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Retrieving crop leaf area index by assimilation of MODIS data into a crop growth model

science china Earth sciences 2010年第5期53卷 721-730页

作者： wang dongwei1,2.3, wang jindi1,2.& liang shunlin4.1 state key laboratory of remote sensing science, jointly sponsored by beijing normal university and institute of remote sensing applications, cas, beijing 100875, china 2 School of Geography, beijing key laboratory of Environmental remote sensing and Digital City, beijing normal university, beijing 100875, china 3 Haihe River Water Conservancy Commission, Tianjin 300170, china 4 Department of Geography, university of Maryland, College Park, MD 20742, USA 1. state key laboratory of remote sensing science Jointly Sponsored by Beijing Normal University and Institute of Remote Sensing Applications CAS Beijing 100875 China2. School of Geography Beijing Key Laboratory of Environmental Remote Sensing and Digital City Beijing Normal University Beijing 100875 China3. Haihe River Water Conservancy Commission Tianjin 300170 China4. Department of Geography University of Maryland College Park MD 20742 USA

Leaf area index (LAI) is an important parameter in monitoring crop growth. One of the methods for retrieving LAI from remotely sensed observations is through inversion of canopy reflectance models. Many model inversion methods fail to account for variable LAI values at different crop growth stages. In this research, we use the crop growth model to describe the LAI changes with crop growth, and consider a priori LAI values at different crop growth stages as constraint information. The key approach of this research is to assimilate multiple canopy reflectance values observed at different growth stages and a priori LAI values into a coupled crop growth and radiative transfer model sequentially using a variational data assimilation algorithm. Adjoint method is used to minimize the cost function. Any other information source can be easily incorporated into the inversion cost function. The validation results show that the time series of MODIS canopy reflectance can greatly reduce the uncertainty of the inverted LAI values. Compared with MODIS LAI product at Changping and Shunyi Counties of beijing, this method has significantly improved the estimated LAI temporal profile.

关键词： inversion leaf area index crop growth model time series MODIS

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Loose architecture of multi-level massive geospatial data based on virtual quadtree

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science china(Technological sciences) 2008年第S1期51卷 114-123页

作者： YANG ChongJun,WU Sheng,REN YingChao,FU Li,ZHANG FuQing,wang Gang,TAN Jian,LIU DongLin,MA ChaoJi & liang Li state key laboratory of remote sensing science,jointly sponsored by the institute of remote sensing applications of Chinese Academy of sciences and beijing normal university,beijing 100101,china 1. state key laboratory of remote sensing science Jointly Sponsored by the Institute of Remote Sensing Applications of Chinese Academy of Sciences and Beijing Normal University Beijing 100101 China

This paper proposed a virtual quadtree (VQT) based loose architecture of multi-level massive geospatial data for integrating massive geospatial data dispersed in the departments of different hierarchies in the same sector into a unified GIS (Geographic Information System) platform. By virtualizing the nodes of the quad-tree,the VQT separates the structure of data organization from data storage,and screens the difference between the data storage in local computer and in the re-mote computers in network environment. And by mounting,VQT easily integrates the data from the remote computers into the local VQT so as to implement seam-less integration of distributed multi-level massive geospatial data. Based on that mode,the paper built an application system with geospatial data over 1200 GB distributed in 12 servers deployed in 12 cities. The experiment showed that all data can be seamlessly rapidly traveled and performed zooming in and zooming out smoothly.

关键词： massive geospatial data virtual quadtree loose architecture

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Application of ensemble kalman filter to geophysical parameters retrieval in remote sensing:A case study of kernel-driven BRDF model inversion

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science china Earth sciences 2006年第6期49卷 632-640页

作者： QIN Jun1, YAN Guangjian1, LIU Shaomin1, liang shunlin2, ZHANG Hao1, wang jindi1 & LI Xiaowen1 1. state key laboratory of remote sensing science, School of Geography and remote sensing, Research Center for RS&GIS, beijing normal university, beijing 100875, china 2. Department of Geography, 2181 Lefrak Hall, university of Maryland, College Park, MD20742, USA 1. state key laboratory of remote sensing science School of Geography and Remote Sensing Research Center for RS&GIS Beijing Normal University Beijing 100875 China 2. Department of Geography 2181 Lefrak Hall University of Maryland College Park MD20742 USA

The use of a priori knowledge in remote sensing inversion has great implications for ensuring the stability of inversion process and reducing uncertainties in retrieved results, especially under the condition of insufficient observations. Common optimization algorithms have difficulties in providing posterior distribution and thus cannot directly acquire uncertainties in inversion results, which is of no benefit to remote sensing application. In this article, ensemble Kalman filter (EnKF) has been introduced to retrieve surface geophysical parameters from remote sensing observations, which has the capability of not merely obtaining inversion results but also giving its posterior distribution. To show the advantage of EnKF, it is compared to standard MODIS AMBRALS algorithm and highly effi-cient global optimization method SCE-UA. The inversion abilities of kernel-driven BRDF models with different kernel combinations at several main cover types are emphatically discussed when observa-tions are deficient and a priori knowledge is introduced into inversion.

关键词： remote sensing inversion, a priori knowledge, posterior distribution, ensemble kalman filter, BRDF, kernel-driven model, albedo.

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High-resolution remote thermometry based on Bi^(3+)-doped 0D zinc halide hybrids

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science china Chemistry 2024年第8期67卷 2599-2605页

作者： Cui-Mi Shi Shu-Hua Xue Jin-Yun wang liang-Jin Xu Zhong-Ning Chen state key laboratory of Structural Chemistry Fujian Institute of Research on the Structure of MatterChinese Academy of SciencesFuzhou 350002China Fujian science Technology Innovation Laboratory for Optoelectronic Information of ChinaFuzhou 350108China university of Chinese Academy of sciences Beijing 100049China

Developing highly sensitive optical thermometers is of great significance due to their capability to enable remote and non-contact temperature measurements, rendering them highly applicable in diverse and harsh environments. Herein we report a temperature-dependent phosphor of 0D metal halide hybrid by incorporating Bi^(3+)into the(MePPh_(3))_(2)ZnCl_(4) matrix. Through Bi^(3+)doping,the initially non-luminescent(MePPh_(3))_(2) ZnCl_(4) matrix exhibits a deep-blue emission centered at 453 nm, with a photoluminescence quantum yield(PLQY) of 5.71% and a Stokes shift of 75 nm at room temperature. Experimental characterization demonstrates that exciton-like luminescence of Bi^(3+)is mainly responsible for the blue emission. Single crystals of Bi^(3+)-doped(MePPh_(3))_(2)ZnCl_(4) show an unusual correlation between photoluminescence(PL) lifetime and temperature. Particularly, the dependence of luminescence lifetime on temperature is most remarkable in the temperature range of 80 to 100 K with an exceptional sensitivity up to 0.09 K^(-1), representing one of the best levels for thermometry based on PL decay lifetime. Our work not only provides a viable strategy for designing a novel, environmentally friendly, and stable blue emitter, but also paves the way for precise thermometric application at low temperature.

关键词： lead-free hybrids Bi^(3+)doping blue emission temperature sensing

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Long-term(2013-2022)mapping of winter wheat in the North china Plain using Landsat data:classification with optimal zoning strategy

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Big Earth Data 2024年第3期8卷 494-521页

作者： Yifei Liu Xuehong Chen Jin Chen Yunze Zang Jingyi wang Miao Lu liang Sun Qi Dong Bingwen Qiu Xiufang Zhu state key laboratory of remote sensing science Institute of Remote Sensing Science and EngineeringFaculty of Geographical ScienceBeijing Normal UniversityBeijingChina beijing Engineering Research Center for Global Land remote sensing Products Institute of Remote Sensing Science and EngineeringFaculty of Geographical ScienceBeijing Normal UniversityBeijingChina state key laboratory of Crop Gene Resources and Breeding Institute of Crop SciencesChinese Academy of Agricultural Sciences(CAAS)BeijingChina state key laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northern china Institute of Agricultural Resources and Regional PlanningChinese Academy of Agricultural SciencesBeijingChina key laboratory of Spatial Data Mining&Information Sharing of Ministry of Education Academy of Digital China(Fujian)Fuzhou UniversityFuzhouChina

security and formulating agricultural *** data are the only available source for long-term winter wheat mapping in the North china Plain due to the fragmented landscape in this *** various methods,such as index-based methods,curve similarity-based methods and machine learning-based methods,have been developed for winter wheat mapping based on remote sensing,the former two often require satellite data with high temporal resolution,which are unsuitable for Landsat data with sparse *** learning is an effective method for crop classification using Landsat ***,applying machine learn-ing for winter wheat mapping in the North china Plain encounters two main issues:1)the lack of adequate and accurate samples for classifier training;and 2)the difficulty of training a single classifier to accomplish the large-scale crop mapping due to the high spatial heterogeneity in this *** address these two issues,we first designed a sample selection rule to build a large sample set based on several existing crop maps derived from recent Sentinel data,with specific consideration of the confusion error between winter wheat and winter rapeseed in the available crop ***,we developed an optimal zoning method based on the quadtree region splitting algorithm with classification feature consistency criterion,which divided the study area into six subzones with uni-form classification *** each subzone,a specific random forest classifier was trained and used to generate annual winter wheat maps from 2013 to 2022 using Landsat 8 OLI *** sample validation confirmed the high accuracy of the produced maps,with an average overall accuracy of 91.1%and an average kappa coefficient of 0.810 across different *** derived winter wheat area also has a good correlation(R2=0.949)with census area at the provincial *** results underscore the reliability of the produced annual winter wheat *** experiments demonstrate that

关键词： Winter wheat mapping Landsat machine learning North china Plain optimal zoning

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Estimating global land surface broadband thermal-infrared emissivityusing advanced very high resolution radiometer optical data

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International Journal of Digital Earth 2013年第S01期6卷 34-49页

作者： Jie Cheng shunlin liang state key laboratory of remote sensing science College of Global Change and Earth System ScienceBeijing Normal UniversityBeijingChina Department of Geographical science University of MarylandCollege ParkMDUSA

An algorithm for retrieving global eight-day 5 km broadband emissivity (BBE)from advanced very high resolution radiometer (AVHRR) visible and nearinfrared data from 1981 through 1999 was presented. Land surface was dividedinto three types according to its normalized difference vegetation index (NDVI)values: bare soil, vegetated area, and transition zone. For each type, BBE at813.5 mm was formulated as a nonlinear function of AVHRR reflectance forChannels 1 and 2. Given difficulties in validating coarse emissivity products withground measurements, the algorithm was cross-validated by comparing retrievedBBE with BBE derived through different methods. Retrieved BBE was initiallycompared with BBE derived from moderate-resolution imaging spectroradiometer (MODIS) albedos. Respective absolute bias and root-mean-square errorwere less than 0.003 and 0.014 for bare soil, less than 0.002 and 0.011 fortransition zones, and 0.002 and 0.005 for vegetated areas. Retrieved BBE wasalso compared with BBE obtained through the NDVI threshold method. Theproposed algorithm was better than the NDVI threshold method, particularly forbare soil. Finally, retrieved BBE and BBE derived from MODIS data wereconsistent, as were the two BBE values.

关键词： earth observation broadband emissivity global land surface satellite soil taxonomy advanced very high resolution radiometer moderate-resolution imaging spectroradiometer

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Improving a PenmanMonteith evapotranspiration model by incorporating soil moisture control on soil evaporation in semiarid areas

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International Journal of Digital Earth 2013年第S01期6卷 134-156页

作者： liang Sun shunlin liang Wenping Yuan Zhongxin Chen state key laboratory of remote sensing science Jointly Sponsored by Beijing Normal University and Institute of Remote Sensing ApplicationsChinese Academy of ScienceBeijingChina College of Global Change and Earth System science Beijing Normal UniversityBeijingChina Department of Geography University of MarylandCollege ParkMDUSA institute of Agricultural Resources and Regional Planning Chinese Academy of Agricultural SciencesBeijingChina

PenmanMonteith(PM)theory has been successfully applied to calculate land surface evapotranspiration(ET)for regional and global ***,soil surface resistance,related to soil moisture,is always difficult to determine over a large region,especially in arid or semiarid *** this study,we developed an ET estimation algorithm by incorporating soil moisture control,a soil moisture index(SMI)derived from the surface temperature and vegetation index *** denoted this ET algorithm as the *** PM-SMI algorithm was compared with several other algorithms that calculated soil evaporation using relative humidity,and validated with Bowen ratio measurements at seven sites in the Southern Great Plain(SGP)that were covered by grassland and cropland with low vegetation cover,as well as at three eddy covariance sites from AmeriFlux covered by forest with high vegetation *** results show that in comparison with the other methods examined,the PM-SMI algorithm significantly improved the daily ET estimates at SGP sites with a root mean square error(RMSE)of 0.91 mm/d,bias of 0.33 mm/d,and R^(2) of *** three forest sites,the PM-SMI ET estimates are closer to the ET measurements during the non-growing season when compared with the other three *** all the 10 validation sites,the PMSMI algorithm performed the ***-SMI 8-day ET estimates were also compared with MODIS 8-day ET products(MOD16A2),and the latter showed negligible bias at SGP *** contrast,most of the PM-SMI 8-day ET estimates are around the 1:1 line.

关键词： ET PenmanMonteith soil moisture MODIS

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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

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EOP预报误差对深空探测器精密定轨结果影响分析

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武汉大学学报（信息科学版） 2024年第9期49卷 1538-1545页

作者：王波鄢建国高梧桐叶茂于亮马卓希李斐武汉大学测绘遥感信息工程国家重点实验室湖北武汉430079 61363部队陕西西安710054

地球定向参数(earth orientation parameters,EOP)是地球参考系和天球参考系之间的重要转换参数,由于复杂的数据处理过程,空间大地测量技术获取的EOP存在几天至2周的延迟。为保障深空探测任务的成功实施,在不能使用实时EOP精确观测值的... 详细信息

地球定向参数(earth orientation parameters,EOP)是地球参考系和天球参考系之间的重要转换参数,由于复杂的数据处理过程,空间大地测量技术获取的EOP存在几天至2周的延迟。为保障深空探测任务的成功实施,在不能使用实时EOP精确观测值的情况下,需研究分析EOP预报误差对深空探测器精密定轨精度的影响。分析了国际地球自转和参考系服务发布的A公报的预报误差,处理了火星快车(Mars express,MEX)的射电跟踪数据和小行星2016HO3探测器的仿真数据,对比分析了EOP预报误差对深空探测器精密定轨精度的影响。结果表明,EOP预报值产生的定轨误差对MEX探测器主要表现在切向和法向上,在深空探测器双程多普勒数据精密定轨中,世界时与协调世界时之间的差异项预报误差为主要误差项;精密定轨误差与EOP预报时长无绝对线性关系,而与差异项预报误差强相关,且在相同预报误差的情况下,该项定轨误差随星地距离变化率的增加而增加。

关键词：地球定向参数精密定轨深空探测器预报误差

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发展几何光学遥感建模理论,推动定量遥感科学前行——深切缅怀李小文院士

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遥感学报 2019年第1期23卷 1-10页

作者：柳钦火阎广建焦子锑肖青闻建光梁顺林王锦地中国科学院遥感与数字地球研究所遥感科学国家重点实验室北京100101 中国科学院大学资源与环境学院北京100190 北京师范大学地理科学学部遥感科学国家重点实验室北京100875 马里兰大学地理科学系美国马里兰20742

李小文长期致力于遥感基础研究,是植被几何光学遥感建模的先驱。他创建了Li-Strahler几何光学模型,论文入选"国际光学工程学会(SPIE)"里程碑系列文集。以李小文为首席科学家,带领国内外科学团队,在植被二向性反射建模、热红外遥感辐射... 详细信息

李小文长期致力于遥感基础研究,是植被几何光学遥感建模的先驱。他创建了Li-Strahler几何光学模型,论文入选"国际光学工程学会(SPIE)"里程碑系列文集。以李小文为首席科学家,带领国内外科学团队,在植被二向性反射建模、热红外遥感辐射方向性建模、遥感信息尺度效应、定量遥感综合实验与真实性检验方面做出了卓越成就。本文总结了李小文的主要学术思想,对定量遥感相关研究进展进行了总结和回顾,对遥感科学及相关领域具有重要的借鉴和参考意义。

关键词：几何光学建模热红外辐射方向性定量遥感反演尺度效应遥感综合试验

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