Remote sensing-supported vegetation parameters for regional climate models: a brief review.
Remote sensing-supported vegetation parameters for regional climate models: a brief review.
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区域气候模型遥感支持的植被参数：简要回顾。
DOI：
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发表时间：
2010
期刊：
影响因子：
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通讯作者：
B. Gálos
中科院分区：
文献类型：
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作者：
Hooman Latifi;B. Gálos
摘要：陆地表面在气候系统中起着关键作用。因此，陆地表面描述通过其对气候的反馈对气候模拟将变得越来越重要。目前正在利用各种形式的主动/被动遥感数据，在全球和区域范围上提供关于地球表面的连续和最新信息。将这些信息纳入气候模式是有用的。本文综述了如何从遥感数据中获取LAI和反照率这两个最重要的地表参数。虽然高采集频率、可及性和空间连续性被认为是潜在的优势，但尺度仍然是一个缺点，可能导致不同遥感数据源对特定气候模式的不兼容等进一步的问题。此外，阴影和大气效应等问题往往是有问题的，特别是在应用光学遥感时。在此，提出了改进建议，并指出了有待解决的问题。
Abstract: Land surface plays a key role in a climate system. Thus, the land surface description will become increasingly important for climate modelling by its feedbacks on the climate. Various forms of active/passive remotely sensed data are nowadays being used to provide continuous and up-to-date information on the earth’s surface on both global and regional scales. This information is useful to be included in climate models. This review summarizes how LAI and albedo, two of the most important land surface parameters, could be derived from remote sensing. Whereas the high acquisition frequency, accessibility, and spatial continuality are referred to potential advantages, the scaling is still a drawback which may cause further problems such as incompatibility of different remote sensing data sources for a specific climate model. Moreover, issues like shadow and atmospheric effects are often problematic, especially when optical remote sensing is applied. Here, suggestions for improvement are made and open questions are pointed out.