Derivation and validation of Canada-wide coarse-resolution leaf area index maps using high-resolution satellite imagery and ground measurements
Derivation and validation of Canada-wide coarse-resolution leaf area index maps using high-resolution satellite imagery and ground measurements
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DOI：
10.1016/s0034-4257(01)00300-5
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发表时间：
2002-04-01
影响因子：
13.5
通讯作者：
Pellikka, PKE
中科院分区：
文献类型：
--
作者：
Chen, JM;Pavlic, G;Pellikka, PKE
叶面积指数（LAI）是在气候、天气和生态研究中具有重要意义的地表参数之一，并且已经常规地通过遥感测量来估计。目前正在使用无云的高级甚高分辨率辐射计图像每10天以1公里分辨率制作全加拿大的叶面积指数图。这些产品的存档始于1993年。为了改进叶面积指数算法并验证这些产品，一组加拿大科学家于1998年夏季在落叶林、针叶林、混交林和农田中获得了叶面积指数测量值。使用商业跟踪辐射和树冠结构（TRAC）和LAI-2000仪器的共同测量标准被遵循。8个30米分辨率的大地卫星专题成像仪场景被用来确定地面站点的位置，并便于将空间比例缩小到1公里像素。在本文中，加拿大范围内的叶面积指数地图的例子后，其准确性使用地面测量和八个Landsat场景的评估。从高分辨率到粗分辨率的图像，考虑表面异质性的混合覆盖类型的缩放方法进行了评估和讨论。以Landsat LAI图像为标准，AVHRR和VEGETATION像元的LAI值精度在50- 75%之间。随机误差和偏差误差都相当大。偏差主要是由Landsat图像的大气校正的不确定性造成的，但混合覆盖类型方面的表面异质性也被发现会导致AVHRR和SPOT植被LAI计算的偏差。随机误差的来源很多，但混合覆盖类型的像素是随机误差的主要原因。由于不同植被类型的辐射信号在相同的LAI下有很大的差异，因此准确地了解不同覆盖类型的亚像素混合信息是提高LAI估计精度的关键。(C)2002年爱思唯尔科技有限公司All rights reserved.
Leaf area index (LAI) is one of the surface parameters that has importance in climate, weather, and ecological studies, and has been routinely estimated from remote sensing measurements. Canada-wide LAI maps are now being produced using cloud-free Advanced Very High-Resolution Radiometer (AVHRR) imagery every 10 days at 1-km resolution. The archive of these products began in 1993. LAI maps at the same resolution are also being produced with images from the SPOT VEGETATION sensor, To improve the LAI algorithms and validate these products, a group of Canadian scientists acquired LAI measurements during the summer of 1998 in deciduous, conifer, and mixed forests, and in cropland. Common measurement standards using the commercial Tracing Radiation and Architecture of Canopies (TRAC) and LAI-2000 instruments were followed. Eight Landsat Thematic Mapper (TM) scenes at 30-m resolution were used to locate ground sites and to facilitate spatial scaling to 1-km pixels. In this paper, examples of Canada-wide LAI maps are presented after an assessment of their accuracy using ground measurements and the eight Landsat scenes. Methodologies for scaling from high- to coarse-resolution images that consider surface heterogeneity in terms of mixed cover types are evaluated and discussed. Using Landsat LAI images as the standard, it is shown that the accuracy of LAI values of individual AVHRR and VEGETATION pixels was in the range of 50-75%. Random and bias errors were both considerable. Bias was mostly caused by uncertainties in atmospheric correction of the Landsat images, but surface heterogeneity in terms of mixed cover types were also found to cause bias in AVHRR and SPOT VEGETATION LAI calculations. Random errors come from many sources, but pixels with mixed cover types are the main cause of random errors. As radiative signals from different vegetation types were quite different at the same LAI, accurate information about subpixel mixture of the various cover types is identified as the key to improving the accuracy of LAI estimates. (C) 2002 Elsevier Science Inc. All rights reserved.