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Study of remote sensing based parameter uncertainty in Production Efficiency Models
Liu, Rui(刘睿); Sun Jiulin; Wang Juanle; Li Xiaolei; Yang Fei; Chen Pengfei
会议名称International Geoscience and Remote Sensing Symposium (IGARSS) ; 2010 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2010 ; International Geoscience and Remote Sensing Symposium (IGARSS) ; 2010 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2010
2010
会议名称International Geoscience and Remote Sensing Symposium (IGARSS) ; 2010 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2010 ; International Geoscience and Remote Sensing Symposium (IGARSS) ; 2010 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2010
通讯作者Liu, Rui
会议日期July 25, 2010 - July 30, 2010
会议地点Honolulu, HI, United states
出版地445 Hoes Lane / P.O. Box 1331, Piscataway, NJ 08855-1331, United States
出版者Institute of Electrical and Electronics Engineers Inc.
会议主办者The Institute of Electrical and Electronics Engineers; Geoscience and Remote Sensing Society
摘要The remote sensing based Production Efficiency Models (PEMs), springs from the concept of "Light Use Efficiency" and has been applied more and more in estimating terrestrial Net Primary Productivity (NPP) regionally and globally. However, global NPP estimates vary greatly among different models in different data sources and handling methods. Because direct observation or measurement of NPP is unavailable at global scale, the precision and reliability of the models cannot be guaranteed. Though, there are ways to improve the accuracy of the models from input parameters. In this study, five remote sensing based PEMs have been compared: CASA, GLO-PEM, TURC, SDBM and VPM. We divided input parameters into three categories, and analyzed the uncertainty of (1) vegetation distribution, (2) fraction of photosynthetically active radiation absorbed by the canopy (fPAR) and (3) light use efficiency (e). Ground measurements of Hulunbeier typical grassland and meteorology measurements were introduced for accuracy evaluation. Results show that a real-time, more accurate vegetation distribution could significantly affect the accuracy of the models, since it's applied directly or indirectly in all models and affects other parameters simultaneously. Higher spatial and spectral resolution remote sensing data may reduce uncertainty of fPAR up to 51.3%, which is essential to improve model accuracy.
关键词Data Handling Geology Remote Sensing Vegetation
收录类别EI
语种英语
文献类型会议论文
条目标识符http://ir.igsnrr.ac.cn/handle/311030/3284
专题研究生部
推荐引用方式
GB/T 7714
Liu, Rui,Sun Jiulin,Wang Juanle,et al. Study of remote sensing based parameter uncertainty in Production Efficiency Models[C]. 445 Hoes Lane / P.O. Box 1331, Piscataway, NJ 08855-1331, United States:Institute of Electrical and Electronics Engineers Inc.,2010.
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