Amplitude Correction Theory Research Articles

A Dense Medium Microwave Backscattering Model for the Remote Sensing of Oil Palm

Very little work has been carried out to examine the potential of synthetic aperture radar (SAR) imaging data for growth monitoring, disease monitoring, and yield prediction of oil palm plantations. In this paper, we present a scattering model for oil palm based on the radiative transfer equations, solved iteratively up to the second order, to better understand the backscattering behavior of oil palm canopies for SAR image interpretation. The oil palm canopy is modeled as a multilayer background host medium embedded with discrete scatterers of different shapes, with elliptic disks representing the leaves and cylinders representing the fronds and trunks. We consider the effects of coherent scattering through the dense medium phase and amplitude correction theory, as well as the near-field interactions between closely spaced scatterers through the Fresnel phase and amplitude corrections. Ground-truth data are used as input parameters for the model. The model is used to calculate the backscattering coefficients of oil palm canopies at different stages of growth and for different polarizations, frequencies, and incident angles. Comparisons between model calculations and C-band scatterometer measurements of multipolarization backscattering coefficients of 4-year-old oil palm canopies in Bangi, Malaysia show good agreement. Our model also predicts that SAR images at L-band are more sensitive to changes in the structure of the canopies, particularly that of the fronds, so will be the best frequency for oil palm growth monitoring and disease detection.

MULTILAYER MODEL FORMULATION AND ANALYSIS OF RADAR BACKSCATTERING FROM SEA ICE

The Antarctic continent is an extremely suitable environ- ment for the application of remote sensing technology as it is one of the harshest places on earth. Satellite images of the terrain can be properly interpreted with thorough understanding of the microwave scattering process. The proper model development for backscatter- ing can be used to test the assumptions on the dominating scattering mechanisms. In this paper, the formulation and analysis of a multi- layer model used for sea ice terrain is presented. The multilayer model is extended from the previous single layer model developed based on the Radiative Transfer theory. The Radiative Transfer theory is chosen because of its simplicity and ability to incorporate multiple scattering efiects into the calculations. The propagation of energy in the medium is characterized by the extinction and phase matrices. The model also incorporates the Dense Medium Phase and Amplitude Correction Theory (DM-PACT) where it takes into account the close spacing ef- fect among scatterers. The air-snow interface, snow-sea ice interface and sea ice-ocean interface are modelled using the Integral Equation Method (IEM). The simulated backscattering coe-cients for co- and cross-polarization using the developed model for 1GHz and 10GHz are presented. In addition, the simulated backscattering coe-cients from the multilayer model were compared with the measurement results ob- tained from Coordinated Eastern Artic Experiment (CEAREX) (Gren- fell, 1992) and with the results obtained from the model developed by Saibun Tjuatja (based on the Matrix Doubling method) in 1992.

A STUDY OF AN INVERSION MODEL FOR SEA ICE THICKNESS RETRIEVAL IN ROSS ISLAND, ANTARCTICA

In this study, an inverse microwave scattering model for sea ice has been developed for the purpose of sea ice thickness retrieval using radar backscatter data. The model is loosely based on the Radiative-Transfer-Thermodynamic Inverse Model for Sea Ice Thickness Retrieval from Time-Series Scattering Data. The developed inverse model is a combination of the Radiative Transfer Theory with Dense Medium Phase and Amplitude Correction Theory (RT- DMPACT) forward model and the Levenberg-Marquardt Optimization algorithm. Using input data from ground truth measurements carried out in Ross Island, Antarctica, together with radar backscatter data extracted from purchased satellite images, the sea ice thickness of an area is estimated using the inverse model developed. The estimated sea ice thickness is then compared with the ground truth measurement data to verify its accuracy. The results have shown good promise, with successful estimation of the sea ice thickness within §0:15m of the actual measurement. A theoretical analysis has also revealed that the model faces di-culty once the sea ice thickness exceeds 1.7m. This can be considered in the future development and improvement of the model.

Passive Microwave Remote Sensing for Sea Ice Thickness Retrieval Using Neural Network and Genetic Algorithm

Over the years, global warming has gained much attention from the global com- munity. The fact that the sea ice plays an important role and has signiflcant efiects towards the global climate has prompted scientists to conduct various researches on the sea ice in the Polar Regions. One of the important parameters being studied is the sea ice thickness as it is a direct key indication towards the climate change. However, to conduct studies on the sea ice scientists are often facing with tough challenges due to the unfavorable harsh weather conditions and the remoteness of the Polar Regions. Thus, microwave remote sensing ofiers an attractive mean for the observation and monitoring of the changes of sea ice in the Polar Regions for the scientists. In this paper, we will be presenting 2 approaches using passive microwave remote sensing to retrieve sea ice thickness. The flrst approach involves the training and testing of the neural network (NN) by using data sets generated from the Radiative Transfer Theory with Dense Medium Phase and Amplitude Correction Theory (RT-DMPACT) forward scattering model. Once training is com- pleted, the inversion for sea ice thickness could be done speedily. The second approach utilizes a genetic algorithm (GA) which would perform a search routine to identify possible solutions in sea ice thickness that would match the corresponding brightness temperatures proflle of the sea ice. The results obtained from both approaches are presented and tested by using Special Scanning Microwave Imager (SSM/I) data with the aid of the sea ice measurements in the Arctic sea. In order to understand the interactions between the wave and sea ice medium, a forward scattering model based on Radiative Transfer Theory was constructed. This forward scattering model was further improved by incorporating Dense Medium Phase and Amplitude Correction Theory (RT- DMPACT) to take into account of the efiect of the closely placed scatterers in the sea ice medium. This forward scattering model formed the basis of our inverse model for the sea ice thickness retrieval process. For the NN approach, multiple pairs of data set consist of difierent sea ice parameters and thicknesses with the corresponding brightness temperatures are flrst generated using the forward scattering model. This data set will be provided to the NN to create a range of sea ice thickness proflle to be used for NN training. The training process is completed when the error generated by NN is acceptably small. After that, inversion is done by providing the brightness temperature proflles of the sea ice to obtain the corresponding sea ice thicknesses. As for GA, a pool of chromosomes representing sea ice thicknesses is created to be fed into the forward scattering model. The chromosomes are then evolved and carried forward to the next generation according to the natural selection concept, whereby the flttest candidate is more likely to survive and to reproduce. The generation and creation continues until the one of the chromosomes has been found to be suitable to be the thickness solution for a given brightness temperature proflle. 2. DATA TRAINING AND SEA ICE THICKNESS INVERSION BY NN The RT-DMPACT Model mentioned above is used to calculate the passive microwave returns in terms of brightness temperatures of vertically (TBv) and horizontally (TBh) polarized wave. The Neural Network (NN) constructed consists of an input layer, two hidden layers and an output layer. Each layer employs several neurons, which are connected to other neurons in the adjacent layer with difierent weights. The signals propagate from input layer, through hidden layers and to the output. The network is trained by the input-output data generated from the RT-DMPACT Model. The training process is carried out by changing the values of the interconnecting weights of the neurons in the layers by using Levenberg-Marquardt Algorithm (Martin H. & Mohammad B. M. 1994), according to the error generated. The weights in the NN are then changed in each iteration to reduce the error to an acceptable margin.

An Inverse Model for Sea Ice Thickness Retrieval Using Active Microwave Remote Sensing

Sea ice encompasses a large area within the polar region and greatly in∞uences the earth's climate system. One particular sea ice parameter of interest in understanding the dynamics of the sea ice cover and the heat exchange between the ocean and the atmosphere is its thickness. Due to this, there has been an increase in interest towards research in the polar region. Yet the harsh environment proves a great challenge to scientists doing research in those regions. The use of microwave remote sensing to retrieve physical data of the polar region, in particular sea ice thickness serves as a practical solution to the problem. In this paper, an RT-DMPACT Inverse Model to retrieve sea ice thickness from active microwave remote sensing data is presented. The inverse model is a combination of the Radiative Transfer Theory with Dense Medium Phase and Amplitude Correction Theory (RT-DMPACT) forward model and the Levenberg-Marquardt Optimization algorithm. The RT-DMPACT forward model is an improved forward model and is applied to generate the radar backscatter data, where the DMPACT is included to account for the close spacing efiect among the scatterers within the medium. The Levenberg-Marquardt Optimization algorithm is then applied to improve on the set of input parameters until the sea ice thickness can be estimated. Data from ground truth measurements carried out in Ross Island, Antarctica, such as sea ice surface roughness and temperature, together with radar backscatter data extracted from purchased satellite images, are used as inputs to estimate the sea ice thickness in an area. The estimated sea ice thickness is then compared with the ground truth measurement data to verify its accuracy. The results from the simulation show promise towards the use of the RT-DMPACT inverse model to retrieve sea ice thickness from actual conditions in the polar region.

Paddy Fields as Electrically Dense Media: Theoretical Modeling and Measurement Comparisons

Early models for paddy fields consist of a single-layered medium in which coherent effects within clusters of leaves are considered but multiple volume scattering is not. In this paper, the paddy canopy is modeled as a multilayered dense discrete random medium consisting of cylindrical and needle-shaped scatterers. Consideration is given to the coherent and near-field effects of the closely spaced scatterers through the Dense Medium Phase and Amplitude Correction Theory and Fresnel corrections, respectively, in the phase matrix. Then, this dense medium phase matrix is applied in the radiative transfer equations and solved up to the second order to consider double-volume scattering. Ground truth measurements of paddy fields were acquired at Sungai Burung, Selangor, Malaysia, for an entire season from the early vegetative stage of the plants to their reproductive stage. Measured parameters are used in the theoretical model to calculate the backscattering coefficients of paddy fields. Theoretical analysis of the simulation results shows in particular that second-order effects are important for cross-polarized backscatter data and that coherent effects need to be considered at lower frequencies. However, the use of needles to represent paddy leaves tends to underestimate the HH-polarized backscattering coefficients especially at the latter stages of plant growth, i.e., when the leaves are broader. The results are also used for comparisons with the backscattering coefficients obtained from RADARSAT images as well as that of earlier models to test the validity of the dense medium model with promising results.

Theoretical Modeling and Measurement Comparison of Season-Long Rice Field Monitoring

The development of a theoretical model to describe the scattering mechanisms involved in the remote sensing of rice crops is essential, as it ensures correct application of remote sensing data for rice monitoring. The theoretical model used in this study is based on the radiative transfer theory applied on a layered dense discrete random medium. The dense medium phase and amplitude correction theory (DM-PACT), which considers the coherent effects of the scatterers, is incorporated in the development of the phase matrices of the scatterers, which are modeled after the physical geometry of the plants. Ground truth measurements of rice fields were acquired at Sungai Burung, Selangor, Malaysia for an entire season. These measurements are used in the theoretical model to calculate the backscattering coefficients of rice fields. The results are then compared to those obtained from RADARSAT images to test the validity of the model. Comparisons show promising results, but further research is required to improve on the current model.

A Theoretical and Measurement Study of Sea Ice and Ice Shelf in Antarctica as Electrically Dense Media

Due to Antarctica's remoteness and harsh weather condition, remote sensing technology has been an attractive tool in equipping Antarctica scientists with the monitoring information and the understanding of ice physical changes in the Antarctican ecological system. Previous Antarctica researches on remote sensing of ice mainly utilize ice and snow scattering models based on independent scattering assumption. The needs of the consideration of coherent effects of the scatterers and the measurement of physical parameters affecting such effects are addressed in this study. Theoretical model based on radiative transfer theory with the incorporation of Dense Medium Phase and Amplitude Correction Theory (DM-PACT) [1] is developed. This concept of DM-PACT derived from antenna array theory requires an understanding of the distribution statistics of the scatterers in the homogeneous host medium. The ground truth measurement of multi-year period has been conducted at sea ice and ice shelf areas near Scott Base, Ross Island in Antarctica and a series of measurement of sea ice and snow physical parameters including distribution statistics of the air bubbles in sea ice are carried out. Theoretical study of the backscattering returns and scattering mechanisms in these electrically dense media is performed and analysis of important physical parameters affecting the radar returns is presented. Satellite measurements of the test areas from Radarsat have been acquired for the comparison of model predictions and measurement data with promising results.