基于Sentinel-1和MODIS数据反演农田地表土壤水分—以REMEDHUS地区为例

土壤水分是陆地生态系统和水循环的重要状态变量,在植被生长监测、农作物产量评估等研究中均发挥着重要作用。为了消除植被散射的影响,进而实现农田地表土壤水分的高精度反演,以时间序列Sentinel-1影像及MODIS产品为实验数据,基于高级积分方程模型和比值植被模型的耦合模型,通过采用不同光学植被参数和VH交叉极化后向散射系数,分别对农田植被散射贡献进行表征,消除植被散射的影响,进而实现土壤水分的高精度反演。结果表明：当利用VH极化进行参数化植被散射贡献时,标定的耦合模型,虽然可消除对光学植被参数的依赖并较好地模拟Sentinel-1卫星观测,但土壤水分反演结果效果欠理想,相关系数R最大仅为0.54;与VH极化相比,利用光学植被参数表征植被散射贡献时,土壤水分整体反演效果较理想,R最大达到0.79,但光学植被参数反演结果在不同站点存在显著的空间差异性,R介于0.07~0.79之间。因此,在未来研究中可尝试将雷达数据与光学数据协同反演,以期在消除植被散射影响的基础上,实现植被覆盖区域土壤水分的高精度反演及动态变化监测。     

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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一种面向建筑物提取的极化雷达影像分解方法

针对基于物理散射模型分解的建筑物与植被混淆的问题,发展了一种面向建筑物提取的全极化SAR影像多分量分解方法,用于区分散射类型易混淆的方位建筑物(走向与雷达方位向不平行的建筑物)与植被。该方法根据植被与建筑物的不同散射机制,预先剔除了植被像元,修正了体散射模型,改进了常规的全极化SAR多分量分解方法。通过H/α/A分解和非反射对称比筛选出植被像元,抑制植被区域对多分量分解效果的影响;引入修正的体散射模型,改进多分量分解模型;对植被区域进行Yamaguchi四分量分解,其他区域进行改进的多分量分解。利用E-SAR和AIRSAR全极化数据进行实验。结果表明,与传统的多分量分解方法相比该方法能够有效去除建筑物中的自然地物虚警从而提高检测精度。     

主被动遥感数据协同估算干旱区草原植被生物量

结合主动微波遥感和被动光学遥感反映地表植被的各自优势,发展了一种主被动遥感协同估算干旱区草原植被生物量的模型。该模型将植被覆盖度作为水云模型的附加参数,将总体散射分为植被覆盖区散射和裸土区散射两部分,将水云模型应用到了植被覆盖稀疏区域。利用改进的水云模型和双极化ASAR数据,通过建立方程组估算植被生物量。将该方法用于乌图美仁草原植被生物量的估算,验证了该方法的有效性。结果表明:该主被动遥感协同估算模型能够成功地估算干旱区草原植被生物量,并且取得了较好的估算精度(R2=0.8562,RMSE=0.1813kg/m2)。最后,分析了该方法估算植被生物量的误差来源。     

基于目标分解理论的全极化SAR图像神经网络分类方法

由于全极化合成孔径雷达(synthetic aperture radar)能够测量每一观测目标的全散射矩阵，即可合成包括线性极化、圆极化及椭圆极化在内的多种极化图像，因此与常规的单极化和多极化SAR相比，在雷达目标探测、识别，纹理特征和几何参数的提取等方面，全极化SAR均具有很多优点，但是由于地物分布的复杂性往往造成不同地物具有相似的后向散射信号特征，因而加大了地物信息提取的难度。同时由于这些极化合成图像具有较高的相关性，从而导致了图像分类精度的降低。为了提高全极化SAR图像的分类精度，基于新疆和田地区的SIR-CL波段全极化雷达数据，利用目标分解理论首先将地物回波的复杂散射过程分解为几种互不相关的单一的散射分量。由于这些单一的散射分量都对应于具有不同物理和几何特征以及分布特征的地物，从而提供了更加丰富的地表覆盖信息，这样就很大程度地改善了地物信息的分类精度；然后利用分解后单一散射分量数据结合传统的极化合成数据，可以得到更多的互不相关的数据源，再使用神经网络分类法对这些数据进行分类。分类结果表明，这种方法大幅度提高了全极化SAR数据用于实验区土地覆盖分类的精度。这种分类方法也可以广泛地用于SAR数据地表覆盖和土地利用动态监测和地表参数的提取。     

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。     

基于物理散射模型的全极化SAR图像增强滤波算法

针对传统的极化SAR滤波方法图像中城镇区域和植被区域地物在滤波中易被混淆, 导致滤波后图像中地物边缘保持效果下降的问题, 提出了一种增强的保持极化散射特性的滤波算法。利用一种增强的四分量极化分解方法获取更加精确的地物散射机制, 并将散射机制信息引入滤波方法中, 使滤波算法中像素的散射机制更精确。增强的四分量极化分解方法引入了极化SAR数据的定向角补偿技术、一种新的体散射模型以及两种散射功率限制条件, 来改进Freeman-Durden分解的结果。理论分析和实验结果表明, 改进后的方法获取了比传统的极化SAR图像滤波算法更加理想的计算结果。     

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。     

被动微波土壤水分反演中消除植被影响的方法研究

针对被动微波反演土壤水分中,ω-τ模型忽略植被内部多次散射的局限,将光线跟踪原理的双矩阵(Matrix Doubling)算法应用到植被覆盖地区的地表辐射以及植被与地表之间多次散射问题.这种方法可以有效地弥补ω-τ模型的缺陷,但需要的参数较多且形式复杂,很难被直接应用到模拟计算和土壤水分反演中.针对这些问题,本文将基于双矩阵(Matrix Doubling)的算法进行简化,分别考虑植被内仅存在一次散射、两次散射、三次散射以及多次散射的情况.将散射矩阵和传输矩阵表达式简化,利用野外楸树林实验得到的植被参数进行模拟.结果显示当仅考虑两次散射时模拟结果与原始模型的差别已经很小.     

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。     

Temporal and spatial soil moisture change pattern detection in an agricultural area using multi‐temporal Radarsat ScanSAR data

Monitoring the characteristics of spatially and temporally distributed soil moisture is important to the study of hydrology and climatology for understanding and calculating the surface water balance. The major difficulties in retrieving soil moisture with Synthetic Aperture Radar (SAR) measurements are due to the effects of surface roughness and vegetation cover. In this study we demonstrate a technique to estimate the relative soil moisture change by using multi‐temporal C band HH polarized Radarsat ScanSAR data. This technique includes two components. The first is to minimize the effects of surface roughness by using two microwave radar measurements with different incidence angles for estimation of the relative soil moisture change defined as the ratio between two soil volumetric moistures. This was done by the development of a semi‐empirical backscattering model using a database that simulated the Advanced Integral Equation Model for a wide range of soil moisture and surface roughness conditions to characterize the surface roughness effects at different incidence angles. The second is to reduce the effects of vegetation cover on radar measurements by using a semi‐empirical vegetation model and the measurements obtained from the optical sensors (Landsat TM and AVHRR). The vegetation correction was performed based on a first‐order semi‐empirical backscattering vegetation model with the vegetation water content information obtained from the optical sensors as the input. For the validation of this newly developed technique, we compared experimental data obtained from the Southern Great Plain Soil Moisture Experiment in 1997 (SGP97) with our estimations. Comparison with the ground soil moisture measurements showed a good agreement for predication of the relative soil moisture change, in terms of ratio, with a Root Mean Square Error (RMSE) of 1.14. The spatially distributed maps of the relative soil moisture change derived from Radarsat data were also compared with those derived from the airborne passive microwave radiometer ESTAR. The maps of the spatial characteristics of the relative soil moisture change showed comparable results.     

基于多源信息融合的土壤含水量估算

遥感信息在大面积土壤水分监测中具有不可替代的优势。通过对试验区域的气象数据、土壤类型数据、土壤和水体的光谱特征曲线、多时相遥感影像数据等进行预处理,提取图像信息和属性数据,并对土地利用类型和植被覆盖度进行划分。基于土壤的光谱响应机制建立像元反射光谱信息分解模型,以此计算出该区域土壤容积含水率。结果表明该方法对于低植被区的监测精度较高(理论精度89.78%),可作为土壤水分监测预警的依据。     

A combined passive/active microwave remote sensing approach for surface variable retrieval using Tropical Rainfall Measuring Mission observations

The backscattering and emission measured simultaneously by radar and radiometer show promise for the estimation of surface variables such as near-surface soil moisture and vegetation characteristics. In this paper, the 10.7 GHz Tropical Rainfall Measuring Mission (TRMM) microwave imager (TMI) channel and 13.8 GHz precipitation radar (PR) observations are simultaneously used for the estimation of the near-surface soil moisture and vegetation properties. The Fresnel model for soil and a simple model for vegetation are used to simulate the passive microwave emission at 10.7 GHz. To determine the PR backscatter signal from a land surface, a theoretical approach is used based on the Geometric Optics Model for simulating bare soil and a semi-empirical water-cloud model for vegetation. The model parameters required in specifying the nature of the soil and vegetation are calibrated on the basis of in situ soil moisture data combined with remotely sensed observations. The calibrated model is subsequently used to retrieve near-surface soil moisture and leaf area index for assumed values of surface roughness and temperature. Algorithm assessment using synthetic passive and active microwave data shows a nonlinearity effect in the system inversion, which results in a varying degree of error statistics in soil wetness and vegetation characteristics retrieval. The technique was applied on TRMM radar/radiometer observations from three consecutive years and evaluated against in situ near-surface (5 cm) soil moisture measurements from the Oklahoma Mesonet showing a consistent performance.     

土壤含水量增墒、退墒预测模型的研建与应用

在天然旱地植被系统中,土壤水大小与大气水、地表水和蒸散量等因素关系密切,且存在水量平衡关系。依据水量平衡关系和实测水文资料,以连续无雨日数为影响因子,创建土壤含水量与连续无雨日数相关关系,该关系式即为单站无雨退墒预测模型;以降水为影响因子,创建土壤含水量增量与降水量相关关系,该关系式即为有雨增墒预测模型,实现短期内土壤含水量动态变化预测。预测模型已应用到2016年吉林省中西部墒情分析评价工作中,应用结果表明:该模型理论依据充分,符合天然旱地植被系统中的水量平衡关系;建模简单,应用便捷,可进行短期土壤含水量增墒、退墒预测,预测结果能够科学反映土壤含水量动态变化规律。     

A model for retrieving soil moisture saturation with Landsat remotely sensed data

Soil moisture saturation indicates the capability of the vegetation humus layer and the soil layer to reabsorb and drain water in an area; it is crucial in predicting natural disasters, such as landslides and droughts. In this article, a model was created to retrieve soil moisture saturation based on multispectral remotely sensed data. Soil brightness and soil wetness, calculated from the tasseled cap transformation, were utilized to obtain soil moisture saturation. With the above model, a soil moisture saturation map of Maoergai District, which is located on the upper Minjiang River in northern Sichuan Province in the south-west of China, was created from a Landsat Enhanced Thematic Mapper Plus (ETM+) image in July 2002. Then, the soil type data and the vegetation distribution data of the year 2000 were used to evaluate the model. The result shows that the model for soil moisture saturation is viable and that the vegetation type, vegetation distribution and soil type have strong correlation with soil moisture saturation.     

GNSS-R信号反演土壤水分研究分析

由于GNSS-R信号具有全天候、穿透性以及不受云的影响,且对土壤水分非常敏感等特性,使其在遥感研究全球变化中具有独特的优势。简要评述了利用GPS地面反射信号在土壤水分反演研究领域的进展,然后侧重分析介绍了GPS反射信号土壤水分反演原理,前向模型分析反演土壤水分的关键技术,并指出目前存在的问题和发展方向。     

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 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.     

The application of compact polarimetric decomposition algorithms to L-band PolSAR data in agricultural areas

ABSTRACT

In this paper, the applicability of the recently developed compact polarimetric decomposition and inversion algorithm to estimate soil moisture under low agricultural vegetation cover is investigated using simulated L-band compact polarimetric synthetic aperture radar (PolSAR) data. The surface scattering component is separated from the volume component of the vegetation through a model-based compact polarimetric decomposition (m-α) under the assumption of randomly orientated vegetation volume and reflection symmetry. The extracted surface scattering component is compared with two physics-based, low frequency surface scattering models such as extended Bragg (X-Bragg) and polarimetric two scale model (PTSM) in order to invert soil moisture for corresponding model- and data-derived surface scattering mechanism parameter α_s. In addition to the parameter α_s from m-α decomposition, the applicability of other scattering mechanism parameters, such as δ (relative phase) and χ (degree of circularity) from m-δ and m-χ decompositions are also investigated for their suitability to invert soil moisture. The algorithm is applied on a time series of simulated L-band compact polarimetric E-SAR data from the AgriSAR’2006 campaign over the Görmin test site in Northern Germany. The compact PolSAR-derived soil moisture is validated against in situ time-domain reflectometry (TDR) measurements. Including various growth stages of three different crop types, the estimated soil moisture values indicate an overall root mean square error (RMSE) of 9–12 and 9–15 vol.% using the X-Bragg model and the PTSM, respectively. The inversion rate for vegetation covered soils ranges from 5% to 40% including all phenological stages of the crops and different soil moisture conditions (range from 4 to 34 vol.%). The time series of soil moisture inversion results using compact polarimetry reveal that the developed algorithm is less sensitive to wet soils under growing agriculture crops due to less sensitivity of scattering mechanism parameters α_s and χ for ε_s > 20. Thus, further developments and investigations are needed to invert soil moisture for compact PolSAR data with high inversion rates and consistently less RMSE (<5 vol.%) over the various crop growing season.