排序方式： 共有2条查询结果，搜索用时 0 毫秒
1
1.
Earth is always changing.Knowledge about where changes happened is the first step for us to understand how these changes affect our lives.In this paper,we use a long-term leaf area index data(LAI) to identify where changes happened and where has experienced the strongest change around the globe during 1981-2006.Results show that,over the past 26 years,LAI has generally increased at a rate of 0.0013 per year around the globe.The strongest increasing trend is around 0.0032 per year in the middle and northern high latitudes(north of 30°N).LAI has prominently increased in Europe,Siberia,Indian Peninsula,America and south Canada,South region of Sahara,southwest corner of Australia and Kgalagadi Basin;while noticeably decreased in Southeast Asia,southeastern China,central Africa,central and southern South America and arctic areas in North America. 相似文献
2.
基于物理模型训练神经网络的作物叶面积指数遥感反演研究 总被引：2，自引：0，他引：2
叶面积指数(LAI)是估算作物生长的关键参数。基于物理模型的LAI反演,被认为是当前最为可靠的方法,但其反演复杂。本文提出了将物理模型和神经网络相结合,从地表反射率反演叶面积指数的算法,利用MOD IS地表反射率和4-scale模型反演作物LAI。(1)利用4-scale模型模拟不同LAI与地表反射率的关系,生成训练数据;(2)利用模型模拟的LAI训练神经网络;(3)以MOD IS地表反射率输入训练后的神经网络,反演LAI。估算的LAI与其他LAI产品进行了比较,结果表明,估算的作物LAI和MOD IS及CYCLOPES LAI产品空间和时间分布一致,均方根误差分别为0.4994和0.6558。以2004年衡水的作物LAI地面观测数据进行了直接验证,估算的LAI与研究区地表植被分布一致,但是,三种卫星LAI产品都小于地表测量,故需针对华北平原浓密作物设计模型参数化方案。 相似文献
1