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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 geography and Remote Sensing, Research Center for RS&GIS, Beijing Normal university, Beijing 100875, China {2., 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.

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Improvement of a 3D radar backscattering model using matrix-doubling method

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Science China Earth Sciences 2010年第7期53卷 1029-1035页

作者： NI WenJian1,2. GUO ZhiFeng1 & SUN GuoQing3 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.Graduate university of Chinese Academy of Sciences, Beijing 100049, China 3 {2. of geography, university of maryland, college park, md 2.042. usa 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 China2. Graduate University of Chinese Academy of Sciences Beijing 100049 China3. Department of Geography University of Maryland College Park MD 27042 USA

Radiative transfer models have been widely used to simulate the radar backscattering from forested areas. A three-dimensional radar backscatter model of forest canopy developed in previous studies takes full account of spatial position of trees in a forest stand, and the interactions among crown, trunk and ground surface. The model predicted well for the co-polarized backscatter measurements, but underestimated the backscattering for cross-polarization, primarily because only the first-order scattering within tree crowns was considered in the model. The backscattering at cross-polarization depends strongly on multiple scatter- ing within tree crowns. To produce good estimations for cross-polarized component, the matrix-doubling method is employed here to compute multiple-scattering within the crown. The modified model is compared with the original model, and the field forest measurements and AIRSAR data are used for validation of the modified model. The cross-polarization backscattering is improved in different degrees for different crown structures and at different bands.

关键词： remote sensing forestry electromagnetic propagation radar backscattering model matrix-doubling

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Vegetation and soil carbon storage in China

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Science China Earth Sciences 2004年第1期47卷 49-57页

作者： LI Kerang, WANG Shaoqiang & CAO MingkuiInstitute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China department of geography, {2. university, college park,md 2.742, usa Correspondence should be addressed to Li Kerang Inst. of Geogr. Sci./Nat. Rsrc. Res. Chinese Academy of Sciences Beijing 100101 China department of geography Maryland University College Park MD 20742 United States

This study estimated the current vegetation and soil carbon storage in China using a biogeochemical model driven with climate, soil and vegetation data at 0.5°latitude-longitude grid spatial resolution. The results indicate that the total carbon storage in China's vegetation and soils was 13.33 Gt C and 82.65 Gt C respectively, about 3% and 4% of the global total. The nationally mean vegetation and soil carbon densities were 1.47 kg C/m2.and 9.17 kg C/m2. respectively, differing greatly in various regions affected by climate, vegetation, and soil types. They were generally higher in the warm and wet Southeast China and Southwest China than in the arid Northwest China; whereas vegetation carbon density was the highest in the warm Southeast China and Southwest China, soil carbon density was the highest in the cold Northeast China and southeastern fringe of the Qinghai-Tibetan Plateau. These spatial patterns are clearly correlated with variations in the climate that regulates plant growth and soil organic matter decomposition, and show that vegetation and soil carbon densities are controlled by different climatic factors.

关键词： carbon storage, vegetation, soil, China.

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Estimating forest aboveground biomass using HJ-1 Satellite CCD and ICESat GLAS waveform data

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Science China Earth Sciences 2010年第S1期53卷 16-25页

作者： GUO ZhiFeng1,CHI Hong1,2.& SUN GuoQing1,3 1State Key Laboratory of Remote Sensing Science,Jointly Sponsored by the Institute of Remote Sensing Applications of Chinese Academy of Sciences and Beijing Normal university,Institute of Remote Sensing Applications,Chinese Academy of Sciences,Beijing 100101,China 2.raduate university of Chinese Academy of Sciences,Beijing 100049,China 3{2. of geography university of maryland,college park,md 2.742,usa 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 Institute of Remote Sensing Applications Chinese Academy of Sciences Beijing 100101 China2. Graduate University of Chinese Academy of Sciences Beijing 100049 China3. Department of Geography University of Maryland College Park MD 20742 USA

The ecosystem in northeastern China and the Russian Far East is a hotspot of scientific research into the global carbon *** aboveground biomass(AGB) is an important component in the land surface carbon *** this study,using forest inventory data and forest distribution data,the AGB was estimated for forest in Daxinganlin in northeastern China by combining charge-coupled device(CCD) data from the Small Satellite for Disaster and Environment Monitoring and Forecast(HJ-1) and Geoscience Laser Altimeter System(GLAS) waveform data from the Ice,Cloud and land Elevation Satellite(ICESat).The forest AGB prediction models were separately developed for different forest types in the research area at GLAS footprint level from GLAS waveform parameters and field survey plot biomass in the Changqing(CQ) Forest Center,which was calculated from forest inventory *** resulted statistical regression models have a R2.0.68 for conifer and R2.0.71 for broadleaf *** models were used to estimate biomass for all GLAS footprints of forest located in the study *** GLAS footprint biomass coupled with various spectral reflectivity parameters and vegetation indices derived from HJ-1 satellite CCD data were used in multiple regression analyses to establish biomass prediction models(R2.0.55 and R2.0.52.for needle and broadleaf respectively).Then the models were used to produce a forest AGB map for the whole study area using the HJ-1 *** data obtained from forest inventory data of the Zhuanglin(ZL) Forest Center were used as independent field measurements to validate the AGB estimated from HJ-1 CCD data(R2.0.71).About 80% of biomass samples had an error less than 2. t ha-1,and the mean error of all validation samples is 5.74 t *** pixel-level biomass map was then stratified into different biomass levels to illustrate the AGB spatial distribution pattern in this *** was found that HJ-1 wide-swath data and GLAS waveform data can be combined to estim

关键词： Disaster and Environment Monitoring and Forecast Satellite ICESat GLAS waveform boreal forest forest aboveground biomass multiple linear regression

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Retrieving crop leaf area index by assimilation of MODIS data into a crop growth model

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Science China Earth Sciences 2010年第5期53卷 721-730页

作者： WANG DongWei1,2.3, WANG JinDi1,2.& LIANG ShunLin4 1 State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal university and Institute of Remote Sensing Applications, CAS, Beijing 100875, China 2.School of geography, Beijing Key Laboratory of Environmental Remote Sensing and Digital City, Beijing Normal university, Beijing 100875, China 3 Haihe River Water Conservancy Commission, Tianjin 300170, China 4 department of geography, university of maryland, college park, md 2.742, usa 1. State Key Laboratory of Remote Sensing Science Jointly Sponsored by Beijing Normal University and Institute of Remote Sensing Applications CAS Beijing 100875 China2. School of Geography Beijing Key Laboratory of Environmental Remote Sensing and Digital City Beijing Normal University Beijing 100875 China3. Haihe River Water Conservancy Commission Tianjin 300170 China4. Department of Geography University of Maryland College Park MD 20742 USA

Leaf area index (LAI) is an important parameter in monitoring crop growth. One of the methods for retrieving LAI from remotely sensed observations is through inversion of canopy reflectance models. Many model inversion methods fail to account for variable LAI values at different crop growth stages. In this research, we use the crop growth model to describe the LAI changes with crop growth, and consider a priori LAI values at different crop growth stages as constraint information. The key approach of this research is to assimilate multiple canopy reflectance values observed at different growth stages and a priori LAI values into a coupled crop growth and radiative transfer model sequentially using a variational data assimilation algorithm. Adjoint method is used to minimize the cost function. Any other information source can be easily incorporated into the inversion cost function. The validation results show that the time series of MODIS canopy reflectance can greatly reduce the uncertainty of the inverted LAI values. Compared with MODIS LAI product at Changping and Shunyi Counties of Beijing, this method has significantly improved the estimated LAI temporal profile.

关键词： inversion leaf area index crop growth model time series MODIS

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Monitoring of rice crop using ENVISAT ASAR data

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Science China Earth Sciences 2006年第7期49卷 755-763页

作者： DONG Yanfang1, 2. SUN Guoqing1,3 & PANG Yong4 1. State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing Applications, Chinese Academy of Sciences, Beijing 100101, China 2. Graduate university of Chinese Academy of Sciences, Beijing 100039, China 3. department of {2., university of maryland, college park, md 2.742, usa 4. Forest Remote Sensing Lab, Research Institute of Forest Resource and Information Technology, Chinese Academy of For-estry, Beijing 100091, China 1. State Key Laboratory of Remote Sensing Science Institute of Remote Sensing Applications Chinese Academy of Sciences Beijing 100101 China 2. Graduate university of Chinese Academy of Sciences Beijing 100039 China 3. department of geography University of Maryland College Park MD 20742 USA 4. Forest Remote Sensing Lab Research Institute of Forest Resource and Information Technology Chinese Academy of Forestry Beijing 100091 China

Because of the cloudy conditions during the rice growth period, rice is one of the agri-cultural crops most suited to monitoring with the SAR instruments. Backscatter response measured by SAR is correlated with rice conditions, including height, density, biomass and structure, which are variable at different growing stages. In this paper, multi-date ENVISAT ASAR Alternating Polarization Mode (APMode) imageries were acquired during the rice crop growing cycle. At the same time, the rice parameters were measured in field. A continuous canopy model was used to compute the back-scattering from rice fields during the growth cycle, and the relationship between rice parameters and radar backscattering coefficients from both ASAR and modeling was analyzed. The effects of polari-zation, incidence angle and polarization on radar backscattering coefficients were analyzed. It was found that simulated radar backscatter has similar trends as ASAR data. This will be meaningful for the further research of rice parameters estimation from ASAR data. Different features show signifi-cantly different signatures in ASAR images and they follow some certain laws, so rice area can be accurately mapped by using multi-temporal SAR images, then rice yield can be estimated.

关键词： rice, ASAR, radar backscattering, model.

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