Soil organic matter content prediction using Vis-NIRS based on different wavelength optimization algorithms and inversion models

Zhou, Wei; Xiao, Jieyun; Li, Haoran; Chen, Qi; Wang, Ting; Wang, Qian; Yue, Tianxiang

doi:10.1007/s11368-023-03480-4

Cited by 7 publications

(5 citation statements)

References 53 publications

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“…Numerous studies have shown that the LASSO, Ridge, MLP, RF, LSTM, CNN, and SVR models used in this study have good prediction results in terms of soil nutrient content, but different spectral pre-processing, different selection of characteristic bands, and different settings of each model parameter in different soil types can affect the prediction accuracy of the model for soil nutrient content [12,17,39]. For example, Zhong Liang et al [40] explored the prediction accuracy SOM content of LeNet-5, MLP-5, RF, SVR, and CNN models under different pre-processing, and the results showed that CNN showed a good prediction effect, and SVR was poorer than the other models.…”

Section: Results Of Single-prediction Modelmentioning

confidence: 99%

“…From the results of the prediction accuracy of single-prediction models (Table 2), it can be seen that for the same set of data sets, the prediction accuracy of different prediction methods on the prediction set, validation set, and training set shows inconsistent phenomena, and the phenomena of underfitting and overfitting may occur; for this reason, it is necessary to take advantage of the advantages of the individual single-prediction models, and innovatively construct the combination model of single models. In the single-prediction models for hyperspectral prediction of organic matter content, the evaluation of prediction accuracy is mostly based on the coefficient of determination, mean absolute error, root mean square error, and other indexes [15][16][17][18][19][20][21][22][23][24][25][26][27][28], and such evaluation indexes mainly take into account the size of the error of each sample, and for the combined prediction model, the sum of the squares of the error or the sum of the absolute value of the error is mostly used as the objective function, with less consideration of the distribution of the error. In this study, the error of prediction accuracy is considered along with the influence of the standard deviation of prediction accuracy on the effectiveness of prediction methods, and the corresponding combined prediction model of prediction effectiveness is established.…”

Section: Discussionmentioning

confidence: 99%

“…Secondly, due to the large number of hyperspectral data bands and the redundancy of information, there have been studies using different spectral transforms for the feature selection; spectral transforms mainly include S-G denoising, differential transforms, wavelet transforms, scattering correction, etc. [9][10][11][12], and feature selection or dimensionality reduction and other methods to screen the sensitive bands, mainly Competitive Adaptive Reweighted Sampling (CARS), Successive Projection Algorithms (SPA), Particle Swarm Optimization algorithms (PSO), Recursive Feature Elimination (RFE), Random Forest importance feature selection, PCA, and other dimensionality reduction [13][14][15][16][17][18]. Third, different prediction methods are used for the calculation of relevant parameters and model construction, mainly using a combination of various optimization algorithms and machine learning models, mainly genetic optimization, PSO, and other algorithms combined with machine learning and deep learning algorithms, such as Partial Least Squares Regression (PLSR), Random Forests (RF), Support Vector Machines (SVM), Decision Trees, Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), Ridge regression, ensemble method and other machine learning and deep learning algorithms for model construction [16][17][18][19][20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

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Visible Near-Infrared Hyperspectral Soil Organic Matter Prediction Based on Combinatorial Modeling

Zhang,

Liu,

et al. 2024

Agronomy

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Non-destructive, fast, and accurate prediction of soil organic matter content in farmland is of great significance for soil fertility assessment and rational fertilization. In the process of soil organic matter prediction, it is important to give full play to the advantages of different prediction models and to integrate different prediction models to innovatively construct a combined prediction model of soil organic matter content so as to improve the prediction accuracy and generalization ability of the model. In this study, the soil organic matter content of agricultural soils was taken as the research object, and the visible near-infrared hyperspectral curves of soils were measured by the Starter Kit indoor mobile scanning platform (Headwall Photonics, Bolton, MA, USA), and the original spectral curves were firstly de-noised by Savitzky–Golay (S-G) smoothing. Secondly, the smoothed and denoised spectral data were subjected to a first-order differential transform, and the features were selected based on the first-order differential transformed spectral data using the L1-paradigm algorithm features. Then, secondly, eight algorithms based on the selected feature bands, such as LASSO Regression (LASSO) (Model 1), Multilayer Perceptron (MLP) (Model 2), Random Forest (RF) (Model 3), Gaussian Kernel Regression (GKR) (Model 4), Ridge Regression (Model 5), Long Short-Term Memory (LSTM) (Model 6), Convolutional Neural Networks (CNN) (Model 7), and Support Vector Regression (SVR) (Model 8), were applied to construct a single-prediction model of soil organic matter content. Finally, a superior linear combination-prediction model was proposed by the eight single-prediction models constructed, and the standard deviation-based prediction validity was added to test the model. The results showed the following: (1) the weights of the eight single-prediction models in the combined prediction model were ω1*=0.099, ω2*=0.202, ω3*=0.000, ω4*=0.357, ω5*=0.088, ω6*=0.089, ω7*=0.000, ω8*=0.165, respectively; (2) The average precision E of the predicted values of soil organic matter content constructed based on the eight single-prediction models was 0.856; the average standard deviation σ was 0.181, and the average prediction validity M was 0.702; (3) The accuracy E of the predicted value of soil organic matter content of the combined model was 0.893, which was 4.322% higher than the average accuracy of the single model; the standard deviation of the combined model was 0.129, which was 28.333% lower than the average standard deviation of the single model, and the prediction validity M of the combined model was 0.778, which was 10.826% higher than the average prediction validity of the single model. The combined model can be used for the effective estimation of soil organic matter content in farmland with visible near-infrared spectral data, which can provide a basis and reference for the rapid detection of soil organic matter content in farmland.

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Section: Results Of Single-prediction Modelmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Visible Near-Infrared Hyperspectral Soil Organic Matter Prediction Based on Combinatorial Modeling

Zhang,

Liu,

et al. 2024

Agronomy

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“…On the one hand, these machine learning models can effectively handle multidimensional datasets and Collinearity problem. On the other hand, these models were widely used in SOC retrieval and obtained good simulation effect ( Hengl et al, 2017 ; Liu et al, 2022 ; Zhou et al, 2023 ). For example, SVM exhibits significant advantages in addressing small sample problems.…”

Section: Methodsmentioning

confidence: 99%

Soil organic carbon estimation using remote sensing data-driven machine learning

Chen,

Wang,

Zhu

2024

PeerJ

Self Cite

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Soil organic carbon (SOC) is a crucial component of the global carbon cycle, playing a significant role in ecosystem health and carbon balance. In this study, we focused on assessing the surface SOC content in Shandong Province based on land use types, and explored its spatial distribution pattern and influencing factors. Machine learning methods including random forest (RF), extreme gradient boosting (XGBoost), and support vector machine (SVM) were employed to estimate the surface SOC content in Shandong Province using diverse data sources like sample data, remote sensing data, socio-economic data, soil texture data, topographic data, and meteorological data. The results revealed that the SOC content in Shandong Province was 8.78 g/kg, exhibiting significant variation across different regions. Comparing the model error and correlation coefficient, the XGBoost model showed the highest prediction accuracy, with a coefficient of determination (R²) of 0.7548, root mean square error (RMSE) of 7.6792, and relative percentage difference (RPD) of 1.1311. Elevation and Clay exhibited the highest explanatory power in clarifying the surface SOC content in Shandong Province, contributing 21.74% and 13.47%, respectively. The spatial distribution analysis revealed that SOC content was higher in forest-covered mountainous regions compared to cropland-covered plains and coastal areas. In conclusion, these findings offer valuable scientific insights for land use planning and SOC conservation.

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“…The soil types mainly include paddy soil and acid purple soil. Soil type differences lead to differences in SOM content [26]. Application of remote sensing data introduces a significant improvement in the ability to predict the SOC content [27], due to its high spatial and spectrum resolution.…”

Section: Introductionmentioning

confidence: 99%

Significant Improvement in Soil Organic Carbon Estimation Using Data-Driven Machine Learning Based on Habitat Patches

Yu,

Zhou,

Wang

et al. 2024

Remote Sensing

Self Cite

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Soil organic carbon (SOC) is generally thought to act as a carbon sink; however, in areas with high spatial heterogeneity, using a single model to estimate the SOC of the whole study area will greatly reduce the simulation accuracy. The earth surface unit division is important to consider in building different models. Here, we divided the research area into different habitat patches using partitioning around a medoids clustering (PAM) algorithm; then, we built an SOC simulation model using machine learning algorithms. The results showed that three habitat patches were created. The simulation accuracy for Habitat Patch 1 (R2 = 0.55; RMSE = 2.89) and Habitat Patch 3 (R2 = 0.47; RMSE = 3.94) using the XGBoost model was higher than that for the whole study area (R2 = 0.44; RMSE = 4.35); although the R2 increased by 25% and 6.8%, the RMSE decreased by 33.6% and 9.4%, and the field sample points significantly declined by 70% and 74%. The R2 of Habitat Patch 2 using the RF model increased by 17.1%, and the RMSE also decreased by 10.5%; however, the sample points significantly declined by 58%. Therefore, using different models for corresponding patches will significantly increase the SOC simulation accuracy over using one model for the whole study area. This will provide scientific guidance for SOC or soil property monitoring with low field survey costs and high simulation accuracy.

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Soil organic matter content prediction using Vis-NIRS based on different wavelength optimization algorithms and inversion models

Cited by 7 publications

References 53 publications

Visible Near-Infrared Hyperspectral Soil Organic Matter Prediction Based on Combinatorial Modeling

Visible Near-Infrared Hyperspectral Soil Organic Matter Prediction Based on Combinatorial Modeling

Soil organic carbon estimation using remote sensing data-driven machine learning

Significant Improvement in Soil Organic Carbon Estimation Using Data-Driven Machine Learning Based on Habitat Patches

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