%0 Journal Article
%T Scale effect and error analysis of crop LAI inversion<br>作物LAI的遥感尺度效应与误差分析
%A ZHU Xiaohu
%A FENG Xiaoming
%A ZHAO Yingshi
%A SONG Xiaoning
%A <br>朱小华
%A 冯晓明
%A 赵英时
%A 宋小宁
%J 遥感学报
%D 2010
%I 
%X Leaf area index (LAI) is an important bio-physical character of vegetation and can be effectively achieved through remote sensing technology. However the LAI inversion from low resolution data induces a scaling bias due to the heterogeneous of the surface and model non-linearity, which may cause the scale effect on the LAI estimate. In this work, the Yingke oasis of Heihe River is selected as the study area. Based on Hyperion data, a two-layer canopy reflectance model (ACRM) is introduced to calculate LAI. The low resolution LAI are then achieved in two ways: LAImean, the mean of LAI, is directly calculated from Hyperion; and the LAIp is computed from linear cumulative Hyperion data. Statistics shows that there is a serious underestima-tion of LAIp. On the basis of LAI-NDVI regresion equation, the Taylor Mean Value Theorem is applied to creat an error factor and to conduct scaling error correction. The result of error correction ( LAIr ) has a high relationship with LAImean, which shows that the method is effective and suitable for scale effect correction and can be used to correct other LAI product, such as MODIS LAI. Finally, the causes for scaling bias are discussed. It is found that the spatial heterogeneous is the key factor which may lead to the error in LAI inversion.
%K Hyperion
%K leaf area index
%K scale effect
%K inversion
%K error correction<br>Hyperion
%K 叶面积指数
%K 尺度效应
%K 反演
%K 误差纠正
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=E62459D214FD64A3C8082E4ED1ABABED5711027BBBDDD35B&cid=A41A70F4AB56AB1B&jid=F926358B31AC94511E4382C083F7683C&aid=C213EAEA095CB54A3F45DF40B28A48FA&yid=140ECF96957D60B2&vid=F3583C8E78166B9E&iid=38B194292C032A66&sid=CD26609C367AC9C8&eid=4158386E7B9422C8&journal_id=1007-4619&journal_name=遥感学报&referenced_num=1&reference_num=17