【摘要】 在分析植被光谱特征与光谱和植被水分相关性的基础上,初步确定几个波段或波段组合为植被含水量的光谱指数。利用数据对植被含水量FMC(Fuel Moisture Content)与上述水份光谱指数分别建立最优函数关系。通过分析不同光谱指数的关系及其相对误差,确定以1600 nm和820 nm处反射率的比值S_R作为建立本研究区植被含水量模型的特征参数;并利用实测光潜反射率与植被含水量建立S_R与FMC之间的模型关系。根据植被含水量模型、ETM和ASTER遥感数据,在IDL7.0开发平台下实现研究区植被含水量的定量反演,并利用区内实测数据和本底调查数据对反演结果进行了综合评价与分析。结果表明.光谱指数S_R可以较好地剔除环境背景以及冠层结构等外界因素的影响,植被含水量遥感反演精度较高.能真实反映研究区植被含水量的时空变化规律与特征。更多还原

【Abstract】 Some bands or band combination are used as the spectral indices of fuel moisture content based on analyzing the spectral characteristic and relation of spectrum and fuel moisture content.The optimal function models are respectively established between the fuel moisture content and spectral indices.By analyzing the spectral index and its relative error, it confirms the spectral index S_R=R₁₆₀₀/R₈₂₀ as the characteristic parameters of the model, where R₁₆₀₀ and R₈₂₀ are the reflectivities at 1600 nm and 820 nm.Utilizing the measured vegetation spectral reflectance and fuel moisture content, the model relation between fuel moisture content and S_R spectral index is established.According to the model of fuel moisture content, ETM and ASTER remote sensing data, the quantitative inversion are achieved by the programme based on IDL7.0 platform.The measured data and background data surveyed in the study area are used to evaluate and analyze the inversion results synthetically.The results show that the spectral index S_R can eliminate the outside impact of the background environment, the canopy structure, and other factors.The remote sensing retrieval precision of fuel moisture content superior.It can reflect the variety characteristics of the fuel moisture content in the study area veritably.更多还原