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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 university, beijing 100871, china 2. center of remote sensing and gis of beijing normal university, beijing 100875, china Institute of remote sensing and Geographical Information System Peking University Beijing China center of remote sensing and gis of beijing normal university 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

Information flow and controlling in regularization inversion of quantitative remote sensing

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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 remote 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, beijing 100875, china Department of Mathematics, beijing normal university, beijing 100875, china Research center for remote 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 Beijing China Department of Mathematics Beijing Normal University 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.

A method for blind separation of components information from mixed pixel

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Progress in Natural Science:Materials International 2006年第7期16卷 760-765页

作者： FAN Wenjie and XU Xiru ( Institute of remote sensing and Geographical Information System, Peking university, beijing 100871, china center of remote sensing and gis of beijing normal university, beijing 100875, china) Peking Univ Inst Remote Sensing & Geog Informat Syst Beijing 100871 Peoples R China beijing normal Univ Ctr Remote Sensing & GIS Beijing 100875 Peoples R 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.

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 remote sensing and gis, Department of Geography, beijing normal university, beijing 100875, china National Satellite Meteorological center, china Meteorological Administration, beijing 100081, china Research center for remote sensing and gis Department of Geography Beijing Normal University Beijing China National Satellite Meteorological center China Meteorological Administration 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, beijing, 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.

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, beijing normal university, beijing 100875, china 2. State Key Laboratory of remote sensing Science, beijing 100875, china 3. beijing Key Laboratory for remote sensing 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 Research center for remote sensing and gis School of Geography Beijing Normal University Beijing China State Key Laboratory of remote sensing Science Beijing China beijing Key Laboratory for remote sensing of Environment and Digital Cities Beijing China Department of Information Management College of Information and Electronic Engineering China Agricultural University Beijing 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.

Multiple time scale evaluation of the energy balance during the maize growing season,and a new reason for energy imbalance

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Science china Earth Sciences 2009年第1期52卷 108-117页

作者： GUO JianXia1,2,BIAN LinGen3 & DAI YongJiu2 1 Meteorological Observation center of china Meteorological Administration,beijing 100081,china 2 State Key Laboratory of remote sensing Science,School of Geography and remote sensing Science,beijing normal university,beijing 100875,china 3 Chinese Academy of Meteorological Sciences,beijing 100081,china Meteorological Observation center of china Meteorological Administration Beijing China State Key Laboratory of remote sensing Science School of Geography and Remote Sensing Science Beijing Normal University Beijing China Chinese Academy of Meteorological Sciences Beijing China

Energy imbalance is a common problem associated with the measurement of surface energy using the eddy covariance *** the evaluation of the energy balance,people usually pay more attention to the statistical result that the effective energy(the sum of sensible and latent heat) is systematically lower than the available energy(the difference of net radiation and ground heat flux).However,little attention has been paid to the existence of the reversed situation when the effective energy is larger than the available energy or their contribution to the overall energy closure *** this paper,based on the analysis of the energy balance on multiple time scales across the maize growth season,we conclude that the non-synchronization of energy components is the main reason for the existence of the reversed *** shifting the phase of the effective energy components half an hour ahead,the rates of energy closure over all time scales are improved and dramatically reduce the number of the half-hourly samples when the energy ratio exceeds 1 or is below *** to the characteristics of the energy distribution and transformation over multiple time scales,latent heat is always the main type of energy cost,and the residual of the energy balance increases with the growth of the maize plant surpassing the sensible heat for seventy *** is suggested that the heat storage and photosynthetic energy play an important role in the energy balance during the growing period of maize.

关键词： maize energy balance multiple time scale energy hysteresis

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 beijing normal university,beijing 100101,china 2 Graduate university of the Chinese Academy of Sciences,beijing 100049,china 3 Institute of Forest Resource Information Technique,the Chinese Academy of Forestry,beijing 100091,china 4 Research center for remote sensing and gis,Department of Geography and beijing Key Laboratory for remote sensing of Environment and Digital Sites,beijing normal university,beijing 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 China Graduate university of the Chinese Academy of Sciences Beijing China Institute of Forest Resource Information Technique the Chinese Academy of Forestry Beijing China Research center for remote sensing and gis Department of Geography and Beijing Key Laboratory for Remote Sensing of Environment and Digital Sites Beijing Normal University Beijing 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 ±20.9 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

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 remote sensing and gis, beijing normal Unirersity, Keying 100875, china center for remote sensing, Boston Univ. , MA02215, USA Research center for remote sensing and CIS Beijing Normal University Beijing China center for remote sensing 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.

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, beijing 100101, china 2. Research center for remote sensing and gis, Department of Geography, beijing normal university, beijing 100875, china 3. Graduate university of Chinese Academy of Sciences, beijing 100039, china 4. Department of Atmospheric Physics, Peking university, beijing 100871, china State Key Laboratory of remote sensing Science Institute of Remote Sensing Applications Chinese Academy of Sciences Beijing 100101 China Research center for remote sensing and gis Department of Geography Beijing Normal University Beijing 100875 China Graduate university Chinese Academy of Sciences Beijing 100039 China Department of Atmospheric Physics Peking University Beijing 100871 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 2004. 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.