TY - GEN
T1 - Developing a neural-network-based "BRDF" tool for the UAE coastal and inland zones
AU - Al Suwaidi, Ali
AU - Al Rais, Adnan
AU - Ghedira, Hosni
AU - Temimi, Marouane
PY - 2009
Y1 - 2009
N2 - The radiation reflected by any observed surface is highly dependent on both sun illumination and satellite observation angles. These two angles are also described, respectively, as incident and reflected angles. The geometry-dependence of surface reflectance is usually corrected by a tailored Bidirectional Reflectance Distribution Function (BRDF). It is the most common tool used to eliminate or to reduce the effects of sun-sensor geometry on the reflected radiation. Generally, BRDFs are derived empirically (or semi-empirically) for a specific land cover by analyzing a large set of observations (training set) made under different illumination and observation angles. This approach involves fitting the model to collected observations and inverting it. A strong BRDF model tailored to specific land cover characteristics of the UAE is especially needed for applications that use data acquired with variable sun-sensor geometry. In this paper, a neural-network-based tool "BRDF" was developed and applied to quantify the effect of sun illumination and SEVIRI-MSG observation angles on measured reflectance for both land (mostly desert) and coastal water pixels in the UAE.
AB - The radiation reflected by any observed surface is highly dependent on both sun illumination and satellite observation angles. These two angles are also described, respectively, as incident and reflected angles. The geometry-dependence of surface reflectance is usually corrected by a tailored Bidirectional Reflectance Distribution Function (BRDF). It is the most common tool used to eliminate or to reduce the effects of sun-sensor geometry on the reflected radiation. Generally, BRDFs are derived empirically (or semi-empirically) for a specific land cover by analyzing a large set of observations (training set) made under different illumination and observation angles. This approach involves fitting the model to collected observations and inverting it. A strong BRDF model tailored to specific land cover characteristics of the UAE is especially needed for applications that use data acquired with variable sun-sensor geometry. In this paper, a neural-network-based tool "BRDF" was developed and applied to quantify the effect of sun illumination and SEVIRI-MSG observation angles on measured reflectance for both land (mostly desert) and coastal water pixels in the UAE.
KW - Bidirectional reflectance distribution function (BRDF)
KW - METEOSAT Second Generation (MSG)
KW - Neural networks
KW - SEVIRI-MSG
KW - UAE
UR - http://www.scopus.com/inward/record.url?scp=77951132570&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77951132570&partnerID=8YFLogxK
U2 - 10.1109/IGARSS.2009.5418116
DO - 10.1109/IGARSS.2009.5418116
M3 - Conference contribution
AN - SCOPUS:77951132570
SN - 9781424433957
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - II460-II463
BT - 2009 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2009 - Proceedings
T2 - 2009 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2009
Y2 - 12 July 2009 through 17 July 2009
ER -