论文标题
将常见的多光谱植被指数与高光谱混合物模型联系起来：在多种农业景观中的5 nm，3 m机载成像光谱的结果
Linking Common Multispectral Vegetation Indices to Hyperspectral Mixture Models: Results from 5 nm, 3 m Airborne Imaging Spectroscopy in a Diverse Agricultural Landscape
论文作者
论文摘要
几十年来，农艺师一直使用遥感来监测关键作物参数，例如生物量，分数覆盖和植物健康。植被指数（VIS）为此目的很受欢迎，主要利用多光谱图像中的光谱红色边缘。相比之下，光谱混合模型使用完整的反射光谱来同时估计混合像素中存在的多个末端材料的面积分数。在这里，我们表征了高光谱末端成员分数与加利福尼亚农业作物土壤中6个常见的多光谱景观之间的关系。绿色植被（FV）的分数面积直接从64,000,000 5 nm，3至5 m的反射光谱中估计，从15个Aviris-NG飞行线的马赛克汇编而成。然后从Aviris-NG得出了模拟的行星超高反射光谱，并用于计算6个流行的Vis（NDVI，NIRV，EVI，EVI2，SR，DVI）。使用参数（Pearson相关性，R）和非参数（共同信息，MI）相似性指标将多光谱VES与高光谱FV进行比较。 4 Vis（NIRV，DVI，EVI，EVI2）显示与FV（R> 0.94; MI> 1.2）的线性关系很强。 NIRV和DVI显示出强的相互关系（r> 0.99，MI> 2.4），但相对于FV显着偏离1：1。 EVI和EVI2也有很强的相互关联（r> 0.99，mi> 2.3），更紧密地遵循与FV的1：1关系。相比之下，NDVI和SR显示出与FV的弱，非线性，异性关系（R <0.84，Mi = 0.69）。 NDVI对底物背景反射率显示出特别严重的敏感性（未植根的光谱为-0.05 <ndvi <+0.6）和饱和度（NDVI = 0.7的0.2 <fv <0.8）。在多光谱VI和高光谱混合模型可比性上，这些直接的观察性约束可以用作越来越多的空间和光谱分辨率地球观察时代的农艺应用的定量基准。
For decades, agronomists have used remote sensing to monitor key crop parameters like biomass, fractional cover, and plant health. Vegetation indices (VIs) are popular for this purpose, primarily leveraging the spectral red edge in multispectral imagery. In contrast, spectral mixture models use the full reflectance spectrum to simultaneously estimate area fractions of multiple endmember materials present within a mixed pixel. Here, we characterize the relationships between hyperspectral endmember fractions and 6 common multispectral VIs in crops & soils of California agriculture. Fractional area of green vegetation (Fv) was estimated directly from 64,000,000 5 nm, 3 to 5 m reflectance spectra compiled from a mosaic of 15 AVIRIS-ng flightlines. Simulated Planet SuperDove reflectance spectra were then derived from the AVIRIS-ng, and used to compute 6 popular VIs (NDVI, NIRv, EVI, EVI2, SR, DVI). Multispectral VIs were compared to hyperspectral Fv using parametric (Pearson correlation, r) and nonparametric (Mutual Information, MI) similarity metrics. 4 VIs (NIRv, DVI, EVI, EVI2) showed strong linear relationships to Fv (r > 0.94; MI > 1.2). NIRv & DVI showed strong interrelation (r > 0.99, MI > 2.4), but deviated significantly from 1:1 relative to Fv. EVI & EVI2 were also strongly interrelated (r > 0.99, MI > 2.3) and more closely followed a 1:1 relation with Fv. In contrast, NDVI & SR showed weaker, nonlinear, heteroskedastic relation to Fv (r < 0.84, MI = 0.69). NDVI showed especially severe sensitivity to substrate background reflectance (-0.05 < NDVI < +0.6 for unvegetated spectra) and saturation (0.2 < Fv < 0.8 for NDVI = 0.7). These direct observational constraints on multispectral VI and hyperspectral mixture model comparability can serve as a quantitative benchmark for agronomic applications in the coming era of increasing spatial & spectral resolution Earth observation.
