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The correlation of multi-angle thermal infrared data and the choice of optimal view angles

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Science china Earth Sciences 2004年第6期47卷 570-576页

作者： fan wenjie1 & xu xiru1,2.1.institute of {2. sensing and GIS of {4. University,beijing 100871,china 2.Center of {2. sensing and GIS of beijing Normal University,beijing 100875,china 1. institute of remote {2. and GIS of peking University 100871 Beijing China 2. Center of remote sensing and GIS of {2. Normal University 100875 Beijing China

Based on the matrix formula of thermal infrared radiant system, the components temperature can be retrieved by the inversive matrix. Around the stability problem of retrieved result, the research work we did was focused on (i) the correlation of (wk,j) which is the key to affect the retrieval accuracy, (ii) a general method which can help us find the number of retrievable parameters and evaluate the retrieval error before its performance, (iii) the choice of “optimal viewing angle group” based on the formula of absolute error propagation. The row winter wheat field was chosen as an example. The results can provide a theoretical basis for multi-angle thermal infrared remote sensing and components temperature retrieval.

关键词： components temperature retrieval, correlation, number of retrievable parameters, optimal viewing angle group.

A method for blind separation of components information from mixed pixel

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Progress in Natural Science:Materials International 2006年第07期16卷 760-765页

作者： fan wenjie and {2. xiru ( {4. of remote sensing and Geographical Information System, peking University, beijing 100871, china Center of remote sensing and GIS of {12. Normal University, {12. 100875, china) [a]institute of remote {2. and Geographical Information System Peking University Beijing China [b]Center of remote sensing and GIS of {2. Nornal University Beijing China

In the field of remote sensing, it is important to separate the component information from mixed pixel. If the physical process of remote sensing can be expressed by a set of linear equations, the remote sensing information matrix is equal to the weight matrix multiplied by the component information matrix. Generally speaking, the precondition of retrieval of component information matrix is that the weight matrix is known. However, the blind signal separation (BSS) method can separate the matrix unconditionally, whose basic principle is that the additive information needing separation can be achieved from the statistical characteristics contained in a mass of samples in the remotely sensed information matrix. Therefore, the values of the component information matrix and the weight matrix can be estimated. The wave shape of components can be retrieved by BSS, but the amplitude cannot. In this paper, the plant-soil mixed pixels were chosen as the studying targets in this paper to quantitatively separate the component information and solve the uncertainty of BSS. Simulation and field test verify the reliability of the method. Results show that the BSS can be one of the effective methods of mixed pixel separation, and the foreground of application is very promising.

关键词： mixed pixel component information blind signal separation.

The spatial scaling effect of continuous canopy Leaves Area Index retrieved by remote sensing

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Science china Earth Sciences 2009年第3期52卷 393-401页

作者： xu xiru {2. wenjie TAO Xin institute of remote {2. and GIS Peking UniversityBeijing 100871China

Leave Area Index (LAI) is one of the most basic parameters to describe the geometric structure of plant canopies. It is also important input data for climatic model and interaction model between Earth surface and atmosphere, and some other things. The spatial scaling of retrieved LAI has been widely studied in recent years. Based on the new canopy reflectance model, the mechanism of the scaling effect of con- tinuous canopy Leaf Area Index is studied, and the scaling transform formula among different scales is found. Both the numerical simulation and the field validation show that the scale transform formula is reliable.

关键词： spatial scaling effect remotely sensed LAI the true LAI the reflectance model of continuous canopy

Crop area and leaf area index simultaneous retrieval based on spatial scaling transformation

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Science china Earth Sciences 2010年第11期53卷 1709-1716页

作者： fan wenjie Yan BinYan {2. xiru peking Univ Inst RS & GIS Beijing 100871 Peoples R China

Accurate estimation of crop yields is crucial for ensuring food security. However, crops are distributed so fragmentally in china that mixed pixels account for a large proportion in moderate and coarse resolution remote {2. images. As a result, unmixing of mixed pixel becomes a major problem to estimate crop yield by means of remote {2. method. Aimed at mixed pixels, we developed a new method to introduce additional information contained in the spatial scaling transformation equation to the canopy reflectance model. The crop area and LAI can be retrieved simultaneously. On the basis of a precise and simple canopy reflectance model, directional second derivative method was chosen to retrieve LAI from optimal bands of hyper-spectral data; this method can reduce the impact of the canopy non-isotropic features and soil background. To evaluate the performance of the method, Yingke Oasis, Zhangye City, Gansu Province, was chosen as the validation area. This area was covered mainly by maize and wheat. A Hyperion/EO-1 image with the 30 m spatial resolution was acquired on July 15, 2.08. Images of 180 m and 1080 m resolutions were generated by linearly interpolating the original Hyperion image to coarser resolutions. Then a multi-scale image serial was obtained. Using the proposed method, we calculated crop area and the average LAI of every 1080 m pixel. A SPOT-5 classification figure serves as the validation data of crop area proportion. Results show that the pattern of crop distribution accords with the classification figure. The errors are restrained mainly to -0.1-0.1, and approximate a Normal Distribution. Meanwhile, 85 LAI values obtained using LAI-2.00 Plant Canopy Analyzer, equipped with GPS, were taken as the ground reference. Results show that the standard deviation of the errors is 0.34.. The method proposed in the paper is reliable.

关键词： scale transformation remote sensing crop yield estimation simultaneous retrieval crop area leaf area index

A unified canopy bidirectional reflectance (BRDF) model for row ceops

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Science china Earth Sciences 2012年第5期55卷 824-836页

作者： YAN BinYan xu xiru {2. wenjie institute of RS and GIS Peking UniversityBeijing 100871China

Row sowing is a basic crop sowing method in china,and thus an accurate Bidirectional Reflectance Distribution Function (BRDF) model of row crops is the foundation for describing the canopy bidirectional reflectance characteristics and estimating crop ecological *** of the macroscopically geometric difference,the row crop is usually regarded as a transition between continuous and discrete vegetation in previous *** row treated as the unit for calculating the four components in the Geometric Optical model (GO model),the formula would be too complex and difficult to *** study focuses on the microscopic structure of row *** the row crop as a result of leaves clumped at canopy scale,we apply clumping index to link continuous vegetation and row ***,the formula of clumping index is deduced *** taking leaf as the basic unit,we calculate the four components of the GO model and develop a BRDF model for continuous vegetation,which is gradually extended to the unified BRDF model for row *** is of great importance to introduce clumping index into BRDF *** order to evaluate the performance of the unified BRDF model,the canopy BRDF data collected in field experiment,"Watershed Allied Telemetry Experiment Research (WATER)",from May 30th to July 1st,2.08 are used as the validation dataset for the simulated *** results show that the unified model proposed in this paper is able to accurately describe the non-isotropic characteristics of canopy reflectance for row *** addition,the model is simple and easy to *** general,there is no irreconcilable conflict between continuous and discrete vegetation,so understanding their common and individual characteristics is advantageous for simulating canopy *** is proven that the four components of the GO model is the basic motivational factor for bidirectional reflectance of all vegetation types.

关键词： row crop BRDF clumping clumping index

Study of evolution forecast of the area of the Huanghe River mouth

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Acta Oceanologica Sinica 1993年第2期12卷 249-259页

作者： Guo Yongsheng, xu xuegong, {2. Zhaomu, Wu Peihong and Zhu Xiaoge Department of Geography, Shandong Normal University, Jinan 2.0014. {4. Department of Geography, peking University, beijing 100871, {4. remote sensing Geology Department, Oil Exploration and Exploitation Scientific Research institute, beijing 100083, {4. Department of Geography Shangdong Normal University Linan 250014 China China

From 1979 to 1989, the current Qingshuigou course of the Huanghe River formed a sub - delta which resembles a beak extending into the Laizhou Bay. It covers 618 km2.in area. To meet the needs of developing and constructing the Huanghe River Delta and under the presupposition of keeping the current course for 15-2. a, we forecast mainly by using the OM (1, 1) model that the front border of the sub-delta will be close to 119°30'E and its area will become 92. km2.y the end of the year 2.00. The Huanghe River will make land 760 km2.in area.

关键词： Huanghe River mouth Markov chain forecasting method of grey number series

A unified model of bidirectional reflectance distribution function for the vegetation canopy

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Science china Earth Sciences 2017年第3期60卷 463-477页

作者： xu xiru {2. wenjie LI JuCai ZHAO Peng CHEN GaoXing institute of remote {2. and Geographic Information System Peking UniversityBeijing 100871China beijing Key Laboratory of Spatial Information Integration&Its Applications Beijing 100871China

An accurate and operational bidirectional reflectance distribution function （BDRF） canopy model is the basis of quantitative vegetation remote sensing. The canopy reflectance should be approximated as the sum of the single scattering reflectance arising from the sun, pl, and the multiple scattering reflectance arising from the canopy, fin, as their directional characteristics are dramatically different. Based on the existing BRDF model, we obtain a new analytical expression of ρ1 and ρm in this paper, which is suitable for different illumination conditions and different vegetation canopies. According to the geometrical optic model at the leaf scale, the anisotropy of ρ1 can be ascribed to the geometry of the object, sun and the sensor, multiple scale clumping, and the fraction of direct solar radiation and diffuse sky radiation. Then, we parameterize the area ratios of four components： the sunlit foliage, sunlit ground, shadow foliage and shadow ground based on a Poisson distribution, and develop a new approximate analytical single scattering reflectance model. Assuming G=0.5, a recollision probability theory based scattering model is developed which considers the effects of diffuse sky radiation, scattering inside the canopy and rebounds between the canopy and soil. Validation using ground measurements of maize and black spruce forest proves the reliability of the model.

关键词： Vegetation BRDF Unified model Clumping Index Proportion of direct solar radiation and diffuse sky radiation Recollision probability

Validation methods of LAI products on the basis of scaling effect

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遥感学报 2015年第1期19卷 134-142页

作者： LIAO Yanran GAI Yingying YAO Yanjuan fan wenjie {2. xiru YAN Binyan institute of remote {2. and GIS Peking University Beijing 100871 China beijing Key Laboratory of Spatial Information Integration & Its Applications Beijing 100871 China institute of Oceanographic Instrumentation Shandong Academy of Sciences Qingdao 266001 China Satellite Environment Center Ministry of Environmental Protection Beijing 100094 China Jackson School of Geosciences the University of Texas at Austin Austin 78712 USA

Validation is one of the most important processes used to evaluate whether remotely sensed products can accurately reflect land surface configuration. Leaf Area Index( LAI) is a key parameter that represents vegetation canopy structures and growth conditions. Accurate evaluation of LAI products is the basis for applying them to land surface models. In this study,validation methods of coarse resolution MODIS and GLASS LAI products for heterogeneous pixels are established on the basis of the scaling effect and the scaling transformation. Considering spatial heterogeneity and growth difference,we transformed LAI from field measurements into a 1 km resolution scale with the aid of middle resolution images. We used average LAI and apparent LAI separately to validate the algorithms and products of MODIS and GLASS LAI. Two study areas,Hebi City and the Yingke Oasis,were selected for validation. Both MODIS and GLASS LAI products underestimate the true LAI in crop area. However,this result cannot be completely attributed to their algorithms. Instead,the primary reason is the heterogeneity and nonuniformity of the coarse *** is evident in the Yingke Oasis,where heterogeneity is significant. Given that GLASS LAI product is the fusion of multiple LAI products,the mean value of this product is closer to the real situation,but the dynamic range is narrower than that of MODIS LAI product.

关键词： validation leaf area index scaling effect scaling transformation

Unraveling adaptive changes in electric vehicle charging behavior toward the postpandemic era by federated meta-learning

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The Innovation 2024年第2期5卷 21-23页

作者： Linlin You Rui Zhu Mei-Po Kwan Min Chen fan Zhang Bisheng Yang Man Sing Wong Zheng Qin School of Intelligent Systems Engineering Sun Yat-Sen UniversityShenzhen 518107China institute of High Performance Computing(IHPC) Agency for ScienceTechnology and Research(A*STAR)1 Fusionopolis WaySingapore 138632Republic of Singapore institute of Space and Earth Information Science Chinese University of Hong KongHong KongChina Key Laboratory of Virtual Geographic Environment(Ministry of Education) Nanjing Normal UniversityNanjing 210023China institute of remote {2. and Geographical Information System School of Earth and Space SciencesPeking UniversityBeijing 100871China State Key Laboratory of Information Engineering in Surveying Mappingand Remote SensingWuhan UniversityWuhan 430079China Department of Land Surveying and Geo-Informatics The Hong Kong Polytechnic UniversityHong KongChina

Dear Editor,Electric vehicle(EV)sales have significantly grown over the years to fulfill growing demands for economic travel and greenhouse gas mitigation.1 However,the surge in the number of EVs has led to charging anxiety as users struggle to find an available charging station before running out of electricity,resulting in longer reserve and waiting times.2.Moreover,severe mobility restrictions caused by infectious diseases,such as coronavirus disease 2.19(COVID-19),have greatly affected people’s travel behavior3,4.and hindered their willingness to use EVs,given that charging in public spaces consumes time and increases the risk of contracting the virus.5 This implies that in the postpandemic era,in which individuals coexist with the virus,the interplay between the two important trends,namely vehicle electrification and mobility restrictions,can extensively affect people’s daily commuting by using EVs.

关键词： travel behavior charging