Generation of 250m MODIS LAI time series by temporal regression

R. Colditz, R. Llamas
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Abstract

Vegetation productivity models and many others for hydrology and biogeochemistry studies require biophysical variables such as the leaf area index (LAI). LAI is part of the 13 essential terrestrial variables to monitor climate change. The index can be retrieved by various methods from optical satellite data and is a standard product in the MODIS processing chain at 1km spatial resolution. This study explores the temporal relations between LAI and vegetation indices and applies regression functions to obtain a 250m LAI product.
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用时间回归生成250m MODIS LAI时间序列
植被生产力模型和许多其他用于水文和生物地球化学研究的模型需要诸如叶面积指数(LAI)之类的生物物理变量。LAI是监测气候变化的13个基本陆地变量之一。该指数可通过多种方法从光学卫星数据中检索,是1km空间分辨率MODIS处理链中的标准产品。本研究探讨了LAI与植被指数的时间关系,并运用回归函数得到了250m的LAI产品。
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