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A dual-pass data assimilation scheme for estimating surface fluxes with FY3A-VIRR land surface temperature

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Science China Earth Sciences 2015年第2期58卷 211-230页

作者： xu tongren LIU ShaoMin xu ZiWei LIANG ShunLin xu Lu State Key Laboratory of Remote Sensing Science and School of Geography Beijing Normal University State Key Laboratory of Remote Sensing Science and College of Global Change and Earth System ScienceBeijing Normal University Department of Geographical Sciences University of Maryland College Park Information Technology Department National Library of China

In this work, a dual-pass data assimilation scheme is developed to improve predictions of surface flux. Pass 1 of the dual-pass data assimilation scheme optimizes the model vegetation parameters at the weekly temporal scale, and Pass 2 optimizes the soil moisture at the daily temporal scale. Based on ensemble Kalman filter(EnKF), the land surface temperature(LST) data derived from the new generation of Chinese meteorology satellite(FY3A-VIRR) are assimilated into common land model(CoLM) for the first time. Six sites, Daman, Guantao, Arou, BJ, Miyun and Jiyuan, are selected for the data assimilation experiments and include different climatological conditions. The results are compared with those from a dataset generated by a multi-scale surface flux observation system that includes an automatic weather station(AWS), eddy covariance(EC) and large aperture scintillometer(LAS). The results indicate that the dual-pass data assimilation scheme is able to reduce model uncertainties and improve predictions of surface flux with the assimilation of FY3A-VIRR LST data.

关键词： assimilation moisture latent weekly vegetation weather pixel covariance aperture Figure

Assessment and improvement of Noah-MP for simulating water and heat exchange over alpine grassland in growing season

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Science China Earth Sciences 2022年第3期65卷 536-552页

作者： Shuang SUN Donghai ZHENG Shaomin LIU Ziwei xu tongren xu Hui ZHENG Xiaofan YANG State Key Laboratory of Earth Surface Processes and Resource Ecology Faculty of Geographical ScienceBeijing Normal UniversityBeijing 100875China National Tibetan Plateau Data Center(TPDC) State Key Laboratory of Tibetan Plateau Earth System Environment and Resources(TPESER)Institute of Tibetan Plateau ResearchChinese Academy of SciencesBeijing 100101China CAS Key Laboratory ofRegional Climate-Environment Research for Temperate East Asia Institute ofAtmospheric PhysicsChinese Academy of SciencesBeijing 100029China

Alpine grassland is the main ecosystem of the Tibetan Plateau(TP),thus accurate simulation of water and heat exchange in the grassland will significantly enhance the understanding of the land-atmosphere interaction process on the *** this study,we assessed and improved the ensemble numerical simulations of the community Noah land surface model with multiparameterization options(Noah-MP)by using observations collected from four alpine grassland observation *** four observation sites belong to the upper Heihe River Basin Integrated Observatory Network located in the northeastern part of the ***,an ensemble of 1008 numerical simulation experiments,based on multiparameterization options of seven physical processes/variables in the Noah-MP,was carried out for the vegetation growing *** Taylor skill score was then used to assess the model performance and select the optimal combination of parameterization options for a more exact simulation of the water and heat exchange in alpine *** accuracy of Noah-MP simulation was further improved by introducing new parameterizations of thermal roughness length,soil hydraulic properties,and vertical root *** was found that:(1)Simulation of water and heat exchange over alpine grassland in the growing season was mainly affected by the parameterizations of dynamic vegetation,canopy stomatal resistance,runoff and groundwater dynamics,and surface exchange coefficient for heat *** of different parameterization options for these four physical processes/variables led to large differences in the simulation of water and heat fluxes.(2)The optimal combination of parameterization options selected in the current Noah-MP framework suffered from significant overestimation of sensible heat flux(H)and underestimation of soil moisture(θ)at all observation sites.(3)The overestimation of H was significantly improved by introducing a new parameterization of thermal roughness ***,the u

关键词： Noah-MP land surface model Alpine grassland Ensemble numerical experiments Parameterization options Parameterization improvement

Retrieval of Land-surface Temperature from AMSR2 Data Using a Deep Dynamic Learning Neural Network

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Chinese Geographical Science 2018年第1期28卷 1-11页

作者： MAO Kebiao ZUO Zhiyuan SHEN Xinyi xu tongren GAO Chunyu LIU Guang National Hulunber Grassland Ecosystem Observation and Research Station Institute of Agricultural Resources and Regional Planning Chinese Academy of Agricultural Sciences State Key Laboratory of Remote Sensing Science Institute of Remote sensing and Digital Earth Research Chinese Academy of Science and Beijing Normal University College of Resources and Environments Hunan Agricultural University Hydrometeorology and Remote Sensing Laboratory University of Oklahoma

It is more difficult to retrieve land surface temperature(LST) from passive microwave remote sensing data than from thermal remote sensing data, because the emissivities in the passive microwave band can change more easily than those in the thermal infrared band. Thus, it is very difficult to build a stable relationship. Passive microwave band emissivities are greatly influenced by the soil moisture, which varies with time. This makes it difficult to develop a general physical algorithm. This paper proposes a method to utilize multiple-satellite, sensors and resolution coupled with a deep dynamic learning neural network to retrieve the land surface temperature from images acquired by the Advanced Microwave Scanning Radiometer 2(AMSR2), a sensor that is similar to the Advanced Microwave Scanning Radiometer Earth Observing System(AMSR-E). The AMSR-E and MODIS sensors are located aboard the Aqua satellite. The MODIS LST product is used as the ground truth data to overcome the difficulties in obtaining large scale land surface temperature data. The mean and standard deviation of the retrieval error are approximately 1.4° and 1.9° when five frequencies(ten channels, 10.7, 18.7, 23.8, 36.5, 89 V/H GHz) are used. This method can effectively eliminate the influences of the soil moisture, roughness, atmosphere and various other factors. An analysis of the application of this method to the retrieval of land surface temperature from AMSR2 data indicates that the method is feasible. The accuracy is approximately 1.8° through a comparison between the retrieval results with ground measurement data from meteorological stations.

关键词： radiometry Advanced Microwave Scanning Radiometer 2 (AMSR2) passive remote sensing inverse problem

Regional and Global Land Data Assimilation Systems: Innovations,Challenges, and Prospects

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Journal of Meteorological Research 2019年第2期33卷 159-189页

作者： Youlong XIA Zengchao HAO Chunxiang SHI Yaohui LI Jesse MENG tongren xu Xinying WU Baoqing ZHANG I.M.Systems Group at Environmental Modeling Center(EMC) National Centers for Environmental Prediction (NCEP)National Oceanic and Atmospheric Administration (NOAA) College of Water Sciences Beijing Normal University National Meteorological Information Center China Meteorological Administration Institute of Arid Meteorology China Meteorological Administration State Key Laboratory of Earth Surface Processes and Resource Ecology School of Natural ResourcesFaculty of Geographical Science Beijing Normal University Key Laboratory of Western China's Environmental Systems of Ministry of Education College of Earth and Environmental Sciences Lanzhou University

Since the North American and Global Land Data Assimilation Systems(NLDAS and GLDAS) were established in2004, significant progress has been made in development of regional and global LDASs. National, regional, projectbased, and global LDASs are widely developed across the world. This paper summarizes and overviews the development, current status, applications, challenges, and future prospects of these LDASs. We first introduce various regional and global LDASs including their development history and innovations, and then discuss the evaluation, validation, and applications(from numerical model prediction to water resources management) of these LDASs. More importantly, we document in detail some specific challenges that the LDASs are facing: quality of the in-situ observations, satellite retrievals, reanalysis data, surface meteorological forcing data, and soil and vegetation databases; land surface model physical process treatment and parameter calibration; land data assimilation difficulties; and spatial scale incompatibility problems. Finally, some prospects such as the use of land information system software, the unified global LDAS system with nesting concept and hyper-resolution, and uncertainty estimates for model structure,parameters, and forcing are discussed.

关键词： land data assimilation system (LDAS) regional and global LDASs in-situ observation satellite retrieval land surface model (LSM)

The study of estimation method of broadband emissivity from EOS/MODIS data

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High Technology Letters 2014年第1期20卷 88-91页

作者：毛克彪 Ma Ying Shen Xinyi Sun Zhiwen He Tianjue Xia Lang xu tongren National Hulunber Grassland Ecosystem Observation and Research Station Institute of Agricultural Resources and Regional PlanningChinese Academy of Agricultural Sciences Guangzhou Institute of Geography Department of Geography University of Toronto Hydrometeorology and Remote Sensing Laboratory The University of Oklahoma Institute No.503 of China Academy of Space Technology State Key Laboratory of Remote Sensing Science School of GeographyBeijing Normal University

The broadband emissivity is an important parameter for estimating the energy balance of the Earth. This study focuses on estimating the window （8 -12 μm） emissivity from the MODIS （mod- erate-resolution imaging spectroradiometer） data, and two methods are built. The regression method obtains the broadband emissivity from MODllB1 - 5KM product, whose coefficient is developed by using 128 spectra, and the standard deviation of error is about 0.0118 and the mean error is about O. 0084. Although the estimation accuracy is very high while the broadband emissivity is estimated from the emissivity of bands 29, 31 and 32 obtained from MOD11B1 _ 5KM product, the standard deviations of errors of single emissivity in bands 29, 31, 32 are about 0.009 for MOD11B1 5KM product, so the total error is about O. 02 and resolution is about 5km × 5km. A combined radiative transfer model with dynamic learning neural network method is used to estimate the broadband emis- sivity from MODIS 1B data. The standard deviation of error is about 0.016, the mean error is about 0.01, and the resolution is about 1 km x 1 km. The validation and application analysis indicates that the regression is simpler and more practical, and estimation accuracy of the dynamic learning neural network method is higher. Considering the needs for accuracy and practicalities in application, one of them can be chosen to estimate the broadband emissivity from MODIS data.

关键词： moderate-resolution imaging spectroradiometer (MODIS) broadband emissivity land surface temperature

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

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Chinese Geographical Science 2020年第5期30卷 855-875页

作者： ZHANG Yuan LIU Shaomin HU Xiao WANG Jianghao LI Xiang xu Ziwei MA Yanfei LIU Rui xu tongren YANG Xiaofan State Key Laboratory of Earth Surface Processes and Resource Ecology Faculy of Geographical ScienceBeijing Normal Univer-sityBeijing 100875China State Key Laboratory of Resources and Environmental Information Systems Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBejing 100101China Depariment of Geography HandanCollegeHebei 056005China

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 stati

关键词： land surface hydrothermal conditions(LSHCs) evapotranspiration spatial heterogeneity remote sensing evaluation scheme

Changes in Global Cloud Cover Based on Remote Sensing Data from 2003 to 2012

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Chinese Geographical Science 2019年第2期29卷 306-315页

作者： MAO Kebiao YUAN Zijin ZUO Zhiyuan xu tongren SHEN Xinyi GAO Chunyu National Hulunber Grassland Ecosystem Observation and Research Station Institute of Agricultural Resources and Regional Planning Chinese Academy of Agricultural Sciences State Key Laboratory of Remote Sensing Science Institute of Remote Sensing and Digital Earth Research Chinese Academy of Science and Beijing Normal University Hydrometeorology and Remote Sensing Laboratory University of Oklahoma

As is well known,clouds impact the radiative budget,climate change,hydrological processes,and the global carbon,nitrogen and sulfur *** understand the wide-ranging effects of clouds,it is necessary to assess changes in cloud cover at high spatial and temporal *** this study,we calculate global cloud cover during the day and at night using cloud products estimated from Moderate Resolution Imaging Spectroradiometer(MODIS)*** indicate that the global mean cloud cover from 2003 to 2012 was 66%.Moreover,global cloud cover increased over this recent ***,cloud cover over land areas(especially North America,Antarctica,and Europe)decreased(slope=–0.001,R^2=0.5254),whereas cloud cover over ocean areas(especially the Indian and Pacific Oceans)increased(slope=0.0011,R^2=0.4955).Cloud cover is relatively high between the latitudes of 36°S and 68°S compared to other regions,and cloud cover is lowest over Oceania and *** highest rates of increase occurred over Southeast Asia and Oceania,whereas the highest rates of decrease occurred over Antarctica and North *** global distribution of cloud cover regulates global temperature change,and the trends of these two variables over the 10-year period examined in this study(2003–2012)oppose one another in some *** findings are very important for studies of global climate change.

关键词： global cloud cover climate change remote sensing MODIS global change

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论社会主义和谐社会的科学内涵及目标要素

江淮论坛 2005年第4期 13-18页

作者：徐统仁莱阳农学院山东青岛266109

“和谐社会”思想的确立,体现了我们党执政理念的升华和把握、驾驭人类社会发展规律,特别是社会管理规律的能力。它作为我国社会转型期一种社会发展的目标选择模式,所表明的是社会总体上处于相对稳定的态势,是一个文明、法治、公平、有... 详细信息

“和谐社会”思想的确立,体现了我们党执政理念的升华和把握、驾驭人类社会发展规律,特别是社会管理规律的能力。它作为我国社会转型期一种社会发展的目标选择模式,所表明的是社会总体上处于相对稳定的态势,是一个文明、法治、公平、有序、融洽、可持续发展的社会。社会主义和谐社会是一个由众多目标要素构成的、全面系统的有机体系。

关键词：社会主义和谐社会科学内涵目标要素转型期

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土壤蒸发和植被蒸腾遥感估算与验证

遥感学报 2017年第6期21卷 966-981页

作者：宋立生刘绍民徐同仁徐自为马燕飞西南大学遥感大数据应用重庆市工程研究中心地理科学学院重庆400715 北京师范大学地理科学学部地表过程与资源生态国家重点实验室北京100875 邯郸学院经济管理学院地理系邯郸056005

地表蒸散发是土壤—植被—大气系统中能量和水循环的重要环节,它包括土壤、水体和植被表面的蒸发,以及植被蒸腾。随着地表参数多源遥感产品的快速发展,利用不同地表参数遥感产品估算地表蒸散发以及其组分土壤蒸发和植被蒸腾成为日常监... 详细信息

地表蒸散发是土壤—植被—大气系统中能量和水循环的重要环节,它包括土壤、水体和植被表面的蒸发,以及植被蒸腾。随着地表参数多源遥感产品的快速发展,利用不同地表参数遥感产品估算地表蒸散发以及其组分土壤蒸发和植被蒸腾成为日常监测越来越便利,监测尺度已从单站扩展到田块、区域乃至全球。目前地表蒸散发双层遥感估算模型按照建模机理的不同可分为:系列模型、平行模型、基于特征空间的模型、结合传统方法的模型以及数据同化方法。本文从模型构建物理机制、模型驱动数据以及模型输出结果验证等方面总结了上述模型的发展历史和现状,并指出在模型结构与参数化方案的优化、高分辨率模型驱动数据的发展、土壤蒸发和植被蒸腾像元尺度"地面真值"的获取等方面都仍需进一步完善。

关键词：土壤蒸发植被蒸腾遥感模型估算验证