基于ERS散射计数据的土壤水分反演方法

在全球能量与水循环研究中,地表土壤水分是非常关键的参数之一。ERS散射计因具有观测尺度大、重复周期短等优势而在地表土壤水分监测方面日益受到关注。采用目前最先进的理论模型AIEM(改进的积分方程模型),根据ERS散射计的参数设计模拟出一个涵盖较宽地表粗糙度和介电常数输入范围的数据库,利用这个数据库发展出一个参数化模型。该模型采用了一个综合均方根高度(RMS height)与相关长度(Correlation length)的粗糙度参数,该参数在每个入射角度下都可以用同一个函数来描述,解决了多角度数据情况下粗糙度参数的表达问题。应用新发展的参数化模型进行土壤水分的反演结果表明,该模型具有较高的精度。     

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。     

利用ANFIS方法反演裸土区土壤水分含量

为了更好地进行土壤水分反演,发展了一种基于ALOS/PALSAR数据、利用自适应神经模糊推理系统(adaptive neuro fuzzy inference system,ANFIS)反演土壤水分的方法.首先,根据研究区实际情况,利用AIEM和Oh模型模拟了试验区裸土区的后向散射特性,建立了后向散射系数与地表粗糙度之间的关系;然后,考虑到研究区地表粗糙度几乎没有变化这一情况,设定了地表粗糙度对后向散射系数的影响为常量;在此基础上,分别利用ANFIS,BP神经网络、多元线性回归和多元非线性回归方法构建了裸土区土壤水分的反演模型,并利用野外实测数据对模型进行了验证.研究结果表明,采用ANFIS方法构建的模型反演精度最高,其均方根误差为0.030,相对误差为14.5％.因此,可以利用ANFIS方法反演裸土区的土壤水分含量,其反演结果具有较高的精度.     

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。     

利用双极化微波遥感数据反演土壤水分的新方法

提出了一种基于微波双极化数据的土壤水分反演经验模型,该模型引入了新的综合粗糙度参数Rs=S2/L(1/2)来描述地表粗糙状况,将两个粗糙度参数均方根高度S和相关长度L合二为一,因而模型的未知量仅为Rs与法向菲涅尔反射系数Г0。基于AIEM模型数值模拟,建立了后向散射系数与Rs、Г0的经验关系,并利用两个极化的微波数据同时反演得到粗糙度参数Rs和Г0,进而得到地表土壤水分。实测数据表明,该模型反演的土壤水分与地表实测值相关性较高(R2=0.681,RMS=0.043),在土壤水分反演方面具有较大的潜力。     

ERS风散射计反演地表参数——一种混合像元方法

基于混合像元的方法,利用ERS风散射计(WSC)数据估算植被覆盖率和同时期NDVI有较高的相关性(0.78),计算出的垂直入射菲涅耳反射系数的空间分布状况也比较合理。     

基于HJ-1B数据和SEBAL模型的陆面蒸散发遥感估算

利用HJ-1B卫星数据和SEBAL模型,进行了淮河上游段的陆面蒸散发(ET)遥感估算。选取2010年4期少云的HJ-1B卫星影像,首先对ET估算中需要的中间变量——地表温度、地表反照率进行了反演,反演结果与MODIS产品结果基本一致;在此基础上,结合部分地面气象观测数据,基于SEBAL模型进行ET估算,并利用水文站点的实测蒸发皿数据对估算的日蒸散发结果进行验证,相对误差在10%之内。将ET估算值与土地利用覆盖类型、地形因子对比分析发现,不同土地覆盖类型的ET量不同,ET空间变异性与地形特征有一定关联。     

耦合MODIS数据的SMAP被动微波土壤水分空间降尺度研究——以闪电河流域为例

土壤水分不仅是陆面过程中重要的变量,同时也是全球水循环中的关键参数。为了获得高分辨率的土壤水分数据,本文将基于自适应窗口的土壤水分降尺度方法应用在闪电河流域,以1 km MODIS产品(地表温度和归一化植被指数)作为辅助数据,对9 km的SMAP被动微波土壤水分(SMAP土壤水分)数据进行降尺度,得到研究区1 km的降尺度土壤水分数据。利用地面站点实测土壤水分和机载被动微波土壤水分(机载土壤水分)对降尺度土壤水分和SMAP土壤水分进行了验证,并对辅助数据和降尺度方法本身展开分析以探讨降尺度过程中的不确定性来源。结果表明:(1)本文使用的基于自适应窗口的土壤水分降尺度方法能够有效地提高SMAP土壤水分的空间分辨率,在进一步丰富土壤水分分布细节变化信息的同时,还能够保留SMAP土壤水分的空间变化特征并与其保持值域一致。(2) 3种基于像元尺度的土壤水分数据(机载土壤水分、SMAP土壤水分和降尺度土壤水分)与站点实测土壤水分之间的相关性并不高,这主要与点、面数据之间的空间匹配不一致、空间代表性不同以及有效验证的数据量有限有关。而与站点数据验证相比,降尺度土壤水分和SMAP土壤水分均和机载土壤水分数据相关性较好。(3) SMAP土壤水分与辅助数据之间的相关性比机载土壤水分与辅助数据之间的较高,而这两种土壤水分数据之间存在的这种偏差主要受到空间尺度、观测配置、参数反演算法和选用的辅助数据等因素的影响。(4)针对验证结果的不确定性,通过增加辅助数据或改变土壤水分估算模型结构进而修改降尺度模型的方式在本研究中并不能显著提高降尺度结果的精度,如何进一步提高降尺度精度仍是未来需要研究的重点。     

基于CASI数据的黑河绿洲区叶面积指数反演

叶面积指数(leaf area index,LAI)作为植被冠层的重要参数,对作物长势监测及产量估算具有重要意义。本研究以黑河流域张掖绿洲试验区为例,基于机载航空高光谱遥感影像(compact airborne spectrographic imager,CASI)数据,利用物理模型与统计模型对研究区的LAI进行估测反演。首先,利用归一化植被指数(normalized difference vegetation index,NDVI)与相应实测LAI数据建立最佳线性回归模型;然后,基于混合像元分解模型和多次散射植被冠层模型构建物理模型;最后,以线性回归模型为参比修正多次散射植被冠层模型,构建半经验LAI反演模型,并比较上述模型拟合效果。研究结果表明,半经验模型为绿洲区LAI反演最优模型,模型估算精度R2达到0.89,精度提高较显著。研究对提升作物LAI的估算精度有一定意义,并将进一步推动精细农业定量遥感理论的研究与应用。     

干旱区土壤水分遥感反演与同化模拟系统研究

利用遥感反演和同化模拟方法估算大范围陆表土壤水分,建立了适用于干旱区的土壤水分反演模型。在.NET平台下运用C#、IDL与ArcEngine控件,对反演模型与同化模拟模型进行了软件实现,并集成为土壤水分遥感反演与同化模拟系统,为环保部门获取大范围土壤水分信息提供了支撑软件工具。     

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。     

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。     

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.     

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。     

Long-term soil moisture dynamics derived from GNSS interferometric reflectometry: a case study for Sutherland,South Africa

Soil moisture is a geophysical key observable for predicting floods and droughts, modeling weather and climate and optimizing agricultural management. Currently available in situ observations are limited to small sampling volumes and restricted number of sites, whereas measurements from satellites lack spatial resolution. Global navigation satellite system (GNSS) receivers can be used to estimate soil moisture time series at an intermediate scale of about 1000 m². In this study, GNSS signal-to-noise ratio (SNR) data at the station Sutherland, South Africa, are used to estimate soil moisture variations during 2008–2014. The results capture the wetting and drying cycles in response to rainfall. The GNSS Volumetric Water Content (VWC) is highly correlated (r ² = 0.8) with in situ observations by time-domain reflectometry sensors and is accurate to 0.05 m³/m³. The soil moisture estimates derived from the SNR of the L1 and L2P signals compared to the L2C show small differences with a RMSE of 0.03 m³/m³. A reduction in the SNR sampling rate from 1 to 30 s has very little impact on the accuracy of the soil moisture estimates (RMSE of the VWC difference 1–30 s is 0.01 m³/m³). The results show that the existing data of the global tracking network with continuous observations of the L1 and L2P signals with a 30-s sampling rate over the last two decades can provide valuable complementary soil moisture observations worldwide.     

An Assessment of QuikSCAT Ku-Band Scatterometer Data for Soil Moisture Sensitivity

The QuikSCAT enhanced (2.225-km) backscattering product is investigated for sensitivity to changes in soil moisture and its potential for spatial disaggregation of Advanced Microwave Scanning Radiometer (AMSR-E) soil moisture. Specifically, an active–passive methodology based on temporal change detection is tested using data from the 2006 National Airborne Field Experiment data set. This campaign was carried out from October 29 to November 20, 2006 in a 60 km $times$ 40 km area of the Murrumbidgee catchment, southeast Australia. Temporal change detection analysis and accuracy in terms of spatial pattern distribution throughout the domain were assessed using a passive microwave airborne product derived from the Polarimetric L-band Multibeam Radiometer at 1-km spatial resolution. QuikSCAT–AMSR-E intercomparisons indicated higher correlations when using C-band observations. The greatest sensitivity to soil moisture was observed when using V-polarized backscatter measurement. While backscattering data showed adequate temporal sensitivity to changes in soil moisture due to precipitation events, the spatial agreement was complicated by the presence of irrigation and standing water (rice fields). This resulted in low Cramer's Phi values (less than 0.06), which were used as a measure of spatial correspondence in terms of change in soil moisture and backscatter. In addition, the high QuikSCAT sensor frequency and existence of noise in the observed data contributed to the observed discrepancies.      

基于GPS-IR的地表土壤湿度估算方法

全球定位系统干涉反射测量（GPS-IR）是一种新型的遥感技术，可用于估算近地表土壤水分含量。本文从多卫星融合角度出发，提出了一种基于多星融合的地表土壤湿度估算方法。首先通过低阶多项式拟合分离出卫星反射信号；然后建立反射信号正弦拟合模型，获取相对延迟相位；最后基于多卫星相对延迟相位建立多元线性回归模型。利用美国板块边界观测计划（PBO）提供的监测数据，对比分析不同建模序列长度的反演效果，从而确定最佳的建模长度。试验结果表明，采用多元线性回归模型可实现多颗卫星的有效融合，运用于土壤湿度估算是可行的。     

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.     

Improving a Penman–Monteith evapotranspiration model by incorporating soil moisture control on soil evaporation in semiarid areas

Penman–Monteith (PM) theory has been successfully applied to calculate land surface evapotranspiration (ET) for regional and global scales. However, soil surface resistance, related to soil moisture, is always difficult to determine over a large region, especially in arid or semiarid areas. In this study, we developed an ET estimation algorithm by incorporating soil moisture control, a soil moisture index (SMI) derived from the surface temperature and vegetation index space. We denoted this ET algorithm as the PM-SMI. The PM-SMI algorithm was compared with several other algorithms that calculated soil evaporation using relative humidity, and validated with Bowen ratio measurements at seven sites in the Southern Great Plain (SGP) that were covered by grassland and cropland with low vegetation cover, as well as at three eddy covariance sites from AmeriFlux covered by forest with high vegetation cover. The results show that in comparison with the other methods examined, the PM-SMI algorithm significantly improved the daily ET estimates at SGP sites with a root mean square error (RMSE) of 0.91 mm/d, bias of 0.33 mm/d, and R² of 0.77. For three forest sites, the PM-SMI ET estimates are closer to the ET measurements during the non-growing season when compared with the other three algorithms. At all the 10 validation sites, the PM-SMI algorithm performed the best. PM-SMI 8-day ET estimates were also compared with MODIS 8-day ET products (MOD16A2), and the latter showed negligible bias at SGP sites. In contrast, most of the PM-SMI 8-day ET estimates are around the 1:1 line.     

An Effective Model to Retrieve Soil Moisture from L- and C-Band SAR Data

This study investigated an appropriate method for soil moisture retrieval from radar images and coincident ground measurements acquired over bare soil and sparsely vegetated regions. The adopted approach based on a single scattering integral equation method (IEM) was developed to establish the relationship between backscatter coefficient and surface soil parameters including volumetric soil moisture content and surface roughness. The performance of IEM in 0–7.6 cm is better than that in 0–20 cm. Moreover, IEM can simulate correctly the backscatter coefficients only for the root mean square (RMS) height s < 1.5 cm at C-band and s < 2.5 cm at L-band by using an exponential correlation function and for s > 1.5 cm at C-band and s > 2.5 cm at L-band by using Gaussian function. However, due to the difficulties involved in the parameterization of soil surface roughness, the estimated accuracy is not satisfactory for the inversion of IEM. This paper used a combined roughness parameter and Fresnel reflection coefficient to develop an empirical model. Simulations were performed to support experimental results and to highlight soil moisture content and surface roughness effects in different polarizations. Results showed that a good agreement was found between the IEM simulations and the SAR measurements over a wide range of soil moisture and surface roughness characteristics. The model had a significant operational advantage in soil moisture retrieval. The correlation coefficients were 77.03 % at L-band and 81.45 % at C-band with the RMSEs of 0.515 and 0.4996 dB, respectively. Additionally, this work offered insight into the required application accuracy of soil moisture retrieval at a large area of arid regions.