The approach was applied to a study area consisting of homogeneous agricultural fields, which were used as objects for applying the spatial constraints. LAI and Cab were estimated from at-sensor radiance data of the Airborne Prism EXperiment (APEX) imaging spectrometer by inverting the coupled SLC-MODTRAN4 canopy-atmosphere model. The estimation was implemented in two steps. In the first step, up to six variables were estimated for each object using a Bayesian optimization algorithm. In the second step, a look-up-table (LUT) was built for each object with only LAI and Cab as free variables, constraining the values of all other variables to the values obtained in the first step. The results indicated that the Bayesian object-based approach estimated LAI more accurately (R2 = 0.45 and RMSE = 1.0) than a LUT with a Bayesian cost function (LUT-BCF) approach (R2 = 0.22 and RMSE = 2.1), and Cab with a smaller absolute bias (鈭?#xA0;9 versus 鈭?#xA0;23 渭g/cm2).
The results of this study are an important contribution to further improve the regularization of ill-posed RT model inversions. The proposed approach allows reducing uncertainties of estimated vegetation variables, which is essential to support various environmental applications. The definition of objects and a priori data in cases where less extensive ground data are available, as well as the definition of the observation covariance matrix, are critical issues which require further research.