Land, sea, sub-surface temperature mapping and forest fire detection from space

This paper describes a number of pieces of work that we have carried out in connection with surface temperature, making use if satellite thermal infrared data. The study involves estimating land surface temperature, sea surface temperature, land sub-surface temperature and forest fires. A plane-parallel radiative transfer model was used to derive a multi-channel method for retriving land surface temperature from space. The perfomance and reliability of this algorithm has been tested for a dry atmosphere whose value of surface temperature lies between 255 and 280 K. Comparison of the retrieved land surface temperatures witha near coincident poin temperature data set resulted in a standard deviation (+-) 0.5 degK. For a wet atmosphere (such as tropical region), the effect of spectral variation in emissivity is significant where a different of 1% in spectral variation would yield an error of 0.2 degK in the retrieved LST. On the other hand, a 1% uncertainty in the value of emissivity channel 4 would yield an error of 0.3 degK in the retrieved LST. In deriving sea surface temperatures from channel 4 and 5 of NOAA AVHRR data, the algorithm has been tested with several scenes of AVHRR data acquired over the western Mediterranean Sea. However, the calculated surface temperatures could not be verified due to unavailability of near coincident data. The potential utility of thermal infrared and short wavelength infrared data for detecting and mapping sub-surface high temperature sources is analysed. In this study, AVHRR and TM data were used to detect and map sub-surface coal fires. The brightness temperature depicted by AVHRR channel 3 data exibited high thermal anomalies in the suspected area. The thermal channel (TM channel 6) has been useful in distinguishing gross thermal anomaies from the background of solar warming. The resultant surface temperature anomalies are compared to surface temperatures depicted from thermal infrared aerial survey and ground measurement. Correlation of these data indicate that TM data could be used to detect, delineate and quantify sub-surface coal fire zones. Finally, the potential usefulness of thermal infrared sensors onboard NOAA polar-orbiting satellites for detecting fires is discussed. Several images over Sumatera have been selected to demonstrate the utility of the NOAA-series 3.0-micron channel for fire detection.