A new method to estimate soil organic matter using the combination model based on short memory fractional order derivative and machine learning model

Fu, Chengbiao; Gan, Shu; Xiong, Heigang; Tian, Anhong

doi:10.1016/j.infrared.2023.104922

Cited by 6 publications

(2 citation statements)

References 35 publications

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“…Geometrically, the integer-order derivative is an arbitrary order slope of a function curve, which has the physical meaning of fractional flow and generalized amplitude. It can refine the information of spectral data and effectively denoise and improve the modeling accuracy [33,34]. The more commonly used fractional-order differentials are defined in the form of Grunwald-Letnikov, Riemann-Liouvile, and Caputo [35].…”

Section: Hyperspectral Data Processingmentioning

confidence: 99%

Hyperspectral Estimation of Chlorophyll Content in Grape Leaves Based on Fractional-Order Differentiation and Random Forest Algorithm

Li,

Xu,

et al. 2024

Remote Sensing

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Chlorophyll, as a key component of crop leaves for photosynthesis, is one significant indicator for evaluating the photosynthetic efficiency and developmental status of crops. Fractional-order differentiation (FOD) enhances the feature spectral information and reduces the background noise. In this study, we analyzed hyperspectral data from grape leaves of different varieties and fertility periods with FOD to monitor the leaves’ chlorophyll content (LCC). Firstly, through sensitive analysis, the fractional-order differential character bands were identified, which was used to construct the typical vegetation index (VI). Then, the grape LCC prediction model was built based on the random forest regression algorithm (RFR). The results showed the following: (1) FOD differential spectra had a higher sensitivity to LCC compared with the original spectra, and the constructed VIs had the best estimation performance at the 1.2th-order differential. (2) The accuracy of the FOD-RFR model was better than that of the conventional integer-order model at different fertility periods, but there were differences in the number of optimal orders. (3) The LCC prediction model for whole fertility periods achieved good prediction at order 1.3, R2 = 0.778, RMSE = 2.1, and NRMSE = 4.7%. As compared to the original reflectance spectra, R2 improved by 0.173; RMSE and NRMSE decreased, respectively, by 0.699 and 1.5%. This indicates that the combination of FOD and RFR based on hyperspectral data has great potential for the efficient monitoring of grape LCC. It can provide technical support for the rapid quantitative estimation of grape LCC and methodological reference for other physiological and biochemical indicators in hyperspectral monitoring.

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Section: Hyperspectral Data Processingmentioning

confidence: 99%

Hyperspectral Estimation of Chlorophyll Content in Grape Leaves Based on Fractional-Order Differentiation and Random Forest Algorithm

Li,

Xu,

et al. 2024

Remote Sensing

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“…The coefficient of determination (R 2 ), root mean square error (RMSE), and relative analysis error (RPD) were used to assess model robustness and stability. The closer R 2 is to 1, the smaller the RMSE; RPD < 1.40 indicates that the model has a poor ability to estimate accuracy, 1.4 < RPD < 2 indicates that the model has an average ability to estimate accuracy, and RPD > 2 indicates that the model has an excellent ability to estimate accuracy [38][39][40]. The specific calculation formulae are as follows:…”

Section: Model Accuracy Evaluationmentioning

confidence: 99%

Improving the Accuracy of Soil Organic Carbon Estimation: CWT-Random Frog-XGBoost as a Prerequisite Technique for In Situ Hyperspectral Analysis

Yang,

Li,

2023

Remote Sensing

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Rapid and accurate measurement of the soil organic carbon (SOC) content is a pre-condition for sustainable grain production and land development, and contributes to carbon neutrality in the agricultural industry. To provide technical support for the development and utilization of land resources, the SOC content can be estimated using Vis-NIR diffuse reflectance spectroscopy. However, the spectral redundancy and co-linearity issues of Vis-NIR spectra pose extreme challenges for spectral analysis and model construction. This study compared the effects of different pre-processing methods and feature variable algorithms on the estimation of the SOC content. To this end, in situ hyperspectral data and soil samples were collected from the lakeside oasis of Bosten Lake in Xinjiang, China. The results showed that the combination of continuous wavelet transform (CWT)-random frog could rapidly estimate the SOC content with excellent estimation accuracy (R2 of 0.65–0.86). The feature variable selection algorithm effectively improved the estimation accuracy (average improvement of (0.30–0.48); based on their ability to improve model estimation on average, the algorithms can be ranked as follows: particle swarm optimization (PSO) > ant colony optimization (ACO) > random frog > Boruta > simulated annealing (SA) > successive projections algorithm (SPA). The CWT-XGBoost model based on random frog showed the best results, with R2 = 0.86, RMSE = 2.44, and RPD = 2.78. The feature bands accounted for only 0.57% of the Vis-NIR bands, and the most important sensitive bands were distributed at 755–1195 nm, 1602 nm, 1673 nm, and 2213 nm. These findings are of significance for the extraction of precise information on lakeside oases in arid areas, which would aid in achieving human–land sustainability.

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Quantitative prediction of soil chromium content using laboratory-based visible and near-infrared spectroscopy with different ensemble learning models

Fu,

Jiang,

Tian

2024

Advances in Space Research

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A new method to estimate soil organic matter using the combination model based on short memory fractional order derivative and machine learning model

Cited by 6 publications

References 35 publications

Hyperspectral Estimation of Chlorophyll Content in Grape Leaves Based on Fractional-Order Differentiation and Random Forest Algorithm

Hyperspectral Estimation of Chlorophyll Content in Grape Leaves Based on Fractional-Order Differentiation and Random Forest Algorithm

Improving the Accuracy of Soil Organic Carbon Estimation: CWT-Random Frog-XGBoost as a Prerequisite Technique for In Situ Hyperspectral Analysis

Quantitative prediction of soil chromium content using laboratory-based visible and near-infrared spectroscopy with different ensemble learning models

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