Assimilation of ASAR data with a hydrologic and semi-empirical backscattering coupled model to estimate soil moisture

The most promising approach for studying soil moisture is the assimilation of observation data and computational modeling.However,there is much uncertainty in the assimilation process,which affects the assimilation results.This research developed a one-dimensional soil moisture assimilation scheme based on the Ensemble Kalman Filter(EnKF)and Genetic Algorithm(GA).A two-dimensional hydrologic model-Distributed Hydrology-Soil-Vegetation Model(DHSVM)was coupled with a semi-empirical backscattering model(Oh).The Advanced Synthetic Apertture Radar(ASAR)data were assimilated with this coupled model and the field observation data were used to validate this scheme in the soil moisture assimilation experiment.In order to improve the assimilation results,a cost function was set up based on the distance between the simulated backscattering coefficient from the coupled model and the observed backscattering coefficient from ASAR.The EnKF and GA were used to re-initialize and re-parameterize the simulation process,respectively.The assimilation results were compared with the free-run simulations from hydrologic model and the field observation data.The results obtained indicate that this assimilation scheme is practical and it can improve the accuracy of soil moisture estimation significantly.     

A methodology for estimating Leaf Area Index by assimilating remote sensing data into crop model based on temporal and spatial knowledge

In this paper,a methodology for Leaf Area Index(LAI) estimating was proposed by assimilating remote sensed data into crop model based on temporal and spatial knowledge.Firstly,sensitive parameters of crop model were calibrated by Shuffled Complex Evolution method developed at the University of Arizona(SCE-UA) optimization method based on phenological information,which is called temporal knowledge.The calibrated crop model will be used as the forecast operator.Then,the Taylor′s mean value theorem was applied to extracting spatial information from the Moderate Resolution Imaging Spectroradiometer(MODIS) multi-scale data,which was used to calibrate the LAI inversion results by A two-layer Canopy Reflectance Model(ACRM) model.The calibrated LAI result was used as the observation operator.Finally,an Ensemble Kalman Filter(EnKF) was used to assimilate MODIS data into crop model.The results showed that the method could significantly improve the estimation accuracy of LAI and the simulated curves of LAI more conform to the crop growth situation closely comparing with MODIS LAI products.The root mean square error(RMSE) of LAI calculated by assimilation is 0.9185 which is reduced by 58.7% compared with that by simulation(0.3795),and before and after assimilation the mean error is reduced by 92.6% which is from 0.3563 to 0.0265.All these experiments indicated that the methodology proposed in this paper is reasonable and accurate for estimating crop LAI.     

土壤水分微波反演方法进展和发展趋势

土壤水分是连接地表水循环和能量循环的关键参量,精确获取该参量对于理解气候变化、地表水文过程、地气间能量交换机理等具有重要意义。微波遥感由于其较为合适的探测深度和坚实的理论基础在观测地表浅层土壤水分上具有很大优势,结合反演方法可以获取空间连续的土壤水分含量,有助于更加客观认知土壤水分的时空演变机理。随着微波遥感数据的不断丰富,多种微波遥感土壤水分反演方法相继涌现,为了更好地了解其发展和趋势,本文总结了当前土壤水分微波反演常用的卫星遥感数据并分析其发展趋势,后从主动微波反演、被动微波反演和多源协同反演3个方面梳理了各类土壤水分微波反演方法的原理、发展和优缺点,最终总结出目前微波遥感土壤水分反演方法的发展趋势：即土壤水分微波反演方法的时空普适性逐渐增强、面向高时空分辨率的土壤水分微波协同反演方法快速发展以及土壤水分微波反演方法的智能化水平不断提高。     

融合土地覆盖和土壤水分产品的近地表空气温度空间化方法

空气温度是评价人居环境的重要指标,与人类的生产生活息息相关;其观测对于水文、环境、生态和气候变化等方面的研究具有重要意义。传统的大范围空气温度观测数据一般通过气象站点获取,但由于气象观测站点空间分布离散稀疏的特点,所获取的数据不能精确描述空间连续的空气温度变化情况。因此,实现基于遥感数据的近地表空气温度精准估算具有重要的现实意义。本研究基于精细的地表覆盖类型、空间连续的土壤水分、地表温度（LST）数据,并结合其他辅助数据,构建了近地表空气温度空间化模型,并对近地表空气温度影响因子进行评估,发现地表覆盖类型对近地表空气温度的影响最大,土壤水分为最活跃的影响因素,经验证,模型精度较高,R²接近0.85,RMSE为0.5℃。本研究获取的精确空间连续的近地表空气温度信息,能够充分表达其空间异质性,为农业气象灾害灾变过程监测、农作物生长过程模拟、区域气候变化分析等研究提供良好的近地表空气温度数据支撑。     

Evaluating Spatial Heterogeneity of Land Surface Hydrothermal Conditions in the Heihe River Basin

Land surface hydrothermal conditions(LSHCs) reflect land surface moisture and heat conditions, and play an important role in energy and water cycles in soil-plant-atmosphere continuum. Based on comparison of four evaluation methods(namely, the classic statistical method, geostatistical method, information theory method, and fractal method), this study proposed a new scheme for evaluating the spatial heterogeneity of LSHCs. This scheme incorporates diverse remotely sensed surface parameters, e.g., leaf area index-LAI, the normalized difference vegetation index-NDVI, net radiation-Rn, and land surface temperature-LST. The LSHCs can be classified into three categories, namely homogeneous, moderately heterogeneous and highly heterogeneous based on the remotely sensed LAI data with a 30 m spatial resolution and the combination of normalized information entropy(S') and coefficient of variation(CV). Based on the evaluation scheme, the spatial heterogeneity of land surface hydrothermal conditions at six typical flux observation stations in the Heihe River Basin during the vegetation growing season were evaluated. The evaluation results were consistent with the land surface type characteristics exhibited by Google Earth imagery and spatial heterogeneity assessed by high resolution remote sensing evapotranspiration data. Impact factors such as precipitation and irrigation events, spatial resolutions of remote sensing data, heterogeneity in the vertical direction, topography and sparse vegetation could also affect the evaluation results. For instance, short-term changes(precipitation and irrigation events) in the spatial heterogeneity of LSHCs can be diagnosed by energy factors, while long-term changes can be indicated by vegetation factors. The spatial heterogeneity of LSHCs decreases when decreasing the spatial resolution of remote sensing data. The proposed evaluation scheme would be useful for the quantification of spatial heterogeneity of LSHCs over flux observation stations toward the global scale, and also contribute to the improvement of the accuracy of estimation and validation for remotely sensed(or model simulated) evapotranspiration.     

黑河流域中游地区作物种植结构的遥感提取

及时、准确地获取农作物种植结构对区域水资源管理与作物产量估测等具有重要意义。随着对通过遥感手段获得作物种植结构的深入研究,如何优选遥感数据和分类器成为需要重点考虑的关键问题。针对黑河流域中游地区的作物分布与种植特点,提出一种基于多时相遥感影像与多分类器组合的作物种植结构提取方法。利用2018年18景16 m分辨率的GF-1 WFV影像,构建NDVI时间序列。根据NDVI时间序列表征的作物季相节律和物候变化规律特点,采用分层的策略,首先解译一级土地覆被类型,再解译二级土地覆被类型。一级土地覆被类型解译中,使用决策树分类方法先将NDVI特殊且易提取的水体进行解译,再使用面向对象分类方法通过分区将需借助NDVI纹理信息提取的建设用地进行解译,最后使用随机森林分类方法解译耕地、林地、草地、裸地和湿地。在对耕地的进一步分类中,使用决策树分类方法首先将具有特殊物候规律且易于区分的苜蓿类别解译出来,再将与其他类别物候差异较大的小麦解译,最后将物候相似的玉米、蔬菜及其他解译。黑河流域中游研究区内一级土地覆被分类总体精度为97.24%,卡帕系数为0.96;作物种植结构解译总体精度为86.58%,卡帕系数为0.80。此外,还分析了影响黑河流域中游研究区解译精度的4个因素：对土地覆被类别的定义、混合像元、影像分割时基础影像的选择以及分类方法的选择。通过对不同分类方法的比较发现,与仅使用最大似然分类方法、支持向量机分类方法或随机森林分类方法相比,本文提出方法的解译结果更好,解译精度更高。     

STUDY ON GIS FOR YIELD ESTIMATION BY REMOTE SENSING IN JILIN MAIZE BELT

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A homogeneous linear estimation method for system error in data assimilation

In this paper, a new bias estimation method is proposed and applied in a regional ensemble Kalman filter (EnKF) based on the Weather Research and Forecasting (WRF) Model. The method is based on a homogeneous linear bias model, and the model bias is estimated using statistics at each assimilation cycle, which is different from the state augmentation methods proposed in previous literatures. The new method provides a good estimation for the model bias of some specific variables, such as sea level pressure (SLP). A series of numerical experiments with EnKF are performed to examine the new method under a severe weather condition. Results show the positive effect of the method on the forecasting of circulation pattern and meso-scale systems, and the reduction of analysis errors. The background error covariance structures of surface variables and the effects of model system bias on EnKF are also studied under the error covariance structures and a new concept ‘correlation scale’ is introduced. However, the new method needs further evaluation with more cases of assimilation.     

克里格法的土壤水分遥感尺度转换

 尺度效应往往会制约着定量遥感反演的精度,对地学信息进行空间尺度转换是生产实践的必然要求,而常用的尺度转换模型多利用光谱数据进行差值计算,不适合升尺度和降尺度转换。由于土壤含水量数据具有区域变化量的随机性和结构性特点,本文以15m分辨率的ASTER图像像元为基本单元,采用点克里格法完成ASTER 15m至7.5m分辨率的土壤含水量数据降尺度转换,从分维数的相似程度上来看,转换结果是合理的;并利用块状克里格法对地面实测样点数据进行点到7.5m分辨率的面数据升尺度转换,将升尺度和降尺度转换结果与实测样点均值相比较,结果表明:7.5m分辨率的实测样点土壤水均值误差在1.5782-5.019之间,块状克里格法获取的升尺度土壤含水量数据与点克里格法获取的降尺度土壤含水量数据之间误差则为1.2825-5.0481,可见克里格法考虑了点与周边的关系,所获得的土壤含水量值要优于未考虑空间异质性的土壤含水量平均值。     

潮滩表层沉积物含水量的高光谱预测模型与反演分析

潮滩土壤含水量具有变化频率快、空间变化大的特征,是影响潮滩地表反射率的重要因素。潮滩土壤含水量的精确提取,可为潮滩特征地物信息遥感反演提供基础。本文利用江苏大丰王港潮滩4种典型沉积物、449组不同含水量对应的实测光谱曲线数据进行特征分析,构建高光谱预测模型,实现了潮滩沉积物含水量的遥感反演。研究结果表明:(1)在短波红外波段,沉积物含水量与反射率之间存在良好的分段线性相关关系,分段点对应的含水量分别为42%和62%;(2)1165nm、1336nm、1568nm和1780nm特征波段反射率,对含水量变化具有良好响应,由特征波段组合计算得到的差值水指数DWI、比值水指数RWI和归一化水指数NDWI与含水量呈显著线性相关,可有效改善单波段反射率与含水量之间的分段线性关系;(3)3个水指数中,DWI反演的含水量精度优于RWI和NDWI,且对不同含水量大小均有良好适应性,而RWI和NDWI更适合含水量变化范围中等的情况;(4)对于粉砂、砂质粉砂、粉砂质砂和砂4种沉积物类型,DWI1336,1780验证组模拟含水量与实测含水量的相关系数,分别为0.891、0.915、0.920和0.905,均方根误差分别为9.87%、3.56%、4.24%和2.98%,表明由DWI构建的高光谱遥感反演模型,可有效实现潮滩表层含水量的时空变化预测。     

面向对象的特色农作物种植遥感调查方法研究

宁夏自治区具有土地、光能、引黄灌溉等优势,为宁夏特色农作物（硒砂瓜、枸杞、大枣）的生长提供了先天条件。快速准确地获取特色农作物的种植信息不仅是宁夏特色农作物监测、估产和灾害评估的重要依据,同时也是分析特色农作物结构分布变化和评价区域特色农业生产影响的重要凭证。近年来,随着航天技术和卫星传感器的不断发展,越来越多的学者将遥感技术运用到农作物种植信息的提取研究中。但是传统的遥感调查模型都是基于中低分辨遥感数据建立的,对于新的高分数据没有完备的信息提取模型。此外,基于GF-1遥感影像对类似宁夏特色农作物（硒砂瓜、枸杞、大枣）的信息提取研究相对较少,决策条件和分类模型的选择也难以满足高分农业的需求。基于此,本文利用国产GF-1 PMS遥感影像,在分析3类特色农作物光谱特征和纹理特征的基础上,建立了面向对象的支持向量机（SVM）分类模型,总体分类精度达到94.94%,Kappa系数为0.9174。同时将分类结果与传统的SVM分类结果相比较,研究发现面向对象的SVM模型的精度更高,效果最好,纹理信息的引入使光谱特征差异较小的枸杞和大枣更容易区分,有效降低了模型错分和漏分误差,改善了模型分类结果。研究结果为实现宁夏特色农作物的快速自动化提取提供了有效途径,也为开展农作物承保和受灾定损评估体系建设提供技术支撑。     

黑河中游地区作物用水效率比较及种植结构调整方向研究

黑河流域地处西北干旱区,水资源短缺是限制其中游绿洲农业发展、下游生态环境保护的首要原因。该流域的中游绿洲农业用水约占总用水量的80%,因此农业节水对流域发展至关重要。在干旱区绿洲农业节水探索中,众多学者主张通过节水技术来提高用水效率,而关于农业种植结构调整对农业节水影响的定量研究较少。本文采用2012年黑河流域蒸散发数据、土地利用数据、降水数据和农业经济统计数据,定量分析黑河中游主要作物需水特征和用水效率差异,尝试从调整作物种植结构角度为其绿洲农业节水提供依据。结果表明：(1)研究区4种主要作物中,玉米生长期需水量最大,其次为小麦、油菜和大麦;(2)考虑降水补给,发现大麦和油菜生长需水可很大程度上依赖降水,而小麦和玉米则需要灌溉,且玉米灌溉需水量远超小麦;(3)作物用水效率由高到低依次为大麦、油菜、小麦和玉米。从用水效率角度而言,考虑种植区位,在黑河中游适当扩大小麦种植规模更有利于提高中游农业用水效率。     

同化叶面积指数和蒸散发双变量的冬小麦产量估测方法

同化遥感信息到作物生长过程模拟模型,是估测区域作物产量的重要方法之一。同化变量的选取对同化结果精度至关重要。本文在标定WOFOST作物模型参数的基础上,优化了WOFOST模型的默认灌溉参数。利用ET和LAI作为同化变量,分别构建了时间序列趋势信息的代价函数和四维变分代价函数;采用SCE-UA算法最小化代价函数, 重新初始化WOFOST模型初始参数——作物初始干物质重、作物35 ℃生命期和灌溉量。最后利用MODIS LAI产品（MCD15A3）、MODIS ET产品（MOD16A2）,同化到作物模型估测产量,并对比分析了水分胁迫模式下同化单变量（ET或LAI）和同化双变量（ET和LAI）的估产精度。结果表明：同化双变量ET和LAI的策略,优于同化单变量LAI或ET,双变量策略的冬小麦产量估测精度为R²=0.432,RMSE=721 kg/hm²;单独同化高精度LAI对提高估产精度具有重要作用,其冬小麦产量估测精度为R²=0.408,RMSE=925 kg/hm²;单独同化ET的趋势信息改善了WOFOST模型模拟水分平衡的参数,但是,产量估测精度（R²=0.013,RMSE=1134 kg/hm²）与模型模拟估测产量精度（R²=0.006,RMSE=1210 kg/hm²）相比改善效果有限。本研究为其他区域的遥感数据与作物模型的双变量数据同化的作物产量估测研究提供了参考价值。     

基于GNSS+R裸土延迟多普勒图理论研究

侧重散射机理研究,以GPS相关函数和相关功率模型为基础,主要通过对双站雷达散射截面计算模型进行修改,得到裸土的GPS散射信号模型。其中裸土的双站圆极化雷达散射截面采用双站随机粗糙面散射模型计算得到,利用极化合成的方法使模型具备全极化计算功能。通过修改后的模型,模拟分析不同极化下裸土参数（土壤水分和地表粗糙度）对延迟多普勒图DDM（delay Doppler map）的影响。该理论模型研究有助于机载/星载GNSS-R接收机数据解释、GNSS-R实验设计以及后向反演算法的开发。同时,延迟多普勒图模型的研究结果也为GNSS+R土壤水分遥感研究提供了一定的理论依据。     

基于组件技术的地表蒸散发遥感监测系统设计与应用

针对遥感模型与水热模型集成应用的需求,以C#语言结合ENVI/IDL和ArcGIS Engine混合编程技术,设计开发了地表蒸散发遥感监测系统的基本流程,对系统数据库设计及遥感模型与水热模型集成模式进行了研究,研发了单元蒸散发参数反演与结果校验功能,实现了遥感模型与水热模型的集成应用.系统对黄河三花间流域以MODIS遥...     

Advances in Research on Soil Moisture by Microwave Remote Sensing in China

Soil moisture is an important factor in global hydrologic circulation and plays a significant role in the research of hydrology, climatology, and agriculture. Microwave remote sensing is less limited by climate and time, and can measure in large scale. With these characteristics, this technique becomes an effective tool to measure soil moisture. Since the 1980s, Chinese researchers have investigated the soil moisture using microwave instruments. The active re- mote sensors are characteristic of high spatial resolution, thus with launch of a series of satellites, active microwave remote sensing of soil moisture will be emphasized. The passive microwave remote sensing of soil moisture has a long research history, and its retrieval algorithms were developed well, so it is an important tool to retrieve large scale moisture information from satellite data in the future.     

基于无人机遥感的滨海盐碱地土壤空间异质性分析与作物光谱指数响应胁迫诊断

在已集中连片改造为农田的盐碱地上,开展无人机遥感作物土壤空间异质性分析与光谱指数响应胁迫诊断对于提升盐碱地利用效率、创造更多经济效益与生态价值具有重要意义。本研究以山东省东营市黄河三角洲典型滨海盐碱地集中连片旱作农田的主要作物——高粱和玉米为研究对象,利用固定翼无人机获取400 hm²滨海盐碱地多光谱遥感数据,并结合地面195个采样点的3个土层（0~10 cm、10~20 cm、20~40 cm）的土壤属性数据,对该研究区域内作物生长的土壤环境因子进行空间异质性分析与光谱指数响应胁迫诊断。基于土壤属性数据,利用反距离加权插值法,绘制该研究区域内土壤盐分、pH、有机质、全氮和速效氮共5个指标含量的水平与垂直空间分布图。插值结果显示,5种土壤属性指标存在显著水平和垂直空间异质性。基于随机森林模型,采用递归特征消除法,结合土壤指标对光谱指数的重要性值,探讨影响作物生长的主要土壤环境胁迫因子。结果表明,5种土壤属性因子均会对玉米和高粱生长造成影响,但主要胁迫因子分别为土壤速效氮含量（10~20 cm）和3个土层的盐分含量。本研究为大面积农情胁迫监测提供了一项有效的地面与航空协同监测方案,为盐碱地旱作农田管理与决策提供了理论依据和技术支持。     

干旱区绿洲典型地物MESMA模拟分解与验证

混合像元作为遥感信息的不确定性,一直是定量遥感科学研究的核心领域之一,干旱区由于下垫面均匀、气象条件单一等先天条件,已成为定量遥感产品真实性检验的理想场所。本文以塔里木盆地北缘的库车河绿洲为研究区,首先,针对不同地物类型分别采用不同方法进行地物端元提取;然后,以端元均方根EAR(Endmember Aver-age RMSE,EAR)和最小平均波谱角(Minimum Average Spectral Angle,MASA)值来选取最优端元;最后,用多端元光谱混合分析(Multiple Endmember Spectral Mixture Analysis,MESMA)模型进行光谱混合分解,并对结果作了精度评价与比较分析。结果表明:MESMA模型能有效提高像元内基本组分丰度信息精度,从而为典型地物高精度提取提供了科学方法。     

基于小波分析辅助GPS-IR反演土壤湿度研究

GPS-IR是一种基于卫星反射信号的新的遥感技术,利用SNR观测值中的卫星反射信号可实现土壤湿度的反演。针对卫星反射信号分离问题,提出一种基于小波分析的卫星反射信号分离新方法。实验表明,采用小波分析能够针对不同卫星的信噪比观测值作出相应变化,有效分离卫星直射信号,获取更加准确的卫星反射信号;采用线性回归模型能够很好地表示相对延迟相位与土壤湿度之间的相关性,通过小波分析获取的结果相对于低阶多项式结果有明显提高。     

Study on model for remote sensing estimation of maize yield

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