Remote detection of bare soil moisture using a surface-temperature-based soil evaporation transfer coefficient

An approach for estimating soil moisture is presented and tested by using surface-temperature-based soil evaporation transfer coefficient (h_a), a coefficient recently proposed through the equation h_a = (T_s − T_a)/(T_sd − T_a), where T_s, T_sd, and T_a are land surface temperature (LST), reference soil (dry soil without evaporation) surface temperature, and air temperature respectively. Our analysis and controllable experiment indicated that h_a closely related to soil moisture, and therefore, a relationship between field soil moisture and h_a could be developed for soil moisture estimation. Field experiments were carried out to test the relationship between h_a and soil moisture. Time series Aqua-MODIS images were acquired between 11 Sep. 2006 and 1 Nov. 2007. Then, MODIS derived h_a and simultaneous measured soil moisture for different soil depths were used to establish the relations between the two variables. Results showed that there was a logarithmic relationship between soil moisture and h_a (P < 0.01). These logarithmic models were further validated by introducing another ground-truth data gathered from 46 meteorological stations in Hebei Province. Good agreement was observed between the measured and estimated soil moisture with RMSE of 0.0374 cm³/cm³ and 0.0503 cm³/cm³ for surface energy balance method at two soil depths (10 cm and 20 cm), with RMSE of 0.0467 cm³/cm³ and 0.0581 cm³/cm³ for maximum temperature method at two soil depths. For vegetated surfaces, the ratio of h_a and NDVI suggested to be considered. The proposed approach has a great potential for soil moisture and drought evaluation by remote sensing.     

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.     

Large area soil moisture estimation and mapping using space-borne multi-frequency passive microwave data

The paper reports the estimation of surface soil moisture (SM) using surface wetness Index (SWI) retrieved from multi-frequency passive microwave radiometer. A change detection algorithm was followed which transforms SWI variations in to SM variations using per pixel soil property of field capacity and air-dry status. Estimated soil moisture was compared with the point measurements made at the Monmouth and De Kalb sites of Illinois (USA) for the validation. Sensitivity of the SWI to the variations of rainfall at various vegetation fractions is analyzed. RMS error of volumetric soil moisture is found to be in the range of 6.35 to 8.85 %. The method works well up to the vegetation fraction of 40 %. Applications of the technique are demonstrated by the spatio-temporal analysis of estimated soil moisture maps for India. Characteristic increase in soil moisture was observed with the progress of monsoon from 25 to 32 week in northern India and 46 to 52 week in the costal parts of Tamil Nadu in south.     

A practical algorithm for estimating surface soil moisture using combined optical and thermal infrared data

Surface soil moisture (SSM) is a critical variable for understanding the energy and water exchange between the land and atmosphere. A multi-linear model was recently developed to determine SSM using ellipse variables, namely, the center horizontal coordinate (x₀), center vertical coordinate (y₀), semi-major axis (a) and rotation angle (θ), derived from the elliptical relationship between diurnal cycles of land surface temperature (LST) and net surface shortwave radiation (NSSR). However, the multi-linear model has a major disadvantage. The model coefficients are calculated based on simulated data produced by a land surface model simulation that requires sufficient meteorological measurements. This study aims to determine the model coefficients directly using limited meteorological parameters rather than via the complicated simulation process, decreasing the dependence of the model coefficients on meteorological measurements. With the simulated data, a practical algorithm was developed to estimate SSM based on combined optical and thermal infrared data. The results suggest that the proposed approach can be used to determine the coefficients associated with all ellipse variables based on historical meteorological records, whereas the constant term varies daily and can only be determined using the daily maximum solar radiation in a prediction model. Simulated results from three FLUXNET sites over 30 cloud-free days revealed an average root mean square error (RMSE) of 0.042 m³/m³ when historical meteorological records were used to synchronously determine the model coefficients. In addition, estimated SSM values exhibited generally moderate accuracies (coefficient of determination R² = 0.395, RMSE = 0.061 m³/m³) compared to SSM measurements at the Yucheng Comprehensive Experimental Station.     

An integrated methodology for soil moisture analysis using multispectral data in Mongolia

Soil moisture (SM) content is one of the most important environmental variables in relation to land surface climatology, hydrology, and ecology. Long-term SM data-sets on a regional scale provide reasonable information about climate change and global warming specific regions. The aim of this research work is to develop an integrated methodology for SM of kastanozems soils using multispectral satellite data. The study area is Tuv (48°40′30″N and 106°15′55″E) province in the forest steppe zones in Mongolia. In addition to this, land surface temperature (LST) and normalized difference vegetation index (NDVI) from Landsat satellite images were integrated for the assessment. Furthermore, we used a digital elevation model (DEM) from ASTER satellite image with 30-m resolution. Aspect and slope maps were derived from this DEM. The soil moisture index (SMI) was obtained using spectral information from Landsat satellite data. We used regression analysis to develop the model. The model shows how SMI from satellite depends on LST, NDVI, DEM, Slope, and Aspect in the agricultural area. The results of the model were correlated with the ground SM data in Tuv province. The results indicate that there is a good agreement between output SM and SM of ground truth for agricultural area. Further research is focused on moisture mapping for different natural zones in Mongolia. The innovative part of this research is to estimate SM using drivers which are vegetation, land surface temperature, elevation, aspect, and slope in the forested steppe area. This integrative methodology can be applied for different regions with forest and desert steppe zones.     

一种裸露土壤湿度反演方法

针对目前土壤湿度反演方法研究较少且缺少实时性的现状,该文提出一种土壤湿度反演方法——最小二乘支持向量机技术。以积分方程模型为正向算法,数值模拟不同雷达参数(频率、入射角及极化)下后向散射系数随土壤含水量和地表粗糙度的变化情况。经过数据敏感性分析,选取C-波段和X-波段、小入射角下的同极化后向散射系数作为支持向量回归的训练样本信息;经过适当的训练,利用支持向量回归技术对土壤含水量进行了反演研究;并考虑通过多频率、多极化、多入射角数据的组合,消除地表粗糙度的影响,提高反演精度。模拟结果表明,该方法反演土壤湿度具有较高的精度和较好的实时性;同时,与人工神经网络方法的结果比较,证明了该方法的有效性,为土壤湿度的反演研究提供了一种方法。     

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。     

基于FY-3B/MWRI数据的裸土区土壤湿度反演

使用高级积分方程模型,模拟多个地表参数条件下的风云三号B星微波成像仪(FY-3B/MWRI)资料。基于模拟数据,分析地表微波辐射特性,利用粗糙地表发射率Qp模型,建立我国西部地区裸露地表土壤湿度反演模型。将该模型用于我国西部地区4个日期(2011年10月8日、10月18日、10月28日和11月8日)的土壤湿度反演,并将反演结果用实测数据进行交叉验证。结果表明:反演土壤湿度与实测土壤湿度的决策系数R2为0.604,均方根误差为0.030 5 cm3/cm3,反演模型具有较高的反演精度。     

Long-term soil moisture content estimation using satellite and climate data in agricultural area of Mongolia

The purpose of this study is to estimate long-term SMC and find its relation with soil moisture (SM) of climate station in different depths and NDVI for the growing season. The study area is located in agricultural regions in the North of Mongolia. The Pearson’s correlation methodology was used in this study. We used MODIS and SPOT satellite data and 14 years data for precipitation, temperature and SMC of 38 climate stations. The estimated SMC from this methodology were compared with SM from climate data and NDVI. The estimated SMC was compared with SM of climate stations at a 10-cm depth (r² = 0.58) and at a 50-cm depth (r² = 0.38), respectively. From the analysis, it can be seen that the previous month’s SMC affects vegetation growth of the following month, especially from May to August. The methodology can be an advantageous indicator for taking further environmental analysis in the region.     

利用半经验模型的土壤水分反演方法

针对土壤水分反演经验模型使用范围受限、而理论模型相对复杂的问题,该文利用积分方程模型及经验相关长度定标模型,分析均方根高度、地表相关长度、土壤体积含水量对雷达后向散射系数组合的影响;结合RADARSAT-2全极化C波段雷达数据和野外实测数据,构建研究区均方根高度反演模型;将该模型与Dubois模型、介电模型进行结合以反演表层土壤含水量,并对反演值进行线性校正,最终实现裸土区表层土壤水分反演。实验结果表明,HH、VV极化下土壤水分实测值与反演值绝对误差在0.06内的点数分别达到总点数的78%和68%,证明该方法具有一定的可靠性和实用性,可为地表均方根高度、土壤水分反演提供方法和借鉴。     

基于被动微波遥感的地表粗糙度及土壤水分反演研究

被动微波遥感具有监测面积大、重复周期短、对土壤水分更为敏感等优点,成为反演土壤水分最有潜力的方式.论文针对地表粗糙度和植被覆盖变化对土壤水分反演过程中带来的误差和不确定性展开研究,发展更适合大区域土壤水分反演的算法.     

The inversion method study of sub-canopy soil moisture estimation using repeat pass SIR-C PolInSAR data

From repeat pass SIR-C L band polarimetric SAR interferometric data and fully maximum likelihood inversion decomposition model of PolInSAR, a method for sub-canopy soil moisture estimation using repeat pass SIR-C PolInSAR data is proposed. At the same time, the potential and validity of fully maximum likelihood inversion decomposition model of PolInSAR for sub-canopy soil moisture inversion is investigated. Firstly, from the random oriented volume over ground two layer coherent scattering model and the statistical characteristics of Pol-InSAR coherency matrix, the fully maximum likelihood inversion decomposition model is used to reconstruct or recover the surface polarimetric coherency matrix with volume scattering components significantly removed; then, from recovered surface polarimetric coherency matrix, co-HH, VV and cross-HV polarization backscattering coefficient are obtained, and the sub-canopy soil moisture are inverted from Oh and Dihedral scattering model. At last, Compared the inversion result with the field measurement and the climate data of hetan region from 1951 to 2006, the preliminary result indicates that the proposed method based on fully maximum likelihood inversion decomposition model has enough high inversion accuracy, if the new spaceborne or airborne polarimetric SAR interferometric data with synchronously spaceborne or airborne-ground measurement will be acquired, the validity and accuracy of proposed inversion method will be further investigated and validated.     

重复轨道SIR-C极化干涉SAR数据植被覆盖区土壤水分反演研究

从理论和试验方面对图像的噪声评估方法进行了分析。结合北京1号小卫星特性, 进行了该类方法应用效能的评价, 讨论了分块评估噪声方法的最佳参数设置。选取满足噪声评估环境的图像, 实现了综合不同地表覆盖条件的北京1号小卫星图像噪声的评估。噪声评估结果与在轨测试情况的对比表明, 北京1号小卫星经过近3年的运行, 仍保持了较好的性能。     

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.     

多星融合的土壤湿度滚动式估算模型

全球定位系统干涉反射测量(GPS-IR)是一种新的遥感技术,可用于估算近地表土壤水分含量。考虑到多卫星融合的优势和土壤湿度的时空尺度性,提出一种基于多星融合的土壤湿度最小二乘支持向量机(LS-SVM)滚动式估算模型。首先通过低阶多项式拟合分离GPS卫星直射和反射信号,进而建立反射信号正弦拟合模型,获取相对延迟相位。最后,通过线性回归模型有效分析和选取多卫星相对延迟相位,并建立基于多星融合的最小二乘支持向量机模型进行滚动式估算土壤湿度。以美国板块边界观测计划PBO提供的监测数据为例,对比分析利用单颗、多颗GPS卫星进行土壤湿度滚动式估算的可行性和有效性。经理论分析和两个测站实验表明:该模型充分发挥了LS-SVM的优势,有效综合了各卫星的性能,改善了采用单颗卫星进行土壤湿度估算时,其结果极易出现异常跳变的现象;模型只需较少的建模数据,采用滚动式能实现较长时间的估算,估算误差较为稳定;模型所估算的结果与土壤湿度实测值之间的相关系数R2以及均方根误差分别为0.942和0.962、0.072和0.032,相对于部分单一卫星至少提高了18.18%。因此,土壤湿度问题可作为非线性事件处理,采用多卫星融合估算是可行和有效的。     

遥感土壤水分对蒸散发估算的影响

地表实际蒸散发是联系陆表水循环、能量平衡和碳收支等物理过程的重要生态水文变量,同时也是目前水循环研究中的薄弱点,定量化土壤水分对蒸散发的胁迫作用是估算地表蒸散发的一个关键过程和难点.本研究基于2018年9月闪电河流域水循环与能量平衡遥感综合试验星—机—地联合观测数据,采用机载观测和卫星遥感反演土壤水分输入到ETMoni...     

利用GPS-IR监测土壤湿度的多星线性回归反演模型

全球定位系统干涉反射测量(GPS-interferometric reflectometry,GPS-IR)是一种新的遥感技术,利用测量型接收机记录的信噪比(SNR)观测值可实现近地表土壤湿度的监测。考虑到目前利用多星组合反演土壤湿度的研究较少,本文提出一种土壤湿度多星线性回归反演模型。试验表明:①多星组合反演能够更全面地反映测站附近有效监测范围内的土壤湿度信息,有效改善采用单颗卫星反演时反演过程极易出现异常跳变的现象,提高了突发性降雨时段的土壤湿度反演精度。②当组合卫星数达到6颗以上时,其反演结果与土壤湿度参考值之间的相关系数均大于0.9,相对于单颗卫星至少提高了20.8%。     

Retrieval of soil moisture using SIR-C polarimetric data

Spaceborne Imaging Radar (SIR-C) polarimetric data acquired over Gujarat test site, India, during April and October 1994 were processed to retrieve soil moisture and surface roughness using multi-polarization techniques. Synchronous field data were collected and compared with the results obtained using SIR-C data. Indian Remote Sensing Satellite (IRS) images in visible region were used for locating groundtruth points. Multi-polarization inversion techniques are found to be sensitive to retrieval of soil moisture and surface roughness parameters. However, the accuracy is not adequate. There is a need to improve the existing inversion models to suit to the Indian agricultural fields.     

An Information fusion approach for PALSAR data to retrieve soil moisture

Estimation of vegetation covered soil moisture with satellite images is still a challenging task. Several models are available for soil moisture retrieval in which water cloud model (WCM) is most common. But, it requires an estimation of accurate vegetation parameterization. Thus, there is a need to develop such an approach for soil moisture retrieval which minimize these limitations. Therefore, this paper deals with the soil moisture retrieval using fully polarimetric SAR data by fusing the information from different bands. Various polarimetric indices and observables were critically analysed, and found that the index; SPAN (total scattered power) gives better information of vegetation cover as compared to other indices/observables. Based on this, WCM model has been modified using SPAN as parameter and soil moisture content were retrieved.     

An advanced GNSS code multipath detection and estimation algorithm

A novel maximum likelihood-based range estimation algorithm is designed to provide robustness to multipath, which is recognized as a dominant error source in DS-CDMA-based navigation systems. The detection–estimation problem is jointly solved to sequentially estimate the parameters of each individual multipath component and predict the existence of a next possible component. A comparison between contemporary maximum likelihood-based multipath estimation techniques and this new technique is provided. A selection of realistic channel simulation models is used to assess relative performance under different operating situations. A set of real GPS L1/CA data processing results are also presented to further assess the applicability of the proposed algorithm for urban navigation.