Abstract

Several organizations provide satellite Leaf Area Index (LAI) data regularly, at various scales, at high frequency, but at low spatial resolution. This study attempted to enhance the spatial resolution of the MODIS LAI product to the Landsat resolution level. Four climatically diverse sites in Europe and Africa were selected as study areas. Regression analysis was applied between MODIS Enhanced Vegetation Index (EVI) and LAI data. The regression equations were used as input in a downscaling model, along with Landsat EVI images and land-cover maps. The estimated LAI values showed high correlation with field-measured LAI during the dry period. The model validation gave statistically significant results, with correlation coefficient values ranging from relatively low (0.25–0.32), to moderate (0.48–0.64) and high (0.72–0.94). Limited samples per vegetation type, the diversity of species within the same vegetation type, land-use/land-cover changes and saturated EVI values affected the accuracy of the downscaling model.

摘要

一些组织定期提供各种尺度的卫星叶面积指数 (LAI) 数据，频率高，但空间分辨率低。本研究试图将 MODIS LAI 产品的空间分辨率提高到 Landsat 分辨率水平。欧洲和非洲的四个气候多样的地点被选为研究区域。在 MODIS 增强植被指数 (EVI) 和 LAI 数据之间应用回归分析。回归方程与 Landsat EVI 图像和土地覆盖图一起用作缩小模型的输入。估计的 LAI 值与旱季现场测量的 LAI 具有高度相关性。模型验证给出了具有统计学意义的结果，相关系数值范围从相对较低（0.25-0.32）到中等（0.48-0.64）和高（0.72-0.94）。