基于Triple Collocation方法的微波土壤水分产品不确定性分析与时空变化规律研究

土壤水分是连接地—气系统的重要状态变量,微波遥感为准确获取大面积土壤水分信息提供新的技术手段。准确解读微波土壤水分产品质量、深入了解其误差的时空分布特征是通过数据同化等方法将其融入陆面模型,从而成功应用于地球科学领域的重要先决条件。基于Triple Collocation(TC)方法检验了风云三号C星(FY-3C)、土壤水分主被动卫星(SMAP)及高级微波散射计(ASCAT)这3种常用微波土壤水分产品在中国陆域的质量,并通过Hovm?ller图评估了3套产品捕捉土壤水分时空变化的能力。结果显示:①TC方法得到的分析结论与地面实测资料的验证结果一致,整体上SMAP优于ASCAT和FY-3C,不同土地利用类型下SMAP信噪比均最高,三者的TC信噪比分别为1.668 dB、-0.316 dB和-2.182 dB,同时三者与实测值的相关系数分别为0.514、0.501和0.209;②FY-3C和ASCAT产品的精度在中国西北地区整体优于南部地区,3种产品均能较好地刻画土壤水分随纬度和经度变化的情况,3种产品展现的季节波动整体高于实测,其中FY-3C的季节波动在3种产品中最为剧烈;③FY-3C的质量比ASCAT和SMAP更易受到植被影响,但在裸土区FY-3C优于ASCAT。本研究基于TC分析提供了全国范围内3种主流微波土壤水分产品的误差和信噪比的空间分布,并通过Hovm?ller图评估了其描述土壤水分时空变化的能力。研究结论可为微波土壤水分产品的同化研究提供一定参考。     

联合同化MODIS地表温度与机载L波段微波亮度温度估计土壤水分

构建了基于通用陆面模型(CoLM,Common Land Model)、微波辐射传输模型L-MEB(Lband Microwave Emission of the Biosphere)和集合平滑算法(EnKS,Ensemble Kalman Smoother)的土壤水分数据同化框架,用于联合同化MODIS地表温度和机载L波段被动微波亮温数据。以2012年HiWATER试验期间中游大满超级站为实验站点,分析了3种LAI数据产品对土壤温度模拟结果的影响,进而分析了联合同化地表温度和微波亮度温度对土壤水分估计结果的影响。研究结果表明:3种LAI数据对土壤温度模拟结果的影响显著,MODIS LAI产品在该研究区显著低估,导致土壤温度模拟结果高估4~6K;同化亮度温度、同化地表温度以及联合同化两者均可以改进土壤水分的估计精度,联合同化地表温度和亮度温度对于土壤水分的改进最为显著,土壤水分同化结果的RMSE减少31%~53%。     

微波遥感土壤湿度研究进展

发展实用的微波遥感土壤湿度卫星反演算法,以提供区域尺度上的土壤湿度信息,对水文学、气象学以及农业科学研究与应用至关重要。简要分析了主动微波遥感土壤湿度的研究进展情况,重点对被动微波遥感土壤湿度的原理、算法发展及研究趋势进行了详细论述。主动微波具有卫星数据空间分辨率高的特点,随着携载主动微波器的一系列卫星的发射,主动微波遥感土壤湿度将受到重视。被动微波遥感研究历史长、反演算法特别是卫星遥感算比较成熟,是今后区域尺度乃至全球尺度监测土壤湿度的重要手段。     

像元尺度土壤水分监测网络及其对L波段土壤水分产品的初步验证结果

土壤水分是作物生长、地—气水热交换及全球水循环过程中的关键变量,对于旱情监测、水文陆面过程及气候变化的研究具有重要的意义。被动微波遥感凭借对于土壤水分的敏感性已经成为监测土壤水分的主要手段。研究中针对吉林省农田下垫面,利用土壤水分传感器网络监测数据,开展了SMAP(Soil Moisture and Active and Passive)和SMOS(Soil Moisture and Ocean Salinity)被动微波土壤水分产品的真实性检验研究,得出了以下结论:(1)与实测数据相比较,SMOS L3(升降轨)和SMAP L3被动微波土壤水分产品存在低估现象,伴随降雨事件会出现高于实测土壤水分的情况;两种被动微波土壤水分产品的无偏均方根误差(unRMSE)都大于0.07m3/m3,但SMAP L3被动微波土壤水分产品数据的ubRMSE略低,为0.078m3/m3;(2)由于L波段的感应深度要浅于传感器的探测深度5cm,降雨后土壤表层的变干现象导致土壤水分的垂直不均匀性,这是SMOS和SMAP被动微波土壤水分产品低估土壤水分的原因之一;(3)SMOS与SMAP亮温分布范围对比结果表明:由于电磁射频干扰(RFI)的影响,RFI对于SMOS的影响更为严重,这或许是SMOS土壤水分产品的RMSE高于SMAP被动微波土壤水分产品的原因。     

机载L波段微波辐射计数据反演表层土壤水分研究

土壤水分是联系地球表层物质能量交换的重要纽带,准确监测土壤水分对区域气候、生态、水文及农业生产研究意义重大。机载L波段微波辐射计提供了获取区域土壤水分"真值"的有效手段。结合黑河中游航空试验中的多源遥感及地面观测,发展了一种基于0°入射角的L波段被动微波亮温数据的单通道土壤水分反演方法,获得了研究区3景约700m空间分辨率的土壤水分反演结果。并对其反演结果进行了点尺度、面尺度和村社尺度3种不同空间尺度上的验证,结果显示:L波段被动微波遥感反演土壤水分在点尺度上的验证精度在0.035~0.055m3/m3之间;面尺度上验证精度略高于点尺度,其验证偏差在0.02m3/m3以内;反演土壤水分与村社尺度的灌溉数据,即距前次灌溉的间隔日数,在空间上负相关关系明显,二者间相关系数约为0.3。     

陆面数据同化系统的研究综述

大气、海洋数据同化系统的完善和发展,促进了陆面数据同化系统的研究。本世纪初,随着北美(全球)陆面数据同化系统的建立,利用卫星、雷达数据同化地表土壤水分、地表温度、能量通量等工作正逐步展开。与此同时,陆面数据同化的研究也已经成为当前陆面过程和水文过程研究的热点。以北美(全球)陆面数据同化系统、欧洲陆面数据同化系统、中国西部陆面数据同化系统为例,对当前陆面数据同化系统的基本框架作了详细介绍;并指出了当前陆面数据同化系统发展中有待解决的若干问题。     

AMSR-E亮温同化系统的数据分析方法研究

低频微波卫星观测信号由于其对土壤水分非常敏感,经常被同化到陆面模式来提高土壤水分和其它地表状态变量的模拟和预报。常用的同化算法主要利用统计学,优化理论等数学知识,对改进和理解模型的物理过程意义不大。通过研究发展一个数据分析方法,判断AMSR\|E亮温同化系统土壤水分的预报误差,为将来从物理角度定性分析提供基础。     

基于AMSR-E的全球陆表被动微波发射率数据集

地表微波发射率表征了地物向外发射微波辐射的能力,星载被动微波发射率估算可在宏观、大尺度上对陆表微波辐射进行整体表达,是被动微波地表参数定量反演中重要基础数据,也是在大尺度上获取陆表微波辐射特征的一种途径。本数据集利用搭载在Aqua卫星上的高级微波扫描辐射计(AMSR-E)和中分辨率成像光谱仪(MODIS)的同步观测特点,采用MODIS的地表温度和大气水汽产品数据作为输入,基于考虑大气影响的发射率估算模型,生产了全球晴空条件下AMSR-E传感器运行期间(2002年6月~2011年10月)的陆表多通道双极化微波瞬时发射率。通过产品低频无线电信号影响、数据间比对、分布统计、不同地表覆盖条件的发射率特征、频率依赖和相关性研究等开展验证性分析,结果表明:瞬时发射率的动态大、细节表达丰富,月内日变化标准差在0.02以内,其时空变化、频率依赖和相关性等符合微波理论分析和自然物理过程理解。此套数据集还包括AMSR-E全生命周期的全球陆表逐日、侯、旬、半月及月产品,可用于开展星载被动微波遥感模拟、陆面模型以及陆表温度、积雪、大气降水/水汽/可降水量等反演研究。     

被动微波遥感反演土壤水分进展研究

在地球系统中, 地表土壤水分是陆地和大气能量交换过程中的重要因子, 并对陆地表面蒸散、水的运移、碳循环有很强的控制作用, 大面积监测土壤水分在水文、气象和农业科学领域具有较大的应用潜力。被动微波遥感是监测土壤含水量最有效的手段之一, 相比红外与可见光, 它具有波长长, 穿透能力强的优势, 相比主动微波雷达, 被动微波辐射计具有监测面积大、周期短, 受粗糙度影响小, 对土壤水分更为敏感, 算法更为成熟的优势。然而微波辐射计观测到的亮温除了受土壤水分影响外, 还要考虑如植被覆盖、土壤温度、雪覆盖以及地形、地表粗糙度、土壤纹理和大气效应以及地表的异质性等其它因子的影响。目前, 已研究出许多使用被动微波辐射计反演土壤水分的方法,这些方法大部分是围绕着土壤湿度与亮温温度之间的关系进行, 同时也考虑其它各种不同因子对 地表微波辐射的影响。从介绍被动微波反演地表参数的原理入手, 重点介绍被动遥感反演土壤水分当前的算法进展、研究趋势等。     

被动微波遥感数据超分辨率增强与混合像元分解研究综述

星载被动微波遥感数据以其全天候、穿透性以及不受云干扰等特点,在全球变化研究领域取得了广泛的应用,然而其较低的空间分辨率,限制了后期地物参数的反演精度。对国内外被动微波遥感数据空间分辨率提高方法进行介绍,重点介绍了基于图像处理技术的超分辨率增强和混合像元分解方法。通过对两类方法的介绍和评价,展望被动微波遥感数据混合像元分解方法的研究前景。被动微波遥感数据空间分辨率的有效提高,可以为更多的研究和应用领域服务。     

Experiments of one-dimensional soil moisture assimilation system based on ensemble Kalman filter

Ensemble Kalman filter is a new sequential data assimilation algorithm which was originally developed for atmospheric and oceanographic data assimilation. It can be applied to calculate error covariance matrix through Monte-Carlo simulation. This approach is able to resolve the nonlinearity and discontinuity existed within model operator and observation operator. When observation data are assimilated at each time step, error covariances are estimated from the phase-space distribution of an ensemble of model states. The error statistics is then used to calculate Kalman gain matrix and analysis increments. In this study, we develop a one-dimensional soil moisture data assimilation system based on ensemble Kalman filter, the Simple Biosphere Model (SiB2) and microwave radiation transfer model (AIEM, advanced integration equation model). We conduct numerical experiments to assimilate in situ soil surface moisture measurements and low-frequency passive microwave remote sensing data into a land surface model, respectively. The results indicate that data assimilation can significantly improve the soil surface moisture estimation. The improvement in root zone is related to the model bias errors at surface layer and root zone. The soil moisture does not vary significantly in deep layer. Additionally, the ensemble Kalman filter is predominant in dealing with the nonlinearity of model operator and observation operator. It is practical and effective for assimilating observations in situ and remotely sensed data into land surface models.     

A comparison study on empirical microwave soil moisture downscaling methods based on the integration of microwave-optical/IR data on the Tibetan Plateau

Soil moisture plays a vital role in land surface energy and the water cycle. Microwave remote sensing is widely used because of the physically based relationship between the land surface emission observed and soil moisture. However, the application of retrieved soil moisture data is restricted by its coarse spatial resolution. To overcome this weakness, downscaling methods should be developed to disaggregate coarse resolution microwave soil moisture data to fine resolution. The traditional method is the microwave-optical/IR synergistic approach, in which land surface temperature, vegetation index, and surface albedo are key parameters. Five purely empirical methods based on the triangle feature are selected in this study. To evaluate their performance on downscaling microwave soil moisture, these methods are applied to the Zoige Plateau in China using the Advanced Microwave Scanning Radiometer on the Earth Observing System (AMSR-E) Land Parameter Retrieval Model (LPRM) soil moisture product and Moderate Resolution Imaging Spectroradiometer (MODIS) optical/IR products. The coarse-resolution AMSR-E LPRM soil moisture data are disaggregated into the high resolution of the MODIS product, and the surface soil moisture measurements of the Maqu soil moisture observation network located in the plateau are used to validate the downscaling results. Results show that (1) the relationship models used in these methods can generally capture the variation in soil moisture, with R² around 0.6, but have a relatively high uncertainty under conditions of high soil moisture; (2) the methods can provide high-resolution soil moisture distribution, but the downscaled soil moisture presents a low level correlation with field measurements at different spatial and temporal scales. This comparative study provides insight into the performance of popular purely empirical downscaling methods on enhancing the spatial resolution of soil moisture on the Tibetan Plateau. Although synergistic methods can improve the spatial resolution of AMSR-E soil moisture data, additional studies are needed to exclude the uncertainty from AMSR-E soil moisture estimation, the low sensitivity of the relationship model under high soil moisture, and the spatial representativeness difference between coarse pixels and point measurement.     

被动微波遥感土壤水分反演研究综述

由于微波具有全天候、穿透性以及不受云的影响等特征,使其在遥感研究全球变化中具有越来越大的优势。在微波传感器技术发展的过程中,人们通过研究发现被动微波遥感是反演土壤水分的各种技术中最有效的方法之一,而植被覆盖地区的土壤水分反演是反演算法中的难点。简略地介绍针对裸地的Q/P模型和针对植被的τ-ω模型,以及主要土壤水分反演算法。     

A soil moisture assimilation scheme based on the microwave Land Emissivity Model and the Community Land Model

Applications of microwave remote-sensing data in land data assimilation are a topic of current interest and importance due to their high temporal and spatial resolution and availability. However, there have been few studies on land surface sub-grid scale heterogeneity and calculating microwave wetland surface emissivity when directly assimilating gridded Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) satellite brightness temperature (BT) data to estimate soil moisture. How to assimilate gridded AMSR-E BT data for land surface model (LSM) grid cells including various land cover types, especially wetland, is worthy of careful study. The ensemble Kalman filter (EnKF) method is able to resolve the non-linearity and discontinuity in forecast and observation operators, and is widely used in land data assimilation. In this study, considering the influences of land surface sub-grid scale heterogeneity, a satellite data simulation scheme based on the National Center for Atmosphere Research (NCAR) Community Land Model version 2.0 (CLM2.0), microwave Land Emissivity Model (LandEM), Shuffled Complex Evolution (SCE-UA) algorithm and AMSR-E BT observation data is presented to simulate AMSR-E BT data and calibrate microwave wetland surface emissivity; then, a soil moisture data assimilation scheme is developed to directly assimilate the gridded AMSR-E BT data, which consists of the CLM2.0, LandEM and EnKF. The experimental results indicate that the calibrated microwave wetland surface emissivities possess excellent transportability, and that the assimilation scheme is practical and can significantly improve soil moisture estimation accuracy. This study provides a promising solution to improve soil moisture estimation accuracy through directly assimilating gridded AMSR-E BT data for various land cover types such as bare soil, vegetation, snow, lake and wetland.     

Using remotely-sensed estimates of soil moisture to infer soil texture and hydraulic properties across a semi-arid watershed

Near-surface soil moisture is a critical component of land surface energy and water balance studies encompassing a wide range of disciplines. However, the processes of infiltration, runoff, and evapotranspiration in the vadose zone of the soil are not easy to quantify or predict because of the difficulty in accurately representing soil texture and hydraulic properties in land surface models. This study approaches the problem of parameterizing soil properties from a unique perspective based on components originally developed for operational estimation of soil moisture for mobility assessments. Estimates of near-surface soil moisture derived from passive (L-band) microwave remote sensing were acquired on six dates during the Monsoon '90 experiment in southeastern Arizona, and used to calibrate hydraulic properties in an offline land surface model and infer information on the soil conditions of the region. Specifically, a robust parameter estimation tool (PEST) was used to calibrate the Noah land surface model and run at very high spatial resolution across the Walnut Gulch Experimental Watershed. Errors in simulated versus observed soil moisture were minimized by adjusting the soil texture, which in turn controls the hydraulic properties through the use of pedotransfer functions. By estimating within a continuous range of widely applicable soil properties such as sand, silt, and clay percentages rather than applying rigid soil texture classes, lookup tables, or large parameter sets as in previous studies, the physical accuracy and consistency of the resulting soils could then be assessed.In addition, the sensitivity of this calibration method to the number and timing of microwave retrievals is determined in relation to the temporal patterns in precipitation and soil drying. The resultant soil properties were applied to an extended time period demonstrating the improvement in simulated soil moisture over that using default or county-level soil parameters. The methodology is also applied to an independent case at Walnut Gulch using a new soil moisture product from active (C-band) radar imagery with much lower spatial and temporal resolution. Overall, results demonstrate the potential to gain physically meaningful soil information using simple parameter estimation with few but appropriately timed remote sensing retrievals.     

Impact of surface heterogeneity on surface soil moisture retrievals from passive microwave data at the regional scale: The Upper Danube case

Water and energy fluxes at the interface between the land surface and atmosphere are strongly depending on the surface soil moisture content which is highly variable in space and time. The sensitivity of active and passive microwave remote sensing data to surface soil moisture content has been investigated in numerous studies. Recent satellite borne mission concepts, as e.g. the SMOS mission, are dedicated to provide global soil moisture information with a temporal frequency of 1-3 days to capture it's high temporal dynamics. Passive satellite microwave sensors have spatial resolutions in the order of tens of kilometres. The retrieved soil moisture fields from that sensors therefore represent surface information which is integrated over large areas. It has been shown that the heterogeneity within an image pixel might have considerable impact on the accuracy of soil moisture retrievals from passive microwave data.The paper investigates the impact of land surface heterogeneity on soil moisture retrievals from L-band passive microwave data at different spatial scales between 1 km and 40 km. The impact of sensor noise and quality of ancillary information is explicitly considered. A synthetic study is conducted where brightness temperature observations are generated using simulated land surface conditions. Soil moisture information is retrieved from these simulated observations using an iterative approach based on multiangular observations of brightness temperature. The soil moisture retrieval uncertainties resulting from the heterogeneity within the image pixels as well as the uncertainties in the a priori knowledge of surface temperature data and due to sensor noise, is investigated at different spatial scales. The investigations are made for a heterogeneous hydrological catchment in Southern Germany (Upper Danube) which is dedicated to serve as a calibration and validation site for the SMOS mission.     

Soil moisture estimation using MODIS and ground measurements in eastern China

Recent technological advances in remote sensing have shown that soil moisture can be measured by microwave remote sensing under some topographic and vegetation cover conditions. However, current microwave technology limits the spatial resolution of soil moisture data. It has been found that the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) are related to surface soil moisture; therefore, a relationship between ground observed soil moisture and satellite NDVI and LST products can be developed. Three years of 1 km NDVI and LST products from the Moderate Resolution Imaging Spectroradiometer (MODIS) have been combined with ground measured soil moisture to determine regression relationships at a 1 km scale. Results show that MODIS NDVI and LST are strongly correlated with the ground measured soil moisture, and regression relationships are land cover and soil type dependent. These regression relationships can be used to generate soil moisture estimates at moderate resolution for study area.