Study of remote sensing based parameter uncertainty in Production Efficiency Models

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.

黄河源区气候温暖化及其对植被生产力影响评价

在分析黄河源区气温、降水变化的基础上,用Turc经验模式计算黄河源区的蒸散量,同时分析了近十几年来土壤湿度、植被地上净初级生产力的变化特征.结果表明,1959-2005年,黄河源区年平均气温按0.0284 ℃/a的变率升高,降水变化态势平稳,但蒸散量增加趋势明显,上升倾向率达0.7315 mm/a,气温上升趋势与年代增加具有明显的相关性.从1987年以来土壤湿度的监测结果分析得知,黄河源区下垫面蒸散量的加大使土壤向干暖化发展.这种气候因素的影响,导致近十几年来植被地上净初级生产力按9.506 g/(m2a)的倾向率下降.