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在甘 (Saccharum spp.) 中叶子的预测. 通过Vis-NIR-SWIR光谱辐射计

Peterson Ricardo Fiorio¹, Carlos Augusto Alves Cardoso Silva¹, Rodnei Rizzo²

¹Department of Biosystems Engineering, "Luiz de Queiroz" College of Agriculture, University of São Paulo, 13418900, Piracicaba, São Paulo, Brazil.

|March 5, 2024

概括

使用高光谱数据估计甘中的叶子含量 (LNC) 对作物管理至关重要. 这项研究开发了强大的光谱模型,特别是在可见和红边带,用于准确的LNC预测.

科学领域:

农业科学农业科学
遥感遥感遥感遥感
植物生理学植物生理学

背景情况:

是农作物必需的营养物质,对于土壤-植物-大气相互作用至关重要.
精确的气监测对于农业的经济和环境可持续性至关重要.
甘作物的产量和质量受到的可用性显著的影响.

研究的目的:

用超光谱反射率数据估计甘中的叶子含量 (LNC).
开发和验证用于在不同作物发育阶段预测LNC的光谱模型.
确定在甘中对LNC估计最相关的光谱频段.

主要方法:

实地实验是在不同的应用速度 (0-180公斤/ha-1) 进行的.
在整个甘生长周期 (67-313天切割后) 中,在多个时间点收集了超谱数据.
部分最小平方回归 (PLSR) 用于构建预测模型,在预测中变量重要性 (VIP) 确定关键的光谱频段.

主要成果:

增加的LNC通常导致可见波长 (400-680 nm) 的反射率下降.
PLSR模型实现了可接受的准确性 (R2 > 0.70,RMSE < 1.41 g kg-1).
最强大的模型使用可见 (400-680 nm) 和红边 (680-750 nm) 频谱区域,产生R2 > 0.81和RMSE < 1.24 g kg−1.1.

结论:

超光谱反射分析是估计甘LNC的一种可行的方法.
可见和红边光谱带对于开发精确的LNC预测模型非常有效.
经过验证的模型证明了在新数据集中预测LNC的潜力,支持精准农业实践.

关键词:

通过交叉验证验证.化肥的受精方式遥感是一种远程传感.频谱反射率是指光谱的反射率.

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