一种基于空间虚拟场景的森林冠层雷达相干散射模型

建立了一种基于分形树和森林动态生长模型的森林冠层雷达相干散射模型.和非相干散射模型相比,相干散射模型不仅考虑了每个散射体的空间位置信息,还保留了后向散射信号中的相位信息.利用已建立的模型模拟了大兴安岭常青林场某一区域白桦纯林的雷达后向散射信号,验证结果说明模型是可靠的,是可以用来反演森林的几何结构和生物物理参数的.     

多参数SAR数据森林应用潜力分析

利用多参数机载全球雷达(GlobeSAR)数据和航天飞机成像雷达(SIR-C/X-SAR)数据,分别在我国南、北方两个试验区进行森林识别与分类,以及蓄积量估测的试验.为了更好地了解雷达后向散射与森林结构特征的关系,分别从雷达图像上提取了后向散射系数和强度,进行森林类型识别效果的分析,以及森林结构参数与雷达后向散射强度的相关分析.结果显示多波段、多极化SAR数据能有效地识别不同类型的森林.雷达的后向散射强度对森林的结构参数,尤其是森林的平均胸径和高度较为敏感,据此对试验区的森林蓄积量进行了估测,并分析了多参数SAR在森林应用中的潜力.     

ENVISAT ASAR 数据用于水稻监测和参数反演

用雷达后向散射模型模拟了水稻生长周期内入射角对雷达后向散射的影响关系。用模拟结果归一化雷达数据的后向散射系数，得到同一入射角下水稻周期内后向散射系数时间序列值。分析了归一化ASAR数据与水稻生物参数的关系，实验结果表明，ASAR数据可以用来估测水稻参数。     

Sentinel-1时序后向散射特征的海岸带盐沼植被分类——以长江口为例

盐沼是中高纬度海岸带区域生产力极高的生态系统,不同种类盐沼植被提供的生态服务功能具有明显差异。围垦、互花米草入侵、海平面上升等人类活动和自然要素复合作用,导致中国海岸带盐沼植被结构和空间分布快速变化。现有光学遥感方法在海岸带区域受潮汐、云雾干扰严重;高光谱、LiDAR等数据方法难以大范围高时效获取盐沼植被信息。本文以长江河口为研究区域,提出了基于植被物候期多时相雷达后向散射特征优选的海岸带盐沼植被分类方法。采用Sentinel-1雷达数据,分析盐沼、潮间森林沼泽、光滩和水体的雷达后向散射全年时序特征。结合盐沼植被物候特点,基于分离阈值法计算典型盐沼植被月际后向散射特征间分离度。根据最优时序雷达分类特征,采用随机森林方法获取盐沼植被种类、结构和空间分布。结果表明：（1）全年VH极化后向散射均值能较好将水体、光滩、潮间森林沼泽与盐沼区分。（2）4月VV极化、11月VH极化与3月VV极化后向散射均值分别为海三棱藨草/藨草、互花米草与芦苇的最优分类特征。（3）基于年际和月际时序雷达最优特征和随机森林分类算法获得的盐沼植被总体分类精度达到85%,Kappa系数为0.80。相较光学遥感,雷达遥感影像可有效获取盐沼植被年际、月际时序雷达后向散射特征,准确得到海岸带盐沼植被空间动态,在海岸带研究中具有较好的应用潜力,可为海岸带生物多样性保护、湿地生态系统功能提升与生态环境管理等提供重要技术手段和数据支撑。     

水稻微波后向散射系数的模拟分析

海洋雷达后向散射回波主要来自短重力波的Ｂｒａｇｇ 散射,这种散射与海面风场信息、边界层涡旋等密切相关。因此,可以从雷达散射截面反演风场信息。对１９９４ 年４ 月航天飞机成像雷达（ＳＩＲＣ／ＸＳＡＲ）获取的南中国海合成孔径雷达（ＳＡＲ） 图像进行了分析研究。利用ＳＩＲＣ 数据,从ＳＡＲ 图像谱提取了风向;根据ＣＭＯＤ４ 模型,从Ｃ波段雷达后向散射系数反演风速;利用双尺度散射模型对反演的风速进行了对比分析。结果表明,从ＳＩＲＣ雷达数据可以反演海面风矢量,星载ＳＡＲ是提取海面风场信息的有效技术手段之一。     

航天飞机成像雷达海面风矢量观测研究——以1994年4月南中国海试验区为例

海洋雷达后向散射回波主要来自短重力波的Ｂｒａｇｇ散射,这种散射与海面风场信息、边界层涡旋等密切相关。因此,可以从雷达散射截面反演风场信息。对１９９４年４月航天飞机成像雷达（ＳＩＲ－Ｃ／Ｘ－ＳＡＲ）获取的南中国海合成孔径雷达（ＳＡＲ）图像进行了分析研究。利用ＳＩＲ－Ｃ数据,从ＳＡＲ图像谱提取了风向;根据ＣＭＯＤ４模型,从Ｃ波段雷达后向散射系数反演风速;利用双尺度散射模型对反演的风速进行了对比分析。结     

利用后向散射特性从高分辨率SAR影像中提取建筑物高度

研究了一种基于雷达后向散射特性,从单张星载高分辨率SAR影像提取建筑物高度的方法。该方法通过检测建筑物二次散射强度,同时获取目标方位角、入射角等参数以及相关介质特性等信息,根据后向散射几何光学模型估算建筑物高度。实验验证了对于相当一部分典型建筑物都能够获得较为理想的高程估计值,进而证明了该方法提取建筑物高度的可行性和有效性。     

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。     

地基雷达的微波面散射模型对比与土壤水分反演

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。     

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。     

Tropical Dry Deciduous Forest Stand Variable Estimation Using SAR Data

Optical remote sensing data have been extensively used to derive biophysical properties that relate forest type and composition. However, stand density, stand height and stand volume cannot be estimated directly from optical remote sensing data owing to poor sensitivity between these parameters and spectral reflectance. The ability of microwave energy to penetrate within forest vegetation makes it possible to extract information on both the crown and trunk components from radar data. The type of polarization employed determines the radar response to the various shapes and orientations of the scattering mechanisms within the canopy or trunk. This study mainly presents experimental results obtained with airborne E-SAR using polarimetric C and L bands over the tropical dry deciduous forest of Chandrapur Forest Division, Maharashtra. A detailed documentation of the relationship between SAR C & L bands backscattering and forest stand variables has been provided in the present study through linear correlation. Linear correlation of the single channel SAR derived estimates with the field measured means show a good correlation between L HV backscattering coefficient with stand volume (r² = 0.71) and L HH backscattering coefficient with stand density (r² = 0.75). The results imply that SAR data has significant potential for stand menstruation in operational forestry.     

Influence of surface and vegetation characteristics on C-band radar measurements for soil moisture content

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.     

森林地上生物量遥感反演方法综述

森林地上生物量反演对理解和监测生态系统及评估人类生产生活的影响有着重要作用,日益发展的遥感技术使全球及大区域的生物量估算成为可能。近年来,不同的遥感技术和反演方法被广泛用于估算森林生物量。本文首先总结了现有的全球及区域生物量产品及其不确定性,然后综述了3类方法在森林地上生物量遥感反演中的应用,即基于单源数据的参数化方法、基于多源数据的非参数化方法和基于机理模型的反演方法,阐述了各类反演方法的特点、优势及局限性。最后从机理模型研究、多源遥感数据协同、生物量季节变化研究和遥感数据源不断丰富4个方面对今后的生物量遥感反演研究进行了展望。     

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。     

PolInSAR森林高度反演中方法及波段选择引起的不确定性分析

森林高度是反映森林资源数量和质量的重要参数,极化干涉合成孔径雷达PolInSAR （Polarimetric Synthetic Aperture Radar Interferometry）技术在森林高度反演中极具潜力。由于森林散射特征受波长影响明显,由此引起的散射机理差异使得基于PolInSAR技术反演的森林高度结果具有很大的不确定性。为了定量化该不确定性的影响,本文以模拟森林场景为例,对PolInSAR技术森林高度反演中常用的4种方法——极化相位中心高度估测法、复相干相位中心差分法、复相干幅度反演法以及相干幅度、相位联合反演法,以及它们在常用的4个微波波段P、L、C和X中的森林高度估测结果进行了分析;明确了匀质森林场景中,算法、波段选择引起的森林高度估测结果的不确定性。研究结果表明：在森林场景基本一致的情况下,估测算法的选择直接影响森林高度估测结果,其中复相干幅度反演法在4个波段的估测结果中精度均最高,但各估测点的估测结果离散度及不确定度较大。波长对4类估测方法估测结果的影响差异明显：复相干幅度反演法的反演结果几乎不受波长的影响,而相干幅度、相位联合反演法受波长影响明显,在P和L波段反演结果中精度较高,在C和X波段反演结果中精度降低明显。此外,以传统的交叉极化（HV）相位代表冠层散射相位中心,水平同极化与垂直同极化的相位差（HH-VV）代表地表散射相位中心,采用复相干相位中心差分法进行森林高度估测会出现严重低估现象。估测结果不确定度具有波长和算法选择依赖性,在C和X波段采用复相干相位中心差分法估测结果不确定度最低,在P和L波段采用极化相位中心高度估测法估测结果不确定度最低,而复相干幅度反演法估测结果则在多个波段中的不确定度均最高。     

Modeling and mapping aboveground biomass of the restored mangroves using ALOS-2 PALSAR-2 in East Kalimantan,Indonesia

Accurate estimation of forest aboveground biomass (AGB) using remote sensing is a requisite for monitoring, reporting and verification (MRV) system of the United Nations Programme on Reducing Emissions from Deforestation and Forest Degradation. However, attaining high accuracy remains a great challenge in the diverse tropical forests. Among available technologies, l-band Synthetic Aperture Radar (SAR) estimates AGB with reasonably high accuracy in the terrestrial tropical forests. Nevertheless, the accuracy is relatively low in the mangrove forests. In this context, the study was carried out to model and map AGB using backscatter coefficients of Advanced Land Observing Satellite-2 (ALOS-2) Phased Array l-band SAR-2 (PALSAR-2) in part of the restored mangrove forest at Mahakam Delta, Indonesia. PALSAR-2 data was acquired with image scene observation during the peak low tide on 30 July 2018 from Japan Aerospace Exploration Agency. The forest parameters namely tree height and diameter at breast height were measured from 71 field plots in September-October 2018. The parameters were used in mangrove allometry to calculate the field AGB. Finally, HV polarized backscatter coefficients of PALSAR-2 were used to model AGB using linear regression. The model demonstrated a comparatively high performance using three distinct methods viz. independent validation (R² of 0.89 and RMSE of 23.16 tons ha⁻¹), random k-fold cross validation (R² of 0.89 and RMSE of 24.59 tons ha⁻¹) and leave location out cross validation (LLO CV) (R² of 0.88 and RMSE of 24.05 tons ha⁻¹). The high accuracy of the LLO CV indicates no spatial overfitting in the model. Thus, the model based on LLO CV was used to map AGB in the study area. This is the first study that successfully obtains high accuracy in modeling AGB in the mangrove forest. Therefore, it offers a significant contribution to the MRV mechanism for monitoring mangrove forests in the tropics and sub-tropics.     

Lidar-aided analysis of boreal forest backscatter at Ku band

This paper analyzes the backscatter of the microwave signal in a boreal forest environment based on a Ku -band airborne Frequency-Modulated Continuous Waveform (FMCW) profiling radar—Tomoradar. We selected a half-managed boreal forest in the southern part of Finland for a field test. By decomposing the waveform collected by the Tomoradar, the vertical canopy structure was achieved. Based on the amplitude of the waveform, the Backscattered Energy Ratio of Canopy-to-Total (BERCT) was calculated. Meanwhile, the canopy fraction was derived from the corresponding point cloud recorded by a Velodyne VLP-16 LiDAR mounted on the same platform. Lidar-derived canopy fraction was obtained by counting the number of the first/ the strongest returns versus the total amount of returns. Qualitative and quantitative analysis of radar-derived BERCT on lidar-derived canopy fraction and canopy height are investigated. A fitted model is derived to describe the Ku-band microwave backscatter in the boreal forest to numerically analyze the proportion contributed by four factors: lidar-derived canopy fraction, radar-derived canopy height, the radar-derived distance between trees and radar sensor and other factors, from co-polarization Tomoradar measurements. The Root Mean Squared Error (RMSE) of the proposed model was 0.0958, and the coefficient of determination R2 was 0.912. The fitted model reveals that the correlation coefficient between radar-derived BERCT and lidar-derived canopy fraction is 0.84, which illustrates that lidar surface reflection explains the majority of the profiling /waveform radar response. Thus, vertical canopy structure derived from lidar can be used for the benefit of radar analysis.     

Radar Polarization Orientation Shifts in Built-Up Areas

Polarization orientation angle shifts can be seen not only in rugged terrain areas but also in urban areas. The latter is explained by backscatter from a wall of a building or house, which is equivalent to a tilted ground-surface patch. From the scattering model of built-up areas, the polarization orientation angle shift in the built-up areas is given as , where is the wall or street orientation angle, and is the radar incidence angle. Japan Aerospace Exploration Agency Pi-SAR L-band polarimetric data of Gifu, Japan, show a good agreement with the theory. The phase difference between VH and HH polarizations is used to demonstrate the contribution of double-bounce scattering ground-wall and wall-ground over a wide range of wall orientation angles.     

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。