Relating MODIS-derived surface albedo to soils and rock types over Northern Africa and the Arabian peninsula
Article first published online: 15 MAY 2002
Copyright 2002 by the American Geophysical Union.
Geophysical Research Letters
Volume 29, Issue 9, pages 67-1–67-4, May 2002
How to Cite
Relating MODIS derived surface albedo to soils and landforms over Northern Africa and the Arabian peninsula, Geophys. Res. Lett., 29(9), doi:10.1029/2001GL014096, 2002., , , , , , and ,
- Issue published online: 15 MAY 2002
- Article first published online: 15 MAY 2002
- Manuscript Accepted: 20 DEC 2001
- Manuscript Revised: 30 NOV 2001
- Manuscript Received: 14 SEP 2001
 We use the MODerate resolution Imaging Spectroradiometer (MODIS) aboard the Terra spacecraft to derive surface albedo for the arid areas of Northern Africa and the Arabian peninsula. Albedo in seven MODIS spectral bands for land and three broad bands (for shortwave, near infrared, and visible portions of the spectrum) is produced. Surface albedo is derived from MODIS observations during a sixteen-day period and is analyzed at 1 km spatial resolution. MODIS data show considerable spatial variability of surface albedo in the study region that is related to soil and geological characteristics of the surface. For example, solar shortwave white-sky albedo varies by a factor of about 2.5 from the darkest volcanic terrains to the brightest sand sheets. Vegetation contribution to surface reflectance is essentially negligible since we only considered pixels with under 10 percent fractional canopy cover. Few, if any, coupled land-atmosphere global or regional models capture this observed spatial variability in surface reflectance or albedo. Here we suggest a scheme that relates soil groups (based on the United Nations Food and Agriculture Organization, FAO, soil classification) and rock types (based on the United States Geological Survey, USGS, geological maps) to MODIS derived surface albedo statistics. This approach is a first step towards the incorporation of the observed spatial variability in surface reflective properties into climate models.