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Plant nitrogen concentration in paddy rice from field canopy hyperspectral radiometry

机译:田间高光谱辐射法测定水稻中的植物氮含量。

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摘要

In the context of precision farming, quantitative information on plant nitrogen concentration (PNC) is necessary to apply variable rate technologies of top-dressing fertilization. Radiometric measurements are useful for monitoring crop conditions and, in particular, for nitrogen/chlorophyll assessment. This work aims to quantify PNC from canopy spectra collected in the field with a hand-held spectro-radiometer. We propose a vegetation index that is able to predict PNC in rice crops through a regressive model that was calibrated and validated with data from two field campaigns carried out in Northern Italy in 2004 and 2006. The index exploits availability of hyperspectral data (numerous very narrow bands, <10nm) that can guide the choice of spectral band combinations for PNC estimation. The most suitable bands were selected in the visible (blue/green) region of the electromagnetic spectrum where nitrogen/chlorophyll compounds play a key role in radiation absorption. The index was also shown to beleast affected by leaf area index and aboveground biomass variability thus assuring the highest sensitivity to PNC (R po =0.65, ***p <0.001). The regressive model was applied, via a spatial interpolation algorithm, to field data in order to derive, mapsof PNC; these showed a high correlation with experimental design and crop conditions through the rice growing cycle. Model precision and accuracy appear suitable for detecting spatial and temporal variations in rice crops and for supporting decisions forapplication of variable rate technology.
机译:在精耕细作的背景下,需要使用可变速率的追肥技术来获得有关植物氮浓度(PNC)的定量信息。辐射测量可用于监测作物状况,尤其是用于氮/叶绿素评估。这项工作旨在利用手持式光谱辐射计从野外收集的冠层光谱中量化PNC。我们提出了一种植被指数,该指数能够通过回归模型预测稻米中的PNC,并使用2004年和2006年在意大利北部进行的两次野外活动的数据进行了校准和验证。该指数利用了高光谱数据的可用性(许多非常窄小于10nm的波段)可以指导PNC估计的光谱波段组合选择。在电磁光谱的可见(蓝色/绿色)区域中选择了最合适的谱带,其中氮/叶绿素化合物在辐射吸收中起关键作用。该指数还显示出受到叶面积指数和地上生物量变异性的最小影响,从而确保了对PNC的最高敏感性(R po = 0.65,*** p <0.001)。通过空间插值算法将回归模型应用于现场数据,以得出PNC的地图。这些表明在水稻生长周期中与实验设计和作物条件高度相关。模型的精确度和准确性似乎适用于检测稻米作物的时空变化,并支持采用可变速率技术的决策。

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