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。     

MPDI在微波辐射计植被覆盖区土壤水分反演中的应用

大尺度上的土壤水分变化监测对于建立全球的水循环模型意义重大，是实现气候变化预测和洪涝监测的基础。星载辐射计为实现大尺度上土壤水分的监测提供了监测途径。但是在星载辐射计观测时，地表植被层的吸收和散射作用会对土壤向上的微波辐射产生衰减影响，这种影响在反演土壤水分的过程中必须予以计算和消除。原有的反演算法中，在计算这部分影响的时候，需要大量的关于地表植被状况的辅助数据，而这些即时的辅助数据往往不易获得。以AMSR—E数据为例，研究证明了微波极化差异指数（MPDI）能够反映地表植被覆盖状况。以中国华北、华东地区为实验区，选择2004年4月8日的AMSR—E亮温数据和MODIS数据为样本数据，建立起MPDI与NDVI之间的负指数关系方程。基于对NDVI的认识，得到植被覆盖度高、中、低三种状况所对应的MPDI域值，以此域值为依据对中等植被覆盖度地区作出自动判断，并用MPDI计算植被层不透明度。     

土壤水分反演中的主动微波散射模型

作为地表水循环的重要组成部分,土壤水分含量的监测已经成为农业、水文、气象和生态环境等领域的研究热点之一,尤其对现代农业中的精确灌溉、旱情监测和产量估计具有深刻的现实意义。微波后向散射强度与土壤水分之间密切的相关关系使主动微波遥感技术成为高空间分辨率的土壤水分监测中最有效的方法之一。高性能微波散射模型的缺乏是限制土壤水分反演应用的主要因素。分别针对裸露地表和植被覆盖地表,首先分析了常用的微波散射模型,然后对影响土壤水分监测的因素进行探讨;并在实际应用举例中,对常用的主要影响因素校正方法进行分析总结。     

用ERS风散射计数据估算土壤水分方法的研究

欧空局ERS1/2卫星上的风散射计(WSC),分辨率是50km,4天内能覆盖全球超过80%的范围,并可在多角度下对地物目标进行观测。本文研究利用该散射计数据估算土壤水分的方法。首先,利用基于ERS散射计数据建立的全球C波段雷达后向散射系数数据库,根据传统的几何光学模型(GOM),反演得到与土壤含水量密切相关的法线方向Fresnel反射率,并与两个采样点(安多和那曲)上的实测降雨量及土壤水分相对比,证明了ERS散射计数据与土壤水分的高相关性;第二步,以水云模型为基础,结合AIEM模型,发展了一种简化模型来估算土壤水分绝对值,分别利用气象站实测点数据和同时期的Basist湿度指数(BWI)进行验证,表明反演结果能较好反映土壤水分的空间分布状况。     

利用AMSR-E微波辐射计对地表粗糙度参数的一种新标定方法

在利用微波辐射计进行对地观测的过程中，陆地表面特性参数（如土壤水分、土壤粗糙度和植被冠层）是土壤微波辐射的重要影响因素。地表粗糙度的标定对于利用微波辐射计数据反演地表参数而言是十分重要的工作。地表粗糙度参数（h和Q）随着观测频率而变化。通常的标定方法是，假设h的空间分布是变化的，Q在全球均一地分布，则在沙漠地区首先采取h=0的近似，再对Q进行标定。但是事实上，h和Q在全球的分布都是变化着的，这与地面环境状况有关。以AMSR—E数据为例，在对MPD1分析的基础上，推导给出了简单的、基于理论模型的参数厂。厂可以直接由观测亮温值计算得到，它是一个与土壤水分无关，仅与植被层含水量7．0，和土壤粗糙度σ有关的参量，因此它可以用于地表粗糙度的标定和对植被层含水量、植被生长／变化的估计。本文选择干旱季节里的北非地区，在没有对h采取任何假设的前提下，利用参数厂实现了对地表粗糙度参数h和σ的标定，并与原有标定方法的结果做了比较分析。     

植被覆盖区土壤水分反演研究——以北京市为例

以北京市为研究区域,联合使用光学遥感数据和雷达数据,对植被覆盖区地表土壤水分进行反演研究。在利用同期光学数据提取出归一化水分指数(normalized differential water index,NDWI)之后,利用water-cloud模型去除植被层在土壤水分后向散射中的贡献,然后考虑到地表粗糙度,在构建后向散射数据库的基础上分别利用HH和HV极化方式的后向散射系数构建土壤水分反演模型,并对反演结果进行对比研究。结果表明,采用HH极化方式反演土壤水分的均方根误差为0.044,相对误差为15.5%;采用HV极化方式反演土壤水分的均方根误差为0.057,相对误差为20.3%;相比而言,HH极化的反演效果更好。     

用双矩阵法和积分方程模型估算地表的亮度温度

提出了一种估计覆盖植被的地表亮度温度模型。模型中的植被看作是不同大小和朝向的离散散射体如叶、茎、杆构成。植被层内的体散射以及植被与地表之间的多次散射采用了双矩阵法(M atrix Doub ling)计算,地表辐射采用了积分方程模型(Integral Equation M ethod)。较高频率上的模拟结果显示植被的辐射是主要的,植被对地表辐射的衰减作用较明显。模拟的亮度温度跟SGP99机载C波段以及AMSR-E的X和Ku波段实测数据相比接近一致。     

UHV/VHF波段SAR对次地表层土壤湿度探测与反演

首先采用一层非球形粒子植被模型,计算各波段矢量辐射传输方程Mueller矩阵一阶散射解,对比微扰法所得各波段地表粗糙面直接后向散射解,结果证明L波段植被层的散射对观测结果仍有影响,与下垫土壤粗糙表面的散射不易分离.因此,宜采用更低频率的UHF和VHF波段,对地表和次地表层能有较大的渗透深度,并可忽略植被层影响.接着,运用矢量辐射传输的3层土壤全极化Mueller矩阵解,计算UHF/VHF波段分层土壤的散射与传输,分析该两波段探测深度的差异,证实UHF波段可探测大致10-60cm深处的土壤湿度,而VHF波段探测深度能更大一些.根据第3层中土壤体湿度变化0.1时能否引起土壤表面观测的后向散射系数变化0.1dB这一判据,分析VHF波段反演第3层土壤体湿度的必要条件,证实当第2层的体湿度较小时(<0.25)才能反演层3的体温度.基于UHF/VHF两波段探测深度的差异,耗散土壤层的贡献有不同的权重,先后采用UHF和VHF,迭代法实现3层土壤湿度廓线反演.误差分析表明,该方法是有意义的.     

植被覆盖地表土壤水分遥感反演

以地域特色突出的新疆渭干河-库车河三角洲绿洲为研究区,联合使用雷达数据和光学遥感数据,对干旱区绿洲土壤和植被水分信息进行提取。在同期光学遥感影像数据提取植被归一化差分水分指数基础上,利用"水-云模型"从雷达数据总的后向散射中去除植被影响,建立土壤后向散射系数与土壤含水量的关系,相关系数为HH极化R2=0.5227,HV极化R2=0.3277。结果表明利用C波段HH极化雷达影像数据结合光学影像数据,进行干旱半干旱地区棉花、玉米等农作物种植区地表土壤水分反演时,在中等覆盖条件下去除植被影响有较好的效果。     

全球地表覆盖高分辨率遥感制图

全球地表覆盖分布及变化是气候变化研究、生态环境评估、地理国情监测、宏观调控分析等不可或缺的重要基础信息。国际上现有全球五套地表覆盖数据产品的空间分辨率为1km或300m,数据精度、分类体系、时空分辨率等均存在不足。为了满足全球变化研究与地球模式模拟的需求,应该研制具有较高时空分辨率、更符合全球变化需要、精度较好的全球地表覆盖数据产品。本文简要介绍了全球地表覆盖遥感制图的情况和数据产品的不足,讨论了对新一代地表覆盖数据产品的需求,介绍了我国研制全球30m地表覆盖数据产品的863重点项目。     

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.     

AMSR微波遥感数据进行土壤湿度的计算机反演

为了有效解决大尺度区域土壤水分时、空间变化监测的问题,在总结了被动微波遥感反演土壤湿度规律的基础上,基于先进的AMSR星载被动微波遥感数据,提出了利用双谱模型计算土壤表面发射率的计算机算法。首先需要由双站散射系数计算反射率和发射率,然后应用人工神经网络反演土壤湿度,实现了在随机粗糙面状况下基于被动微波遥感的土壤表面水分反演,并在实验区进行了成功的应用。     

中国西部地区基于经验模型的土壤水分反演

地表土壤水分含量的时空分布信息是十分重要的,常常作为水文模型、气候模型、生态模型的输入参数,同时,也是干旱预报、农作物估产等工作的重要指标。被动微波遥感是监测土壤含水量最有效的手段之一。相比红外与可见光,它具有波长长,穿透能力强的优势。相比主动微波雷达,被动微波辐射计具有监测面积大、周期短,受粗糙度影响小,对土壤水分更为敏感,算法更为成熟的优势。目前,已研究出许多反演土壤水分的方法.本课题的主要内容是借助AMSR-E土壤水分影像数据、MODIS归一化植被指数(NDVI)影像数据和MODIS分类影像数据,利用ENVI软件进行遥感图像数据处理,运用统计分析方法建立NDVI与土壤水分的经验模型,研究中国西部地区稀疏植被覆盖区土壤水分的反演。     

植被层对被动微波遥感土壤水分反演影响的研究

在很多对土壤水分被动微波遥感的研究中 ,为简单起见 ,覆盖的植被层使用了一种简单的模型来表征其散射和衰减特性。本文中使用了一种基于辐射传输理论的离散模型来研究植被的发射率、传输率。这种方法可以更加真实地刻划组成植被的散射个体如叶、茎、树枝、树干等对这两个参数的影响 ,因而能更准确地描述植被对下垫面的影响。为了减少土壤水分反演算法中未知量的数目 ,该文给出了这两个参数的模拟结果分别在AMSR E三种不同频率下的简单关系。     

Improved Understanding of Soil Surface Roughness Parameterization for L-Band Passive Microwave Soil Moisture Retrieval

Surface roughness parameterization plays an important role in soil moisture retrieval from passive microwave observations. This letter investigates the parameterization of surface roughness in the retrieval algorithm adopted by the Soil Moisture and Ocean Salinity mission, making use of experimental airborne and ground data from the National Airborne Field Experiment held in Australia in 2005. The surface roughness parameter is retrieved from high-resolution (60 m) airborne data in different soil moisture conditions, using the ground soil moisture as input of the model. The effect of surface roughness on the emitted signal is found to change with the soil moisture conditions with a law different from that proposed in previous studies. The magnitude of this change is found to be related to soil textural properties: in clay soils, the effect of surface roughness is higher in intermediate wetness conditions (0.2–0.3 v/v) and decreases on both the dry and wet ends. Consequently, this letter calls for a rethink of surface roughness parameterization in microwave emission modeling.      

Combined Passive and Active Microwave Observations of Soil Moisture During CLASIC

An important research direction in advancing higher spatial resolution and better accuracy in soil moisture remote sensing is the integration of active and passive microwave observations. In an effort to address this objective, an airborne instrument, the passive/active L-band sensor (PALS), was flown over two watersheds as part of the cloud and land surface interaction campaign (CLASIC) conducted in Oklahoma in 2007. Eleven flights were conducted over each watershed during the field campaign. Extensive ground observations (soil moisture, soil temperature, and vegetation) were made concurrent with the PALS measurements. Extremely wet conditions were encountered. As expected from previous research, the radiometer-based retrievals were better than the radar retrievals. The standard error of estimates (SEEs) of the retrieved soil moisture using only the PALS radiometer data were 0.048 m³/m³ for Fort Cobb (FC) and 0.067 m³/m³ for the Little Washita (LW) watershed. These errors were higher than typically observed, which is likely the result of the unusually high soil moisture and standing water conditions. The radar-only-based retrieval SEEs were 0.092 m³/m³ for FC and 0.079 m³/ m³ for LW. Radar retrievals in the FC domain were particularly poor due to the high vegetation water content of the agricultural fields. These results indicate the potential for estimating soil moisture for low-vegetation water content domains from radar observations using a simple vegetation model. Results also showed the compatibility between passive and active microwave observations and the potential for combining the two approaches.     

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.     

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.     

多角度遥感估算稀疏团粒分布型土壤表面粗糙度方法初探

表面粗糙度是影响土壤在微波波段发射和散射辐射的主要因素之一,也是微波遥感研究与应用的重要参量。由于微波后向散射还受介电特性、穿透深度等因素的影响,在微波遥感应用中往往难以单独考虑介质表面粗糙度,给参数估算与反演带来了一定困难。在可见光、近红外波段,粗糙度作为土壤表面重要的结构参数之一,直接影响着土壤的二向反射分布特征。因此,本文尝试利用光学多角度观测信息,反演土壤表面粗糙度。基于地表二向反射几何光学模型,假设裸土像元由随机分布于平坦表面的土壤团粒组成,将团粒近似为半椭球体,建立裸露土壤表面二向反射模型,模拟不同粗糙度条件下土壤表面像元的二向反射分布特征。进一步尝试采用多角度观测数据反演模型,估算土壤团粒的几何结构参数,进而计算土壤表面均方根高度,作为表面粗糙度的衡量指标。地面实测多角度数据的初步验证结果表明,多角度光学遥感估算土壤表面粗糙度的方法是可行的。