共查询到18条相似文献，搜索用时 125 毫秒
1.
Envisat-1双极化雷达数据建模及应用 总被引：1，自引：0，他引：1
根据欧空局Envisat-1卫星上ASAR传感器的系统参数和双极化特点，利用AIEM模型模拟，建立了裸露地表同极化后向散射模型和粗糙度参数计算模型。前者把同极化总后向散射系数表达成人射角和两个地表参数（土壤水分和粗糙度）的函数；后者给出了用双极化雷达数据计算粗糙度的方法。把这两个模型结合，用于土壤水分反演，分别用模拟数据和实测数据验证，良好的结果证明了这两个模型的可靠性和实用性。双极化后向散射模型的建立，将为以后PALSAR（日本）和RADARSAT-2（加拿大）多极化雷达数据的应用打下基础。 相似文献
2.
提出了一种基于微波双极化数据的土壤水分反演经验模型,该模型引入了新的综合粗糙度参数Rs=S2/L(1/2)来描述地表粗糙状况,将两个粗糙度参数均方根高度S和相关长度L合二为一,因而模型的未知量仅为Rs与法向菲涅尔反射系数Г0。基于AIEM模型数值模拟,建立了后向散射系数与Rs、Г0的经验关系,并利用两个极化的微波数据同时反演得到粗糙度参数Rs和Г0,进而得到地表土壤水分。实测数据表明,该模型反演的土壤水分与地表实测值相关性较高(R2=0.681,RMS=0.043),在土壤水分反演方面具有较大的潜力。 相似文献
3.
合成孔径雷达反演裸露地表土壤水分的新方法 总被引：4，自引：0，他引：4
提出了一种新的合成孔径雷达(SAR)反演裸露地表土壤水分的经验模型,该模型同时考虑了均方根高度S和相关长度L的影响,并将两个粗糙度参数合二为一,然后利用VV和VH极化的后向散射系数即可反演得到土壤水分。通过实测数据对模型进行了验证,发现在θ020°时,模型反演值与模拟值有着良好的相关关系(R2=0.71)。该模型在不需要测量地面粗糙度的情况下可以反演得到比较好的土壤水分精度,尤其适用于地表情况复杂、难以精确测量的地区。 相似文献
4.
地基雷达的微波面散射模型对比与土壤水分反演 总被引：1，自引：1，他引：0
为了探究地基合成孔径雷达(c GBSAR)后向散射信号的时空变化规律和研究雷达土壤水分反演的影响因素,在内蒙古闪电河流域的昕元牧场站进行了地基雷达观测试验,本文结合以上观测试验的地基雷达数据进行波段、入射角度、极化通道3个雷达参数以及地表粗糙度参数对雷达的后向散射系数影响的分析,然后利用以上分析结果选择地表微波面散射模型,最后利用选定的地表微波面散射模型构建人工神经网络数据集来反演地表土壤水分。结果表明:(1)在地基雷达视场内,各地表微波面散射模型的模拟结果与地基雷达实测的L波段全极化数据拟合效果最佳的是AIEM-Oh模型。(2)通过对20°—60°范围内的雷达入射角度的AIEM-Oh模型后向散射系数模拟的绝对残差分析发现,雷达入射角为25°、41°和53°时模拟结果最接近雷达实测值。(3)最后通过分析土壤水分反演结果发现,当雷达入射角度为41°时的土壤水分反演精度最高,相关系数R是0.8080,RMSE是0.0385 m~3m~3。本文的结论是雷达后向散射信号受到雷达入射角度和地表粗糙度相互作用的影响,因此通过考虑地表粗糙度来合理的选取雷达入射角能够提高土壤水分的反演精度。 相似文献
5.
为了更好地进行土壤水分反演,发展了一种基于ALOS/PALSAR数据、利用自适应神经模糊推理系统(adaptive neuro fuzzy inference system,ANFIS)反演土壤水分的方法.首先,根据研究区实际情况,利用AIEM和Oh模型模拟了试验区裸土区的后向散射特性,建立了后向散射系数与地表粗糙度之间的关系;然后,考虑到研究区地表粗糙度几乎没有变化这一情况,设定了地表粗糙度对后向散射系数的影响为常量;在此基础上,分别利用ANFIS,BP神经网络、多元线性回归和多元非线性回归方法构建了裸土区土壤水分的反演模型,并利用野外实测数据对模型进行了验证.研究结果表明,采用ANFIS方法构建的模型反演精度最高,其均方根误差为0.030,相对误差为14.5％.因此,可以利用ANFIS方法反演裸土区的土壤水分含量,其反演结果具有较高的精度. 相似文献
6.
参数不确定性是SAR反演土壤水分的重要不确定性来源,为控制土壤水分反演精度,提出一种基于参数不确定性的有效控制土壤水分反演精度的方法,使用该方法可以控制参数的误差范围。首先使用全局敏感性分析方法,确定后向影响散射系数输出的主要参数;在不同量级高斯噪声随机扰动下,将大量各参数采值输入AIEM模型中,得到带噪声的后向散射系数集合;再使用LUT法反演土壤水分,计算反演结果满足误差量级控制范围。以此为基础,利用ENVISAT ASAR双极化数据(VV、VH)和实测土壤水分数据进行验证,利用LUT法反演得到带噪声的土壤水分,计算ASAR影像中采样点土壤水分反演值RMSE0.04cm3/cm3。结果表明各影响参数误差量级控制范围可有效控制土壤水分反演精度,在较大的入射角范围内都适用。 相似文献
7.
为了提供更适用的地表参数反演方案,对Njoku等模拟AMSRE-E数据的多参数反演工作重新进行正向模拟和算法改进,算法用MATLAB开发实现.反演结果表明,改进后的反演方法不仅在精度上略高于原方法,而且还能反演裸露地表可实际测量的粗糙度参数,并在更大的植被含水量范围内达到较高的反演精度. 相似文献
8.
9.
基于改进的BP神经网络裸露地表土壤水分反演模型对比 总被引：1，自引：0，他引：1
《国土资源遥感》2016,(1)
土壤水分对于全球水循环十分重要,大面积、快速获取土壤水分信息具有重要意义。微波遥感数据可以用于反演土壤水分。以Matlab为平台建立BP神经网络,通过改进BP神经网络的权值、阈值和网络结构,对该算法进行了优化;在研究区范围,分别利用积分方程模型(integral equation model,IEM)、Oh模型、Shi模型生成模拟数据,训练改进的BP神经网络,构建裸露地表土壤水分反演模型,并用野外实测土壤水分数据对模型进行了验证。结果表明,改进后的BP神经网络算法反演精度明显提高,且Shi模型训练网络反演精度较其他2种模型更高,绝对误差为2.47 g/cm3,相对误差仅为7.78%。 相似文献
10.
一种裸露土壤湿度反演方法 总被引：1，自引：0，他引：1
针对目前土壤湿度反演方法研究较少且缺少实时性的现状,该文提出一种土壤湿度反演方法——最小二乘支持向量机技术。以积分方程模型为正向算法,数值模拟不同雷达参数(频率、入射角及极化)下后向散射系数随土壤含水量和地表粗糙度的变化情况。经过数据敏感性分析,选取C-波段和X-波段、小入射角下的同极化后向散射系数作为支持向量回归的训练样本信息;经过适当的训练,利用支持向量回归技术对土壤含水量进行了反演研究;并考虑通过多频率、多极化、多入射角数据的组合,消除地表粗糙度的影响,提高反演精度。模拟结果表明,该方法反演土壤湿度具有较高的精度和较好的实时性;同时,与人工神经网络方法的结果比较,证明了该方法的有效性,为土壤湿度的反演研究提供了一种方法。 相似文献
11.
12.
Influence of surface and vegetation characteristics on C-band radar measurements for soil moisture content 总被引：1，自引：0，他引：1
Shakil Ahmad Romshoo Masahiro Koike Sadayukihir Onaka Taikan Oki Katumi Musiake 《Journal of the Indian Society of Remote Sensing》2002,30(4):229-244
Soil moisture estimation using microwave remote sensing faces challenges of the segregation of influences mainly from roughness and vegetation. Under static surface conditions, it was found that Radarsat C-band SAR shows reasonably good correlation and sensitivity with changing soil moisture. Dynamic surface and vegetation conditions are supposed to result in a substantial reduction in radar sensitivity to soil moisture. A C-band scatterometer system (5.2 GHz) with a multi-polarization and multi-angular configuration was used 12 times to sense the soil moisture over a tall vegetated grass field. A score of vegetation and soil parameters were recorded on every occasion of the experiment. Three radar backscattering models Viz., Integral Equation Model (IEM), an empirical model and a volume scattering model, have been used to predict the backscattering phenomena. The volume scattering model, using the Distorted Born Approximation, is found to predict the backscattering phenomena reasonably well. But the surface scattering models are expectedly found to be inadequate for the purpose. The temporal variation of soil moisture does show good empirical relationship with the observed radar backscattering. But as the vegetation biomass increases, the radar shows higher sensitivity to the vegetation parameters compared to surface characteristics. A sensitivity analysis of the volume scattering model for all the parameters also reveals that the radar is more sensitive to plant parameters under high biomass conditions, particularly vegetation water content, but the sensitivity to surface characteristics, particularly to soil moisture, is also appreciable. 相似文献
13.
时序双极化SAR开采沉陷区土壤水分估计 总被引：1，自引：0，他引：1
开采沉陷地质灾害诱发矿区生态环境恶化的关键因子是土壤水分变化。研究提出了一种利用Sentinel-1A双极化SAR和OLI地表反射率数据联合反演土壤含水量的方法,即基于归一化水体指数(NDWI)反演植被含水量;采用Water-Cloud Model(WCM)模型消除植被对Sentinel-1A后向散射系数产生的影响,将其转化为裸土区的后向散射系数;利用基于AIEM模型和Oh模型建立的经验模型反演研究区地表参数,并用OLI光学反演结果进行验证;最后比较了开采沉陷区内外土壤水分含量。研究表明:(1)与基于OLI的土壤水分监测指数(SMMI)的土壤水分含量反演结果相比,两种极化方式中VH极化反演的水分结果具有更好的一致性,且两种极化方式反演结果也表明荒漠化草原区比黄土丘陵沟壑区反演效果更好,说明地形对后向散射的影响不可忽略。(2)在2016年内72期数据中,VH极化反演结果对比区土壤水分含量大于沉陷区的有41期,所占比例为57%;VV极化反演结果对比区土壤水分含量大于沉陷区的有36期,所占比例为50%,且不同矿区内的沉陷区受到的影响不同。说明开采沉陷造成的地表粗糙度的增加会对地表土壤水分产生负面影响,但不同矿区之间又有差异。 相似文献
14.
Abstract Various inversion algorithms have been developed to obtain estimates of soil moisture and surface roughness parameters from multifrequency, multiangle, and multipolarization radar reflectances. Since the penetration depth for radar signals increases with wavelength, an inversion algorithm using widely separated frequencies does not yield comparable probing depths. Furthermore, existing algorithms assume a linear relationship between the radar backscatter coefficient (in dB) and soil parameters, such as the volumetric soil moisture, soil surface roughness and surface slope. This assumption is valid only over a narrow range of soil parameters, thereby restricting its operational use under realistic conditions. Our research specifically explored the use of inversion algorithms based on L‐Band radar reflectances at 1 GHz and 2 GHz frequencies in order to retain relatively consistent probing depths. In order to extend the range of applicability, a non‐linear exponential‐type relationship was developed between radar reflectance at a specified frequency, polarization and incidence angle combination, and soil parameters of interest, viz., soil moisture, surface roughness, and surface slope. An over‐constrained inversion algorithm using a six‐parameter combination was found to yield relatively accurate estimates of soil parameters over a wide range of soil conditions even in the presence of system error. 相似文献
15.
Paloscia S. Macelloni G. Pampaloni P. Santi E. 《Geoscience and Remote Sensing Letters, IEEE》2004,1(3):201-205
The sensitivity of radar backscattering to the principal hydrological parameters, such as vegetation biomass, soil moisture, and surface roughness, is discussed. Results obtained by using multifrequency synthetic aperture radar (SAR) data measured by the Jet Propulsion Laboratory Airborne Synthetic Aperture Radar, Spaceborne Imaging Radar-C, and European Remote Sensing 1/2 sensors are summarized. The sensitivity of L- and C-bands to spatial variations of plant and soil parameters is masked by the presence of surface roughness, which in turn affects the radar signal. However, from the observation of data collected at different dates and averaged over a relatively wide area that includes several fields, the correlation to soil moisture and vegetation biomass is found to be significant, since the effects of spatial variations are smoothed. On the other hand, the sensitivity to surface roughness becomes appreciable when multitemporal data are averaged in time, thus reducing the effects of temporal moisture variations. 相似文献
16.
Liangliang Tao Jing Li Xi Chen Qingkong Cai Yunfei Zhang 《Journal of the Indian Society of Remote Sensing》2017,45(4):621-629
This study investigated an appropriate method for soil moisture retrieval from radar images and coincident ground measurements acquired over bare soil and sparsely vegetated regions. The adopted approach based on a single scattering integral equation method (IEM) was developed to establish the relationship between backscatter coefficient and surface soil parameters including volumetric soil moisture content and surface roughness. The performance of IEM in 0–7.6 cm is better than that in 0–20 cm. Moreover, IEM can simulate correctly the backscatter coefficients only for the root mean square (RMS) height s < 1.5 cm at C-band and s < 2.5 cm at L-band by using an exponential correlation function and for s > 1.5 cm at C-band and s > 2.5 cm at L-band by using Gaussian function. However, due to the difficulties involved in the parameterization of soil surface roughness, the estimated accuracy is not satisfactory for the inversion of IEM. This paper used a combined roughness parameter and Fresnel reflection coefficient to develop an empirical model. Simulations were performed to support experimental results and to highlight soil moisture content and surface roughness effects in different polarizations. Results showed that a good agreement was found between the IEM simulations and the SAR measurements over a wide range of soil moisture and surface roughness characteristics. The model had a significant operational advantage in soil moisture retrieval. The correlation coefficients were 77.03 % at L-band and 81.45 % at C-band with the RMSEs of 0.515 and 0.4996 dB, respectively. Additionally, this work offered insight into the required application accuracy of soil moisture retrieval at a large area of arid regions. 相似文献
17.
18.
S. Saran G. Sterk P. J. van Oevelen V. K. Dadhwal 《Journal of the Indian Society of Remote Sensing》2012,40(3):541-550
Soil moisture estimation is considered to be one of the important parameters in hydrological studies. The extraction of information on near surface soil moisture from the synthetic aperture radar is well established. The available Advanced Synthetic Aperture Radar (ASAR) data onboard ENVISAT with multi-incidence and multi-polarization mode for soil moisture estimation on sloping terrain was investigated. Empirical models were developed to estimate near surface soil moisture in the fallow agricultural fields by incorporating the effects of surface roughness using multi-incidence angle ASAR data. Medium incidence angle (IS-4) with VV polarization of ASAR data had higher correlation coefficient to volumetric soil moisture content. The ratio of medium (IS-4) to high incidence (IS-6) angle could further reduce the effect of surface roughness. The effect of topography on the radar data is taken care by calculating local incidence angle derived from ASTER DEM data. The VV polarization in the sloping terrain provided better results in comparison to VH polarization. 相似文献