Microwave radiometer is the main sensor collecting the passive microwave signals emitted by the forest areas. Usually, the passive microwave models simulate the emissivity in terms of the radiation transfer equations with the energy equilibrium. The widely used ω-τ model ignores the signal of volume scattering within the vegetation, so it is limited in the case of dense forested area and high frequencies. The first-order model considers the first-order volume scattering in the vegetation. It has relatively improvement compared with the ω-τ model, but still underestimates the emissivity. However, the Matrix-Doubling model, which is the numerical solution of radiation transfer equation, can fully deal with the volume scattering. And it is established to fit the observed data quite well. Moreover, the MD model can coupled high-accuracy surface model, such as AIEM as the solution of the boundary condition. The model adopted on this platform is the MD-AIEM model.

Synthetic aperture radar is one kind of active microwave sensors which is widely used to map forest aboveground biomass due to its all-weather and all-day data acquisition ability as well as its penetration ability over forested areas. The SAR observations used in the 1990s were multi-polarization backscattering coefficients. Therefore SAR models developed over this period were incoherent model which only simulated backscattering coefficients over forested areas. For example, the Michigan Microwave Canopy Scattering Model (MIMICS), which was developed by Ulaby et al in 1990, could simulate backscattering coefficients of forest stands with vertical variations. The 3D radar backscattering model of forest canopies developed by Sun and Ranson in 1995 (Sun&Ranson model). The incoherent model collected on this platform is the Sun&Ranson model.

With the development of Interferometric SAR (InSAR) and Polarimetric Interferometric SAR (PolInSAR) techniques and enrichments of InSAR and PolInSAR data sources, coherent models were developed to simulate behaviors of both backscattering coefficients and scattering phase center over different forest structures. For example, the three-dimensional coherent radar backscattering model developed by Liu et al in 2010, which is based on real three-dimensional forest scenes. The Sun&Ranson model was further developed to semi-coherent model (SCSR) by Ni in 2014.

The basis of LiDAR is ranging to the surface obtained by precise timing of the roundtrip travel time of short-duration pulses of backscattered, near-infrared laser radiation. Several spaceborne LiDAR systems have been used to make measurements of vegetation. For example, the Shuttle laser altimeter (SLA) missions flown in 1996 and 1997 provided laser profile data from vegetation canopies and other surface features from space. These data provide information about the surface elevation, vegetation height, and the vertical distribution of vegetation components. How to derive vegetation physical parameters from LiDAR data depends on our knowledge of the relationship between LiDAR waveforms and the spatial structure and optical properties of the vegetation stand. A LiDAR waveform simulator presents a good tool to study these relationships because in a model, the vegetation scene structure can be exactly described and the mis-registration between target and signature, which is always a problem in remote sensing field experiments, can be avoided. With these considerations, A LiDAR waveform model was developed by Sun & Ranson in 2000.

With the development of optical models of vegetation, it can be divided into four categories: radiative transfer models, geometrical optical models, hybrid models and computer simulation models. BRDF models suitable for forest canopy include the FRT model developed by KUUSK, the Li-Strahler geometrical-optical model, GORT, GeoSAIL and other hybrid models as well as computer simulation models like DART and RGM. Xiaowen Li created the Li-Strahler geometrical-optical model in 1985. This successfully explained the non-Lambert phenomenon of vegetation surface pixels due to crown geometry. The gap probability model established for discrete vegetation crown in 1988 bridges the gap between geometrical optical model and radiation transfer model. This model greatly simplified the solution complexity in three dimensional spaces with complex border conditions. In 1992, Li built the GOMS model which considers the mutual shadowing effect between illumination direction and reflected direction. An exhaustive validation established the academic status for the Li-Strahler geometrical-optical model, which is adopted by this platform.