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Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance

机译:从叶片和冠层反射率估算玉米叶片叶绿素浓度

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Farmers must balance the competing goals of supplying adequate N for their crops while minimizing N losses to the environment. To characterize the spatial variability of N over large fields, traditional methods (soil testing, plant tissue analysis, and chlorophyll meters) require many point samples. Because of the close link between leaf chlorophyll and leaf N concentration, remote sensing techniques have the potential to evaluate the N variability over large fields quickly. Our objectives were to (1) select wavelengths sensitive to leaf chlorophyll concentration, (2) stimulate canopy reflectance using a radiative transfer model, and (3) propose a strategy for detecting leaf chlorophyll status of plants using remotely sensed data. A wide range of leaf chlorophyll levels was established in field-grown corn (Zea mays L.) with the application of 8 N levels: 0%, 12.5%, 25%, 50%, 75%, 100%, 125%, and 150% of the recommended rate. Reflectance and transmittance spectra of fully expanded upper leaves were acquired over the 400-nm to 1,000-nm wavelength range shortly after anthesis with a spectroradiometer and integrating sphere. Broad-band differences in leaf spectra were observed near 550 nm, 715 nm, and >750 nm. Crop canopy reflectance was simulated using the SAIL (Scattering by Arbitrarily Inclined Leaves) canopy reflectance model for a wide range of background reflectances, leaf area indices (LAI), and leaf chlorophyll concentrations. Variations in background reflectance and LAI confounded the detection of the relatively subtle differences in canopy reflectance due to changes in leaf chlorophyll concentration. Spectral vegetation indices that combined near-infrared reflectance and red reflectance (e.g. OSAVI and NIR/Red) minimized contributions of background reflectance, while spectral vegetation indices that combined reflectances of near-infrared and other visible bands (MCARI and NIR/Green) were responsive to both leaf chlorophyll concentrations and background reflectance. Pairs of these spectral vegetation indices plotted together produced isolines of leaf chlorophyll concentrations. The slopes of these isolines were linearly related to leaf chlorophyll concentration. A limited test with measured canopy reflectance and leaf chlorophyll data confirmed these results. The characterization of leaf chlorophyll concentrations at the field scale without the confounding problem of background reflectance and LAI variability holds promise as a valuable aid for decision making in managing N applications. Published by Elsevier Science Inc. [References: 30]
机译:农民必须权衡相互竞争的目标,即为作物提供足够的氮,同时最大程度地减少对环境的氮损失。为了表征大田中N的空间变异性,传统方法(土壤测试,植物组织分析和叶绿素计)需要许多点样。由于叶片叶绿素和叶片氮素浓度之间的紧密联系,遥感技术具有迅速评估大田地氮素变异性的潜力。我们的目标是(1)选择对叶片叶绿素浓度敏感的波长,(2)使用辐射转移模型刺激冠层反射率,以及(3)提出使用遥感数据检测植物叶片叶绿素状态的策略。在田间种植的玉米(Zea mays L.)中,通过应用8种氮素,可以确定各种叶绿素含量:0%,12.5%,25%,50%,75%,100%,125%和建议费率的150%。在使用分光辐射计和积分球后不久,在400nm至1,000nm波长范围内获得了完全展开的上部叶片的反射和透射光谱。在550 nm,715 nm和> 750 nm附近观察到叶片光谱的宽带差异。使用SAIL(任意倾斜叶子的散射)冠层反射模型模拟了作物冠层反射率,该模型具有广泛的背景反射率,叶面积指数(LAI)和叶绿素浓度。背景反射率和LAI的变化混淆了由于叶绿素浓度变化而引起的冠层反射率相对细微差异的检测。结合近红外反射率和红色反射率的光谱植被指数(例如OSAVI和NIR / Red)使背景反射率的贡献最小化,而结合近红外和其他可见波段(MCARI和NIR / Green)的反射率的光谱植被指数具有响应性叶片叶绿素浓度和背景反射率。成对的这些光谱植被指数对产生了叶绿素浓度的等值线。这些等值线的斜率与叶片叶绿素浓度线性相关。对测得的冠层反射率和叶绿素数据进行的有限测试证实了这些结果。在田间尺度上表征叶绿素浓度而没有背景反射和LAI变异性的混淆问题,有望作为管理N种应用决策的宝贵帮助。由Elsevier Science Inc.发布[参考:30]

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