现象阶段和植被指数用于预测在雨水环境下玉米产量
Amrit Shrestha1, Raju Bheemanahalli2, Ardeshir Adeli3
1Department of Agricultural & Biological Engineering, Mississippi State University, Mississippi State, MS, United States.
Frontiers in plant science
|August 7, 2023
概括
准确的玉米产量预测依赖于确定最佳植被指数 (VIs) 和生长阶段. 叶叶叶绿素和MERIS陆地叶叶绿素指数在繁殖阶段 (R1) 周围显示了改善作物管理的最大希望.
科学领域:
- 农业遥感 农业遥感
- 精准农业 精准农业 精准农业
- 植物生理学作物生理学
背景情况:
- 无人机提供高分辨率数据用于作物监测,但由于环境变化,传统的产量预测方法缺乏一致性.
- 准确的季节性产量估计对于优化农业管理决策和提高作物产量至关重要.
研究的目的:
- 确定最佳的现象阶段和植被指数 (VI) 以在雨天条件下准确估计玉米产量.
- 用多光谱图像识别最一致和最强大的VI来预测玉米产量.
主要方法:
- 收集了玉米作物的三个生长季节 (2020-2022) 的多光谱图像.
- 分析了50多个植被指数 (VIs) 及其与最终玉米产量的相关性.
- 采用相关性分析和随机森林模型来确定关键的预测VI和增长阶段.
主要成果:
- 在为期三年的研究期间,31个VI显示出显著的收益相关性 (r ≥ 0.7).
- 五个VI,特别是那些基于红色,红边和近红外反射率的VI,在三年内都保持了显著的相关性.
- 叶子叶绿素指数,MERIS陆地叶绿素指数和705的修改正常化差异在生殖阶段测量时是最一致的预测指标 (R1).
结论:
- 该研究强调了树冠反射的动态性质及其与玉米产量的密切联系.
- 将特定植被指数与生殖生长阶段 (R1) 整合起来,可显著提高玉米产量预测的准确性.
- 这些发现支持使用UAS衍生的光谱数据进行更可靠的季节性作物产量预测.
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