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Information flow and controlling in regularization inversion of quantitative remote sensing

Science china Earth Sciences 2005年第1期48卷 74-83页

作者： YAng Hua, XU Wangli, ZHAO Hongrui, CHEN Xue & WAng Jindi research center for {2. sensing and gis, School of geography, beij.ng normal university, State Key Laboratory of {2. sensing Science, beij.ng Key Laboratory for {2. sensing of Environment and Digital Cities,{12., china department of Mathematics, beij.ng normal university,{12., china 1. research center for {2. sensing and gis School of Geography Beijing Normal University State Key Laboratory of Remote Sensing Science Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities 100875 Beijing China 2. department of Mathematics Beijing Normal University 100875 Beijing China

In order to minimize uncertainty of the inversed parameters to the largest extent by making full use of the limited information in remote sensing data, it is necessary to understand what the information flow in quantitative remote sensing model inversion is, thus control the information flow. Aiming at this, the paper takes the linear kernel-driven model inversion as an example. At first, the information flow in different inversion methods is calculated and analyzed, then the effect of information flow controlled by multi-stage inversion strategy is studied, finally, an information matrix based on USM is defined to control information flow in inversion. It shows that using Shannon entropy decrease of the inversed parameters can express information flow more properly. Changing the weight of a priori knowledge in inversion or fixing parameters and partitioning datasets in multi-stage inversion strategy can control information flow. In regularization inversion of remote sensing, information matrix based on USM may be a better tool for quantitatively controlling information flow.

关键词： regularization inversion, information flow, Shannon entropy decrease, information matrix.

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Integrative inversion of land surface component temperature

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Science china Earth Sciences 2005年第11期48卷 2011-2019页

作者： FAN Wenjie1 & XU Xiru1,2.1. Institute of remote sensing and Geographical Information System, Peking {2., beij.ng 100871, china 2. center of remote sensing and gis of beij.ng normal university, beij.ng 100875, china 1. Institute of remote sensing and Geographical Information System Peking University 100871 Beijing China 2. center of remote {2. and gis of beij.ng normal university 100875 Beijing China

In this paper, the row winter wheat was selected as the example to study the com-ponent temperature inversion method of land surface target in detail. The result showed that the structural pattern of row crop can affect the inversion precision of component temperature evi-dently. Choosing appropriate structural pattern of row crop can improve the inversion precision significantly. The iterative method combining inverse matrix was a stable method that was fit for inversing component temperature of land surface target. The result of simulation and field ex-periment showed that the integrative method could remarkably improve the inversion accuracy of the lighted soil surface temperature and the top layer canopy temperature, and enhance inver-sion stability of components temperature. Just two parameters were sufficient for accurate at-mospheric correction of multi-angle and multi-spectral thermal infrared data: atmospheric trans-mittance and the atmospheric upwelling radiance. If the atmospheric parameters and component temperature can be inversed synchronously, the really and truly accurate atmospheric correction can be achieved. The validation using ATSRII data showed that the method was useful.

关键词： components temperature, inverse pattern, atmospheric correction

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Principle and application of three-band gradient difference vegetation index

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Science china Earth Sciences 2005年第2期48卷 241-249页

作者： TAng Shihao, ZHU Qijiang, WAng Jindi, ZHOU Yuyu & ZHAO Feng research center for {2. sensing and gis, department of geography, beij.ng normal university, beij.ng 100875, china National Satellite Meteorological center, china Meteorological Administration, beij.ng 100081, china 1. research center for {2. sensing and gis Department of Geography Beijing Normal University 100875 Beijing China 2. National Satellite Meteorological center China Meteorological Administration 100081 Beijing China

Vegetation index is a simple, effective and experiential measurement of terrestrial vegetation activity, and plays a very important role in qualitative and quantitative remote sensing. Aiming at shortages of current vegetation indices, and starting from the analysis of vegetation spectral characteristics, we put forward a new vegetation index, the three-band gradient difference vegetation index (TGDVI), and established algorithms to inverse crown cover fraction and leaf area index (LAI) from it. Theoretical analysis and model simulation show that TGDVI has high saturation point and the ability to remove the influence of background to some degree, and the explicit functional relation with crown cover fraction and LAI can be established. Moreover, study shows that TGDVI also has the ability to partly remove the influence of thin cloud. Experiment in the Shunyi District, {2., china shows that reasonable result can be reached using the vegetation index to retrieve LAI. We also theoretically analyzed the reason why the normalized difference vegetation index (NDVI) owns the low saturation point, and show that it is determined by the definition of NDVI and the characteristic of vegetation spectra, and is unavoidable to some degree. Meanwhile, through model simulation, we also indicate that the relationship between simple ratio vegetation index (SR) and LAI closes to a piecewise linear one instead of a linear one, which is mainly caused by the influence of background and different change rates of reflectance in red and infrared bands with LAI increasing.

关键词： vegetation index, remote sensing, LAI, crown cover fraction, remote sensing inversion.

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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 remote sensing and {2. of Peking university,beij.ng 100871,china 2.center of remote sensing and {2. of beij.ng normal university,beij.ng {12.,china 1. Institute of remote sensing and {2. of Peking university 100871 Beijing China 2. center of remote {2. and gis of beij.ng 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 {2. and components temperature retrieval.

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

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Study on NDVI-T_s space by combining LAI and evapotranspiration

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Science china Earth Sciences 2006年第7期49卷 747-754页

作者： HAN Lijuan1,2,3, WAng Pengxin4, YAng Hua1,2,3, LIU Shaomin1,2,3 & WAng Jindi1,2,3 1. research center for remote sensing and gis, School of geography, beij.ng normal university, beij.ng 100875, china 2. State Key Laboratory of remote sensing Science, beij.ng 100875, china 3. beij.ng Key Laboratory for remote sensing of Environment and Digital Cities, beij.ng 100875, china 4. department of Information Management, College of Information and Electronic Engineering, china Agricultural university, beij.ng 100083, china 1. research center for {2. sensing and gis School of Geography Beijing Normal University Beijing 100875 China 2. State Key Laboratory of remote sensing Science Beijing 100875 China 3. beij.ng Key Laboratory for remote {2. of Environment and Digital Cities Beijing 100875 China 4. department of Information Management College of Information and Electronic Engineering China Agricultural University Beijing 100083 China

This paper focuses on interpreting the different spatial relationships between NDVI and Ts, a triangular or a trapezoid, and on analyzing transformation conditions, the physical and ecological meanings of the vegetation index-surface temperature space as well. Further, we use the Tempera-ture-Vegetation Dryness Index (TVDI) to explain the existent meaning of a triangular space after NDVI reaches its saturated state by employing the relationships between NDVI, LAI and evapotranspiration. The specific relations between NDVI and Ts are useful for describing, validating and updating land surface models.

关键词： NDVI, land surface temperature, evapotranspiration, leave area index.

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Estimating surface evapotranspiration using combined MODIS and CBERS-02.data

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Science china(Technological Sciences) 2005年第S2期48卷 145-160页

作者： XIN Xiaozhou1,2. LIU Qinhuo1, TAng Yong1,3, TIAN Guoliang1, GU Xingfa1, LI Xiaowen1,2. ZHEng Hongsheng4 & CHEN Jiayi4 1. State Key Laboratory of remote sensing Science, Institute of remote sensing Applications, Chinese Academy of Sciences, beij.ng 100101, china 2. research center for remote sensing and gis, department of geography, beij.ng normal university, beij.ng 100875, china 3. Graduate university of Chinese Academy of Sciences, beij.ng 100039, china 4. department of Atmospheric Physics, Peking university, beij.ng 100871, china 1. State Key Laboratory of remote sensing Science Institute of Remote Sensing Applications Chinese Academy of Sciences 100101 Beijing China2. Research Center for Remote Sensing and GIS Department of Geography Beijing Normal University 100875 Beijing China3. Graduate University of Chinese Academy of Sciences 100039 Beijing China4. Department of Atmospheric Physics Peking University 100871 Beijing China

Spatial scale error is one of the most serious problems in the estimates of land surface heat fluxes of sensible and latent from satellite-borne data such as MODIS 1km resolution reflectance and emissive data. One of the feasible and economic ways to decrease the spatial scale error is to use high resolution land use class data together with the MODIS data. CBERS-02.data were used to produce land use class of Baiyangdian area, Hebei Province, china in the autumn of 2.04. The area ratio of each class in MODIS pixel was calculated, and used to derive the heat fluxes of the mixed pixel. The results showed that the estimated heat fluxes of soil, sensible and latent have been changed remarkably after using the high resolution land class data. It could be concluded from the comparison between simulated and ground-measured fluxes as well as the theoretical analysis that high resolution land class data are useful to diminishing the scale error of heat fluxes estimated from low resolution satellite data.

关键词： surface evapotranspiration spatial scale error MODIS CBERS land class.

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The definition of effective emissivity of land surface at the scale of remote sensing pixels

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Chinese Science Bulletin 1999年第23期44卷 2154-2158页

作者： LI Xiaowen and WAng Jindi research center for {2. sensing and gis, beij.ng normal Unirersity, Keying 100875, china center for {2. sensing, Boston Univ. , MA02.15, USA research center for {2. sensing and CIS Beijing Normal University Beijing China center for remote {2. Boston Univ. USA

remote sensing (RS) of land surface temperature (LST) is a very challenging problem at the present development stage of RS science. Tremendous efforts have been devoted to atmosphere correction and temperature emissivity separation (TES) of new LST product algorithms. However, the mechanism of directionality of thermal emission from land surface remains unknown, and even worse, there are confusions on the definition of the effective emissivity of land surface at the scale of RS pixels. The mechanism of directionality of thermal emission

关键词： non-isothermal pixel effective emissivity. directionality of thermal emission.

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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 XU Xiru ( Institute of remote sensing and Geographical Information System, Peking {2., beij.ng 100871, china center of remote sensing and gis of beij.ng normal university, beij.ng 100875, china) [a]Institute of remote sensing and Geographical Information System Peking University Beijing China [b]center of remote {2. and gis of beij.ng 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.

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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 {2. and remote sensing, research center for RS&gis, beij.ng normal university, beij.ng 100875, china 2. department of geography, 2.81 Lefrak Hall, {12. of Maryland, College Park, MD2.742. 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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Synchronous estimation of DTM and fractional vegetation cover in forested area from airborne LIDAR height and intensity data

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Science china(Technological Sciences) 2008年第S2期51卷 176-187页

作者： BAO YunFei1,2,CAO ChunXiang1,ZHAng Hao1,CHEN ErXue3,HE QiSheng1,2,HUAng HuaBing1,LI ZengYuan3,LI XiaoWen1,4 & GOng Peng1 1 State Key Laboratory of remote sensing Science,jointly sponsored by the Institute for remote sensing Applications of Chinese Academy of Sciences and beij.ng normal university,beij.ng 100101,china 2 Graduate university of the Chinese Academy of Sciences,beij.ng 100049,china 3 Institute of Forest Resource Information Technique,the Chinese Academy of Forestry,beij.ng 100091,china 4 research center for remote sensing and gis,department of geography and beij.ng Key Laboratory for remote sensing of Environment and Digital Sites,beij.ng normal university,beij.ng 100875,china State Key Laboratory of remote sensing Science jointly sponsored by the Institute for Remote Sensing Applications of Chinese Academy of Sciences and Beijing Normal University Beijing 100101 China Graduate university of the Chinese Academy of Sciences Beijing 100049 China Institute of Forest Resource Information Technique the Chinese Academy of Forestry Beijing 100091 China research center for {2. sensing and gis Department of Geography and Beijing Key Laboratory for Remote Sensing of Environment and Digital Sites Beijing Normal University Beijing 100875 China

We proposed a method to separate ground points and vegetation points from discrete return,small footprint airborne laser scanner data,called skewness change *** method,which makes use of intensity of laser scanner data,is especially applicable in steep,and forested *** does not take slope of forested area into account,while other algorithms consider the change of slope in steep forested *** ground points and vegetation points can be used to estimate digital terrain model(DTM) and fractional vegetation cover,respectively.A few vegetation points which were classified into the ground points were removed as noise before the generation of *** method was tested in a test area of 10000 square meters.A LiteMapper -5600 laser system was used and a flight was carried out over a ground of 700―800 *** this tested area,a total number of 1546 field measurement ground points were measured with a total station TOPCON GTS-602.and TOPCON GTS -7002.for validation of DTM and the mean error value is -18.5 cm and the RMSE(root mean square error) is ±2..9 cm.A data trap sizes of 4m in diameter from airborne laser scanner data was selected to compute vegetation fraction *** of fractional vegetation cover was carried out using 15 hemispherical photographs,which are georeferenced to centimeter accuracy by differential *** gap fraction was computed over a range of zenith angles 10° using the gap light analyzer(GLA) from each hemispherical *** R2.for the regression of fractional vegetation cover from these ALS data and the respective field measurements is *** this study presents a method for synchronous estimation of DTM and fractional vegetation cover in forested area from airborne LIDAR height and intensity data.

关键词： LIDAR intensity skewness synchronous DTM fractional vegetation cover

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