Geospatial Intelligence
Thermal Data& Emissions
Under DevelopmentMonitor Land Surface Temperature (LST), thermal heat anomalies, and global VIIRS nightlight footprints.
Thermal Infrared Sensing
Thermal remote sensing records electromagnetic radiation emitted from the Earth's surface in the thermal infrared range (8 - 14 $\mu m$). This radiation is directly related to surface kinetic temperature and emissivity.
Brightness Temperature Equation
Raw sensor values are first converted to Top-of-Atmosphere (TOA) spectral radiance, and then to Brightness Temperature using Planck's Law calibration coefficients.
PLANCK RADIATION TEMPERATURE CALIBRATION
Where T is Brightness Temperature in Kelvin, L_lambda is spectral radiance, and K_1, K_2 are sensor constants.
T = \frac{K_2}{\ln\left(\frac{K_1}{L_{\lambda}} + 1\right)}
LST Calibration Pipeline
The following script converts TOA radiance matrices to Brightness Temperature (Kelvin) for Landsat-8 Band 10 raster arrays.
thermal_lst.py
| 1 | # Python function to convert raw Top-of-Atmosphere (TOA) radiance to Brightness Temperature (Kelvin) |
| 2 | import numpy as np |
| 3 | |
| 4 | def toa_radiance_to_brightness_temp(band_10_raster, k1_const=774.89, k2_const=1321.08): |
| 5 | # k1 and k2 are standard calibration constants for Landsat-8 TIRS Band 10 |
| 6 | brightness_temp = k2_const / np.log((k1_const / band_10_raster) + 1.0) |
| 7 | return brightness_temp |