重庆市墒情分析评价预测系统的设计与实现

结合重庆市墒情、水雨情等自动监测系统,考虑主要作物种类、分布区域、播种面积、耕作制度、生育期间各生长发育指标,以及不同区域、深度的田间持水量,对已建立土壤墒情监测点的地区,采用土壤相对湿度评估农业墒情;对于尚未建立墒情监测站但已建立雨量监测站点的雨养农业区,采用降水量距平法或连续无雨日数法,进行墒情分析评价,用衰减系数法预测墒情的变化趋势。采用B/S开发模式,利用Flex通过天地图在线服务进行地图显示,采取IIS发布模式,基于Web Services的数据服务模式,设计一套基于Web GIS的墒情监测分析评价预测系统,通过相关评价指标反映农林作物土壤的干旱情况,并能结合天气情况预测未来墒情数据,为安排农业用水提供技术支撑,减少干旱灾害损失。     

玉米植株水分动态及其影响因子的研究(英)

本研究供试品种为先锋783,总叶数为19片,试验地为砂壤土,在全自动防雨棚下进行。通过调控喷水量,使正常供水的对照区土壤含水量接近田间持水量,干旱处理区于第14片叶伸出时停止供水18d。采用改良热平衡法连续监测玉米植株茎中汁液流速,直接确定整株蒸腾速率和冠层日蒸腾量。结果表明,当土壤含水量接近田间持水量时,植株蒸腾速率主要受制于太阳辐射等气象因子,并与蒸散势呈同步平行变化;当土壤含水量减少到正常水分含量的75％时,蒸腾速率迅速下降,不再反映气象因子的影响。与同步测定的叶片气孔传导率、叶卷曲指数及植株生长速率相比较,整株日蒸腾率能更准确地反映植株与土壤水分的关系。     

基于信道相关因子的自适应滤波模型及其应用

对信道衰落趋势进行预测,并有针对性地制定接收机策略,是改善无线接收机性能的一项重要课题.信道相关因子能一定程度反映了移动终端接收信号强度变化的规律,将信道相关因子引入自适应滤波模型的建立,并从理论上对模型的收敛速度和稳态特性进行分析.应用该模型对移动终端在衰落信道下信号强度的变化趋势进行预测,结果证明该方法具有良好的预测精度和较快的收敛速度.应用该预测模型的移动终端接收机在典型的衰落信道下解调性能得到一定程度的提高.     

基于MPSO-SVM巷道围岩松动圈预测研究

针对目前巷道围岩松动圈确定方法的种种缺陷,提出了一种新的预测方法,采用改进的粒子群算法（MPSO）优化支持向量机（SVM）对巷道围岩松动圈进行预测。在标准PSO中引入压缩因子,实现了算法全局搜索和局部寻优的有效平衡;应用MPSO对SVM的参数C和g进行优化,建立MPSO-SVM回归预测模型;将该预测模型应用于巷道围岩松动圈的预测,将预测性能与PSO-SVM、GA（遗传算法）-SVM、GSM（网格搜索）-SVM模型、BP神经网络进行对比分析。结果表明：该模型具有较强的泛化能力,较高的预测精度,可以对围岩松动圈厚度进行有效预测。     

生物地球化学循环模型DNDC及其应用

生物地球化学模型是模拟研究化学元素动态的新兴领域,可用于陆地生态系统内植物、有机物和无机营养元素动态变化和循环。DNDC模型(DeNitrification-DeComposition Model)是美国新罕布什尔州大学陆地海洋空间研究中心开发研制的,最初是为了模拟农田生态系统固碳、氮流失和水平衡而创建,目前该模型可以模拟草地、湿地、林地等陆地生态系统碳氮动态过程。DNDC模型已经在美洲、欧洲、澳洲以及亚洲的一些地区得到了验证和运用。DNDC模型可用来分析陆地植物生长规律、土壤硝化和反硝化作用、温室气体和痕量气体排放预测研究、不同土壤类型及气候条件对森林生态系统碳氮通量变化的影响以及气候变化对生物地球化学循环的影响预测等。     

基于二次逼近神经网络的反应釜预测控制

针对在化工生产过程中使用连续搅拌反应釜(Continuous Stirred Tank Reactor,CSTR)时存在的控制方式不便,调节的精确度不高等问题;在对实际问题进行分析建模的基础上,提出了一种基于二次逼近神经网络模型的预测控制方法;该方法首先利用多层前馈神经网络模型去逼近连续搅拌反应釜系统的多步预测值,其次在已创建的预测模型的基础上优化并求解预测控制的二次目标函数,以得到最优的控制参数,最后由通过泰勒展开式的二次逼近得到非线性预测控制器的最优解;通过对控制模型的模拟以及带入相关参数进行仿真实验,对连续搅拌反应釜控制系统的仿真结果进行分析表明:该方法控制精确度较高,并且是可行有效的,能够使生产效率得到显著提高且保证了产品的质量,具有较高的实用价值。     

基于实例加权和双分类器的稳定学习算法

稳定学习的目标是利用单一的训练数据构造一个鲁棒的预测模型,使其可以对任意与训练数据具有相似分布的测试数据进行精准的分类.为了在未知分布的测试数据上实现精准预测,已有的稳定学习算法致力于去除特征与类标签之间的虚假相关关系.然而,这些算法只能削弱特征与类标签之间部分虚假相关关系并不能完全消除虚假相关关系;此外,这些算法在构建预测模型时可能导致过拟合问题.为此,提出一种基于实例加权和双分类器的稳定学习算法,所提算法通过联合优化实例权重和双分类器来学习一个鲁棒的预测模型.具体而言,所提算法从全局角度平衡混杂因子对实例进行加权来去除特征与类标签之间的虚假相关关系,从而更好地评估每个特征对分类的作用.为了完全消除数据中部分不相关特征与类标签之间的虚假相关关系以及弱化不相关特征对实例加权过程的干扰,所提算法在实例加权之前先进行特征选择筛除部分不相关特征.为了进一步提高模型的泛化能力,所提算法在训练预测模型时构建两个分类器,通过最小化两个分类器的参数差异来学习一个较优的分类界面.在合成数据集和真实数据集上的实验结果表明了所提方法的有效性.     

数据挖掘和动态神经网络在负荷预测中的研究与应用

中长期负荷变化规律与社会经济指标的关系很难用一个准确的数学模型来表达。将数据挖掘技术应用到全社会用电量增长的关联分析中,从2000年以来的社会经济指标中选取多项组成相关因素数据库,对缺失数据进行了补全,使用聚类分析挖掘出与全社会用电量关系密切的若干指标,并对指标中的失真数据进行修正,构建了更加科学的负荷预测模型。通过时间序列的动态神经网络,对负荷预测模型进行了测试和验证,结果表明该预测模型具有很好的收敛性,效果令人满意。     

一种基于因子图的搜索广告转化预测模型

基于转化的广告方式在应用和研究中逐渐得到重视,采用该方式的搜索广告在广告排序时需要对候选广告的转化概率进行预测,以提高广告的转化率,优化搜索引擎的广告收益。该文在对搜索广告中影响转化的各特征进行提取与分析的基础上,提出了描述广告、查询、用户三个因素与转化事件关系的概率因子图模型,并基于该模型对广告转化进行预测。最后我们使用从某商业搜索引擎采集的实际数据对预测模型进行评价并与朴素贝叶斯方法进行对比,实验结果表明,三类因素对转化具有不同程度的影响,我们提出的因子图模型可以较好地预测广告的转化。     

带反馈输入BP神经网络的应用研究

为了有效解决具有非线性特征的水文预报精准度的问题,通过对反向传播BP神经网络的学习和研究,分析了变量间的相互信息,提出了系统间相关信息熵的概念,并建立了适合水文预测的自迭代反向传播神经网络模型.该模型通过对迭代因子的及时修正,在反向传播中不断调整网络的权值和阈值,从而在很大程度上改善了传统BP算法所带来的不足,提高了预测的精度.实际的应用研究表明,自迭代反向传播模型的预测效果优于传统预测模型.     

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。     

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     

Retrieval of daily evolution of soil moisture from satellite-derived land surface temperature and net surface shortwave radiation

Soil moisture is a key parameter in water balance, and it serves as the core and link in atmosphere–vegetation–soil–groundwater systems. Soil moisture directly affects the accuracy of the simulation and prediction conducted by hydrological and atmospheric models. This article aims to develop a new model to retrieve the daily evolution of soil moisture with time series of land surface temperature (LST) and net surface shortwave radiation (NSSR). First, for the time series of soil moisture, LST and NSSR daytime data were simulated by the common land model (CoLM) with different soil types in bare soil areas. Based on these data, the variations between soil moisture and LST-NSSR during the daytime with different soil types were analysed, and a plane function was used to fit the daily evolution of soil moisture and the time series of LST and NSSR data. Further study proved that the coefficients of the soil moisture retrieval model are not sensitive to soil type. Then, a relationship model between the daily evolution of soil moisture and the time series of LST-NSSR was developed and validated using the data simulated by CoLM with different soil types and different atmospheric conditions. To demonstrate the feasibility of the soil moisture retrieval method proposed in this study, it was applied to the African continent with data from the METEOSAT Second Generation Spinning Enhanced Visible and Infrared Imager (MSG–SEVIRI) geostationary satellite. The results show that the variation of soil moisture content can be quantitatively estimated directly by the method at the regional scale with some reasonable assumptions. This study can provide a new method for monitoring the variation of soil moisture, and it also indicates a new direction for deriving the daily variation of soil moisture using the information from the time series of the land surface variables.     

Dynamic response of NDVI to soil moisture variations during different hydrological regimes in the Sahel region

Over the last few decades, the African Sahel has become the focus of many studies regarding vegetation dynamics and their relationships with climate and people. This is because rainfall limits the production of biomass in the region, a resource on which people are directly dependent for their livelihoods. In this study, we utilized a remote-sensing approach to answering the following two questions: (1) how does the dynamic relationship between soil moisture and plant growth vary across hydrological regimes, and (2) are vegetation-type-dependent responses to soil moisture availability detectable from satellite imagery? In order to answer these questions, we studied the relationship between monthly modelled soil moisture as an indicator for water availability and the remotely sensed normalized difference vegetation index (NDVI) as a proxy for vegetation growth between a “recovery rainfall period” (1982 to 1997) and a “stable rainfall period” (1998 to 2013), at different time lags across the Sahel region. Using windowed cross-correlation, we find a strong significant positive relationship between NDVI and soil moisture at a concurrent time and at NDVI lagging behind soil moisture by 1 month for grassland, cropland, and deciduous shrubland vegetation – the dominant vegetation classes in the Sahel. South of the Sahel (the Sudanian and Guinean areas), we find longer optimal lags (soil moisture lagged by 1–3 months) in association with mixed forest and deciduous shrubland. We find no major significant change in optimal lag between the recovery and stable periods in the Sahelian region; however, in the Sudanian and Guinean areas, we observe a trend towards shorter time lags. This change in optimal lag suggests a vegetation change, which may be a response to a climatic shift or land-use change. This approach of identifying spatiotemporal trends in optimal lag correlations between modelled soil moisture and NDVI could prove to be a useful tool for mapping vegetation change and ecosystem behaviour, in turn helping inform climate change mitigation approaches and agricultural planning.     

Experimental data for separation of vegetation and soil and estimation of soil moisture using passive microwaves

Abstract

Microwave radiometer measurements of soil moisture content were made over bare and vegetated fields with dual polarized microwave radiometers at 1·55GHz (L-band) and 19·1 GHz (K.-band). Two typical Indian crops Bazra and Gawar have been studied. The bare field measurements were used to investigate the effect of soil texture on sensitivity of a radiometer to soil moisture and for soil moisture sampling depth. It is found that expression of soil moisture as available moisture content in the soil can minimize the texture effect. The estimated soil moisture sampling depth for L-band is 2-5 cm, while for K-band it is less than 2 cm. The vegetation cover affects the sensitivity of the radiometer to soil moisture. This effect is more pronounced the denser the vegetation and higher the frequency of observation. The measured polarization factor over a vegetated field at L-band was found to be appreciably reduced compared to that over a bare field. The difference between normalized brightness temperature from L-band and K-band is sensitive to vegetation type. The soil moisture under vegetation cover at L-band can be predicted well using Jackson's parametric model.     

基于数字相机的草地物候模拟及其与气象因子关系的研究

研究植被物候及其与气候之间的关系对于理解全球生态环境变化意义重大。近地面数字相机凭借其监测频率高、数据质量好等优势已成为一种有效的监测植被物候的遥感平台。以北美地区瓦瑞(Vaira Ranch)牧场为例,对研究区植被的春季生长情况进行监测,利用近地表数字相机获取的影像计算绿度相对亮度(Greenness Chromaticity Coordinates,Gcc)并构成时间序列,模拟植被春季物候,将所得植被物候信息分别与地面同步实测的总初级生产力(Gross Primary Production,GPP)以及气象数据进行对比分析。结果表明:研究区植被春季生长季开始于第20d,结束于第145d,Gcc与GPP的总体相关性为0.88,二者提取的7项物候指标平均相对差异为0.05;降雨、土壤湿度、空气温度、土壤温度、太阳辐射通量对植被生长存在影响:空气温度、土壤温度、太阳辐射通量三者整体对于Gcc变化的解释力为91.3%,其中,气温和土温的单因子解释力分别为30.9%和49.0%,此外,由于水分缺乏,降水成为制约研究区植被生长的重要因素。     

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。     

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.     

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.