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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 {2. 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 {2. 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 {2. 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 12.0 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,{2. & LI Xiaowen1 1. state key laboratory of Remote sensing science, School of Geography and Remote sensing, Research Center for RS&GIS, beijing normal university,{12., china 2. Department of Geography, 2.81 Lefrak Hall, university of Maryland, College Park, MD2.742. USA 1. state key {2. 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 {2. 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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Impact of Different Rates of Nitrogen Supplementation on Soil PhysicochemicalProperties and Microbial Diversity in Goji Berry

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Phyton-International Journal of Experimental Botany 2024年第3期93卷 467-486页

作者： Xiaojie liang Wei An Yuekun Li Yajun wang Xiaoya Qin Yanhong Cui Shuchai Su state key {2. of Efficient Production of Forest Resources The College of ForestryBeijing Forestry UniversityBeijing100083China National Wolfberry Engineering Research Center Wolfberry Science Research InstituteNingxia Academy of Agriculture and Forestry SciencesYinchuan750002China

Goji berry(Lycium barbarum L.)is substantially dependent on nitrogen fertilizer application,which can signiﬁ-cantly enhance fruit yield and Goji berry industrial development in Ningxia,*** study aimed to analyze the functions of differential nitrogen application rates including low(N1),medium(N2.,and high(N3)levels in soil microbial community structure(bacterial and fungal)at 2.diverse soil depths(0-2.,2.-40 cm)through high-throughput sequencing technology by targeting 16S RNA gene and ITS1&ITS2.*** the observed physicochemical parameters exhibited signiﬁcant improvement(p0.05)differences among three group treatments(N1,N2.and N3).Furthermore,the functional enrichment analysis demonstrated signiﬁcant(psensing,cysteine and methionine metabolism,and transcriptional machinery for bacterial communities,while various saprotrophic functional roles for fungal ***,moderately reducing the use of N-supplemented fertilizers is conducive to increasing soil nitrogen utilization rate,which can contribute to sustainable agriculture practices through improved soil quality,and microbial community structure and functions.

关键词： Goji berry production Ningxia,china differential nitrogen supplementation rates 16S RNA gene and IT1&IT2.region sequencing soil physicochemical properties

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Long-term(2.13-2.2.)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 {2. 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 {2. of Crop Gene Resources and Breeding Institute of Crop SciencesChinese Academy of Agricultural Sciences(CAAS)BeijingChina state key {2. 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 2.13 to 2.2. 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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Organic-inorganic hybrid piezotronic bipolar junction transistor for pressure sensing

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Microsystems & Nanoengineering 2024年第3期10卷 229-244页

作者： Emad Iranmanesh Zihao liang Weiwei Li Congwei Liao Shunyu Jin Chuan Liu Kai wang Shengdong Zhang Charalampos Doumanidis Gehan A.J.Amaratunga Hang Zhou Guangdong Provincial key laboratory of In-Memory Computing Chips School of Electronic and Computer EngineeringPeking University Shenzhen Graduate SchoolShenzhen 518055P.R.China School of Mechanical Engineering Guangdong Technion-Israel Institute of TechnologyShantou 515063P.R.China state key {2. of Microelectronics Device and Integrated Technology Institute of MicroelectronicsChinese Academy of SciencesBeijing 100029P.R.China Hefei National Research Center for Physical sciences at the Microscale University of Science and Technology of ChinaHefei 23000PR China School of Electronics and Information Technology Sun Yat-sen UniversityNo.132 East Waihuan RoadGuangzhou 510006P.R.China Department of Mechanical Aerospaceand Biomedical EngineeringUniversity of South AlabamaShelby Hall3128MobileAL 36688USA Electrical Engineering Division Department of EngineeringUniversity of Cambridge9 JJ Thomson AvenueCambridge CB30FAUnited Kingdom Zhejiang university International CampusHainingChina

With the rapid development of the Internet of Things(IoTs),wearable sensors are playing an increasingly important role in daily monitoring of personal health and *** signal-to-noise-ratio has become the most critical performance factor to *** enhance it,on the one hand,good sensing materials/devices have been employed;on the other hand,signal amplification and noise reduction circuits have been ***,most of these devices and circuits work in an active sampling mode,requiring frequent data acquisition and hence,entailing high-power *** this scenario,a flexible and wearable event-triggered sensor with embedded signal amplification without an external power supply is of great ***,we report a flexible two-terminal piezotronic n-p-n bipolar junction transistor(PBJT)that acts as an autonomous and highly sensitive,current-and/or voltage-mediated pressure *** PBJT is formed by two back-to-back piezotronic diodes which are defined as emitter-base and collectorbase *** force exertion on the emitter side,as a result of the piezoelectric effect,the emitter-base diode is forward biased while the collector-base diode is reverse *** to the inherent BJT amplification effect,the PBJT achieves record-high sensitivities of 139.7 kPa^(-1)(current-based)and 88.66 kPa^(-1)(voltage-based)in sensing *** PBJT also has a fast response time of110 ms under exertion of dynamic stimuli ranging from a flying butterfly to a gentle finger ***,the PBJT advances the {2. of the art not only in terms of sensitivity but also in regard to being self-driven and autonomous,making it promising for pressure sensing and other IoT applications.

关键词： bipolar transistor IoT

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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 {2. 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 {2. 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 {2. of Remote sensing science and School of Geography Beijing Normal University state key {2. 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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