基于LAI-2200的草地LAI测量与聚集度系数分析

Validation of Grassland Leaf Area Index and Clumping Index Retrievals from LAI-2200

叶面积指数（LAI）是陆地表层生态系统最重要的植被结构参数之一。近年来,利用LAI-2200观测草地叶面积指数的研究逐渐增多,但是其精度验证研究却很少。本文利用LAI-2200获取19个草地样地的有效植被面积指数（PAI_e）,并利用草地孔隙率模型模拟LAI-2200测量值,然后与收获法得到的植被面积指数（PAI）进行比较,进而评估LAI-2200的测量精度,并分析草地聚集度系数（CI）的变化规律。结果表明LAI-2200观测天顶角较小的4圈数据计算的PAI_e比5圈数据计算的PAI_e的精度更高;PAI_e与PAI相关性极显著（R2=0.951）;当PAI小于3时,PAIe略小于PAI;随着PAI继续增大,PAI_e低估逐渐严重。这种低估现象主要原因是叶片的聚集效应,当PAI小于3时,CI均值为0.97;当PAI为3~6时,CI均值为0.88;当PAI大于6时,CI均值为0.71,因此LAI-2200用于浓密草地测量时需要用CI进行订正。

Leaf area index（ LAI） is one of the most important structural parameters of terrestrial ecosystems. In recent years,the use of LAI-2200 to measure leaf area index of grassland has gradually increased,but few studies have evaluated the accuracy of the optical method to estimate LAI in grassland ecosystems. In order to validate the measurement accuracy of LAI-2200 for grassland leaf area index,the effective plant area index（ PAIe） retrievals by LAI-2200 were compared with the simulated LAI-2200 measurements and the plant area index（ PAI） retrievals by destructive sampling in 19 grassland sites. As an additional biophysical parameter of comparable importance to LAI,the clumping index（ CI） of grassland was also analyzed. The PAIeretrievals from LAI-2200 4-ring data with smaller zenith angle perform better than from LAI-2200 5-ring data,and correspond very well with the destructive PAI values（ R2= 0. 951）. Reasonable agreement of the PAIeretrievals from LAI-2200 with the destructive sampling and the simulated results verifies the reliability of LAI-2200 used in sparse grassland. PAIeis slightly less than PAI when PAI is less than 3. As PAI continues to increase,PAIehas been seriously underestimated due to clumping effect： CI = 0. 97 for PAI 3,CI = 0. 88 for 3 PAI 6,and CI = 0. 71 for PAI 6. Therefore,the LAI retrieved from LAI-2200 with the assumption of the random foliage distribution might yield inaccurate results in clumped grass,which need to be corrected using CI values.