The effects of scale on predictions of land surface temperature from a variety of remote sensing platforms

• Published in: IGARSS 2001. Scanning the Present and Resolving the Future. Proceedings. IEEE 2001 International Geoscience and Remote Sensing Symposium (Cat. No.01CH37217) Vol. 3; pp. 1315 - 1317 vol.3
• Main Authors: McCabe, M.F., Prata, A.J., Kalma, J.D.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 2001
• Subjects: Brightness temperature; Data mining; Extraterrestrial measurements; Land surface; Land surface temperature; MODIS; Satellites; Spatial resolution; Temperature measurement; Temperature sensors
• ISBN: 9780780370319; 0780370317
• DOI: 10.1109/IGARSS.2001.976830

Remotely sensed measurements have the potential to provide information on the land surface at a variety of spatial and temporal scales. However, the temporal resolution ideally required for these applications is not generally commensurate with the level of information provided by satellite systems. The trade-off between an appropriate level of spatial versus temporal resolution makes the combination of data from multiple platforms appealing. This paper reports on an analysis of the NOAA-AVHRR and MODIS satellite systems to assess their suitability for this purpose. Land surface temperature from these two platforms are compared with ground based infrared thermometry measurements. The results of a previous study comparing brightness temperatures derived from the GMS-5 and NOAA-AVHRR are also presented. It is observed that for a selected clear-sky period, there is good agreement between the NOAA, MODIS and ground based LST. It is also shown that brightness temperatures from the NOAA-AVHRR and GMS-5 offer some insight into the diurnal trend in LST, and that a combination of the two would increase the confidence in predictions. This has implications for the use of such data in a number of modeling applications. This paper explores the feasibility of extracting the diurnal temperature wave from polar orbiters, and improving knowledge of the degree of spatial variation in LST through comparison with the GMS-5 satellite.