An exhaustive database for the development of a Bidirectional Reflectance Distribution Function (BRDF) model over the Caspian and the Baltic Sea

Information on the ice cover extent, distribution, concentration, ice surface temperature and other physical parameters of the ice pack is needed in numerical weather prediction, ship navigation, water management and regional/global climate change impact assessment. The objective of this research is to infer sea ice extent using estimation of sea ice and water reflectances from remote sensing measurements using a Bidirectional Reflectance Distribution Function (BRDF) model and an Artificial Neural Network. This paper has identified the structure feature, description, identification strategy and modeling method of this model.

This research has incorporated the BRDF because of its simplicity and the availability of the data necessary for its implementation. The ANN is suitable for any nonlinear curve fitting at any precision theoretically and occupies simple structure. Ability of satellites to provide global observations at high temporal frequency has made them the primary tool for monitoring ice cover extent. The issue of Bidirectional Reflectance Distribution Function (BRDF) spatial variability on its linkages to surface characteristics has received little attention.

The Advanced Baseline Imager (ABI) is being designed for future Geostationary Operational Environmental Satellites (GOES) starting with GOES-R in 2012. The ABI will improve over the existing GOES Imager with more spectral bands, higher spatial resolution, faster imaging and broader spectral coverage. The Northern region of the Caspian Sea is characteristic of reliable data sources relative to high-resolution satellite imagery compared with the aerial photography. In prior studies, it has BRDF models have been used to predict spectral reflectance and emissivity in the thermal infrared and has also shown no conclusive violations in the measurements. The results show that the development of this BRDF and ANN combination is a model that can aid in remote sensing of sea and lake ice.