Interrelationship Analysis of L-Band Backscattering Intensity and Soil Dielectric Constant for Soil Moisture Retrieval Using PALSAR Data

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.

PALSAR-FBS L-HH Mode and Landsat-TM Data Fusion for Geological Mapping

Characterized by lithological diversity and rich mineral resources, Benshangul-Gumuz National Regional State located in Asosa Zones, Western Ethiopia has been investigated for geological mapping and morpho-structural lineaments extraction using PALSAR (Phased Array type L-band Synthetic Aperture Radar ) Fine Beam Single (FBS) L-HH polarization and Landsat-5 TM (Thematic Mapper ) datasets. These data were preprocessed to retrieve ground surface reflectance and backscatter coefficients. To overcome the geometry acquisition between the two sensors, they were geometrically and topographically rectified using ASTER-V2 DEM. Intensity-Hue-Saturation, directional filters and automatic lineaments extraction were applied on the datasets for lithological units’ discrimination and structural delimitation for potential mineral exploration. The obtained results showed good relationship among the topographic morphology, rock-substrate, structural variations properties, and drainage network. The spectral variations were easily associated with lithological units. Likewise, the morpho-structural information highlighted in the PALSAR image was visible without altering the radiometric integrity of the details in TM bands through the fusion process. Moreover, predominant lineaments directions trending NE-SW, NS, and NW-SE were identified. Results of this study highlighted the importance of the PALSAR FBS L-HH mode and TM data fusion to enhance geological features and lithological units for mineral exploration particularly in tropical zones.

山区星载ALOS PALSAR微波遥感图像的正射校正

多山地区微波遥感图像的正射校正是实践应用中的关键和瓶颈,该文以星载合成孔径雷达数据ALOS(advanced landobservation satellite)PALSAR为基础数据,GAMMA软件为处理平台,研究微波图像正射校正方法。首先利用数字高程模型(digital elevation model,DEM)及卫星轨道参数生成模拟合成孔径雷达数据(synthetic aperture radar,SAR),再利用图像交叉相关分析算法自动搜索模拟SAR与真实SAR同名地物点偏移量,建立校正多项式模型,在此基础上通过建立校正查找表方法实现真实SAR图像的地理编码及正射校正。校正效果经检验,误差在一个像元以内,校正效果较好。

PALSAR及RADARSAT2全极化雷达数据在地质构造应用中的研究

由于雷达数据不受天气影响,且具有一定的穿透能力,近年来已被广泛的运用在地质构造信息提取研究中。ALOSPALSAR与RADARSAT2是近年发展的最先进的雷达传感器,其全极化模式同时又具有较高的空间分辨率,这为植被区地质构造研究提供了良好的数据源。本文选取非洲埃塞俄比亚西部为研究区,通过对研究区域两种全极化雷达数据进行正射校正、极化合成等处理,对比分析了其在地质构造应用中的优劣,结果表明PALSAR全极化数据较为有利;最后选取PALSAR全极化数据,结合区域地质背景,提取了研究区的构造信息,提取结果与区域整体构造格架一致。

基于ALOS PALSAR数据的卫星轨道拟合方法

以ALOS PALSAR数据为例,介绍了多项式轨道拟合法和轨道根数描述法两种卫星轨道描述方法。利用这两种方法确定了任意时刻的卫星轨道状态矢量,并结合数学软件Matlab进行计算,比较了两种方法的精度。实验结果表明,多项式轨道拟合法比轨道根数描述法的拟合精度更高,与原始数据的误差更小。卫星轨道拟合的目的在于提高雷达定位精度,对雷达数据校正及其相关研究具有重要意义。

金坛盐穴天然气储库区地表变形PSI监测

地面沉降监测对天然气地下储气库安全有效运行具有重要意义。由于盐穴空间分布和应力环境周期性改变的特点,盐穴储库区地面变形具有空间差异性和时间波动性。因而,利用监测时空密度较高的技术才能全面细致地反映盐穴储库区地面变形的时空特征。基于永久散射体雷达干涉测量(PSI)技术,利用PALSAR数据,对金坛盐穴天然气储库区在2007～2010年的地面变形进行监测。与地面GPS监测结果的对比表明,PSI监测在变形的总体趋势和非线性过程方面都具有较高精度,并且监测的空间范围更广、空间密度和时间密度更高。储库区地面变形显示明显的空间差异,沉降高值区与人类工程活动强烈的区域对应。同时,监测时间段内的储库区非线性变形过程可以被划分为3个沉降阶段和2个抬升阶段。但是因为相关资料的缺乏,这种沉降和抬升的交替现象是否与各时段的人类活动特征有关,还有待进一步研究。

2010年青海玉树Ms7.1级地震地表破裂带和形变特征分析

通过对SPOT卫星影像上地表破裂的目视解译,以及对ALOS PALSAR卫星数据进行D-InSAR形变提取和分析,结合地震活动性、震源机制、活动构造等资料,确定了发震断层空间分布、断层性质和同震形变场分布特征.结果显示,玉树地震发生在甘孜玉树断裂带上,总体走向约为300°,断层近乎直立.根据相干性强弱将Ⅰ区地表破裂划分为三段:北段长22 km,中段长5 km,南段长6 km,破裂带总长度约33 km.Ⅱ区内非相干带长约10 km.同震形变场分布在78 km×55 km范围内,主震所在的形变Ⅰ区断层两侧视线向相对位错约为0.78 m,转换成水平位错约为1.5 m;余震所在的Ⅱ区形变相对较小.

基于PALSAR雷达数据的于田绿洲土壤盐渍化反演

土壤盐渍化是当今土地退化和荒漠化的主要形式之一,不仅严重制约农业和经济的发展,并且对生态环境和人类生存造成威胁。本研究以新疆于田绿洲为研究区,利用四极化PALSAR(Phased Array type L-band Synthetic Aperture Radar)数据后向散射系数,土壤含水量,土壤pH值以及土壤盐分实测值,采用多元线性回归模型、地理加权回归模型和BP神经网络模型,以土壤含盐量作为因变量建立了定量反演模型。从土壤盐分反演结果图可以看出,反演结果与地面实地考察结果基本一致。经过模型验证得知,3层BPANN模型的均方根误差RMSE=0.99,平均相对误差MRE=0.31,模型性能指数RPD=5.34,其模型预测能力优于前2种传统模型。本文建立的神经网络模型无需考虑复杂的介电常数,在一定程度上能够满足土壤盐渍化监测的需要,促进PALSAR数据在土壤盐渍化监测中的应用。

卫星轨道和大气延迟误差联合校正的山地DEM提取

针对轨道误差和大气延迟误差是制约ALOS PALSAR数据快速获取大面积及高精度数字高程模型的主要因素这一问题,该文提出一种基于埃尔米特(Hermite)插值和高程大气模型对两种误差进行联合矫正的方法。首先,采用埃尔米特插值法对轨道矢量数据进行插值,以提高去除平地效应的效果;然后,建立高程与相位之间的线性模型,去除与高程相关的大气延迟误差;最后,进行相位高程转换得到数字高程模型。利用陕西省彬县地区的两景ALOS PALSAR数据进行实验分析。结果表明,该方法获取的数字高程模型以航天飞机雷达地形测绘使命为参考的均方根误差为14.48m,比常规干涉方法获取的结果有很大的提高,证明了该方法的有效性。