环境小卫星S波段SAR监测土壤水分变化应用分析

通过IEM正演模型的模拟数据，发展了一种用S波段(3.0GHz)、VV极化数据反演土壤含水量相对变化的算法；选择典型的土壤含水量、地表粗糙度及入射角变化范围，模拟出两幅SAR图像，并把该算法应用到模拟图像中，对算法进行验证和改进； 将结果与输入值对比，结果表明，该算法能较好地提取土壤含水量时间和空间变化信息。     

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。     

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。     

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。     

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。     

Simulation Study of a Robust Algorithm for Soil Moisture and Surface Roughness Estimation Using L‐Band Radar Backscatter

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.     

Snow permittivity retrieval inversion algorithm for estimating snow wetness

The environmental satellite (ENVISAT) advanced synthetic aperture radar (ASAR) offers the opportunity for monitoring snow parameters with dual polarization and multi-incidence angle. Snow wetness is an important index for indicating snow avalanche, snowmelt runoff modelling, water supply for irrigation and hydropower stations, weather forecasts and understanding climate change. We used a first-order scattering model that includes both volume and air/snow surface scattering based on a developed inversion model to estimate snow dielectric constant, which can be further related for estimating snow wetness. Comparison with field measurement showed that the correlation coefficient for snow permittivity estimated from ASAR data was observed to be 0.8 at 95% confidence interval and model bias was observed as 2.42% by volume at 95% confidence interval. The comparison of ASAR-derived snow permittivity with ground measurements shows the average absolute error 2.5%. The snow wetness range varies from 0 to 15% by volume.     

Evidence of a topographic signal in surface soil moisture derived from ENVISAT ASAR wide swath data

The susceptibility of a catchment to flooding is affected by its soil moisture prior to an extreme rainfall event. While soil moisture is routinely observed by satellite instruments, results from previous work on the assimilation of remotely sensed soil moisture into hydrologic models have been mixed. This may have been due in part to the low spatial resolution of the observations used. In this study, the remote sensing aspects of a project attempting to improve flow predictions from a distributed hydrologic model by assimilating soil moisture measurements are described. Advanced Synthetic Aperture Radar (ASAR) Wide Swath data were used to measure soil moisture as, unlike low resolution microwave data, they have sufficient resolution to allow soil moisture variations due to local topography to be detected, which may help to take into account the spatial heterogeneity of hydrological processes. Surface soil moisture content (SSMC) was measured over the catchments of the Severn and Avon rivers in the South West UK. To reduce the influence of vegetation, measurements were made only over homogeneous pixels of improved grassland determined from a land cover map. Radar backscatter was corrected for terrain variations and normalized to a common incidence angle. SSMC was calculated using change detection.To search for evidence of a topographic signal, the mean SSMC from improved grassland pixels on low slopes near rivers was compared to that on higher slopes. When the mean SSMC on low slopes was 30–90%, the higher slopes were slightly drier than the low slopes. The effect was reversed for lower SSMC values. It was also more pronounced during a drying event. These findings contribute to the scant information in the literature on the use of high resolution SAR soil moisture measurement to improve hydrologic models.     

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.     

Modified Dubois Model for Estimating Soil Moisture with Dual Polarized SAR Data

This paper discusses a new methodology to estimate soil moisture in agriculture region using SAR data with the use of HH and HV polarization. In this study the semi empirical model derived by Dubois et al. (IEEE Transactions on Geoscience and Remote Sensing, 33(4), 915–926, 1995) was modified to work using σ _HH instead of two like polarization equations σ_HH, σ_VV so that soil moisture can be obtained for the larger area frequently. The field derived roughness correlated with the cross polarization ratio (HV/HH) to replace the one unknown parameter ‘s’ in the Dubois model and hence the dielectric constant was derived by inverting the Dubois model equation (HH). The Topp et al. (Water Resources Research, 16(3), 574–582, 1980) model was used to retrieve soil moisture using the dielectric constant. The mid incidence angle was used to overcome the incident angle effect and it worked successfully to the larger extent. The result is realistic overall, especially where surface has less variation in the roughness and vegetation since the penetration capability of C-band is limited when plant grows hence model valid in the initial period of cultivation. The derived model is having good scope for soil moisture monitoring with the present availability of Indian RISAT data.     

Polarimetric SAR Data Correction and Terrain Topography Measurement Based on the Radar Target Orientation Angle

Topographic variations caused by the range and the azimuth terrain slopes induce polarization orientation changes which cause the polarization to rotate about the line of sight. The existence of these variations reduce the accuracy measurement of geophysical parameters from polarimetric synthetic aperture radar (PolSAR) images. For this reason most inversion studies are best done in area of flat earth. In area which has significant terrain variations require compensation for topography. In real situations, terrain slopes rotate the polarization basis of the polarimetric scattering matrices by an orientation angle shift, and induce significant cross-polarization power. In this paper, two methods have been investigated using the polarimetric orientation angle (PAO): the first one involves the rotation of the polarimetric scattering and coherency matrices to achieve maximum azimuthally asymmetry for polarimetric data compensation to ensure accurate estimation of geophysical parameters in rugged terrain areas. The second approach has been developed which measures azimuth and range terrain slopes that are related to shifts in polarization orientation angle. Terrain elevation maps relative to a plane parallel to the radar line of sight can then be generated by integrating these slopes requiring only one PolSAR flight pass by combing orientation angle estimation and a shape-from-shading technique (SFS) which is mostly used by the computer vision community. Experimental results with C-band polarimetric RADARSAT2 data are used evaluate the data compensation algorithm and DEM generation.     

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。     

Surface roughness signatures of summer arctic snow-covered sea ice in X-band dual-polarimetric SAR

ABSTRACT

Surface roughness of sea ice is primary information for understanding sea ice dynamics and air–ice–ocean interactions. Synthetic aperture radar (SAR) is a powerful tool for investigating sea ice surface roughness owing to the high sensitivity of its signal to surface structures. In this study, we explored the surface roughness signatures of the summer Arctic snow-covered first-year sea ice in X-band dual-polarimetric SAR in terms of the root mean square (RMS) height. Two ice campaigns were conducted for the first-year sea ice with dry snow cover in the marginal ice zone of the Chukchi Sea in August 2017 and August 2018, from which high-resolution (4 cm) digital surface models (DSMs) of the sea ice were derived with the help of a terrestrial laser scanner to obtain the in situ RMS height. X-band dual-polarimetric (HH and VV) SAR data (3 m spatial resolution) were obtained for the 2017 campaign, at a high incidence angle (49.5°) of TerraSAR-X, and for the 2018 campaign, at a mid-incidence angle (36.1°) of TanDEM-X 1–2 days after the acquisition of the DSMs. The sea ice drifted during the time between the SAR and DSM acquisitions. As it is difficult to directly co-register the DSM to SAR owing to the difference in spatial resolution, the two datasets were geometrically matched using unmanned aerial vehicle (4 cm resolution) and helicopter-borne (30 cm resolution) photographs acquired as part of the ice campaigns. A total of five dual-polarimetric SAR features―backscattering coefficients at HH and VV polarizations, co-polarization ratio, co-polarization phase difference, and co-polarization correlation coefficient ―were computed from the dual-polarimetric SAR data and compared to the RMS height of the sea ice, which showed macroscale surface roughness. All the SAR features obtained at the high incidence angle were statistically weakly correlated with the RMS height of the sea ice, possibly influenced by the low backscattering close to the noise level that is attributed to the high incidence angle. The SAR features at the mid-incidence angle showed a statistically significant correlation with the RMS height of the sea ice, with Spearman’s correlation coefficient being higher than 0.7, except for the co-polarization ratio. Among the intensity-based and polarimetry-based SAR features, HH-polarized backscattering and co-polarization phase difference were analyzed to be the most sensitive to the macroscale RMS height of the sea ice. Our results show that the X-band dual-polarimetric SAR at mid-incidence angle exhibits potential for estimation of the macroscale surface roughness of the first-year sea ice with dry snow cover in summer.     

Comprehensive evaluation of soil moisture retrieval models under different crop cover types using C-band synthetic aperture radar data

In the present study, random forest regression (RFR), support vector regression (SVR) and artificial neural network regression (ANNR) models were evaluated for the retrieval of soil moisture covered by winter wheat, barley and corn crops. SVR with radial basis function kernel was provided the highest adj. R² (0.95) value for soil moisture retrieval covered by the wheat crop at VV polarization. However, RFR provided the adj. R² (0.94) value for soil moisture retrieval covered by barley crop at VV polarization using Sentinel-1A satellite data. The adj. R² (0.94) values were found for the soil moisture covered by corn crop at VV polarization using RFR, SVR linear and radial basis function kernels. The least performance was reported using ANNR model for almost all the crops under investigation. The soil moisture retrieval outcomes were found better at VV polarization in comparison to VH polarization using three different models.     

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 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.     

利用ASAR图像监测土壤含水量和小麦覆盖度

以高级合成孔径雷达（ASAR）影像数据和地面实测数据为基础，分析了裸土、低覆盖（覆盖度为0．2左右）冬小麦麦地的后向散射与土壤含水量、地表粗糙度及小麦覆盖度之间的关系，探讨了裸土和冬小麦麦地土壤含水量及小麦覆盖度的反演方法。分析结果表明：①裸土后向散射系数受地表粗糙度和土壤质地的综合影响较大，裸土的后向散射和土壤含水量正相关关系未达显著，反演裸土土壤含水量必须考虑这两个因素的影响。②冬小麦麦地两种极化后向散射对土壤含水量和小麦覆盖度的敏感性差异明显。由于小麦植株与土壤的水平同极化后向散射差异较大，水平极化后向散射系数和小麦覆盖度及土壤含水量相关性达到显著；冬小麦麦地的垂直同极化后向散射对土壤含水量较敏感，垂直极化后向散射系数和土壤含水量的相关性达到显著，但与小麦覆盖度的相关性相对较低。据此，利用冬小麦麦地的两个同极化后向散射系数，建立了后向散射系数与土壤含水量和小麦覆盖度之间的关系模型，实现了小麦覆盖度和冬小麦覆盖下的土壤含水量反演。验证结果表明：土壤含水量和小麦覆盖度反演结果与地面调查和测量结果一致。     

星载SAR水下地形和水深遥感的最佳雷达系统参数模拟

根据星载合成孔径雷达 (SAR)浅海水下地形和水深成像机理 ,建立了浅海水下地形和水深雷达后向散射截面仿真模型。该模型包括奈维 斯托克斯方程、谱作用量平衡方程和雷达后向散射模式。利用该模型仿真结果 ,探讨了不同波段 (P、L、C和X)、不同极化 (VV和HH)和不同入射角 (2 0°— 70°)的星载SAR测量浅海水下地形和水深的能力。研究结果表明 ,浅海水下地形和水深遥感的最佳波段为P波段 ,L波段次之 ,C波段比X波段要好一些。VV极化SAR的测量能力要强于HH极化。 2 0°— 40°是星载SAR测量浅海水下地形和水深的最佳入射角范围。     

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。