Development of a daily long term record of NOAA-14 AVHRR land surface temperature over Africa

• Published in: Remote sensing of environment Vol. 103; no. 2; pp. 153 - 164
• Main Authors: Pinheiro, A.C.T., Mahoney, R., Privette, J.L., Tucker, C.J.
• Format: Journal Article
• Language: English
• Published: New York, NY Elsevier Inc 30.07.2006; Elsevier Science
• Subjects: Advanced Very High Resolution Radiometer; Africa; Animal, plant and microbial ecology; Applied geophysics; Biological and medical sciences; Earth sciences; Earth, ocean, space; Exact sciences and technology; Fundamental and applied biological sciences. Psychology; General aspects. Techniques; Global Area Coverage; Internal geophysics; Land surface temperature; Split window algorithm; Teledetection and vegetation maps; South Africa; Southern Africa; UNESCO; FAO; Advanced Very High Resolution Radiometer; Land surface temperature; Global Area Coverage; Split window algorithm; Africa; data; Night; NOAA; latitude; Surface temperature; global; aluminum; clouds; inventory; Earth surface; Modeling; Daily; 1995; 1994; Center; development; NASA; cartography; Space flight; windows; Long term; Longitude; Sun; geometry; methodology
• ISSN: 0034-4257; 1879-0704
• DOI: 10.1016/j.rse.2006.03.009

We developed a new 6-year daily, daytime and nighttime, NOAA-14 AVHRR based land surface temperature (LST) dataset over continental Africa for the period 1995 through 2000. The processing chain was developed within the Global Inventory Modeling and Mapping System (GIMMS) at NASA's Goddard Space Flight Center. This paper describes the processing methodology used to convert the Global Area Coverage Level-1b data into LST and collateral data layers, such as sun and view geometries, cloud mask, local time of observation, and latitude and longitude. We used the Ulivieri et al. [Ulivieri, C., M.M. Castronuovo, R. Francioni, and A. Cardillo (1994), A split window algorithm for estimating land surface temperature from satellites, Adv. Space Research, 14(3):59–65.] split window algorithm to determine LST values. This algorithm requires as input values of surface emissivity in AVHRR channels 4 and 5. Thus, we developed continental maps of emissivity using an ensemble approach that combines laboratory emissivity spectra, MODIS-derived maps of herbaceous and woody fractional cover, and the UNESCO FAO soil map. A preliminary evaluation of the resulting LST product over a savanna woodland in South Africa showed a bias of < 0.3 K and an uncertainty of < 1.3 K for daytime retrievals (< 2.5 K for night). More extensive validation is required before statistically significant uncertainties can be determined. The LST production chain described here could be adapted for any wide field of view sensor (e.g., MODIS, VIIRS), and the LST product may be suitable for monitoring spatial and temporal temperature trends, or as input to many process models (e.g., hydrological, ecosystem).