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Hyperspectral indices and model simulation for chlorophyll estimation in open-canopy tree crops

机译:冠层农作物叶绿素估计的高光谱指数和模型模拟

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An investigation of the estimation of leaf biochemistry in open tree crop canopies using high-spatial hyperspectral remote sensing imagery is presented. Hyperspectral optical indices related to leaf chlorophyll content were used to test different radiative transfer modelling assumptions in open canopies where crown, soil and shadow components were separately targeted using 1 m spatial resolution ROSIS hyperspectral imagery. Methods for scaling-up of hyperspectral single-ratio indices such as R{sub}750/R{sub}710 and combined indices such as MCARI, TCARI and OSAVI were studied to investigate the effects of scene components on indices calculated from pure crown pixels and from aggregated soil, shadow and crown reflectance. Methods were tested on 1-m resolution hyperspectral ROSIS datasets acquired over two olive groves in southern Spain during the HySens 2002 campaign conducted by the German Aerospace Center (DLR). Leaf-level biochemical estimation using 1-m ROSIS data when targeting pure olive tree crowns employed PROSPECT-SAILH radiative transfer simulation. At lower spatial resolution, therefore with significant effects of soil and shadow scene components on the aggregated pixels, a canopy model to account for such scene components had to be used for a more appropriate estimation approach for leaf biochemical concentration. The linked models PROSPECT-SAILH-FLIM improved the estimates of chlorophyll concentration from these open tree canopies, demonstrating that crown-derived relationships between hyperspectral indices and biochemical constituents cannot be readily applied to hyperspectral imagery of lower spatial resolutions due to large soil and shadow effects. Predictive equations built on a MCARI/OSAVI scaled-up index through radiative transfer simulation minimized soil background variations in these open canopies, demonstrating superior performance compared to other single-ratio indices previously shown as good indicators of chlorophyll concentration in closed canopies. The MCARI/OSAVI index was demonstrated to be less affected than TCARI/OSAVI by soil background variations when calculated from the pure crown component even at the typically low LAI orchard and grove canopies.
机译:提出了一种利用高空间高光谱遥感影像估算裸露树作物冠层叶片生物化学的方法。与叶片叶绿素含量有关的高光谱光学指数用于测试开放冠层中不同的辐射传递建模假设,在该冠层中,冠层,土壤和阴影成分使用1 m空间分辨率ROSIS高光谱图像分别作为目标。研究了放大高光谱单比率指标(例如R {sub} 750 / R {sub} 710)和组合指标(例如MCARI,TCARI和OSAVI)的方法,以研究场景分量对从纯冠状像素计算的指标的影响以及来自聚集的土壤,阴影和树冠反射率。在德国航空航天中心(DLR)进行的HySens 2002运动期间,在西班牙南部的两个橄榄树上采集的1-m分辨率高光谱ROSIS数据集上测试了方法。以纯橄榄树冠为目标时,使用1-m ROSIS数据进行叶片水平生化估计时,采用了PROSPECT-SAILH辐射转移模拟。在较低的空间分辨率下,因此,土壤和阴影场景分量对聚合像素具有重大影响,因此必须使用考虑此类场景分量的树冠模型来更适当地估算叶片生化浓度。链接的模型PROSPECT-SAILH-FLIM改进了这些开阔树冠的叶绿素浓度估计值,表明由于大的土壤和阴影效应,高光谱指数与生化成分之间的冠状派生关系不易应用于空间分辨率较低的高光谱图像。通过辐射传递模拟基于MCARI / OSAVI放大指数建立的预测方程式使这些开放式林冠中的土壤背景变化最小化,与先前显示为封闭式林冠中叶绿素浓度的良好指标的其他单比率指标相比,证明了其优越的性能。当从纯树冠成分计算时,即使在通常较低的LAI果园和林冠层中,土壤背景变化对MCARI / OSAVI指数的影响也比TCARI / OSAVI少。

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