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玉米产量估计的空间和光谱依赖使用遥感

Nathan Burglewski¹, Subhashree Srinivasagan², Quirine Ketterings²

¹Center for Imaging Science, Rochester Institute of Technology, Rochester, NY 14623, USA.

Sensors (Basel, Switzerland)

|June 27, 2024

概括

准确的玉米产量估计对于全球粮食监测至关重要. 一个七带多谱传感器,利用红边和近红外波段,为玉米料产量建模提供高精度,即使在较粗的分辨率.

科学领域:

农业遥感农业遥感
作物产量建模作物产量建模
精准农业精准农业精准农业

背景情况:

玉米 (Zea mays L.) 是一个全球重要的食品和料作物.
准确的玉米产量估计对于监测粮食生产至关重要.
现有的产量模型使用具有不同空间和光谱分辨率的传感器,需要了解它们的依赖性.

研究的目的:

评估空间和光谱分辨率对玉米料产量估计的影响.
为准确的玉米产量建模确定最佳传感器配置.
评估光谱反射功能的适用性,用于产量估计.

主要方法:

利用来自无人机系统 (UAS) 的高分辨率高光谱图像 (0.063 m GSD, 272 波段).
应用了三带选择算法来识别用于产量估计的关键光谱特征.
导出了11种光谱配置,重新采样到各种地面样本距离 (GSD),并使用支持向量回归 (SVR) 建模了产量.

主要成果:

在所有测试的光谱配置中,模型精度在4米GSD以上显著降低.
七带多光谱配置,包括红边和近红外频段,在90%的试验中实现了更高的准确性.
确定了最佳的光谱和空间分辨率,以可靠地估计玉米料产量.

结论:

一个七带多谱传感器为全球玉米产量建模提供了一个有希望的,准确的解决方案.
在4米GSD以下的空间分辨率对于保持模型准确性至关重要.
需要对时间依赖性的进一步调查,以进行全面的全球玉米产量建模.

关键词:

农业农业农业农业超光谱图像是一种超光谱图像.影像光谱学影像光谱学多光谱的多光谱的在重新抽样时进行重新抽样.频谱指数是指光谱指数.无人驾驶飞行系统无人驾驶飞行系统植物生长植物生长收益率收益率收益率收益率

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