The purpose of this paper is to study about the interrelationship between the backscattering intensity of PALSAR data and the laboratory measurement of dielectric constant and soil moisture. The characterization of th...The purpose of this paper is to study about the interrelationship between the backscattering intensity of PALSAR data and the laboratory measurement of dielectric constant and soil moisture. The characterization of the dielectric constant of arid soils in the 0.3 - 3 GHz frequency range, particularly focused in L-band was analyzed in varied soil moisture content and soil textures. The interrelationship between the relative dielectric constant with soil textures and backscattering of PALSAR data was also analyzed and statistical model was computed. In this study, after collecting the soil samples in the field from top soil (0 - 10 cm) in a homogeneous area then, the dielectric constant was measured using a dielectric probe tool kit. For investigated of the characteristics and behaviors of the dielectric constant and relationship with backscattering a variety of moisture content from 0% to 40% and soil fraction conditions was tested in laboratory condition. All data were analyzed by integrating it with other geophysical data in GIS, such as land cover and soil texture. Thus, the regression model computed between measured soil moisture and backscattering coefficient of PALSR data which were extracted as same point of each soil sample pixel. Finally, after completing the preprocessing, such as removing the speckle noise by averaging, the model was applied to the PALSAR data for retrieving the soil moisture map in arid region of Iran. The analysis of dielectric constant properties result has shown the soil texture after the moisture content has the largest effected on dielectric constant. In addition, the PALSAR data in dual polarization are also able to derive the soil moisture using statistical method. The dielectric constant and backscattering shown have the exponential relationship and the HV polarization mode is more sensitive than the HH mode to soil moisture and overestimated the soil moisture as well. The validation of result has shown the 4.2 Vol-% RMSE of soil moisture. It means that the backscattering analysis should consider about other factors such a surface roughness and mix pixel of vegetation effective.展开更多
In this paper, an empirical methodology to retrieve bare soil moisture by Synthetic Aperture Radar (SAR) is developed. The model is based on Advanced Integral Equation Model (AIEM). Since AIEM cannot express cross-pol...In this paper, an empirical methodology to retrieve bare soil moisture by Synthetic Aperture Radar (SAR) is developed. The model is based on Advanced Integral Equation Model (AIEM). Since AIEM cannot express cross-polarized backscattering coefficients accurately, we propose an empirical model to retrieve soil moisture for bare farmland only with co-polarized SAR data. The soil moisture can be obtained by solving an equation of HH and VV polarized data without any field measurements. Both simulated and real SAR data are used to validate the accuracy of the model. This method is especially effective in a large area where the surface roughness is difficult to be completely measured.展开更多
森林树高的反演是极化干涉合成孔径雷达(polarimetric SAR interferometry,PolInSAR)领域研究的热点,但时间去相关的存在引起了散射体的变化,降低了树高反演的性能,其中土壤含水量的改变是引起时间去相关的因素之一。本文利用PolSARpro...森林树高的反演是极化干涉合成孔径雷达(polarimetric SAR interferometry,PolInSAR)领域研究的热点,但时间去相关的存在引起了散射体的变化,降低了树高反演的性能,其中土壤含水量的改变是引起时间去相关的因素之一。本文利用PolSARpro生成不同土壤含水量的极化SAR模拟数据,通过三阶段算法进行树高反演实验。结果表明:土壤含水量的变化会导致反演结果产生一定偏差,但此偏差相对较小,已远远小于算法本身造成的误差。展开更多
Synthetic aperture radar(SAR) is an effective and important technique in monitoring crop and other agricultural targets because its quality does not depend on weather conditions. SAR is sensitive to the geometrical st...Synthetic aperture radar(SAR) is an effective and important technique in monitoring crop and other agricultural targets because its quality does not depend on weather conditions. SAR is sensitive to the geometrical structures and dielectric properties of the targets and has a certain penetration ability to some agricultural targets. The capabilities of SAR for agriculture applications can be organized into three main categories: crop identification and crop planting area statistics, crop and cropland parameter extraction, and crop yield estimation. According to the above concepts, this paper systematically analyses the recent progresses, existing problems and future directions in SAR agricultural remote sensing. In recent years, with the remarkable progresses in SAR remote sensing systems, the available SAR data sources have been greatly enriched. The accuracies of the crop classification and parameter extraction by SAR data have been improved progressively. But the development of modern agriculture has put forwarded higher requirements for SAR remote sensing. For instance, the spatial resolution and revisiting cycle of the SAR sensors, the accuracy of crop classification, the whole phenological period monitoring of crop growth status, the soil moisture inversion under the condition of high vegetation coverage, the integrations of SAR remote sensing retrieval information with hydrological models and/or crop growth models, and so on, still need to be improved. In the future, the joint use of optical and SAR remote sensing data, the application of multi-band multi-dimensional SAR, the precise and high efficient modeling of electromagnetic scattering and parameter extraction of crop and farmland composite scene, the development of light and small SAR systems like those onboard unmanned aerial vehicles and their applications will be active research areas in agriculture remote sensing. This paper concludes that SAR remote sensing has great potential and will play a more significant role in the various fields of agricultural remote sensing.展开更多
文摘The purpose of this paper is to study about the interrelationship between the backscattering intensity of PALSAR data and the laboratory measurement of dielectric constant and soil moisture. The characterization of the dielectric constant of arid soils in the 0.3 - 3 GHz frequency range, particularly focused in L-band was analyzed in varied soil moisture content and soil textures. The interrelationship between the relative dielectric constant with soil textures and backscattering of PALSAR data was also analyzed and statistical model was computed. In this study, after collecting the soil samples in the field from top soil (0 - 10 cm) in a homogeneous area then, the dielectric constant was measured using a dielectric probe tool kit. For investigated of the characteristics and behaviors of the dielectric constant and relationship with backscattering a variety of moisture content from 0% to 40% and soil fraction conditions was tested in laboratory condition. All data were analyzed by integrating it with other geophysical data in GIS, such as land cover and soil texture. Thus, the regression model computed between measured soil moisture and backscattering coefficient of PALSR data which were extracted as same point of each soil sample pixel. Finally, after completing the preprocessing, such as removing the speckle noise by averaging, the model was applied to the PALSAR data for retrieving the soil moisture map in arid region of Iran. The analysis of dielectric constant properties result has shown the soil texture after the moisture content has the largest effected on dielectric constant. In addition, the PALSAR data in dual polarization are also able to derive the soil moisture using statistical method. The dielectric constant and backscattering shown have the exponential relationship and the HV polarization mode is more sensitive than the HH mode to soil moisture and overestimated the soil moisture as well. The validation of result has shown the 4.2 Vol-% RMSE of soil moisture. It means that the backscattering analysis should consider about other factors such a surface roughness and mix pixel of vegetation effective.
基金Supported by the Research Program of Science and Technology at Universities of Inner Mongolia Autonomous Region (NJZZ11069)the Natural Science Foundation of Inner Mongolia Autonomous Region (2011BS0904)
文摘In this paper, an empirical methodology to retrieve bare soil moisture by Synthetic Aperture Radar (SAR) is developed. The model is based on Advanced Integral Equation Model (AIEM). Since AIEM cannot express cross-polarized backscattering coefficients accurately, we propose an empirical model to retrieve soil moisture for bare farmland only with co-polarized SAR data. The soil moisture can be obtained by solving an equation of HH and VV polarized data without any field measurements. Both simulated and real SAR data are used to validate the accuracy of the model. This method is especially effective in a large area where the surface roughness is difficult to be completely measured.
文摘森林树高的反演是极化干涉合成孔径雷达(polarimetric SAR interferometry,PolInSAR)领域研究的热点,但时间去相关的存在引起了散射体的变化,降低了树高反演的性能,其中土壤含水量的改变是引起时间去相关的因素之一。本文利用PolSARpro生成不同土壤含水量的极化SAR模拟数据,通过三阶段算法进行树高反演实验。结果表明:土壤含水量的变化会导致反演结果产生一定偏差,但此偏差相对较小,已远远小于算法本身造成的误差。
基金supported in part by the National Natural Science Foundation of China (61661136006 and 41371396)
文摘Synthetic aperture radar(SAR) is an effective and important technique in monitoring crop and other agricultural targets because its quality does not depend on weather conditions. SAR is sensitive to the geometrical structures and dielectric properties of the targets and has a certain penetration ability to some agricultural targets. The capabilities of SAR for agriculture applications can be organized into three main categories: crop identification and crop planting area statistics, crop and cropland parameter extraction, and crop yield estimation. According to the above concepts, this paper systematically analyses the recent progresses, existing problems and future directions in SAR agricultural remote sensing. In recent years, with the remarkable progresses in SAR remote sensing systems, the available SAR data sources have been greatly enriched. The accuracies of the crop classification and parameter extraction by SAR data have been improved progressively. But the development of modern agriculture has put forwarded higher requirements for SAR remote sensing. For instance, the spatial resolution and revisiting cycle of the SAR sensors, the accuracy of crop classification, the whole phenological period monitoring of crop growth status, the soil moisture inversion under the condition of high vegetation coverage, the integrations of SAR remote sensing retrieval information with hydrological models and/or crop growth models, and so on, still need to be improved. In the future, the joint use of optical and SAR remote sensing data, the application of multi-band multi-dimensional SAR, the precise and high efficient modeling of electromagnetic scattering and parameter extraction of crop and farmland composite scene, the development of light and small SAR systems like those onboard unmanned aerial vehicles and their applications will be active research areas in agriculture remote sensing. This paper concludes that SAR remote sensing has great potential and will play a more significant role in the various fields of agricultural remote sensing.
