A Modified GORT Model for a Two-Layer Canopy Surface

Forest resources are globally valuable and critical in many ecological processes. The forest canopy remote sensing model is an essential tool for understanding forest growth conditions. Over time, the model has evolved to consider the effects of the vertical heterogeneity of forests and understory vegetation on radiative transfer. Despite the effectiveness of the mainstream three-dimensional radiative transfer model, it still faces challenges related to parameter complexity and poor timeliness. To address these issues, we developed a two-layer model (Two-Layer GORT) based on the geometric optical-radiative transfer hybrid (GORT) model. The Two-Layer GORT extends the GORT model to include a layer of understory vegetation under the canopy. The effectiveness of the Two-Layer GORT model is evaluated by simulating the gap probability and BRDF of the North China larch (Larix principis-rupprechtii) sample plot in the Saihanba Jixie Forest Farm, Hebei Province. Additionally, the model is compared to the model in the RAMI project to evaluate its effectiveness. The results indicate that the Two-Layer GORT model has a good ability to simulate the radiative transfer process, demonstrating the potential of the GORT model to simulate increasingly complex scenarios. This study is effective for understanding the growth state of a forest and sets the future goal of constructing a generalized two-layer model that considers scenarios with increasing complexity.