Soil moisture mapping over the Chinese Loess Plateau using ENVISAT/ASAR data

In this paper, we used the available algorithm for soil moisture estimation over LOPEX05 (the Loess Plateau land surface process Experiment (2005)) area. The available algorithm used ENVISAT/ASAR AP mode VV polarization observational data at a low incidence angle and ground measured soil moistures. The ground measurements were performed in the summer of the 2005 during the LOPEX05 field campaign. The validated results indicate that an average difference between the soil moistures estimated from the microwave remote sensing and ground measurements is less than 0.02 cm³/cm³, with a RMS error of 2.0%, and a maximum less than 0.04 cm³/cm³. The algorithm was applied to the surface soil moisture mapping later. The results show that this algorithm is suitable for monitoring soil moisture information of the agricultural fields over the Chinese Loess Plateau, when ground land cover situation and the resolution of imagery data are taken into account. However, we also find that there are large differences over the steep slope region, the edge of mesa. The results are not acceptable for surface soil moisture estimation in these regions. Thus, the surface soil moisture retrieval in the steep slope region of the Loess Plateau need to be further investigated in the future.     

一种MEMS陀螺随机漂移的高精度建模方法

为补偿MEMS陀螺随机漂移,采用时间序列分析法对其进行自回归滑动平均（ARMA）模型辨识,提出一种滑动平均（MA）参数估计的新方法。先将陀螺随机漂移建模为带观测噪声的ARMA模型,在估计出自回归（AR）部分的参数后,针对AR滤波后的残差,推导出一种方差小的MA自协方差估计值,并将该估计值作为输入,利用Gevers-Wouters（GW）算法估计出MA部分的参数。仿真结果表明,MA参数估计精度得到提升的同时,参数估计可靠性也得到了增强。MEMS陀螺的随机漂移补偿实验进一步验证本文所提算法的补偿精度高于改进前。      

A multilayer perceptron and multiclass support vector machine based high accuracy technique for daily rainfall estimation from MSG SEVIRI data

The current paper introduces a new multilayer perceptron (MLP) and support vector machine (SVM) based approach to improve daily rainfall estimation from the Meteosat Second Generation (MSG) data. In this study, the precipitation is first detected and classified into convective and stratiform rain by two MLP models, and then four multi-class SVM algorithms were used for daily rainfall estimation. Relevant spectral and textural input features of the developed algorithms were derived from the spectral MSG SEVIRI radiometer channels. The models were trained using radar rainfall data set colected over north Algeria. Validation of the proposed daily rainfall estimation technique was performed by rain gauge network data set recorded over north Algeria. Thus, several statistical scores were calculated, such as correlation coefficient (r), root mean square error (RMSE), mean error (Bias), and mean absolute error (MAE). The findings given by: (r = 0.97, bias = 0.31 mm, RMSE = 2.20 mm and MAE = 1.07 mm), showed a quite satisfactory relationship between the estimation and the respective observed daily precipitation. Moreover, the comparison of the results with those of two advanced techniques based on random forests (RF) and weighted ‘k’ nearest neighbor (WkNN) showed higher accuracy obtained by the proposed model.     

疲劳多裂纹扩展随机模型的参数估计方法

建立了用试验数据估计疲劳多裂纹扩展随机模型参数的方法,尤其为利用现有的单裂纹扩展试验数据,建立了用不完全数据估计多裂纹扩展参数的方法,为含多裂纹结构的概率损伤容限分析作准备.应用最小二乘原理,讨论了完全试验数据-并联远源裂纹和完全试验数据-近源裂纹2种情况下裂纹扩展参数的估计方法,考虑了裂纹扩展试验的2种极限情况,给出了估计结果偏于保守的不完全试验数据-近源裂纹情况下的裂纹扩展参数的估计方法.完善了疲劳多裂纹扩展随机模型,为模型的工程应用打下了基础.      

GNSS-IR双频数据融合的土壤湿度反演方法

目前全球导航卫星系统反射信号干涉测量（GNSS-IR）土壤湿度反演研究仅针对单一频点展开,提出用熵值法将2个频点数据进行融合以改进土壤湿度反演精度。首先,利用频谱分析法分别解析出各频点的信噪比（SNR）序列的振荡频率,计算出对应的等效天线高度,并利用最小二乘法求解各频点信噪比序列相位;然后,通过熵值法进行2个频点的相位观测量融合;最后,利用融合结果与实测土壤湿度建立经验模型,实现土壤湿度反演。利用地基观测实验获得的全球定位系统（GPS）L1和L2信噪比数据对该方法进行了验证,结果表明:L1和L2双频融合反演结果平均标准差为0.6%,比L1单频反演结果提高64.73%,比L2单频反演结果提高32.12%;均方根误差为0.37%,比L1频点降低72.8%,比L2频点降低73.4%。      

Remote sensing based continuous hydrologic modeling

There are two principal ways in which remote sensing can be used with continuous hydrological models: (1) by providing a cost effective way for obtaining input data and (2) by providing synoptic measurements of various state variables. This paper discusses existing hydrologic models and the modifications required to adapt them for using remotely sensed data that may significantly improve their simulation performance. Microwave and thermal infrared measurements show promise for use in hydrologic models because they can measure certain physical properties of the watershed (emissivity and temperature) from which a hydrologic condition can be inferred. Additional applications of remote sensing data include the use of spatial data, mechanisms for extrapolating point data and direct measurement of several hydrologic state variables such as soil moisture, surface temperature, snow water equivalent, frozen soils, and rainfall distribution. Results are presented from several aircraft flights where thermal infrared and microwave data were collected over a small research basin. These results are discussed with respect to their application in continuous hydrologic simulation models.     

Modelling the atmospheric boundary layer for remotely sensed estimates of daily evaporation

An energy and moisture balance model of the soil surface was used to estimate daily evaporation from wheat and barley fields in West Germany. The model was calibrated using remotely sensed surface temperature estimates. Complete atmospheric boundary layer models are difficult to use because of the number of parameters involved and a simplified model was used here. The resultant evaporation estimates were compared to eddy correlation evaporation estimates and good agreement was found.     

Improved predictions of equatorial TEC for real time applications

It is important to use models developed specifically for the equatorial ionospheric estimation for real-time applications, particularly in Satellite Navigation. This work demonstrates a methodology for improved predictions of VTEC in real time using the model developed for the equatorial ionosphere by the authors. This work has been done using TEC data of the low solar activity period of 2005 obtained using dual frequency GPS receivers installed under the GAGAN project of ISRO. For the purpose, the model is first used in conjunction with Kriging technique. Improvement in accuracy is observed when compared with the estimations from the model alone using the measurements as true reference. Further improvement is obtained by Bayesian combination of these estimates with independent Neural Network based predictions. Statistical performance of improvement is provided. An improvement of ∼1 m in confidence level of estimation of VTEC is obtained.     

Crop parameters estimation by fuzzy inference system using X-band scatterometer data

Learning fuzzy rule based systems with microwave remote sensing can lead to very useful applications in solving several problems in the field of agriculture. Fuzzy logic provides a simple way to arrive at a definite conclusion based upon imprecise, ambiguous, vague, noisy or missing input information. In the present paper, a subtractive based fuzzy inference system is introduced to estimate the potato crop parameters like biomass, leaf area index, plant height and soil moisture. Scattering coefficient for HH- and VV-polarizations were used as an input in the Fuzzy network. The plant height, biomass, and leaf area index of potato crop and soil moisture measured at its various growth stages were used as the target variables during the training and validation of the network. The estimated values of crop/soil parameters by this methodology are much closer to the experimental values. The present work confirms the estimation abilities of fuzzy subtractive clustering in potato crop parameters estimation. This technique may be useful for the other crops cultivated over regional or continental level.     

A study of forest vegetation dynamics in the south of the Krasnoyarskii Krai in spring

Remote sensing applications have greatly enhanced ability to monitor and manage in the areas of forestry. Accurate measurements of regional and global scale vegetation dynamics (phenology) are required to improve models and understanding of inter-annual variability in terrestrial ecosystem carbon exchange and climate–biosphere interactions. Study of vegetation phenology is required for understanding of variability in ecosystem. In this paper, monitoring of vegetation dynamics using time series of satellite data is presented. Vegetation variability (vegetation rate) in different topoclimatic areas is investigated. Original software using IDL interactive language for processing of satellite long-term data series was developed. To investigate growth dynamics vegetation rate inferred from remote sensing was used. All estimations based on annual time series of Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. Vegetation rate for Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI) was calculated using MODIS data. The time series covers spring seasons of each of 9 years, from 2000 to 2008. Comparison of EVI and NDVI derived growth rates has shown that NDVI derived rates reveal spatial structure better. Using long-term data of vegetation rates variance was estimated that helps to reveal areas with anomalous growth rate. Such estimation shows sensitivity degree of different areas to different topoclimatic conditions. Woods of heights depend on spatial topoclimatic variability unlike woods of lowlands. Principal components analysis shows vegetation with different rate conditions. Also it reveals vegetation of same type in areas with different conditions. It was demonstrated that using of methods for estimating the dynamic state of vegetation based on remote sensing data enables successful monitoring of vegetation phenology.     

Automatic lineament extraction in a heavily vegetated region using Landsat Enhanced Thematic Mapper (ETM+) imagery

Lineament extraction from satellite remotely sensed data has been one of the widely used applications of remote sensing in geology. In fact, recent advances in digital image processing allow such lineament extraction to be accomplished in semi-automatic to fully automatic approaches. However, satellite remotely sensed data acquired in heavily vegetated regions such as tropical rainforest, are vulnerable to higher inherent noise levels attributed to the resultant effects of scattering by clouds and adjacency effects of highly inhomogeneous vegetation cover within the pixel dimension. In this study, we examined the effects of noise levels to lineament extraction using a fully automatic approach, consisting of a combination of edge-line detection algorithms. Ancillary information from a digitized topographic map and image classification was used to discriminate between cultural and natural lineaments from the extracted lineaments. Adapting the combination of edge detection and a line-linking algorithm, we have found the optimal parameters for automatic lineament extraction of such complex areas using Enhanced Thematic Mapper (ETM+) data. A noise level of 30% is the maximum threshold before artifacts are generated. It is therefore concluded that the combination of edge-based and line-linking digital image processing operations with the priori local optimal parameters is crucial in lineament feature extraction in heavily vegetated regions.     

基于GA-SVM的GNSS-IR土壤湿度反演方法

针对提高大范围土壤湿度测量精度的问题,研究了土壤湿度的全球卫星导航系统干涉测量法（GNSS-IR）,提出了一种基于支持向量机（SVM）的土壤湿度反演模型,利用遗传算法（GA）的自动寻优功能寻找SVM的最佳参数。结果表明,GA-SVM模型在测试集上得到的土壤湿度反演值与实测值的平均绝对百分比误差（MAPE）仅为0.69%,最大相对误差（MRE）为1.22%,线性回归方程决定系数达到了0.956 9。进一步与统计回归、粒子群优化的SVM模型（PSO-SVM）及反向传播（BP）神经网络方法进行对比,结果说明:在样本数目有限的情况下,GA-SVM方法更适用于土壤湿度的GNSS-IR技术反演,且反演精度较高,泛化性能良好。      

Improved rainfall estimation over the Indian region using satellite infrared technique

The GOES Precipitation Index (GPI) technique (Arkin, 1979) for rainfall estimation has been in operation for the last three decades. However, its applications are limited to the larger temporal and spatial scales. The present study focuses on the augmentation on GPI technique by incorporating a moisture factor for the environmental correction developed by Vicente et al. (1998). It consists of two steps; in the first step the GPI technique is applied to the Kalpana-IR data for rainfall estimation over the Indian land and oceanic region and in the second step an environmental moisture correction factor is applied to the GPI-based rainfall to estimate the final rainfall. Detailed validation with rain gauges and comparison with Tropical Rainfall Measuring Mission (TRMM) merged data product (3B42) are performed and it is found that the present technique is able to estimate the rainfall with better accuracy than the GPI technique over higher temporal and spatial domains for many operational applications in and around the Indian regions using Indian geostationary satellite data. Further comparison with the Doppler Weather Radar shows that the present technique is able to retrieve the rainfall with reasonably good accuracy.     

Retrieval of crop variables with field-based X-band microwave remote sensing of ladyfinger

Modeling in agriculture has been widely used to retrieve and monitor various soil and crop growth variables. Remote sensing, especially radar sensors can be useful for temporal and spatial monitoring of the soil and plant variables. Therefore, in this paper field measurements of crop ladyfinger were carried out to examine the dependency of radar backscatter on crop–soil variables and to develop a method for monitoring and retrieving crop variables for ladyfinger. A crop-bed was prepared to observe scatterometer response in the angular range of incidence angle 20–70° at 9.89 GHz in the X-band for VV- and HH-polarization. At the same time, soil moisture, plant height, leaf area index and aboveground biomass were measured at various growth stages of crop ladyfinger. The angular variation of scattering coefficient decreases with the age of crop ladyfinger shows the dominance of crop effect on soil moisture effect at the older age. Thus, angular trends are more flat as the plant grows since the effects of soil is masked by developing vegetation. Scattering coefficient increases with the increase of leaf area index for both polarizations (i.e. VV- and HH-). It was found that leaf area index and aboveground biomass of crop ladyfinger are highly correlated with microwave frequency more than with plant height and soil moisture. Leaf area index and biomass of ladyfinger crop were retrieved by polarization based model and non-linear least square optimization model. These two models gave very good results for the retrieval of leaf area index and aboveground biomass.     

On uncertainties in carbon flux modelling and remotely sensed data assimilation: The Brasschaat pixel case

Uncertainty on carbon fluxes is determined by the uncertainties of ecosystem model structure, data and model parameter uncertainties and the temporal and spatial inaccuracy of the input data retrieval. The objective of this paper is to understand the error propagation and uncertainty of evaporative fraction (EF), soil moisture content (SMC) and water limited net ecosystem productivity (NEP). In this respect, C-Fix and spaceborne remote sensing are used for the ‘Brasschaat’ pixel. A simple model based on error theory and a Monte-Carlo approach are used. Different error scenarios are implemented to assess input uncertainty on EF, SMC and NEP as estimated with C-Fix.     

Capability of Bhaskara-II satellite microwave radiometer brightness temperature data to discriminate soil moisture conditions of Indian landmass

With the objective of developing Microwave Remote Sensing technology in the country, India has launched a series of Satellites Bhaskara-I and II with the microwave radiometer capability. In this paper, an attempt is made to demonstrate the capability of the brightness temperature data acquired by these radiometers to discriminate various soil moisture conditions of Indian land mass. The analysis show that large areas assessment of soil moisture is possible to a limited extent.     

BLDC电机温度退化多段Wiener过程建模

无刷直流（BLDC）电机应用广泛,其温度退化过程呈现多段性,需建立多段退化模型,而模型参数较多时,参数估计过程对初始值敏感且易陷入局部最优。首先,针对电机的加速退化数据进行研究,采用正态加权平均（Gauss滤波）的方式滤波,改进实际数据在模型参数的估计中的应用。然后,引入转换函数对Wiener模型改进,建立多段Wiener模型。其次,以极大化似然函数进行参数估计,计算时采用改进粒子群优化（PSO）算法得到估计值,对比非线性模型的残差的正态性,同时分析各模型理论寿命分布及实际该批次失效分布,确定多段模型合理性;得到的模型结果说明电机在退化过程中发生了退化机理的改变,且变换速度快。最后,以非线性模型不同时刻的寿命分布给出该应力下电机的寿命预测,这对电机的预测与健康管理（PHM）有重要意义。      

多轴疲劳寿命分析方法在飞机结构上的应用

针对飞机结构上常见的处于多轴应力应变(比例多轴)状态下的典型结构,采用3种多轴疲劳寿命分析模型,对该结构的疲劳危险部位进行疲劳寿命分析,并与单轴寿命分析方法的分析结果、疲劳试验结果进行了对比分析。首先对该结构进行细节有限元计算,确定结构的应力分布与应力水平,当载荷施加到88%的最大一级的峰值载荷时,疲劳危险部位的孔边即出现显著的塑性应变,因此,选用低周疲劳(LCF)寿命预测模型进行分析。选取的3种分析模型均是基于临界面的分析模型,分别是Wang-Shang模型、Smith-Watson-Topper(SWT)模型以及Morrow-Brown-Miller模型。为验证分析模型工程适用性,开展了该结构的多轴疲劳试验。与试验结果相比,3种分析模型的预测结果均偏大,其中Wang-Shang模型的预测结果最接近试验值,适用于本文这类结构;SWT模型和Morrow-Brown-Miller模型的预测结果误差相对较大。对于处于多轴载荷状态下的结构,应按照多轴疲劳寿命分析方法进行寿命预测,单轴疲劳寿命分析方法将给出过于危险的评定结果。      

Remote sensing and GIS-based landslide hazard analysis and cross-validation using multivariate logistic regression model on three test areas in Malaysia

This paper presents the results of the cross-validation of a multivariate logistic regression model using remote sensing data and GIS for landslide hazard analysis on the Penang, Cameron, and Selangor areas in Malaysia. Landslide locations in the study areas were identified by interpreting aerial photographs and satellite images, supported by field surveys. SPOT 5 and Landsat TM satellite imagery were used to map landcover and vegetation index, respectively. Maps of topography, soil type, lineaments and land cover were constructed from the spatial datasets. Ten factors which influence landslide occurrence, i.e., slope, aspect, curvature, distance from drainage, lithology, distance from lineaments, soil type, landcover, rainfall precipitation, and normalized difference vegetation index (ndvi), were extracted from the spatial database and the logistic regression coefficient of each factor was computed. Then the landslide hazard was analysed using the multivariate logistic regression coefficients derived not only from the data for the respective area but also using the logistic regression coefficients calculated from each of the other two areas (nine hazard maps in all) as a cross-validation of the model. For verification of the model, the results of the analyses were then compared with the field-verified landslide locations. Among the three cases of the application of logistic regression coefficient in the same study area, the case of Selangor based on the Selangor logistic regression coefficients showed the highest accuracy (94%), where as Penang based on the Penang coefficients showed the lowest accuracy (86%). Similarly, among the six cases from the cross application of logistic regression coefficient in other two areas, the case of Selangor based on logistic coefficient of Cameron showed highest (90%) prediction accuracy where as the case of Penang based on the Selangor logistic regression coefficients showed the lowest accuracy (79%). Qualitatively, the cross application model yields reasonable results which can be used for preliminary landslide hazard mapping.