监测花生叶面积指数和地上部生物量的最优植被指数及适宜波段带宽

为推动光谱遥感在快速无损监测花生生长中的应用，明确监测花生叶面积指数（leaf area index，LAI）和地上部生物量（aboveground biomass，AGB）的最优植被指数及适宜的核心波段带宽。设置2个花生品种、4个施氮水平的花生田间试验，在不同生育时期（苗期、开花下针期、结荚期、成熟期）用Analytical Spectral Devices（ASD）公司生产的FieldSpec HandHeld 2型野外高光谱辐射仪，采集325~1075 nm范围冠层反射光谱，筛选敏感植被指数，并研究核心波段带宽对其监测叶面积指数（LAI）和地上部生物量（AGB）时精度的影响。结果显示，对花生LAI和AGB敏感的植被指数均为归一化红边指数（normalized difference red edge），即NDRE（λ₇₉₀, λ₇₂₀）。进一步分析这一指数的监测精度随波段带宽的变化，发现监测LAI时，核心波段带宽（bandwidth，b）在（λ₇₉₀：1~33 nm，λ₇₂₀：41~59 nm）范围内时能使NDRE（λ₇₉₀, λ₇₂₀）保持较高监测精度，其中带宽组合（λ₇₉₀：33 nm，λ₇₂₀：53 nm）的带宽和值最大，对核心波段带宽的要求最低，利用其构建监测模型时决定系数（determination coefficient，R²）为0.7482，利用独立试验数据检验模型时相对均方根误差（relative root mean square difference，RRMSE）为13.88%。监测花生AGB时，核心波段带宽在（λ₇₉₀：1~101 nm，λ₇₂₀：19~101 nm）范围内时能使NDRE（λ₇₉₀, λ₇₂₀）保持较高的监测精度，其中带宽和值最大的核心波段带宽组合为（λ₇₉₀：89 nm，λ₇₂₀：89 nm），其建模R²为0.7103，检验RRMSE为20.42%。综上，在花生整个生长进程中，可用上述两个具有不同核心波段带宽的植被指数NDRE（λ₇₉₀-b₃₃, λ₇₂₀-b₅₃）和NDRE（λ₇₉₀-b₈₉, λ₇₂₀-b₈₉）分别对LAI和AGB进行监测，监测模型为LAI = 0.0296 × exp^{（14.365×NDRE）}和AGB = 0.6240 × exp^{（20.222×NDRE）}。在核心波段适宜带宽上的研究结果，可以为花生长势光谱监测设备研发及评估提供参考。

Sensitive vegetation indices and optimal bandwidths for monitoring peanut LAI and AGB

To promote the application of spectral remote sensing on rapid nondestructive spectral monitoring for peanut production, sensitive vegetation indices and their optimal bandwidths for estimating peanut leaf area index （LAI） and aboveground biomass （AGB） were investigated. Peanut LAI, AGB and hyperspectral reflectance data, were collected from 2 field experiments encompass variations in 2 years, with 2 cultivars and 4 nitrogen application rates. Sensitive vegetation indices for LAI and AGB were identified and effect of optimal bandwidths on sensitive vegetation indices were analyzed using the in-site dataset. Results showed that the normalized difference red edge （NDRE（λ₇₉₀, λ₇₂₀）） was the most sensitive vegetation index for both LAI and AGB. Nevertheless, in the exploration of bandwidth based on data from an independent experiment, the normalized difference red edge （NDRE（λ₇₉₀-b_33,λ₇₂₀-b₅₃））, which contains the 790 nm central band （λ₇₉₀） with 33 nm bandwidth （b₃₃） and 720 nm central band （λ₇₂₀） with 53 nm bandwidth （b₅₃）, exhibited greater practicability in LAI estimation with a determination coefficient （R²） of 0.7482 and a relative root mean square error （RRMSE） of 13.88%. The normalized difference red edge （NDRE（λ₇₉₀-b₈₉, λ₇₂₀-b₈₉））, which contains the 790 nm central band （λ₇₉₀） with 89 nm bandwidth （b₈₉） and 720 nm central band （λ₇₂₀） with 89 nm bandwidth （b₈₉）, performed best for monitoring AGB （R²= 0.7103, RRMSE=20.42%）. Considering the accuracy and convenience in application, it was demonstrated that NDRE（λ₇₉₀-b_33,λ₇₂₀-b₅₃） and NDRE（λ₇₉₀-b₈₉, λ₇₂₀-b₈₉） could be used to monitor peanut LAI and AGB with estimation models of LAI=0.0296×exp^{（14.365×NDRE）} and AGB= 0.6240×exp^{（20.222×NDRE）}, respectively.