Modeling and validation of directional reflectance for heterogeneous agro-forestry scenarios

Abstract:

Landscape heterogeneity is a common natural phenomenon but is seldom considered in current radiative transfer models for predicting the surface reflectance. This paper developed an explicit analytical Radiative Transfer model for heterogeneous Agro-Forestry scenarios (RTAF) by dividing the scenario into non-boundary regions and boundary regions. The scattering contribution of the non-boundary regions that are treated as homogeneous canopies can be estimated from the SAILH model, whereas that of the boundary regions with lengths, widths, canopy heights, and orientations of the field patches, is calculated based on the bidirectional gap probability by considering the interactions and mutual shadowing effects among different patches. The hot spot factor is extended for heterogeneous scenarios, the Hapke model for soil anisotropy is incorporated, and the contributions of the direct and diffuse radiation are separately calculated.

The multi-angular airborne observations and the Discrete Anisotropic Radiative Transfer (DART) model simulations were used for validating and evaluating the RTAF model over an agro-forestry scenario in Heihe River Basin, China. It indicates that the RTAF model can accurately simulate the hemispherical-directional reflectance factors (HDRFs) of the heterogeneous agro-forestry scenario, with an RMSE of 0.0016 and 0.0179 in the red and near-infrared (NIR) bands, respectively. The RTAF model was compared with two widely used models, the dominant cover type (DCT) model and the spectral linear mixture (SLM) model, which either neglected the interactions and mutual shadowing effects between the shelterbets and crops, or did not account for the contribution of the shelterbets. Results suggest that the boundary effect can significantly influence the angular distribution of the HDRFs, and consequently enlarged the HDRF variations between the backward and forward directions in the principle plane. The RTAF model reduced the maximum relative error from 25.7% (SLM) and 23.0% (DCT) to 9.8% in the red band, and from 19.6% (DCT) and 13.7% (SLM) to 8.7% in the NIR band. According to the findings in this paper, the RTAF model provides a promising way to improve the retrieval of biophysical parameters (e.g. leaf area index) from remote sensing data over heterogeneous agro-forestry scenarios.