Radarsat-2/SAR和MODIS数据联合反演黄土高原地区植被覆盖下土壤水分研究

以黄土高原半干旱区定西为试验区,利用Radarsat-2/SAR和MODIS数据,将由MODIS NDVI估算的植被含水量(VWC)应用到微波散射Water-Cloud模型中校正植被的影响。采用交叉极化(VV/VH)组合方案对植被覆盖下土壤水分的反演进行初步探讨,结果表明:在植被影响校正前,模型反演土壤水分值出现明显低估现象;校正植被影响后,相关系数R由0.13提高到0.44,且通过α=0.01的显著性检验,标准差SD由5.02降低到4.30,有效提高了模型反演土壤水分的准确度。卫星反演的研究区土壤含水量大部分介于10%~30%之间,与实地考察情况一致,较好地反映出区域土壤湿度分布信息。表明,光学和微波协同遥感反演对于提高农田土壤水分遥感反演精度具有较大的应用潜力。     

极化分解技术在估算植被覆盖地区土壤水分变化中的应用

地表植被覆盖是影响雷达遥感估算土壤水分的主要因素之一。本文探讨了将极化分解技术与植被覆盖地区的一阶散射模型结合估算土壤水分变化的方法。雷达数据经极化目标分解技术分解后得到的双次散射项和一阶植被散射模型的植被-地表的双次散射项一一对应，再利用多时相雷达数据消除植被层后向散射的影响，从而估算出地表土壤水分变化量。最后应用全极化机载雷达数据（AirSAR）对该方法进行了检验，结果表明该方法能够较好的估算植被覆盖地表的土壤水分变化。     

基于Sentinel-1及 Landsat 8数据的黑河中游农田土壤水分估算

土壤水分是陆地表层系统中的关键变量。利用主动微波遥感,特别是合成孔径雷达(Synthetic Aperture Radar,SAR)的观测,在监测和估计表层土壤水分时空分布方面已开展了诸多研究。然而,SAR土壤水分反演仍存在诸多挑战,特别是地表粗糙度和植被的影响。因此,本文提出了一种结合主动微波和光学遥感的优化估计方案,旨在同步反演植被含水量、地表粗糙度和土壤水分。反演算法首先在水云模型的框架下对模型中的植被透过率因子(与植被含水量密切相关)采用3种不同的光学遥感指数——修正的土壤调节植被指数(Modified Soil Adjusted Vegetation Index,MSAVI)、归一化植被指数(Normalized Difference Vegetation Index,NDVI)和归一化水体指数(Normalized Difference Water Index,NDWI)进行参数化估计,用于校正植被层的散射贡献。在此基础上,构造基于SAR观测和Oh模型的代价函数,利用复型洗牌全局优化算法进行土壤水分和地表粗糙度的联合反演。采用Sentinel-1 SAR和Landsat 8多光谱数据在黑河中游开展了反演试验,并利用相应的地面观测数据对结果进行了验证。结果表明反演结果与地面观测具有良好的一致性,其中基于NDWI的植被含水量反演效果最佳,与地面观测比较,土壤水分决定系数(R 2)在0.7以上,均方根误差(RMSE)为0.073 m^ 3/m^ 3;植被含水量R 2大于0.9,RMSE为0.885 kg/m 2,表明该方法能够较准确地估计土壤水分。同时发现植被含水量的估计结果,以及植被透过率的参数化方案对土壤水分的反演精度有一定的影响,在未来的研究中需要进一步探索。     

基于Sentinel-1与FY-3C数据反演植被覆盖地表土壤水分

基于新一代的Sentinel-1SAR数据与FY-3C的MWRI数据,研究植被覆盖地表土壤湿度反演方法。为消除植被对土壤湿度反演影响,首先利用FY-3C/MWRI的微波极化差异指数MPDI,建立植被含水量反演模型;然后,结合植被含水量反演模型和水—云模型,发展一种主被动微波联合反演植被覆盖地表土壤含水量模型;最后,在江淮地区开展反演试验,利用观测的土壤湿度数据进行反演结果的精度验证。结果表明:(1)对于植被覆盖地表土壤湿度反演,由FY3C/MWRI提取的MPDI对于去除植被影响效果较好;(2)相比于VH极化哨兵1号卫星数据,VV极化数据更适用于土壤含水量的反演,能够得到更高的土壤湿度反演精度;(3)哨兵1号卫星数据能够获得较高精度的土壤含水量反演结果,试验反演的土壤湿度值与实测值相关系数为0.561 2,均方根误差为0.044cm~3/cm~3。     

基于TVDI的大范围干旱区土壤水分遥感反演模型研究

温度植被干旱指数TVDI(Temperature Vegetation Dryness Index)是一种基于光学与热红外遥感通道数据进行植被覆盖区域表层土壤水分反演的方法。当研究区域较大、地表覆盖格局差异显著时,利用TVDI模型来反演陆表土壤水分,精度通常较低。对Sandholt的TVDI土壤水分反演模型进行了改进:利用云掩膜校正和多天平均温度合成来减少云的影响;同时对研究区域地形起伏、覆盖类型差异的影响进行了消除;对TVDI模型干边的模拟方法进行了改进。最后,使用铝盒采样等方法利用新疆地区观测得到的地面数据来拟合改进后的模型参数,并对2009年5月和8月的土壤水分进行了反演实验。与实测数据的比较分析表明,该模型能基本满足大区域土壤水分反演的要求,改进后的模型对新疆地区的土壤水分估算精度有较显著的提高。     

基于多源遥感数据协同反演森林地表土壤水分研究

土壤水分在土壤监测中是一项重要的指标,对于农业生产、生态环境以及水资源管理有着重要的影响。随着遥感建模与反演理论的不断成熟,其逐渐成为分析土壤指标的重要技术与手段。因此,利用光学影像与雷达影像数据,以大兴安岭地区漠河市为研究区域,分别建立以Landsat 8为数据源的土壤水分反演模型和由Landsat 8影像数据与GF-3卫星数据协同反演的土壤水分反演模型,将反演结果与实际测得数据进行对比验证,并评价所建立的反演模型。结果表明：①对研究区地温进行反演,利用地表温度（Ts）与归一化差异湿度指数NDMI构建Ts-NDMI特征空间,结合实测数据可以发现Ts-NDMI特征空间土壤水分反演模型的反演结果与实测土壤含水量为负相关性;②协同GF-3卫星数据和Landsat 8遥感影像数据所建立的土壤水分反演模型能得到质量较高的反演结果,且在高植被覆盖度地区,利用该协同反演模型得到的反演结果比利用单一光学数据源所建模型得到的反演结果精度高,为今后高植被覆盖度地区土壤湿度的研究提供了新途径。     

面向双站SAR系统的长波PolInSAR目标自适应分解北大核心CSCD

双站SAR系统无时间去相干的特性,结合长波的强穿透能力,在估计植被结构参数上应用前景极大,借助极化干涉SAR分解技术研究双站SAR系统下的植被区散射过程,对揭示信号与地物的交互过程,构建植被结构参数反演模型具有重要意义。考虑模型适用性和双站SAR系统存在的不可忽略的去相干,将极化干涉矩阵表达为极化方位角扩展的广义表面散射矩阵、广义二次散射矩阵和Neumann自适应体散射矩阵与其对应相干成分乘积的和的形式,基于残差最小二乘准则,使用非线性最小二乘优化技术同时求解所有模型参数。使用BioSAR 2008项目的 L波段全极化机载数据对方法进行测试,获取了实验区不同散射机制的相干成分、相位分布和能量信息,结合机载激光雷达数据进行了分析。结果表明:分解方法对植被区不同散射机制区分良好,有效抑制了体散射功率高估;植被区表面散射在垂直向上的分布与植被高度和穿透程度存在联系,体散射相位中心高度与机载激光雷达植被高接近且趋势一致;有效估计了散射机制的相干性。     

基于Radarsat-2全极化数据的高原牧草覆盖地表土壤水分反演

大面积土壤水分反演对于青海湖流域草场的管理和保护具有重要的意义。利用C波段全极化的Radarsat-2 合成孔径雷达(SAR)影像数据,开展了青海湖流域刚察县附近草场的土壤水分反演研究,在“水-云”模型和Chen模型的基础上,发展了一种新的土壤水分反演算法。该算法消除了植被覆盖以及地表粗糙度对雷达后向散射系数的影响。实验结果表明:预测结果能够与实测数据很好地吻合,R²、RMSE和RPD分别达到0.71\,3.77%和1.64,反演精度较高,能够满足研究区土壤水分的反演精度要求。如果能够更细致地刻画植被层以及地表粗糙度对雷达后向散射系数的影响,土壤水分反演精度有望得到进一步提高。
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基于时序Sentinel-1A数据的农田土壤水分变化检测分析

土壤水分是作物生长的基本条件,同时也是作物长势监测、估产和旱情监测的重要参数。基于变化检测模型,利用不同时相Sentinel-1A数据估算农田土壤水分变化信息。首先利用积分方程模型(Integral Equation Model,IEM)模拟数据分析雷达后向散射系数变化与土壤水分变化之间的关系,在土壤表面粗糙度恒定的情况下,土壤湿度变化与雷达后向散射系数变化具有高度相关性,验证了变化检测模型用于估算土壤水分变化的合理性。在此基础上,利用河北邯郸研究区时序Sentinel-1ASAR数据和现场实测数据构建土壤水分变化检测模型,从而利用雷达后向散射系数变化估算土壤水分变化信息。结合现场实测数据得到,最小二乘和支持向量回归模型反演结果的均方根误差(RMSE)分别为5.1vol%和4.6vol%,决定系数(R2)分别为0.65和0.73。验证了时序Sentinel-1A数据用于监测农田土壤水分变化的实用性。     

基于地基微波辐射观测的土壤水分反演算法评估

当前常用的被动微波土壤水分反演算法有水平极化单通道算法、垂直极化单通道算法、双通道算法、微波极化差比值算法和扩展双通道算法,5种反演算法具有不同的差异,对这些反演算法进行系统的评估和分析将有助于反演算法的改进和星载高精度土壤水分产品的发布。为了避免直接采用卫星产品验证时的尺度匹配、空间异质性等问题,基于地基L波段微波辐射观测以及配套的土壤和植被参数测量数据,对这5种反演算法进行了实现、对比和分析,得出以下结论：①单通道算法具有最佳的反演性能,水平极化单通道算法反演结果具有最高的相关性(相关性系数R=0.83),垂直极化单通道算法反演结果具有最小的反演误差(均方根误差RMSE=0.028 m³/m³,偏差BIAS= -0.011 m³/m³),但单通道算法需要精确的植被含水量输入;②其余3种算法能脱离植被辅助数据的使用,性能略差但也能满足星载微波传感器的探测指标要求(小于等于0.04 m³/m³);其中,扩展双通道算法和微波极化差比值算法的土壤水分反演结果比双通道算法略差,但本例中扩展双通道算法在植被含水量反演方面更具优势。     

Soil Moisture Retrieval over Vegetated Areas based on Sentinel-1 and FY-3C Data

This study aims to develop soil moisture retrieval model over vegetated areas based on Sentinel-1 SAR and FY-3C data.In order to remove vegetation effect,the MWRI data from FY-3C was applied to establish the inversion model of vegetation water content.The model was combined with the original water-cloud model,and developing a soil moisture retrieval model by combining active and passive microwave remote sensing data.Finally,the experiment of the soil moisture retrieval was conducted in Jiangsu and Anhui province,and validating the inversion accuracy of soil moisture by measured data.The results showed that：①For the vegetation-covered surface,the Microwave Polarization Difference Index obtain from FY-3C/MWRI was suitable for removing vegetation effect.②Compared with the Sentinel-1 VH polarization data,the backscattering coefficient of VV polarization was more suitable for soil moisture retrieval and get a higher accuracy of soil moisture retrieval.③Sentinel\|1 data can obtain high precision soil moisture estimation results,and the correlation coefficient between the estimated and measured soil moisture is 0.561 2 and RMSE is 0.044 cm³/cm³.     

空间遥感现状及未来发展

从第三十五届国际宇航联合会的空同遥感专业小组会议上可以看出,目前空间遥感的现状及未来发展前景。今后空间遥感将从具有单一遥感能力向具有综合遥感能力方面发展,不仅能对陆地,而且对海     

Potential of Dubois model for soil moisture retrieval in prairie areas using SAR and optical data

Soil moisture retrieval is often confounded by the influence of vegetation and surface roughness on the backscattered radar signal in vegetated areas. In this study, a semi-empirical methodology is proposed to retrieve soil moisture in prairie areas. The effect of vegetation is eliminated by the ratio vegetation method and water cloud model (WCM), respectively. The conditions of vegetation are characterized by leaf area index (LAI), vegetation water content (VWC), normalized difference vegetation index (NDVI), and enhanced vegetation index (EVI), respectively. To remove the dependence on surface roughness, the dielectric constant is explicitly expressed as the function of co-polarization backscattering coefficients and sensor parameters based on the Dubois model. The ground measurements and satellite data collected from the Ruoergai and Wutumeiren prairies of China allow for validating the feasibility and effectiveness of the proposed methodology. From the perspective of soil moisture retrieval accuracy, the ratio vegetation method performs better than WCM. In the Ruoergai prairie, the best soil moisture retrieval result is obtained when EVI is used, with correlation coefficient (r) and root mean square error (RMSE) of 0.87 and 3.50 vol.%, respectively. While in the Wutumeiren prairie, the lowest retrieval error is obtained when LAI is used, with r and RMSE values of 0.79 and 5.73 vol.%, respectively. These results demonstrate that the Dubois model has a potential for enhancing soil moisture retrieval in prairie areas using synthetic aperture radar (SAR) and optical data.     

土壤湿度微波遥感中的植被散射模型进展

植被是影响土壤湿度微波遥感的主要因子之一,土壤湿度微波遥感的主要任务是建立含有地表土壤信息的植被散射模型。植被散射模型的建立可以加深我们对植被和土壤散射机理的理解,定量分析微波后向散射系数对于各散射因子的敏感性,进一步达到从微波信息中反演土壤湿度的目的。植被散射模型可以分为经验模型、理论模型和半经验模型,各种模型都具有自身的优势和局限性。经验模型的建立比较简单,但一般只适用于特定的研究条件;理论模型是建立在一定的理论基础之上,对于散射因子的考虑相对详尽,但一般模型比较复杂,反演相对困难;半经验模型是前两者的折中,它以植被的宏观物理参量为模型参数,模型的建立和反演比理论模型要简单,但同时也具有一定的理论依据,适用性也较经验模型广。     

Soil moisture estimation over vegetated terrains using multitemporal remote sensing data

A new method for retrieving soil moisture content over vegetated fields, employing multitemporal radar and optical images, is presented. It is based on the integration of the temporal series of radar data within an inversion scheme and on the correction of the vegetation effects. The retrieval algorithm uses the Bayesian maximum posterior probability and assumes the existence of a relation among the soil conditions at the different times of the series. The correction of the vegetation effects models the variation, with respect to the initial time of the series, of the component of the backscattering coefficient due to the soil characteristics as function of the variations of the measured backscattering coefficient and of the biomass. The method is tested on the data acquired throughout the SMEX02 experiment. The results show that measured and estimated soil moistures are fairly well correlated and that the performances of multitemporal retrieval algorithm are better than those obtained by employing one radar acquisition, especially in terms of capability to detect soil moisture changes. Although the approach to correct the vegetation effects on radar observations needs to be further assessed on different sets of data, this finding demonstrates that the proposed method has a potential to improve the quality of the soil moisture retrievals.     

Remote sensing of bare surface soil moisture using EMAC/ESAR data

In this paper we present first results of bare surface soil moisture retrieval using data from the European Multisensor Airborne Campaign/ Experimental Synthetic Aperture Radar (EMAC/ESAR) collected on 9 April 1994 in the Zwalm catchment, Belgium. Data from EMAC Reflective Optics System Imaging Spectrometer (ROSIS) collected on 12 July 1994 over the same catchment were used to develop land use maps. Concurrent to the EMAC/ESAR overflights field data were collected in two subcatchments of the Zwalm catchment. The paper first presents the data processing procedures used for the radar images. Then we apply a theoretical backscattering model to investigate the sensitivity of EMAC/ESAR backscattering coefficients to surface parameters (topography, surface roughness, vegetation and soil moisture). By comparing the predicted backscattering coefficients to the observed ones, we can conclude that classical measurement techniques for surface roughness parameters in remote sensing campaigns are not accurate enough for retrieving soil moisture using theoretical models. A method based on simultaneous retrieval of surface roughness parameters and soil moisture using multiple ESAR measurements is hence proposed. Promising results for retrieved soil moisture confirm the validity of the proposed method.     

Calculations of radar backscattering coefficient of vegetation-covered soils

A model for simulating the measured radar backscattering coefficient of vegetation-covered soil surfaces is presented in this study. The model consists of two parts: the first is a soil surface model to describe the backscattered radar pulses from a rough soil surface, and the second part takes into account the effect of vegetation cover. The soil surface is characterized by two parameters, the surface height standard deviation σ and the horizontal correlation length l. The effect of vegetation canopy scattering is incorporated into the model by making the radar pulse subject to two-way attenuation and volume scattering when it passes through the vegetation layer. These processes are characterized by the two parameters, the canopy optical thickness τ and the volume scattering factor η. The model results agree well with the measured angular distributions of the radar backscattering coefficient for HH polarization at the 1.6 GHz and 4.75 GHz frequencies over grass-covered fields. These observations were made from an aircraft platform during six flights over a grass watershed in Oklahoma. It was found that the coherent scattering from soil surfaces is very important at angles near nadir, while the vegetation volume scattering is dominant at larger incident angles (> 30°). The results show that least-squares fits to scatterometer data can provide reliable estimates of the surface roughness parameters, particularly the surface height standard deviation σ. The range of values for σ for the six flights is consistent with a 2 or 3 dB uncertainty in the magnitude of the radar response.     

基于无人机与卫星遥感的草原地上生物量反演研究

草原生物量是评价草原生态系统功能的重要参数.为了快速、准确、有效地估算草原地上生物量,以呼伦贝尔草原为研究区,基于无人机多光谱影像和卫星遥感(Sentinel-2)影像,选择GNDVI、LCI、NDRE、NDVI、OSAVI、EVI等6个植被指数,结合实测地上生物量数据,建立植被指数回归模型,并采用留一法交叉验证进行精...