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Estimating surface evapotranspiration using combined MODIS and CBERS-02 data

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, {3.,{3.,2, ZHENG Hongsheng4 & CHEN Jiayi4 1. State Key Laboratory of remote sensing Science, institute of remote sensing applications, chinese academy of {13., 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 {13., beijing 10003., 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 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.

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The quantification of remote sensing

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Science china(Technological sciences) 2005年第S2期48卷 1-11页

作者： gu xingfa1,2,3.tian guoliang1,2, li xiaowen1,2 & {3.O Jianning4 1. State Key Laboratory of {3. sensing Science, jointly sponsored by the institute of remote sensing applications of chinese academy of sciences and beijing normal university, beijing 100101, china 2. The Center for National Spaceborne Demonstration, beijing 100101, china 3. Institut National de la Recherche Agronomique, Climat Sol Environnement, 84914 Avignon, France 4. china Center for Resource Satellite Data and Application, beijing 10083., china chinese Acad Sci Inst Remote Sensing Applicat State Key Lab Remote Sensing Sci Beijing 100101 Peoples R China beijing normal Univ State Key Lab Remote Sensing Sci Beijing 100101 Peoples R China Ctr Natl Spaceborne Demonstrat Beijing 100101 Peoples R China Inst Natl Rech Agron F-84914 Avignon France china Ctr Resource Satellite Data & Applicat Beijing 100830 Peoples R China

The paper firstly defined the remote sensing information quantification, analyzed the necessity of developing remote sensing quantification, figured out the application guidelines requirement, and pointed out the importance of quantification research. Then taking the remote sensing application research of CBERS-02 data quantification as the example, the paper described the whole quantification system of “remotely sensed digital signal-radiation information-field parameter inversion”. Finally the paper gave the prospect for the development trend of the quantitative remote sensing.

关键词： remote sensing information quantification CBERS-02 radiometric calibration surface parame- ter reversion.

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Biomass estimation and uncertainty analysis based on CBERS-02 CCD camera data and field measurement

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Science china(Technological sciences) 2005年第S2期48卷 116-128页

作者： CHEN liangfu1,2, GAO Yanhua1,3. CHENG Yu1,3. WEI Zheng1,3. XIAO Qing1,4, liU Qinhuo1, YU Tao1, liU Qijing5, {3. & tian guoliang1 1. State Key Laboratory of {3. sensing Science, institute of {3. sensing applications, chinese {13. of sciences, beijing 100101, china 2. Geography and Environment College, Jiangxi normal university, Nanchang 3.0027, china 3. Graduate university of chinese {13. of sciences, beijing 10003., china 4. beijing Research institute of Uranium Geology, beijing 100875, china 5. institute of Geographic sciences and Natural Resources Research, chinese {13. of sciences, beijing 100101, china 1. State Key Laboratory of remote sensing Science Institute of Remote Sensing Applications Chinese Academy of Sciences 100101 Beijing China 2. Geography and Environment College Jiangxi Normal University 330027 Nanchang China 3. Graduate university of chinese academy of {3. 100039 Beijing China 4. beijing Research institute of Uranium Geology 100875 Beijing China 5. institute of Geographic sciences and Natural Resources Research Chinese Academy of Sciences 100101 Beijing China

The study site is located in Qianyanzhou experimental station and the surrounding area. Based on CBERS-02 satellite data and field measurement, we not only discussed the relationship between NDVI and biomass of two species of coniferous plantations, namely, Pinus massoniana Lamb and Pinus elliottii Engelm, but also introduced the biomass models based on NDVI. The comparison between measured biomass in Qianyanzhou and biomass derived from CBERS-02 CCD data showed that it is feasible to estimate biomass based on NDVI. But its limitations cannot be ignored. This kind of model depends on the dominant vegetation species. There are some effect factors in estimating biomass based on NDVI. This paper analyzes these factors based on fine-resolution CBERS-02 CCD data and some conclusions are drawn: In Qianyanzhou, the area with good vegetation coverage, the nonlinearity of NDVI has little influence on scaling-up of NDVI. As a result of surface heterogeneity, scaling-up can cause NDVI within each pixel to change. Because scaling-up can cause pixel attribute to change, the applicability of biomass model is one of the sources of error in estimating biomass.

关键词： NDVI biomass scaling effect scaling-up.

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