Zhengxiao Chen

Inversion of snow parameters from passive microwave remote sensing measurements by a neural network trained with a multiple scattering model

The inversion of snow parameters from passive microwave remote sensing measurements is performed with a neural network trained with a dense-media multiple-scattering model. The input-output pairs generated by the scattering model are used to train the neural network. Simultaneous inversion of three parameters, mean-grain size of ice particles in snow, snow density, and snow temperature from five brightness temperatures, is reported. It is shown that the neural network gives good results for simulated data. The absolute percentage errors for mean-grain size of ice particles and snow density are less than 10%, and the absolute error for snow temperature is less than 3 K. The neural network with the trained weighting coefficients of the three-parameter model is also used to invert SSMI data taken over the Antarctic region. >

Inversion of Snow Parameters by Neural Network with Iterative Inversion

The inversion of snow parameters from passive microwave remote sensing measurements is performed with a neural network trained with a dense media multiple scattering model. A constrained iterative inversion scheme is used. Inversion of four parameters has been performed from five brightness temperatures. The four parameters are: mean-grain size of ice particles in snow, snow density, snow temperature and snow depth. The five brightness temperatures are that of 19 GHz vertical polarization, 19 GHz horizontal polarization, 22 GHz vertical polarization, 37 GHz vertical polarization, and 37 GHz horizontal polarization which are available from SSMI satellites. Based on the neural network constrained iterative inversion algorithm, we have also performed synthetic mapping of the terrain. Retrieval of synthetic mapping has been achieved. The incorporation of ground truth information is also considered.

Microwave scattering by plants generated by a stochastic Lindenmayer system

Recent studies indicate that at high frequencies, the branches and leaves in vegetation scatter independently. However, at low frequencies, they scatter collectively so that the structure of the trees and plants needs to be taken into account. Also, the near-field interaction among branches and leaves at low frequencies can give strong polarization signatures and cross polarization. Such collective scattering effects can contribute to a variety of frequency and polarimetric dependence. To take into account the tree and plant structures, the authors have used a stochastic Lindenmayer system to generate plant and tree structures with realistic appearances. They then solve Maxwells equations for vegetation grown in this manner.

Collective scattering effects in vegetation canopies at microwave frequencies based on Monte Carlo simulations

Vector radiative transfer theory based on independent scattering can be invalid for certain cases of vegetation canopy where the scatterers can scatter collectively. Collective scattering effects include correlated scattering and the mutual coherent wave interactions between scatterers in close proximity of each other. The authors perform Monte Carlo simulations of scattering by vegetation with clustering structures. Two cases are considered. The first case is scattering from a large number of trees with clustering branches and leaves. The location and orientation of each branch and leaf is generated by using the stochastic Lindenmayer system (L-system) which is a technique of computer growth of vegetation and plants. The second case is vertical dielectric cylinders placed randomly on a dielectric surface. Results of both cases are compared with independent scattering.<<ETX>>

Retrieval of semi-arid region parameters from passive microwave measurements using neural networks within a Bayesian framework

The problem of recovering geophysical parameters from microwave measurements is examined. A multilayer perceptron neural network was built, and then trained with data produced from a passive radiative transfer model. The model is applicable to semi-arid regions, and produces dual-polarised temperatures for frequencies of 6.6, 10.7, and 37 GHz from specification of soil moisture, soil temperature, and vegetation moisture. Synthetic semi-arid terrains, characterized by physically realizable moisture and temperature values, were created and used to test the inversion process. The model generated brightness temperatures for these terrains were inverted by the trained neural network within the Bayesian framework. Microwave brightness temperatures obtained from the Seasat Scanning Multichannel Microwave Radiometer (SMMR) over the Sahara Desert were also inverted. The values of soil moisture, temperature and vegetation moisture recovered from the inversion produced contours that follow expected trends for that region.<<ETX>>