气候模式中关键陆面植被参量遥感估算的研究进展

doi:1001-8166(2014)01-0056-12

卫星遥感资料对于改善气候模式的强迫场, 改进相关物理参数, 提高数值模式模拟的准确性具有重要作用。目前, 全球已经积累了多年的卫星遥感资料, 并且已有多种陆面参量遥感产品。然而, 卫星遥感资料在气候模式中的应用还非常有限。充分利用卫星遥感资料, 对于提高气候模式模拟能力具有重要作用。选择植被覆盖度（Fractional Vegetation Cover, FVC）、叶面积指数（Leaf Area Index, LAI）和地表反照率（Albedo）3个关键陆面参量的遥感估算方法进行评述, 并分析了陆面参量真实性检验的尺度转换问题, 还以WRF （Weather Research and Forecasting model）为例, 阐述了遥感估算的陆面参量应用于模式的表达方式。最后讨论了关键陆面参量遥感估算的不确定性和遥感参量应用于气候模式的尺度匹配等亟待解决的问题, 并对这些问题的未来改进方向进行了展望。

关键词: 气候模式, 陆面参量, 卫星遥感

Satellite remote sensing data play an important role in the improvement of climate models forcing field, relevant physical parameters and simulation accuracy. At present, there are many years of satellite remote sensing data and a variety of products about land surface attributes. However, the application of satellite remote sensing data to climate models is still very limited. Fully using satellite remote sensing data is important to improving the simulation ability. In the paper, remote sensing estimates methods of three key land surface parameters including Fractional Vegetation Coverage（FVC）, Leaf Area Index（LAI）and surface albedo（Albedo）is reviewed and upor downscaling land surface variables in validation process is analyzed. Secondly, taking WRF（Weather Research and Forecasting）model as an example, three parameters in climate model are described. Finally, the key problems of using remote sensing data in climate models are discussed, which comprise the uncertainties and scales of remote sensing estimation parameters and the future direction is prospected.

Key words: Climate model, Land surface parameter, Satellite remote sensing.

中图分类号:

图1 遥感产品真实性检验的升尺度框架

Fig.1 The upscale frame of validatingremote sensing products

表1 不同遥感估算方法的比较

Table 1 Comparison of different remote sensing estimation methods

方法	方法描述	陆面参量	主要优点	主要缺陷
经验线性或非线性回归模型	建立地面实测的参量值与植被指数之间的经验线性或非线性回归模型进行估算	LAI, FVC	简单易行, 计算效率高, 在一定的时空范围内精度较高	需要大量的实测数据确定经验系数, 并且回归模型依赖于特定的植被类型, 只适用于具体的研究区域
经验线性或非线性回归模型	计算各波段在太阳辐射中所占的比重	Albedo	简单易行, 计算效率高, 在一定的时空范围内精度较高	需要大量的实测数据确定经验系数, 并且回归模型依赖于特定的植被类型, 只适用于具体的研究区域
物理模型	几何光学模型、辐射传输模型, 以及混合模型, 并利用代价函数与优化方法	LAI	物理意义明确	难以获取辐射传输模型的最优参数
	利用二向反射模型和“核”模型进行联合反演	Albedo		难以获取辐射传输模型的最优参数
	光谱混合像元线性或非线性分解模型	FVC		确定端元时有不确定性
机器学习	用已知参量和对应的反射率数据训练神经网络, 然后将反射率数据输入到已经训练的神经网络来模拟相应的参量	LAI, FVC	适用于复杂现象的研究, 可以替代比较复杂的模型, 运行效率高	物理机制不明确, 神经网络的中间层属于黑箱操作, 合理控制需要反演的参数还存在困难, 结果与训练数据的选取有很大关系
数据同化	利用遥感反射率数据或实测反射率数据, 将LAI作为传递参量, 结合物理模型或其他植被生长过程模型, 得到最优的LAI	LAI	利用多种数据, 提高了分析值精度	计算速度慢、难以满足全球尺度遥感产品的生成

表1 不同遥感估算方法的比较

Table 1 Comparison of different remote sensing estimation methods

方法	方法描述	陆面参量	主要优点	主要缺陷
经验线性或非线性回归模型	建立地面实测的参量值与植被指数之间的经验线性或非线性回归模型进行估算	LAI, FVC	简单易行, 计算效率高, 在一定的时空范围内精度较高	需要大量的实测数据确定经验系数, 并且回归模型依赖于特定的植被类型, 只适用于具体的研究区域
经验线性或非线性回归模型	计算各波段在太阳辐射中所占的比重	Albedo	简单易行, 计算效率高, 在一定的时空范围内精度较高	需要大量的实测数据确定经验系数, 并且回归模型依赖于特定的植被类型, 只适用于具体的研究区域
物理模型	几何光学模型、辐射传输模型, 以及混合模型, 并利用代价函数与优化方法	LAI	物理意义明确	难以获取辐射传输模型的最优参数
	利用二向反射模型和“核”模型进行联合反演	Albedo		难以获取辐射传输模型的最优参数
	光谱混合像元线性或非线性分解模型	FVC		确定端元时有不确定性
机器学习	用已知参量和对应的反射率数据训练神经网络, 然后将反射率数据输入到已经训练的神经网络来模拟相应的参量	LAI, FVC	适用于复杂现象的研究, 可以替代比较复杂的模型, 运行效率高	物理机制不明确, 神经网络的中间层属于黑箱操作, 合理控制需要反演的参数还存在困难, 结果与训练数据的选取有很大关系
数据同化	利用遥感反射率数据或实测反射率数据, 将LAI作为传递参量, 结合物理模型或其他植被生长过程模型, 得到最优的LAI	LAI	利用多种数据, 提高了分析值精度	计算速度慢、难以满足全球尺度遥感产品的生成

图2 MODIS LAI 遥感产品真实性验证结果

Fig.2 The validation result of MODIS LAI product

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Remote Sensing Estimates of Key Land Surface Vegetation Variables Used in Climate Model: A Review