Hyperspectral estimation of soil organic matter content using grey relational local regression model

Cao, Xuesong; Li, Xican; Ren, Wei; Wu, Yanan; Liu, Jieya

doi:10.1108/gs-08-2020-0099

Cited by 7 publications

(7 citation statements)

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“…Its mathematical and physical meaning is clear, and its calculation is simple (Deng, 1985). At present, grey relational degree has been widely used in natural science, social science and engineering management fields (Liu, 2017), such as the analysis of crop yield affecting factors (Chen et al , 2020; Li et al , 2019), flood identification and flood forecast (Feng et al , 2021), evaluation of multilevel dispatching rules in wafer fabrication (Yee et al , 2021), evaluation of technological innovation ability in universities (Sang et al , 2019), hospital readmission prediction based on improved feature selection (Miswan et al , 2021), China's overcapacity industry evaluation with mixed attributes (Hao et al , 2021), decision-making in manufacturing industry upgrading (Yu et al , 2020), hyperspectral estimation of soil organic matter content (Cao et al , 2021) and so on. With the deepening of grey relational analysis, based on the Deng's relational degree model, many scholars put forward B-type relational degree (Wang, 1989), C-type relational degree (Wang et al , 1999), T-type relational degree (Tang, 1995), slope relational degree (Dang, 1994), absolute relational degree and relative relational degree (Liu et al , 2006), similarity relational degree and proximity relational degree (Liu et al , 2011), comprehensive distance relational degree (Cao et al , 2020) and so on.…”

Section: Introductionmentioning

confidence: 99%

Analysis on the related factors of China's technological innovation ability using greyness relational degree

2021

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PurposeIn order to make grey relational analysis applicable to the interval grey number, this paper discusses the model of grey relational degree of the interval grey number and uses it to analyze the related factors of China's technological innovation ability.Design/methodology/approachFirst, this paper gives the definitions of the lower bound domain, the value domain, the upper bound domain of interval grey number and the generalized measure and the generalized greyness of interval grey number. Then, based on the grey relational theory, this paper proposes the model of greyness relational degree of the interval grey number and analyzes its relationship with the classical grey relational degree. Finally, the model of greyness relational degree is applied to analyze the related factors of China's technological innovation ability.FindingsThe results show that the model of greyness relational degree has strict theoretical basis, convenient calculation and easy programming and can be applied to the grey number sequence, real number sequence and grey number and real number coexisting sequence. The relational order of the four related factors of China's technological innovation ability is research and development (R&D) expenditure, R&D personnel, university student number and public library number, and it is in line with the reality.Practical implicationsThe results show that the sequence values of greyness relational degree have large discreteness, and it is feasible and effective to analyze the related factors of China's technological innovation ability.Originality/valueThe paper succeeds in realizing both the model of greyness relational degree of interval grey number with unvalued information distribution and the order of related factors of China's technological innovation ability.

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Section: Introductionmentioning

confidence: 99%

Analysis on the related factors of China's technological innovation ability using greyness relational degree

2021

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“…Chen et al [20] used PLSR to estimate the SOM contents in forest soil in the Yunnan Province of China and suggested that PSLR after the LFR (logarithmic first-derivative reflectance) transformation was the best method for SOM estimation of forest soil. Cao et al [37] proposed a multiple linear regression (MLR)-based hyperspectral estimation model based on the gray correlation for overcoming the interference of abnormal soil samples on the constructing of linear regression models. Wang et al [38] and Sun et al [39] reported that MLR was the best multivariate technique to predict SOM content of soil.…”

Section: Discussionmentioning

confidence: 99%

Hyperspectral Estimation Model of Organic Matter Content in Farmland Soil in the Arid Zone

Subi,

Eziz,

Zhong

2023

Sustainability

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Soil organic matter (SOM) is one of the most important indicators of soil quality. Hyperspectral remote sensing technology has been recognized as an effective method to rapidly estimate SOM content. In this study, 173 samples (0–20 cm) were collected from farmland soils in the northwestern arid zones of China. Partial least squares regression (PLSR), support vector machine regression (SVMR), and random forests regression (RFR), based on 15 types of mathematical transformations of the original spectral data of soil, were applied for identifying the optimal estimation method. Distribution of SOM content was mapped using both ground-measured values and predicted values estimated based on the optimum models. Obtained results indicated that the important spectral wavebands with the highest correlation were identified as 421 nm, 441 nm, 1014 nm, 1045 nm, and 2351 nm for SOM in the soil. Spectral transformations had obvious effects on the spectral characteristics of SOM. The optimal estimation was obtained when RFR was combined with the reciprocal logarithmic first-order differential (RLFD) (R2 = 0.884, RMSE = 2.817%, MAE = 2.222) for SOM contents. Finally, the RFR-RLFD method had much better performance compared with the PLSR and SVMR models. Results of this study can provide an alternative to the application of the hyperspectral estimation of SOM in farmland soils in arid zones.

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“…In order to further improve the estimation accuracy of grey relational identification, Li et al (2016) established a grey relational estimation model with residual error correction and applied it to the spectral estimation of soil water content and organic matter. On this basis, some new estimation models based on grey relational analysis are constantly proposed, such as spectral estimation correction model based on the difference information between the sample to be identified and its corresponding pattern sample (Miao et al, 2018(Miao et al, , 2019, grey relational local regression model (Cao et al, 2020(Cao et al, , 2021, positive and inverse grey relational degree estimation model (Zhong, 2020), estimation model based on greyness of grey number (Ding et al, 2022), grey relational estimation model based on grey information ) and so on. These studies not only enriched the grey system theory and effectively improved the spectral estimation accuracy of soil organic matter content, but also combined statistical analysis and grey relational analysis to effectively deal with randomness and greyness in spectral estimation.…”

Section: Introductionmentioning

confidence: 99%

Grey fuzzy prediction model of soil organic matter content using hyper-spectral data

Cao

et al. 2023

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PurposeIn order to overcome the uncertainty and improve the accuracy of spectral estimation, this paper aims to establish a grey fuzzy prediction model of soil organic matter content by using grey theory and fuzzy theory.Design/methodology/approachBased on the data of 121 soil samples from Zhangqiu district and Jiyang district of Jinan City, Shandong Province, firstly, the soil spectral data are transformed by spectral transformation methods, and the spectral estimation factors are selected according to the principle of maximum correlation. Then, the generalized greyness of interval grey number is used to modify the estimation factors of modeling samples and test samples to improve the correlation. Finally, the hyper-spectral prediction model of soil organic matter is established by using the fuzzy recognition theory, and the model is optimized by adjusting the fuzzy classification number, and the estimation accuracy of the model is evaluated using the mean relative error and the determination coefficient.FindingsThe results show that the generalized greyness of interval grey number can effectively improve the correlation between soil organic matter content and estimation factors, and the accuracy of the proposed model and test samples are significantly improved, where the determination coefficient R2 = 0.9213 and the mean relative error (MRE) = 6.3630% of 20 test samples. The research shows that the grey fuzzy prediction model proposed in this paper is feasible and effective, and provides a new way for hyper-spectral estimation of soil organic matter content.Practical implicationsThe research shows that the grey fuzzy prediction model proposed in this paper can not only effectively deal with the three types of uncertainties in spectral estimation, but also realize the correction of estimation factors, which is helpful to improve the accuracy of modeling estimation. The research result enriches the theory and method of soil spectral estimation, and it also provides a new idea to deal with the three kinds of uncertainty in the prediction problem by using the three kinds of uncertainty theory.Originality/valueThe paper succeeds in realizing both the grey fuzzy prediction model for hyper-spectral estimating soil organic matter content and effectively dealing with the randomness, fuzziness and grey uncertainty in spectral estimation.

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Hyperspectral estimation of soil organic matter content using grey relational local regression model

Cited by 7 publications

References 4 publications

Analysis on the related factors of China's technological innovation ability using greyness relational degree

Analysis on the related factors of China's technological innovation ability using greyness relational degree

Hyperspectral Estimation Model of Organic Matter Content in Farmland Soil in the Arid Zone

Grey fuzzy prediction model of soil organic matter content using hyper-spectral data

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