The goal of this study is to develop an algorithm for estimating the surface soil moisture and surface roughness using polarimetric Synthetic Aperture Radar (SAR) data. In this study, an algorithm was applied to polarimetric airborne SAR data to estimate distributions of surface soil moisture and roughness. To validate the estimated soil moisture, we simultaneously conducted an experiment in October 1999 in Tsukuba Science City, Ibaragi Prefecture of Japan. Surface soil moisture was obtained by the Time- Domain Reflectometry (TDR) method, and the horizontal profiles of the land surface height were measured by a comb- style instrument for calculating the surface roughness parameters in test sites. Because the problem is site- specific and depends upon the measurement accuracy of both the ground truth data, the SAR system including speckle noise, and the effects of vegetation and artificial constructions, such as buildings, houses, roads, and roadside trees, the comparison results did not agree well with measured and inferred soil moisture.
In recent years, the application of radar polarimetry for remote sensing of land cover types has attracted extensive interest. Numerous microwave scattering models have been developed and used to interpret the polarimetric SAR data. In this paper, existing L-band backscatter models were used to model land-cover types, such as smooth and slightly rough surfaces (single scattering), urban area and tree trunks (double-bounce scattering) and forested area (diffuse scattering). Using these models, it is possible to construct the amplitude scattering matrix, Mueller matrix, Stokes parameter, etc. for each target. However, a state- vector was created using the Stokes parameters, degree of unpolarization and the phase difference between the HH and VV polarizations. The angle between two state-vectors (the theoretical state-vector derived from the calculation using the existing models and the state-vector derived from the observation or image data) was calculated for each land cover-type. We found that there is a strong correlation between the model predicted and the observed state-vectors for the same land cover types. The angle between the calculated and observed state-vectors is very useful for contrast enhancement and classifying the polarimetric radar data. For this purpose, polarimetric L-band airborne SAR data acquired over a variety of geographic targets are analyzed with the support of field investigations of forest, bare land and smooth surface (or ground and water), urban and rough surfaces. The classification results were presented.
We have conducted a feasibility study on measuring the tree height distribution by using polarimetric SAR interferometry datasets acquired by the NASDA-CRL's L-band Polarimetric Interferometric Synthetic Aperture Radar (PI-SAR). Test site is the Tottori Dune and its surrounding area, where pine trees were planted. Coherence and the phase differences obtained by the interferometric analysis were evaluated. As a result, we could estimate the tree height distribution if the polarimetric SAR images (co-pol and cross-pol) are acquired with larger baselines and smaller slant ranges. Quantitative evaluation is required to relate the phase center differences and the tree types and the polarization combinations.
The purpose of this paper is to evaluate JERS-1/SAR data for determining vegetation types in arid regions. First, a noise speckle filter was applied to the original JERS-1/SAR image data using a Map Filter with an adaptive 7*7 window. Second, a small part of the study area was extracted for the full scene image for further analysis. The NRCS values of each extracted image data were computed with the known Calibration Factor for the NASDA supplied JERS-1/SAR data. Each image was assigned to one of the three categories with two selected threshold levels. These two threshold levels can be obtained by Otsu's Automatic threshold selection method.In order to generate color composite images, multi- temporal SAR images were registered with the JERS-1/OPS image using a second-order polynomial function. The accuracy of registration was within 0.5 pixel RMS error. Following this color composite an image based on above three scenes was generated to identify the training samples. Finally, the color composite image was evaluated for vegetation type discrimination in the study area. A test site along the Tarim River in the Tarim Basin, China, was selected for this purpose.
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