Estimating plant root water uptake using a neural network approach
Estimating plant root water uptake using a neural network approach
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使用神经网络方法估计植物根系吸水量
DOI：
10.1016/j.agwat.2010.08.017
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发表时间：
2010-12
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影响因子：
--
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中科院分区：
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--
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植物根系对水分的吸收是水循环中的一个重要过程，不仅对植物生长有重要作用，而且在形成微生物群落和给土壤带来物理生化变化方面也起着重要作用。根系提取水分的能力是由土壤和植物的综合特性决定的，如何对其建模多年来一直是人们感兴趣的问题。大多数宏观吸水模型都是在土壤剖面尺度上运行的，假设吸收率取决于根系密度和土壤湿度。虽然被证明是适当的，但这些模型需要时空根密度分布，这在原位测量是繁琐的，而且由于隐藏在不透明土壤中的根结构的复杂性，容易产生不确定性。因此，开发不明确需要根密度来估计根吸水性的替代方法实际上是有用的，但尚未得到解决。本文提出并验证了这种方法。该方法基于神经网络模型，利用易于在田间测量的不同类型的数据来估计吸水量。以向日葵在沙质壤土中生长为例，对水分胁迫和盐分胁迫进行了研究。神经网络模型的输入包括土壤湿度、土壤溶液电导率、植物茎高和直径、潜在蒸散量、大气湿度和气温。输出为土壤剖面中不同深度的根系吸水速率。为了对模型进行训练和测试，我们直接根据质量平衡和达西定律，从土壤表面到100cm深度的剖面测量土壤含水量和土壤水基质势，评估根系吸水率。实测的根系吸水量与神经网络模型预测的结果吻合较好。该模型的成功运行为在田间尺度上估算根系水分吸收提供了一种更实用的方法。
Water uptake by plant roots is an important process in the hydrological cycle, not only for plant growth but also for the role it plays in shaping microbial community and bringing in physical and biochemical changes to soils. The ability of roots to extract water is determined by combined soil and plant characteristics, and how to model it has been of interest for many years. Most macroscopic models for water uptake operate at soil profile scale under the assumption that the uptake rate depends on root density and soil moisture. Whilst proved appropriate, these models need spatio-temporal root density distributions, which is tedious to measure in situ and prone to uncertainty because of the complexity of root architecture hidden in the opaque soils. As a result, developing alternative methods that do not explicitly need the root density to estimate the root water uptake is practically useful but has not yet been addressed. This paper presents and tests such an approach. The method is based on a neural network model, estimating the water uptake using different types of data that are easy to measure in the field. Sunflower grown in a sandy loam subjected to water stress and salinity was taken as a demonstrating example. The inputs to the neural network model included soil moisture, electrical conductivity of the soil solution, height and diameter of plant shoot, potential evapotranspiration, atmospheric humidity and air temperature. The outputs were the root water uptake rate at different depths in the soil profile. To train and test the model, the root water uptake rate was directly measured based on mass balance and Darcy's law assessed from the measured soil moisture content and soil water matric potential in profiles from the soil surface to a depth of 100cm. The ‘measured’ root water uptake agreed well with that predicted by the neural network model. The successful performance of the model provides an alternative and more practical way to estimate the root water uptake at field scale.
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