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叶片等效水厚度(EWT)是评估果树生长状况及产量的一个重要参数。为了快速、准确地估算此参数,该文建立苹果叶片EWT归一化近红外水分指数(NDIWI)和扩展傅里叶幅度灵敏度检测方法和偏最小二乘回归(EFAST-PLS)估算模型并验证。使用2012年和2013年在中国山东省肥城县潮泉镇获取的整个生育期苹果叶片EWT和配套的光谱数据,比较NDIWI和EFAST-PLS联合模型。在EFAST-PLS联合模型中,EFAST用来选择光谱敏感波段,PLS用来回归分析。NDIWI与EFAST-PLS模型的决定系数(R2)分别为0.2831和0.5628,标准均方根误差(NRMSE)分别为8.00%和6.25%。研究结果表明:EFAST-PLS模型估算苹果叶片EWT潜力巨大,考虑到应用简单,NDIWI也有可取之处。
Leaf equivalent water thickness (EWT) is an important parameter to evaluate the growth status and yield of fruit trees. In order to estimate this parameter quickly and accurately, an EWT normalized near-infrared moisture index (NDIWI) and an extended Fourier amplitude sensitivity detection method and partial least-squares regression (EFAST-PLS) estimation model were established and validated. Using EWT and matching spectral data of apple leaves during the whole growth period obtained from 2012 and 2013 in Chaoquan, Feicheng County, Shandong Province, China, the NDIWI and EFAST-PLS joint models were compared. In the EFAST-PLS joint model, EFAST is used to select the spectral sensitive bands and PLS is used for regression analysis. The determination coefficients (R2) of NDIWI and EFAST-PLS models were 0.2831 and 0.5628, respectively, and the standard root mean square error (NRMSE) were 8.00% and 6.25%, respectively. The results show that EFAST-PLS model has great potential for estimating EWT in apple leaves. Considering the simple application, NDIWI is also a good choice.