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用FDR-HY2D用于基于设施的草灌的根区和精密灌的时空空间湿度数字重建

Ruipeng Tang¹, Loy Chee Luen², Jianxun Tang³

¹NUS Centre for Urban Agriculture (CUVA), National University of Singapore, Singapore, Singapore. tang823662722@gmail.com.

NPJ science of food

|February 26, 2026

概括

使用频域反射计 (FDR) 传感器和HYDRUS-2D模型的智能灌方法提高了草温室用水的效率. 这种方法增强了土壤水分模拟,并减少了深层透.

科学领域:

农业工程农业工程
土壤科学土壤科学
水文学水文学

背景情况:

温室草的传统滴灌方式受到深层透和低水利用效率的影响.
精确的时空地土壤水分分布在根区难以重建.

研究的目的:

开发一种智能灌方法,将频域反射计 (FDR) 传感器与HYDRUS-2D模型集成在一起.
为了提高温室草生产中的土壤湿度模拟和水资源管理的准确性.

主要方法:

来自FDR传感器的实时土壤湿度数据被纳入HYDRUS-2D模型.
集成的FDR-HY2D框架为2D模拟动态校准了土壤根系参数.
分析了水平衡的组成部分,包括透,根吸收,蒸发和深层透.

主要成果:

与SIMDualKc和AquaCrop.c相比,FDR-HY2D方法在模拟土壤水分动态方面表现出更高的准确性 (增加R2,减少RMSE和MAE).
该模型有效地复制了根区的每日湿度变化和垂直梯度.
智能灌将水分配从深层透转移到作物透气,提高了用水效率.

结论:

拟议的FDR-HY2D方法为精密灌提供了一个数字决策支持工具.
这种方法显著提高了温室草生产中的有效用水和用水效率.
它为优化水肥管理提供了一条途径.

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