Modelling the radiometric response of a dynamic, 3D structural model of Scots pine in the optical and microwave domains

A dynamic 3D structural model is used to simulate the structural growth stages of a Scots pine canopy from age 5 to 50 years. The 3D structural output of the model agrees with observed measures of Scots pine canopy structure. Needles are added to the structural model according to measured density and phyllotaxy (distribution). The 3D structural models are used to drive both optical and microwave models of canopy radiometric response. Simulated canopy radiometric response is compared with airborne hyperspectral reflectance data (HyMAP) and airborne synthetic aperture radar (ASAR) backscatter data, recorded during the SAR and Hyperspectral Airborne Campaign (SHAC) conducted over the UK during 2000. Simulations are shown to agree well in general with observations. This method is shown to be suitable for exploring the impact of canopy structure on the measured remotely sensed signal.

[1]  Ranga B. Myneni,et al.  The role of canopy structure in the spectral variation of transmission and absorption of solar radiation in vegetation canopies , 2001, IEEE Trans. Geosci. Remote. Sens..

[2]  Mathias Disney,et al.  Monte Carlo ray tracing in optical canopy reflectance modelling , 2000 .

[3]  C Andrieu,et al.  Development of architectural vegetation growth models for remote sensing applications, ESA Final Report, Contract 14940 , 2004 .

[4]  Philip Lewis.  Three-dimensional plant modelling for remote sensing simulation studies using the Botanical Plant Modelling System , 1999 .

[5]  Kamal Sarabandi,et al.  Electromagnetic scattering from grassland. I. A fully phase-coherent scattering model , 2000, IEEE Trans. Geosci. Remote. Sens..

[6]  Kamal Sarabandi,et al.  Electromagnetic scattering from grassland. II. Measurement and modeling results , 2000, IEEE Trans. Geosci. Remote. Sens..

[7]  Kamal Sarabandi,et al.  A Monte Carlo coherent scattering model for forest canopies using fractal-generated trees , 1999, IEEE Trans. Geosci. Remote. Sens..

[8]  J. Clevers,et al.  Combined use of optical and microwave remote sensing data for crop growth monitoring , 1996 .

[9]  B. Andrieu,et al.  Adel-maize: an l-system based model for the integration of growth processes from the organ to the ca , 1999 .

[10]  Kamal Sarabandi,et al.  Simulation of interferometric SAR response for characterizing the scattering phase center statistics of forest canopies , 2000, IEEE Trans. Geosci. Remote. Sens..

[11]  Mathias Disney,et al.  Biophysical parameter retrieval from forest and crop canopies in the optical and microwave domains using 3D models of canopy structure , 2003, IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477).

[12]  S. Running,et al.  Synergistic algorithm for estimating vegetation canopy leaf area index and fraction of absorbed photosynthetically active , 1998 .

[13]  Mathias Disney,et al.  The botanical plant modelling system , 1997 .

[14]  Marc L. Imhoff,et al.  A theoretical analysis of the effect of forest structure on synthetic aperture radar backscatter and the remote sensing of biomass , 1995, IEEE Transactions on Geoscience and Remote Sensing.