一个通用的模型来估计小麦在生长季节的LAI,无论土壤类型的背景
Qiaomin Chen1,2, Bangyou Zheng2, Karine Chenu3
1School of Agriculture and Food Sciences, The University of Queensland, St Lucia, QLD, Australia.
Plant phenomics (Washington, D.C.)
|May 26, 2023
概括
一个新的通用模型使用无人机跨多种土壤的多谱数据准确估计小麦叶面积指数 (LAI),而无需地面校准. 这种方法增强了精准农业和作物育种应用.
科学领域:
- 农业遥感 农业遥感
- 植物科学 植物科学
- 地理空间分析是什么?
背景情况:
- 准确估计小麦叶面积指数 (LAI) 对于作物管理至关重要.
- 现有的模型通常需要进行地面校准,并与各种土壤背景作斗争.
- 基于无人驾驶飞行器 (UAV) 的多谱数据为非侵入性LAI估计提供了一个有希望的途径.
研究的目的:
- 从无人机的多光谱数据开发一个通用的,无校准的模型来估计小麦的LAI.
- 为了在各种土壤类型和作物条件中提高模型的稳定性.
- 提高遥感在精准农业和作物育种中的应用性.
主要方法:
- 使用随机森林回归 (RFR) 模型与辐射转移模型 (PROSAIL) 模拟进行训练.
- 实施了两个策略:扩大土壤反射率域和优化输入指标 (带反射率/植被指数).
- 通过在不同澳大利亚土壤中使用两年实地试验数据验证了该模型.
主要成果:
- 开发的通用模型准确地估计了在不同土壤背景的小麦LAI,预测准确度高 (RMSE:0.23-0.89 m2m-2).
- 该模型表明,对于稀疏的天花板 (LAI < 0.3 m2m−2) 具有低RMSE (0.02-0.25 m2m−2) 的强度.
- 在不同的作物管理实践下可靠地捕获季节性LAI动态 (相关性:0.82-0.98).
结论:
- 增强的RFR模型提供了一个强大的和准确的解决方案,用于小麦LAI估计来自无人机多谱数据,独立于土壤背景.
- 这种框架可以适应各种传感器和物种,为作物育种和精准农业提供了巨大的潜力.
- 该研究强调了多样化的培训数据和优化的输入特征对于开发通用遥感模型的重要性.
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