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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 remote sensing and gis, Department of Geography, beijing normal University, beijing 100875, china National Satellite Meteorological center, china Meteorological Administration, beijing 100081, china 1. research center for remote 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, 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.

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, 2181 Lefrak Hall, University of Maryland, College Park, MD20742, 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.

Bi-directional normalized difference vegetation index: concept and application

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Progress in Natural Science:Materials International 2002年第2期 37-41页

作者： li xiaowen , GAO Feng , wang jindi , YANG Hua, A. STRAHLER and C. SCHAAF( research center for remote sensing and Department of Geography, beijing normal University, beijing 100875, china Department of Geography and center for remote sensing, Boston University, Boston MA02215, USA) research center for remote sensing and Department of Geography，beijing normal University，beijing 100875，china Department of Geography and center for remote sensing，Boston University，Boston MA02215，USA Department of Geography and center for remote sensing，Boston University，Boston MA02215，USA research center for remote sensing and Department of Geography，beijing normal University，beijing 100875，china

The data products of land surface bi-directional reflectance distribution function (BRDF) from the space-borne Polarization and Directionality of the Earth Reflectance (POLDER) of France and the Moderate Resolution Imaging Spectroradiometer (MODIS) of USA are available recently, but the atmospheric correction for meeting the requirement of quantitative remote sensing is still a very difficult problem. This paper presents a concept: bi-directional normalized difference vegetation index (Bi-NDVI), in order to consider simultaneously the effects of both land surface BRDF and atmospheric path scattering. An atmospheric quality index is thus defined for satellite multi-angular observations. The quality of MODIS/BRDF data products can be improved notably through iterative inversion weighed by this index.

关键词： bi-directional reflectance normalized difference vegetation index atmospheric correction.

An improved TES algorithm based on the corrected ALPHA difference spectrum

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Science china Earth Sciences 2007年第2期50卷 274-282页

作者： TANG ShiHao li xiaowen wang jindi ZHU QiJiang ZHANG liHua National Satellite Meteorological center Key Laboratory of Radiometric Calibration and Validation for Environmental SatellitesChinese Meteorological AdministrationBeijing 100081China research center of remote sensing&gis State Key Lab Remote Sensing ScienceBeijing Normal UniversityBeijing 100875China

Different from visible signals, thermal infrared radiances depend on both temperature and emissivity. It is a key problem for us to separate temperature and emissivity in thermal infrared remote sensing re- search. Another difficulty encountered in the retrieval of surface temperature is the correction of downwelling sky irradiance, because it is closely related to surface emissivity. When emissivity is un- known, the downwelling sky irradiance is difficult to be removed. In this paper, we introduce a correc- tion term of downwelling sky irradiance developed by li and Becker into Wien’s approximation, to de- rive an improved ALPHA difference spectrum which is independent of temperature, and furthermore develop a correction term to remove the error of Wien’s approximation. Under the support of the above work, attractive features of Alpha derived emissivity method and ASTER TES algorithm are combined together to acquire a new Improved TES algorithm based on Corrected ALPHA Difference Spectrum (ICADS TES). Because a multi-band inversion technique is applied, and the operations of band ratios and differences are included in the algorithm, it can partly remove the influence of atmosphere and noises. Numerical simulation experiments show that for various combinations of atmosphere, land covers and surface temperatures, the algorithm is applicable and stable. Its accuracy for temperature is 0―1.5 K, and that for emissivity is 0―0.015. Compared with current TES algorithms, our method has clear physical meaning, is easy to be implemented, and is applicable for a wide temperature range and surface types. The results are not influenced by the directional characteristic of emissivity. Because ICADS TES does not need the support of a priori information of surface types, it is also not influenced by the accuracy of classification and the problem of mixture pixels. Compared with our former TES algorithm based on corrected Alpha difference spectra (CADS TES), the new algorithm take

关键词： thermal infrared remote sensing TES algorithm emissivity land surface temperature