Combining Multiple Solution and Cost Function for Better LAI Estimation

Vegetation is a key component of the earth planet, which controls the energy and water exchanges between atmosphere and the Earth surface and plays an important role in the global energy cycles, such as oxygen, carbon dioxide, and water. Monitoring and management of vegetation are done using its biophysical and biochemical parameters such as LAI. Leaf area index (LAI) is one of the most important vegetation parameters that used in most of the applications such as water and carbon cycles modeling. Remote sensing in terms of their continuous and extensive cover is a unique tool for generating vegetation variables. Different retrieval approaches have been developed to extract biophysical parameters information from remote sensing data, which is divided into two broad classes, the statistical/experimental approaches and the physical approach. In the present study, the PROSAIL RT model (Radiation Transfer Model) based on the LUT table have been used to retrieve the LAI variable. Ground reference data collected during the SPARC 2003 campaign were also used to evaluate the accuracy of the retrieved variable. To drawback, the ill-posed problem, four categories of cost functions have been used: Information Measurement (IM), Minimum contrast (MC), Angle Measurement (SAM) and Least Square Error (LSE) and used the multiple Best solution instead of Single best solution. The results showed improvement in the LAI estimation of up to 12% for the multi-species canopy.