Jiawei Wang, Dong Zhang, Yulu Zhang, Hu Liu, Linkang Zhou, Hua Jin
{"title":"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","authors":"Jiawei Wang, Dong Zhang, Yulu Zhang, Hu Liu, Linkang Zhou, Hua Jin","doi":"10.1007/s11368-024-03868-w","DOIUrl":null,"url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Purpose</h3><p>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.</p><h3 data-test=\"abstract-sub-heading\">Materials and methods</h3><p>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.</p><h3 data-test=\"abstract-sub-heading\">Results and discussion</h3><p>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 R<sup>2</sup> ranging from 0.919 to 0.991 and corresponding RMSE values from 0.619% to 0.982%.</p><h3 data-test=\"abstract-sub-heading\">Conclusions</h3><p>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.</p>","PeriodicalId":17139,"journal":{"name":"Journal of Soils and Sediments","volume":"30 1","pages":""},"PeriodicalIF":2.8000,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Prediction of soil moisture content using genetic algorithm-optimized backpropagation algorithm from spectral data\",\"authors\":\"Jiawei Wang, Dong Zhang, Yulu Zhang, Hu Liu, Linkang Zhou, Hua Jin\",\"doi\":\"10.1007/s11368-024-03868-w\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<h3 data-test=\\\"abstract-sub-heading\\\">Purpose</h3><p>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.</p><h3 data-test=\\\"abstract-sub-heading\\\">Materials and methods</h3><p>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.</p><h3 data-test=\\\"abstract-sub-heading\\\">Results and discussion</h3><p>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 R<sup>2</sup> ranging from 0.919 to 0.991 and corresponding RMSE values from 0.619% to 0.982%.</p><h3 data-test=\\\"abstract-sub-heading\\\">Conclusions</h3><p>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.</p>\",\"PeriodicalId\":17139,\"journal\":{\"name\":\"Journal of Soils and Sediments\",\"volume\":\"30 1\",\"pages\":\"\"},\"PeriodicalIF\":2.8000,\"publicationDate\":\"2024-07-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Soils and Sediments\",\"FirstCategoryId\":\"97\",\"ListUrlMain\":\"https://doi.org/10.1007/s11368-024-03868-w\",\"RegionNum\":3,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENVIRONMENTAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Soils and Sediments","FirstCategoryId":"97","ListUrlMain":"https://doi.org/10.1007/s11368-024-03868-w","RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
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.
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.