Assimilation of Remotely-Sensed Leaf Area Index into a Dynamic Vegetation Model for Gross Primary Productivity Estimation
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Yi Zheng | Li Zhang | Rui Ma | Xiang Zhao | Wenping Yuan | Xiangjun Tian | Tomomichi Kato | Jiancai Zhang | Li Zhang | Tomomichi Kato | W. Yuan | Xiang Zhao | X. Tian | Yi Zheng | Rui Ma | Jiancai Zhang
[1] Philip Lewis,et al. Assimilating canopy reflectance data into an ecosystem model with an Ensemble Kalman Filter , 2008 .
[2] W. Ju,et al. Changes of net primary productivity in China during recent 11 years detected using an ecological model driven by MODIS data , 2013, Frontiers of Earth Science.
[3] Peter Scarth,et al. Prediction and validation of foliage projective cover from Landsat-5 TM and Landsat-7 ETM+ imagery , 2009 .
[4] Yanxia Zhao,et al. Assimilating remote sensing information with crop model using Ensemble Kalman Filter for improving LAI monitoring and yield estimation , 2013 .
[5] T. D. Mitchell,et al. An improved method of constructing a database of monthly climate observations and associated high‐resolution grids , 2005 .
[6] G. Liu,et al. Estimation of net primary productivity of terrestrial vegetation in China by remote sensing , 2001 .
[7] I. Prentice,et al. A general model for the light-use efficiency of primary production , 1996 .
[8] Mingguo Ma,et al. Assimilation of soil moisture in LPJ-DGVM , 2009, Remote Sensing.
[9] A. Dai,et al. A dual‐pass variational data assimilation framework for estimating soil moisture profiles from AMSR‐E microwave brightness temperature , 2009 .
[10] P. Houtekamer,et al. Data Assimilation Using an Ensemble Kalman Filter Technique , 1998 .
[11] D. Kuang,et al. Comparison of multiple models for estimating gross primary production using MODIS and eddy covariance data in Harvard Forest , 2010 .
[12] Mekonnen Gebremichael,et al. Evaluation of MODIS Gross Primary Productivity (GPP) in tropical monsoon regions , 2006 .
[13] L. Dente,et al. Assimilation of leaf area index derived from ASAR and MERIS data into CERES - wheat model to map wheat yield , 2008 .
[14] Atul K. Jain,et al. A model-data comparison of gross primary productivity: Results from the North American Carbon Program site synthesis , 2012 .
[15] Guirui Yu,et al. Net ecosystem CO2 exchange and controlling factors in a steppe—Kobresia meadow on the Tibetan Plateau , 2006 .
[16] Robert Argent. Land Surface Observation, Modeling and Data Assimilation , 2014, Environ. Model. Softw..
[17] P. Ciais,et al. Assimilation of global MODIS leaf area index retrievals within a terrestrial biosphere model , 2007 .
[18] G. Evensen. Sequential data assimilation with a nonlinear quasi‐geostrophic model using Monte Carlo methods to forecast error statistics , 1994 .
[19] K. Taylor. Summarizing multiple aspects of model performance in a single diagram , 2001 .
[20] Ranga B. Myneni,et al. Lidar remote sensing for modeling gross primary production of deciduous forests , 2004 .
[21] P. Jones,et al. REPRESENTING TWENTIETH CENTURY SPACE-TIME CLIMATE VARIABILITY. , 1998 .
[22] Hong Sun,et al. Simulation of Forest Carbon Fluxes Using Model Incorporation and Data Assimilation , 2016, Remote. Sens..
[23] W. Ju,et al. Modeling the impact of drought on canopy carbon and water fluxes through parameter optimization using an ensemble Kalman filter , 2009 .
[24] Toshio Koike,et al. A very fast simulated re-annealing (VFSA) approach for land data assimilation , 2004, Comput. Geosci..
[25] Xiangjun Tian,et al. A microwave land data assimilation system: Scheme and preliminary evaluation over China , 2010 .
[26] Benjamin Smith,et al. Estimating potential forest NPP, biomass and their climatic sensitivity in New England using a dynamic ecosystem model , 2010 .
[27] Boris Zeide,et al. Primary Unit of the Tree Crown , 1993 .
[28] I. C. Prentice,et al. Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model , 2003 .
[29] Jiancheng Shi,et al. First Evaluation of Aquarius Soil Moisture Products Using In Situ Observations and GLDAS Model Simulations , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[30] J. Townshend,et al. A long-term Global LAnd Surface Satellite (GLASS) data-set for environmental studies , 2013 .
[31] Jetse D. Kalma,et al. One-dimensional soil moisture profile retrieval by assimilation of near-surface observations: a comparison of retrieval algorithms , 2001 .
[32] C. Peng,et al. Integrating a model with remote sensing observations by a data assimilation approach to improve the model simulation accuracy of carbon flux and evapotranspiration at two flux sites , 2016, Science China Earth Sciences.
[33] Toshio Koike,et al. A New Satellite-Based Data Assimilation Algorithm to Determine Spatial and Temporal Variations of Soil Moisture and Temperature Profiles , 2003 .
[34] I. C. Prentice,et al. BIOME3: An equilibrium terrestrial biosphere model based on ecophysiological constraints, resource availability, and competition among plant functional types , 1996 .
[35] P. Jones,et al. Representing Twentieth-Century Space–Time Climate Variability. Part I: Development of a 1961–90 Mean Monthly Terrestrial Climatology , 1999 .
[36] Y. Guirui,et al. The Changes of Net Primary Productivity in Chinese Terrestrial Ecosystem: Based on Process and Parameter Models , 2012 .
[37] Zhao Lin. Climatic Characteristics over China in 2012 , 2013 .
[38] Jeffrey P. Walker,et al. Assimilation of remotely sensed data for improved latent and sensible heat flux prediction: A comparative synthetic study , 2008 .
[39] P. Jones,et al. Representing Twentieth-Century Space-Time Climate Variability. Part II: Development of 1901-96 Monthly Grids of Terrestrial Surface Climate , 2000 .
[40] Jiyuan Liu,et al. Simulation study of China’s net primary production , 2008 .
[41] J. Sherman,et al. Adjustment of an Inverse Matrix Corresponding to a Change in One Element of a Given Matrix , 1950 .
[42] Fuqing Zhang,et al. Coupling ensemble Kalman filter with four-dimensional variational data assimilation , 2009 .
[43] Hideki Kobayashi,et al. Recent Changes in Terrestrial Gross Primary Productivity in Asia from 1982 to 2011 , 2013, Remote. Sens..
[44] T. Vesala,et al. On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm , 2005 .
[45] A. Pekkarinen,et al. Estimation of forest stand volumes by Landsat TM imagery and stand-level field-inventory data , 2004 .
[46] Wenping Yuan,et al. Accurate representation of leaf longevity is important for simulating ecosystem carbon cycle , 2016 .
[47] M. Wahlen,et al. Interannual extremes in the rate of rise of atmospheric carbon dioxide since 1980 , 1995, Nature.
[48] Benjamin Smith,et al. Parameter uncertainties in the modelling of vegetation dynamics — effects on tree community structure and ecosystem functioning in European forest biomes , 2008 .
[49] M. Monsi. Uber den Lichtfaktor in den Pflanzengesellschaften und seine Bedeutung fur die Stoffproduktion , 1953 .
[50] G. Sun,et al. Effects of spring drought on carbon sequestration, evapotranspiration and water use efficiency in the songnen meadow steppe in northeast China , 2011 .
[51] Xiaodong Liu,et al. Quantifying the hydrological responses to climate change in an intact forested small watershed in Southern China , 2011 .
[52] Stephen Sitch,et al. Effects of parameter uncertainties on the modeling of terrestrial biosphere dynamics , 2005 .
[53] Xiangjun Tian,et al. A POD‐based ensemble four‐dimensional variational assimilation method , 2011 .
[54] D. Etheridge,et al. Natural and anthropogenic changes in atmospheric CO2 over the last 1000 years from air in Antarctic ice and firn , 1996 .
[55] G. Evensen,et al. Analysis Scheme in the Ensemble Kalman Filter , 1998 .
[56] Shaoqiang Wang,et al. Effects of drought and ice rain on potential productivity of a subtropical coniferous plantation from 2003 to 2010 based on eddy covariance flux observation , 2013 .
[57] Adrian Sandu,et al. A hybrid approach to estimating error covariances in variational data assimilation , 2010 .
[58] W. Lucht,et al. Terrestrial vegetation and water balance-hydrological evaluation of a dynamic global vegetation model , 2004 .
[59] Marco Bindi,et al. Application of BIOME-BGC to simulate Mediterranean forest processes , 2007 .
[60] Xiangming Xiao,et al. Satellite-based estimation of evapotranspiration of an old-growth temperate mixed forest , 2009 .
[61] B. Mohanty,et al. Root Zone Soil Moisture Assessment Using Remote Sensing and Vadose Zone Modeling , 2006 .
[62] A. Bondeau,et al. Comparing global models of terrestrial net primary productivity (NPP): overview and key results , 1999 .
[63] Zirui Liu,et al. Evaluating Parameter Adjustment in the MODIS Gross Primary Production Algorithm Based on Eddy Covariance Tower Measurements , 2014, Remote. Sens..
[64] Jindi Wang,et al. Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance , 2014, IEEE Transactions on Geoscience and Remote Sensing.
[65] Shamil Maksyutov,et al. SEVER: A modification of the LPJ global dynamic vegetation model for daily time step and parallel computation , 2007, Environ. Model. Softw..
[66] K. Moffett,et al. Remote Sens , 2015 .
[67] Andrew C. Lorenc,et al. The potential of the ensemble Kalman filter for NWP—a comparison with 4D‐Var , 2003 .
[68] Geir Evensen,et al. The Ensemble Kalman Filter: theoretical formulation and practical implementation , 2003 .
[69] Zhenghui Xie,et al. A joint data assimilation system (Tan-Tracker) to simultaneously estimate surface CO 2 fluxes and 3-D atmospheric CO 2 concentrations from observations , 2013 .
[70] Mingguo Ma,et al. Estimation of gross primary production over the terrestrial ecosystems in China , 2013 .
[71] K. Davis,et al. Global estimates of evapotranspiration and gross primary production based on MODIS and global meteorology data , 2010 .
[72] W. Cramer,et al. A global biome model based on plant physiology and dominance, soil properties and climate , 1992 .
[73] Xiangjun Tian,et al. Implementations of a square-root ensemble analysis and a hybrid localisation into the POD-based ensemble 4DVar , 2012 .
[74] Chunlin Huang,et al. Experiments of one-dimensional soil moisture assimilation system based on ensemble Kalman filter , 2008 .
[75] James Hansen,et al. Assimilation of remotely sensed soil moisture and vegetation with a crop simulation model for maize yield prediction , 2013 .
[76] Jindi Wang,et al. Long-Time-Series Global Land Surface Satellite Leaf Area Index Product Derived From MODIS and AVHRR Surface Reflectance , 2016, IEEE Transactions on Geoscience and Remote Sensing.
[77] Hou We,et al. Climatic Characteristics over China in 2013 , 2014 .
[78] Yanhong Tang,et al. Temperature and biomass influences on interannual changes in CO2 exchange in an alpine meadow on the Qinghai‐Tibetan Plateau , 2006 .
[79] Piero Toscano,et al. Remote sensing of annual terrestrial gross primary productivity from MODIS: an assessment using the FLUXNET La Thuile data set , 2013 .
[80] Jianhua Sun,et al. PODEn4DVar-based radar data assimilation scheme: formulation and preliminary results from real-data experiments with advanced research WRF (ARW) , 2015 .
[81] W. Ju,et al. Net primary productivity of China's terrestrial ecosystems from a process model driven by remote sensing. , 2007, Journal of environmental management.