利用遗传算法优化的反向传播算法从光谱数据中预测土壤含水量

IF 2.8 3区 农林科学 Q3 ENVIRONMENTAL SCIENCES Journal of Soils and Sediments Pub Date : 2024-07-31 DOI:10.1007/s11368-024-03868-w
Jiawei Wang, Dong Zhang, Yulu Zhang, Hu Liu, Linkang Zhou, Hua Jin
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引用次数: 0

摘要

目的准确评估土壤含水量(SMC)对于农业应用和生态可持续性至关重要。然而,由于传统方法的复杂性和环境变量的不断变化,土壤含水量的动态监测和评估面临着相当大的挑战。相关研究表明,可见光和近红外(vis-NIR)光谱是准确、方便地估算 SMC 的一种实用且具有成本效益的替代方法。技术和计算机硬件的进步使得光谱特性和计算机视觉算法在快速、无损地表征土壤特性方面显示出巨大的潜力。本研究的目的是利用可见近红外光谱数据评估 SMC 的预测能力。利用遗传算法-优化反向传播(GA-BP)神经网络建立 C-W 和 W-W 模型,根据室外测量结果预测 SMC:(1) 通过 C-W 和 W-W 模型,可以利用光谱数据成功预测 SMC;(2) C-W 模型的性能优于 W-W 模型,尤其是在深层土壤方面,R2 为 0.919 至 0.991,相应的 RMSE 值为 0.619% 至 0.982%。它进一步证明了 GA-BP 算法在预测室外 SMC 方面仍然有效。该研究成果可能有助于对 SMC 测量的进一步研究。
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Prediction of soil moisture content using genetic algorithm-optimized backpropagation algorithm from spectral data

Purpose

Accurately assessing soil moisture content (SMC) is essential for applications in agriculture and ecological sustainability. However, the dynamic monitoring and assessment of SMC presents considerable challenges due to the intricate traditional methods and the ever-evolving environmental variables. Relevant research has indicated that visible and near-infrared (vis–NIR) spectra are a practical and cost-effective alternative for accurate and convenient estimation of SMC. Advances in technology and computer hardware have enabled spectral characteristics and computer vision algorithms to show enormous potential for rapid and non-destructive characterization of soil properties. The objective of this study was to evaluate the predicted ability of SMC using vis–NIR spectral data.

Materials and methods

A total of 60 topsoil samples (0–5 cm) from the maize test field at the Shanxi Central Irrigation Test station were used as the study object. A set of four spectral parameters was derived and filtered from spectral data, and C-W and W-W models were developed using Genetic Algorithm algorithm-optimized backpropagation (GA-BP) neural networks to predict SMC based on outdoor measurements.

Results and discussion

The results showed that: (1) SMC can be successfully predicted using the spectral data through the C-W and W-W models; (2) the C-W model outperformed the W-W model, particularly in the context of deep soil, with R2 ranging from 0.919 to 0.991 and corresponding RMSE values from 0.619% to 0.982%.

Conclusions

This study introduces two effective methodologies for accurate estimation of SMC at different depths using multispectral remote sensing, which showed a high degree of prediction accuracy. It further proves that GA-BP algorithm is still effective for predicting SMC in outdoor. The research result might be helpful for the further study of SMC measurement.

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来源期刊
Journal of Soils and Sediments
Journal of Soils and Sediments 环境科学-土壤科学
CiteScore
7.00
自引率
5.60%
发文量
256
审稿时长
3.5 months
期刊介绍: The Journal of Soils and Sediments (JSS) is devoted to soils and sediments; it deals with contaminated, intact and disturbed soils and sediments. JSS explores both the common aspects and the differences between these two environmental compartments. Inter-linkages at the catchment scale and with the Earth’s system (inter-compartment) are an important topic in JSS. The range of research coverage includes the effects of disturbances and contamination; research, strategies and technologies for prediction, prevention, and protection; identification and characterization; treatment, remediation and reuse; risk assessment and management; creation and implementation of quality standards; international regulation and legislation.
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