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Analysis of hyperspectral field radiometric data for monitoring nitrogen concentration in rice crops

机译:高光谱场辐射数据监测稻米氮素含量的分析

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摘要

Monitoring crop conditions and assessing nutrition requirements is fundamental for implementing sustainable agriculture. Rational nitrogen fertilization is of particular importance in rice crops in order to guarantee high production levels while minimising the impact on the environment. In fact, the typical flooded condition of rice fields can be a significant source of greenhouse gasses. Information on plant nitrogen concentration can be used, coupled with information about the phenological stage, to plan strategies for a rational and spatially differentiated fertilization schedule. A field experiment was carried out in a rice field Northern Italy, in order to evaluate the potential of field radiometric measurements for the prediction of rice nitrogen concentration. The results indicate that rice reflectance is influenced by nitrogen supply at certain wavelengths although N concentration cannot be accurately predicted based on the reflectance measured at a given wavelength. Regression analysis highlighted that the visible region of the spectrum is most sensitive to plant nitrogen concentration when reflectance measures are combined into a spectral index. An automated procedure allowed the analysis of all the possible combinations into a Normalized Difference Index (NDI) of the narrow spectral bands derived by spectral resampling of field measurements. The derived index appeared to be least influenced by plant biomass and Leaf Area Index (LAI) providing a useful approach to detect rice nutritional status. The validation of the regressive model showed that the model is able to predict rice N concentration (R~2=0.55 [p < 0.01]; RRMSE=29.4; modelling efficiency close to the optimum value).
机译:监测作物状况和评估营养需求是实施可持续农业的基础。合理的氮肥在稻米作物中尤为重要,以确保高产量同时将对环境的影响降至最低。实际上,稻田的典型淹水状况可能是温室气体的重要来源。可以使用有关植物氮浓度的信息以及有关物候阶段的信息,来规划合理和空间差异化施肥计划的策略。在意大利北部的稻田中进行了田间试验,以评估田间辐射测量方法对预测稻米氮浓度的潜力。结果表明,尽管不能根据给定波长下测得的反射率准确预测氮的浓度,但在某些波长下稻米的反射率受氮供应的影响。回归分析强调,当将反射率度量组合为光谱指数时,光谱的可见区域对植物氮浓度最敏感。自动化程序允许将所有可能的组合分析为通过现场测量的频谱重采样获得的窄谱带的归一化差异指数(NDI)。得出的指数似乎受植物生物量的影响最小,叶面积指数(LAI)为检测水稻的营养状况提供了一种有用的方法。回归模型的验证表明该模型能够预测水稻的氮素浓度(R〜2 = 0.55 [p <0.01]; RRMSE = 29.4;建模效率接近最佳值)。

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