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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., Beijing 100871, china 2. center of remote sensing and gis of Beijing normal {2., Beijing 100875, china 1. Institute of remote sensing and Geographical Information System Peking University 100871 Beijing China 2. center of remote {2. and gis of Beijing 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

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 {2. and gis, School of Geography, Beijing normal university, Beijing 100875, china 2. State Key Laboratory of remote {2. Science, Beijing 100875, china 3. Beijing 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 1. Research center for remote {2. 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

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.

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 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.

Polynomial analysis of canopy spectra and biochemical component content inversion

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Science china Earth Sciences 2005年第10期48卷 1780-1791页

作者： YAN Chunyan1, LIU Qiang1, NIU Zheng1, WANG Jihua2. HUANG Wenjiang2.& LIU Liangyun 2.1. 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 2. National Engineering Research center for Information Technology in Agriculture, Beijing 100089, china 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 china National Engineering Research center for Information Technology in Agriculture Beijing china

A polynomial expression model was developed in this paper to describe directional canopy spectra, and the decomposition of the polynomial expression was used as a tool for re-trieving biochemical component content from canopy multi-angle spectra. First, the basic formula of the polynomial expression was introduced and the physical meaning of its terms and coeffi-cients was discussed. Based on this analysis, a complete polynomial expression model and its decomposition method were given. By decomposing the canopy spectra simulated with SAILH model, it shows that the polynomial expression can not only fit well the canopy spectra, but also show the contribution of every order scattering to the whole reflectance. Taking the first scattering coefficients a10 and a01 for example, the test results show that the polynomial coefficients reflect very well the hot spot phenomenon and the effects of viewing angles, LAI and leaf inclination angle on canopy spectra. By coupling the polynomial expression with leaf model PROSPECT, a canopy biochemical component content inversion model was given. In the simulated test, the canopy multi-angle spectra were simulated by two different models, SAILH and 4-SCALE re-spectively, then the biochemical component content was retrieved by inverting the coupled polynomial expression + PROSPECT model. Results of the simulated test are promising, and when applying the algorithm to measured corn canopy multi-angle spectra, we also get relatively accurate chlorophyll content. It shows that the polynomial analysis provides a new method to get biochemical component content independent of any specific canopy model.

关键词： polynomial expression, canopy spectra, decomposition, biochemical component content, inversion.

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 100081 China State Key Laboratory of remote sensing Science School of Geography and Remote Sensing Science Beijing Normal University Beijing 100875 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

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 {2. and gis, School of Geography, Beijing normal university, State Key Laboratory of remote {2. Science, Beijing Key Laboratory for remote {2. of Environment and Digital Cities, Beijing 100875, china Department of Mathematics, Beijing normal university, Beijing 100875, china 1. Research center for remote {2. 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.

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, 2.81 Lefrak Hall, university 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.

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

Principle of small target detection for hyperspectral imagery

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Science china Earth Sciences 2007年第8期50卷 1225-1231页

作者： GENG XiuRui1,2.3 & ZHAO YongChao2.3,4 1 Research center for remote {2. and gis, Department of Geography, Beijing normal university, Beijing 100875, china 2.State Key Laboratory of remote {2. Science, Chinese Academy of Sciences, Beijing 100101, china 3 Institute of Erectronics, Chinese Academy of Sciences, Beijing 100080, china 4 center for for Assessment and Monitoring of Forest and Environmental Resources and Division of Ecosystem Sciences university of California Berkeley, CA 9472.-3114, USA 1. Research center for remote {2. and gis Department of Geography Beijing Normal University Beijing 100875 China 2. State Key Laboratory of remote sensing Science Chinese Academy of Sciences Beijing 100101 China 3. Institute of Erectronics Chinese Academy of Sciences Beijing 100080 China 4. center for for Assessment and Monitoring of Forest and Environmental Resources and Division of Ecosystem Sciences University of California Berkeley CA 94720-3114 USA

This paper generalizes the progress of algorithms in small target detection for hyperspectral imaging, and finds that whitening the image is the key point of many methods in small target detection. An al-gorithm is presented to detect desired targets by converting large targets into small ones based on the weighted sample autocorrelation matrix.

关键词： hyperspectral whiten target detection