CAJ | 학술논문

遥感经验模型反演方法是叶面积指数（LAI， Leaf Area Index）反演的重要途径，而实测数据又是经验统计模型的基本数据来源之一，但对于地面采样样本量对 LAI 遥感经验建模的影响研究较少。文章对单一地表覆盖类型的研究区采用不同样本量多次随机采样获取的采样数据来构建经验模型反演 LAI，探究样本量对于建模精度的影响。研究结果表明：①遥感模型精度评估指数（RMAI， Remote Sensing Model Accuracy Index）随样本量呈幂函数形式逐渐减小；②在样本数量小于30时 RMAI 较为敏感，建模精度较差，当样本量达到45左右时，其精度达到稳定状态；③样本量越大的采样方案构建的遥感经验模型越稳定；④综合 RMAI 平均值及标准差的变化趋势，当样本量达到40时采样数据即可构建精度较高且稳定的 LAI 经验统计模型。图4，参15。
요감경험모형반연방법시협면적지수（LAI， Leaf Area Index）반연적중요도경，이실측수거우시경험통계모형적기본수거래원지일，단대우지면채양양본량대 LAI 요감경험건모적영향연구교소。문장대단일지표복개류형적연구구채용불동양본량다차수궤채양획취적채양수거래구건경험모형반연 LAI，탐구양본량대우건모정도적영향。연구결과표명：①요감모형정도평고지수（RMAI， Remote Sensing Model Accuracy Index）수양본량정멱함수형식축점감소；②재양본수량소우30시 RMAI 교위민감，건모정도교차，당양본량체도45좌우시，기정도체도은정상태；③양본량월대적채양방안구건적요감경험모형월은정；④종합 RMAI 평균치급표준차적변화추세，당양본량체도40시채양수거즉가구건정도교고차은정적 LAI 경험통계모형。도4，삼15。
Empirical-model-based inversion is an important approach to the retrieval of leaf area index (LAI).The ground sampling data is the primary data resource of Empirical modeling,and the sample size affects directly the precision of the empirical model.However, few studies were investigated about the effects of sample size on remote sensing empirical modeling accuracy of LAI.This article,based on the ground sample data sampled repeatedly with different sample sizes,built an empirical model of LAI to explore the effects of sam-ple size on modeling accuracy.The results showed that:①remote sensing model accuracy index ( RMAI) decreases with increase in sample size in the power function form;②when the number of samples is less than 30,RMAI is more sensitive and the modeling accu-racy is lower,while the sample size approaches 45,the modeling accuracy reaches a steady state;③the larger the sample size,the more stable the modeling based on sample data;④giving consideration to the change trends of the mean and standard deviation of RMAI,it comes to the conclusion that the sample data,whose sample size reaches 40,can build a stable empirical model with high precision.