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

机译:稻作物氮浓度监测氮谱辐射辐射辐射数据的分析

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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 ;i 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~=0.55 [p<0.01]; RRMSE=29.4; modelling efficiency close to the optimum value).
机译:监测作物条件并评估营养需求是实施可持续农业的基础。在水稻作物中,合理的氮肥是特别重要的,以保证高产量水平,同时最大限度地减少对环境的影响。事实上,稻田的典型洪水状况可以是温室气体的重要来源。有关植物氮浓度的信息可以使用,加上有关鉴别阶段的信息,以规划理性和空间分化的施肥时间表的策略。在意大利北部的稻田中进行了一个田间实验,以评估用于预测水稻氮浓度的场辐射测量的潜力。结果表明,稻米反射率受到某些波长下的氮气供应的影响,尽管不能基于在给定波长处测量的反射率准确地预测n浓度。回归分析强调,当反射率测量组合成光谱指数时,光谱的可见区域对植物氮浓度最敏感。自动化过程允许分析通过场测量的光谱重采样导出的窄谱带的归一系列差异指数(NDI)分析。衍生指数似乎是植物生物质和叶面积指数(LAI)的最小影响;我有用的方法来检测水稻营养状况。回归模型的验证表明,该模型能够预测水稻浓度(R = 0.55 [P <0.01]; rrmse = 29.4;建模效率接近最佳值)。

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