, README file, Installation - Code Compilation
Making the script i.lansat8.swlst available from within any GRASS-GIS ver.
7.x session, may be done via the following steps:
launch a GRASS-GIS’ ver. 7.x session
navigate into the script’s source directory
execute make MODULE_TOPDIR=$GISBASE
[High Priority]
~~Fix retrieval of adjacent subranges (exclude 6, get it only if there is no match for subranges 1, 2, 3, 4, and 5)~~
Evaluate BIG mapcalc expressions -- are they correct? I guess so ;-)
Why is the LST out of range when using a fixed land cover class? Cloudy
pixels are, mainly, the reason. Better cloud masking is the solution.
~~Why does the multi-step approach on deriving the CWV map differ from the single big mapcalc expression?~~ Fixed
Implement direct conversion of B10, B11 to brightness temperature values.
Done
Get the FROM-GLC map, Found
~~implement mechanism to read land cover classes from it and use it to retrieve emissivities~~ Done
How to use the FVC? Don't. Just use the Look-up table (see [*] for
details).
~~Save average emissivity and delta emissivity maps for caching (re-use in subsequent trials, huge time saver!)~~ Implemented
[Mid]
r.mapcalc can't do this. Best to implement a test checking the values
on-the-fly while they are created. A C-function?[Low]
split_window_lst class, in functions
_build_average_emissivity_mapcalc() and
_build_delta_emissivity_mapcalc()~~[*] Details: the authors followed the CBEM method. Based on the FROM-GLC map, they derived the following look-up table (LUT):
Emissivity Class|TIRS10|TIRS11
Cropland|0.971|0.968
Forest|0.995|0.996
Grasslands|0.97|0.971
Shrublands|0.969|0.97
Wetlands|0.992|0.998
Waterbodies|0.992|0.998
Tundra|0.98|0.984
Impervious|0.973|0.981
Barren Land|0.969|0.978
Snow and ice|0.992|0.998[0] Du, Chen; Ren, Huazhong; Qin, Qiming; Meng, Jinjie; Zhao, Shaohua. 2015. "A Practical Split-Window Algorithm for Estimating Land Surface Temperature from Landsat 8 Data." Remote Sens. 7, no. 1: 647-665. http://www.mdpi.com/2072-4292/7/1/647/htm\#sthash.ba1pt9hj.dpuf
[1] Huazhong Ren, Chen Du, Qiming Qin, Rongyuan Liu, Jinjie Meng, and Jing Li. "Atmospheric Water Vapor Retrieval from Landsat 8 and Its Validation." 3045--3048. IEEE, 2014.
[2] Ren, H., Du, C., Liu, R., Qin, Q., Yan, G., Li, Z. L., & Meng, J. (2015). Atmospheric water vapor retrieval from Landsat 8 thermal infrared images. Journal of Geophysical Research: Atmospheres, 120(5), 1723-1738.
[3] FROM-GLC products, http://data.ess.tsinghua.edu.cn/