双极化SAR数据反演裸露地表土壤水分

为了较高精度地获取大范围地表土壤水分,提出一种基于双极化合成孔径雷达数据的裸露地表土壤水分反演模型即非线性方程组,通过改进的粒子群算法求解非线性方程组从而得到土壤水分。首先通过AIEM模型数值模拟和回归分析,得到一种新的组合粗糙度,然后模拟分析得到土壤水分与雷达后向散射系数的关系,从而建立雷达后向散射系数与组合粗糙度、土壤水分的经验关系。利用ASAR C波段双极化雷达数据,基于经验关系和改进的粒子群算法即可实现土壤水分的反演。经过黑河流域实测土壤水分数据对模型进行验证,反演结果与实测数据具备良好的相关性(R~2=0.778 6)。与以往同一区域研究成果比较,文中的方法反演精度有所提高,更适用于裸露地表土壤水分反演。     

The contribution of multitemporal SAR data in assessing hydrological parameters

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.     

目标分解技术在植被覆盖条件下土壤水分计算中的应用

目标分解技术利用协方差距阵的特征值和特征矢量，将极化雷达后向散射测量值分解为单向散射，双向散射和交叉极化散射三个分量，并建立了植被覆盖地表的一阶物理离散散射模型。通过分解的各分量与该模型的比较，建立重轨极化雷达测量数据估算土壤水分的方法，采用Washita‘92实验区多时相全极化L波段JPL/AIRSAR图像雷达测量数据，利用分解的散射测量值，我们评估了在同一入射角，单频（L波段），多路条件下，分解理论在进行土壤水分估计时减少植被影响的能力。结果表明利用目标分解理论和重轨极化雷达数据可以估算植被覆盖区域土壤水分的变化情况。     

综合主动和被动微波数据监测土壤水分变化

微波遥感测量土壤水分的方法主要分主动和被动两种，它们都是基于干燥土壤和水体之间介电常数的巨大差异。估算植被覆盖土壤表面土壤水分必须要考虑地表粗糙度和植被覆盖影响的问题。植被覆盖土壤表面的后向散射包括来自植被的体散射，来自地表的面散射和植被与地表间的交互作用散射项。本研究建立了一个半经验公式模型，用来计算体散射项，综合时间序列的主动和被动微波数据，消除植被覆盖的影响，估算地表土壤水分的变化状况。并应用1997年美国SGP‘97综合实验中的机载800m分辨辐射计ESTAR数据计算表面反射系数，综合Radarsat的SCAN-SAR数据得到体散射项，然后，由NOAA/AVHRR和TM计算得到的NDVI值加权分配50m分辨率的体散射项，最后计算50m分辨率的表面反射系数的变化值，从而得到土壤水分的变化情况，验证数据表明该计算结果与实测值一致。     

An Effective Model to Retrieve Soil Moisture from L- and C-Band SAR Data

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.     

全极化SAR图像的山地低矮植被区域土壤含水量估计

山区土壤含水量对山区植被生长监测、滑坡预测等工作具有重要意义,因此针对山地低矮植被区域,提出了全极化SAR图像的土壤含水量估计方法。为解决山地区域SAR图像几何形变和极化旋转问题,根据入射角、坡度、坡向信息定义了可测区域与不可测区域,并对可测区域后向散射系数进行校正。其次以密西根模型为基础,发展了低矮植被的散射模型。在假定植被和土壤特征不变的情况下,基于此散射模型并结合校正数据建立了山区土壤含水量反演方法。结果表明,模型反演的土壤含水量和实验点实测值基本一致,两个实验点反演值分别为14%和15%,实测值为11.45%和15.80%,能够满足一般应用的需求。     

地表土壤水分与雷达后向散射系数及入射角之间关系研究

应用雷达技术反演土壤水分较著名的经验模型有Oh模型[1]、Dubois模型[2]以及Shi模型[3]。本文在Shi模型的基础上,对土壤水分与雷达后向散射系数之间的关系进行探讨,反演出土壤水分与雷达后向散射系数及入射角之间的关系。     

Prediction of topsoil properties at field-scale by using C-band SAR data

Designing and validating digital soil mapping (DSM) techniques can facilitate precision agriculture implementation. This study generates and validates a technique for the spatial prediction of soil properties based on C-band radar data. To this end, (i) we focused on working at farm-field scale and conditions, a fact scarcely reported; (ii) we validated the usefulness of Random Forest regression (RF) to predict soil properties based on C-band radar data; (iii) we validated the prediction accuracy of C-band radar data according to the coverage condition (for example: crop or fallow); and (iv) we aimed to find spatial relationship between soil apparent electrical conductivity and C-band radar. The experiment was conducted on two agricultural fields in the southern Argentine Pampas. Fifty one Sentinel 1 Level-1 GRD (Grid) products of C-band frequency (5.36 GHz) were processed. VH and VV polarizations and the dual polarization SAR vegetation index (DPSVI) were estimated. Soil information was obtained through regular-grid sample scheme and apparent soil electrical conductivity (ECa) measurements. Soil properties predicted were: texture, effective soil depth, ECa at 0-0.3m depth and ECa at 0-0.9m depth. The effect of water, vegetation and soil on the depolarization from SAR backscattering was analyzed. Complementary, spatial predictions of all soil properties from ordinary cokriging and Conditioned Latin hypercube sampling (cLHS) were evaluated using six different soil sample sizes: 20, 40, 60, 80, 100 and the total of the grid sampling scheme. The results demonstrate that the prediction accuracy of C-band SAR data for most of the soil properties evaluated varies considerably and is closely dependent on the coverage type and weather dynamics. The polarizations with high prediction accuracy of all soil properties showed low values of σ_VV^o and σ_VH^o, while those with low prediction accuracy showed high values of σ_VV^o and low values of σ_VH^o. The spatial patterns among maps of all soil properties using all samples and all sample sizes were similar. In conditions when summer crops demand large amount of water and there is soil water deficit backscattering showed higher prediction accuracy for most soil properties. During the fallow season, the prediction accuracy decreased and the spatial prediction accuracy was closely dependent on the number of validation samples. The findings of this study corroborates that DSM techniques at field scale can be achieved by using C-band SAR data. Extrapolation y applicability of this study to other areas remain to be tested.     

广东肇庆地区SIR-C森林雷达后向散射特征分析

森林雷达后向散射特征的研究是森林微波遥感应用的重要前提。本文利用不连续树冠森林微波后向散射模型模拟了肇庆地区松树林的雷达后向散射特征,并与从ＳＩＲ－Ｃ图像提取的雷达后向散射特征进行对比,从而分析和探讨了该区松树的雷达后向散射机制。     

Soil Moisture Estimation in a Vegetated Area Using Combination of AIRSAR and Landsat5-TM Images

High difference between dielectric constant of water (dielectric constant about 80) and dielectric constant of dried soil (dielectric constant about 2–3) makes Synthetic Aperture Radar (SAR) highly capable in soil moisture estimation. However, there are other factors which affect on radar backscattering coefficient. The most important parameters are vegetation cover, surface roughness and sensor parameters (frequency, polarization and incidence angle). In this paper, the importance of considering the effects of these parameters on SAR backscatter coefficients is shown by comparing different soil moisture estimation models. Moreover, an experimental soil moisture estimation model is developed. It is shown that this model can be used to estimate soil moisture under a variety of vegetation cover densities. The new developed model is based on combination of different indices derived from Landsat5-Thematic Mapper and AIRSAR images. The AIRSAR image is used for extraction of backscattering coefficient and incidence angle while TM image is used for calculation of Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Normalized Difference Water Index (NDWI) and Brightness Temperature. Then a soil moisture estimation model which is named as Hybrid model is developed based on integration of all of these parameters. The accuracies of this model are assessed in the NDVI ranges of 0–0.2, 0.2–0.4 and 0.4–0.7 by using SAR data in C band and L band frequencies and also in different polarizations of HH, HV, VV and TP. The results show that for instance in L band with HV polarization, R-square values of 0.728, 0.628 and 0.527 are obtained between ground measured soil moisture and estimated soil moisture values using the Hybrid model for NDVI ranges of 0–0.2, 0.2–0.4 and 0.4–0.7, respectively.     

Envisat-1双极化雷达数据建模及应用

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。     

Development of an inversion algorithm for dry snow density estimation and its application with ENVISAT-ASAR dual co-polarization data

Radar remote sensing has great potential to determine the extent and properties of snow cover. Availability of space-borne sensor dual-polarization C-band data of environmental satellite- (ENVISAT-) advanced synthetic aperture radar (ASAR) can enhance the accuracy in measurement of snow physical parameters as compared with single polarization data measurement. This study shows the capability of C-band synthetic aperture radar (SAR) data for estimating dry snow density over snow covered rugged terrain in Himalayan region. The snow density is an important parameter for the snow hydrology and avalanche forecasting related studies. An algorithm has been developed for estimating snow density, based on snow volume scattering and snow-ground scattering components. The radar backscattering coefficients of both horizontal–horizontal (hh) and vertical–vertical (vv) polarization and incidence angle are used as inputs in the algorithm to provide the snow dielectric constant which can be used to derive snow density using Looyenga's semi-empirical formula. Comparison was made between snow density estimated from algorithm using ENVISAT-ASAR hh and vv polarization data and the measured field value. The mean absolute error between estimated and measured snow density was found to be 0.024 g/cm³.     

基于神经网络方法的极化雷达地表参数反演

人工神经网络（Artificial Neural Network)是一个由独立处理单元以一定拓扑结构高度连接而成的并行分布式信息处理结构，适于解决各种非线性问题，积分方程（Integrated Equation Model)单散射模型可模拟各种地表参数条件下裸露地表后向散射系数，以IEM为基础生成训练数据，用L波段的C波段SIR-CHH，VV极化单散射后向散射系数数据为神经网络输入，通过后向反馈（BP）神经网络模型可同时反演得到裸露地表条件下地表介电常数，地表相关长度和均方根高度等地表参数。     

基于参数不确定性分析的SAR土壤水分反演精度控制方法

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。     

时序双极化SAR开采沉陷区土壤水分估计

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。     

The inversion method study of sub-canopy soil moisture estimation using repeat pass SIR-C PolInSAR data

From repeat pass SIR-C L band polarimetric SAR interferometric data and fully maximum likelihood inversion decomposition model of PolInSAR, a method for sub-canopy soil moisture estimation using repeat pass SIR-C PolInSAR data is proposed. At the same time, the potential and validity of fully maximum likelihood inversion decomposition model of PolInSAR for sub-canopy soil moisture inversion is investigated. Firstly, from the random oriented volume over ground two layer coherent scattering model and the statistical characteristics of Pol-InSAR coherency matrix, the fully maximum likelihood inversion decomposition model is used to reconstruct or recover the surface polarimetric coherency matrix with volume scattering components significantly removed; then, from recovered surface polarimetric coherency matrix, co-HH, VV and cross-HV polarization backscattering coefficient are obtained, and the sub-canopy soil moisture are inverted from Oh and Dihedral scattering model. At last, Compared the inversion result with the field measurement and the climate data of hetan region from 1951 to 2006, the preliminary result indicates that the proposed method based on fully maximum likelihood inversion decomposition model has enough high inversion accuracy, if the new spaceborne or airborne polarimetric SAR interferometric data with synchronously spaceborne or airborne-ground measurement will be acquired, the validity and accuracy of proposed inversion method will be further investigated and validated.     

基于MODIS影像的森林火灾火线检测方法

结合归一化火灾差异指数NDBR(normalized difference burn ratio)和MODIS多波段影像梯度边缘分析手段检测火线, 应用B样条函数拟合火线并确定火势蔓延方向。为对比验证, 基于火线的Kriging插值实现火灾外推预测, 与30min后的火灾参考数据目视对比与统计：火线的预测变化与参考影像基本保持一致, 火灾外推影像的均值和熵约为参考影像的86%和81%, 火迹地检测的Kappa系数达80.2%。试验表明, 提出的森林火线特征自动检测方法在动态火灾监测中是可行、有效的。     

Simulation analysis on the relationship between the leaf area index and polarimetric parameters of crops

Abstract

The leaf area index (LAI) is an important parameter to quantitatively describe the structure of vegetation and crops. Uncertainty in the relationship between the LAI and polarimetric parameters is the key problem for LAI estimation from polarimetric synthetic aperture radar (POLSAR) data. However, the existing POLSAR data have difficulties meeting the demand of the aforementioned research. This paper analyses the correlations between the LAI and the polarimetric parameters derived from Cloude and Freeman decompositions using simulated POLSAR data based on a coherent scattering model for maize and wheat. The results show: (1) The POLSAR data at C-band with a large incidence angle (40 degrees) are very suitable for finding the LAI for maize and wheat. (2) For maize there is a strong correlation between the scattering type angle and the LAI at C-band with a large incidence angle, and the coherency entropy, anisotropy, and the power of the double-bounce scattering power component also have significant correlations with the LAI. (3) For wheat at C-band with a high incidence angle, although the correlation coefficient is low, there is still a correlation between the entropy, anisotropy and LAI. Besides, the volume scattering is suitable for extracting the LAI for wheat at X-band.     

雷达后向散射模型及其在雷达图像地形影响纠正中的应用

在从雷达估测森林生物量时，经常遇到的一个问题是地形对雷达信号的影响。地形使得雷达波的入射角度改变，使每个雷达图像像元所包含的地表面积改变，由于地面的起伏，植被本身的结构也不同，纠正这种由地形而不是植被类型引起的雷达图像的变化是一个很复杂的问题，除了需要高质量的地形数据外，还必须理解植被雷达信号随地形变化的规律。提出一种可用来模拟森林及其它植被处于山坡上的雷达后向散射模型。结合DEM数据，模拟的结果可用来进行雷达图像的地形影响纠正，如果多极化或多波段图像存在，通过雷达模型可用从一种极化推导出的地形信息来纠正其它极化的图像数据。     

Application of ENVISAT ASAR Data in Mapping Rice Crop Growth in Southern China

This research letter presents preliminary results of mapping rice crop growth using ENVISAT advanced synthetic aperture radar (ASAR) alternating polarization HH/HV data. Four ASAR HH/HV images were collected in the early rice-growth cycle in the test site in 2006, and the temporal response of ASAR data to the rice field was analyzed. The height and biomass of rice were measured during acquisition of ASAR data, and empirical relationships were established between the backscattering coefficient and these two parameters. Based on the temporal variation of the radar response, a method for mapping a rice growth area was developed using the combination of ASAR HH and HV polarization data between two acquisition dates. The results confirm that C-band SAR data have great potential in the development of an operational system for monitoring rice crop growth in Southern China.