Sensitivity of Near Real-time MODIS Gross Primary Productivity in Terrestrial Forests Based on Eddy Covariance Measurements  

作　　者：TANG Xuguang LI Hengpeng LIU Guihua LI Xinyan YAO Li XIE Jing CHANG Shouzhi 

机构地区：[1]Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences [2]Key Laboratory of Poyang Lake Wetland and Watershed Research (Jiangxi Normal University),Ministry of Education [3]Department of Geography, University of Zurich [4]Jilin University, College of Geo Exploration Science and Technology

出　　处：《Chinese Geographical Science》2015年第5期537-548,共12页中国地理科学（英文版）

基　　金：Under the auspices of National Natural Science Foundation of China(No.41401221,41271500,41201496);Opening Fund of Key Laboratory of Poyang Lake Wetland and Watershed Research(Jiangxi Normal University),Ministry of Education,China(No.PK2014002)

摘　　要：As an important product of Moderate Resolution Imaging Spectroradiometer(MODIS), MOD17A2 provides dramatic improvements in our ability to accurately and continuously monitor global terrestrial primary production, which is also significant in effort to advance scientific research and eco-environmental management. Over the past decades, forests have moderated climate change by sequestrating about one-quarter of the carbon emitted by human activities through fossil fuels burning and land use/land cover change. Thus, the carbon uptake by forests reduces the rate at which carbon accumulates in the atmosphere. However, the sensitivity of near real-time MODIS gross primary productivity(GPP) product is directly constrained by uncertainties in the modeling process, especially in complicated forest ecosystems. Although there have been plenty of studies to verify MODIS GPP with ground-based measurements using the eddy covariance(EC) technique, few have comprehensively validated the performance of MODIS estimates(Collection 5) across diverse forest types. Therefore, the present study examined the degree of correspondence between MODIS-derived GPP and EC-measured GPP at seasonal and interannual time scales for the main forest ecosystems, including evergreen broadleaf forest(EBF), evergreen needleleaf forest(ENF), deciduous broadleaf forest(DBF), and mixed forest(MF) relying on 16 flux towers with a total of 68 site-year datasets. Overall, site-specific evaluation of multi-year mean annual GPP estimates indicates that the current MOD17A2 product works highly effectively for MF and DBF, moderately effectively for ENF, and ineffectively for EBF. Except for tropical forest, MODIS estimates could capture the broad trends of GPP at 8-day time scale for all other sites surveyed. On the annual time scale, the best performance was observed in MF, followed by ENF, DBF, and EBF. Trend analyses also revealed the poor performance of MODIS GPP product in EBF and DBF. Thus, improvements in the sensitivity of MOD17A2 to forest productivity rAs an important product of Moderate Resolution Imaging Spectroradiometer（MODIS）, MOD17A2 provides dramatic improvements in our ability to accurately and continuously monitor global terrestrial primary production, which is also significant in effort to advance scientific research and eco-environmental management. Over the past decades, forests have moderated climate change by sequestrating about one-quarter of the carbon emitted by human activities through fossil fuels burning and land use/land cover change. Thus, the carbon uptake by forests reduces the rate at which carbon accumulates in the atmosphere. However, the sensitivity of near real-time MODIS gross primary productivity（GPP） product is directly constrained by uncertainties in the modeling process, especially in complicated forest ecosystems. Although there have been plenty of studies to verify MODIS GPP with ground-based measurements using the eddy covariance（EC） technique, few have comprehensively validated the performance of MODIS estimates（Collection 5） across diverse forest types. Therefore, the present study examined the degree of correspondence between MODIS-derived GPP and EC-measured GPP at seasonal and interannual time scales for the main forest ecosystems, including evergreen broadleaf forest（EBF）, evergreen needleleaf forest（ENF）, deciduous broadleaf forest（DBF）, and mixed forest（MF） relying on 16 flux towers with a total of 68 site-year datasets. Overall, site-specific evaluation of multi-year mean annual GPP estimates indicates that the current MOD17A2 product works highly effectively for MF and DBF, moderately effectively for ENF, and ineffectively for EBF. Except for tropical forest, MODIS estimates could capture the broad trends of GPP at 8-day time scale for all other sites surveyed. On the annual time scale, the best performance was observed in MF, followed by ENF, DBF, and EBF. Trend analyses also revealed the poor performance of MODIS GPP product in EBF and DBF. Thus, improvements in the sensitivity of

关 键 词：MOD 17A2 FLUXNET community eddy covariance （EC） gross primary productivity （GPP） forest ecosystem evaluation 

分 类 号：S718.5[农业科学—林学]