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, whic...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 require continued efforts.展开更多
The shortcomings of an adaptive Sage filter are analyzed in this paper.An improved adaptive Sage filter is developed by using a weighted average quadratic form of the historical residuals of observations and predicted...The shortcomings of an adaptive Sage filter are analyzed in this paper.An improved adaptive Sage filter is developed by using a weighted average quadratic form of the historical residuals of observations and predicted states to evaluate the covariance matrices of observations and dynamic model errors at the present epoch.The weight function is constructed based on the variances of observational residuals or predicted state residuals and the space distance between the previous and the present epoch.In order to balance the contributions of the measurements and the dynamic model information,an adaptive factor is applied by using a two-segment function and predicted state discrepancy statistics.Two applications,orbit determination of a maneuvered GEO satellite and GPS kinematic positioning,are conducted to verify the performance of the proposed method.展开更多
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)
文摘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 require continued efforts.
基金supported by the Foundation for the Author of National Excellent Doctoral Dissertation of China (Grant No.2007B51)the National Natural Science Foundation of China (Grant Nos.41174008 and 41020144004)+1 种基金China Postdoctoral Science Foundation (Grant Nos.20080430148 and 200902444)the Junior Scientist Exchange Program between the China Scholarship Council and the Helmholtz Association of German Research Centers Council
文摘The shortcomings of an adaptive Sage filter are analyzed in this paper.An improved adaptive Sage filter is developed by using a weighted average quadratic form of the historical residuals of observations and predicted states to evaluate the covariance matrices of observations and dynamic model errors at the present epoch.The weight function is constructed based on the variances of observational residuals or predicted state residuals and the space distance between the previous and the present epoch.In order to balance the contributions of the measurements and the dynamic model information,an adaptive factor is applied by using a two-segment function and predicted state discrepancy statistics.Two applications,orbit determination of a maneuvered GEO satellite and GPS kinematic positioning,are conducted to verify the performance of the proposed method.
