Hyperspectral band selection and modeling of soil organic matter content in a forest using the Ranger algorithm

Shi, Yuanyuan; Zhao, Junyu; Song, Xianchong; Qin, Zuoyu; Wu, Lichao; Wang, Huili; Tang, Jian

doi:10.1371/journal.pone.0253385

Cited by 12 publications

(7 citation statements)

References 62 publications

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“…The CV of Paddy soil was relatively high. The SOM content was divided into seven levels: <15 g/kg, 15-20 g/kg, 20-25 g/kg, 25-30 g/kg, 30-35 g/kg, 35-40 g/kg and >40 g/kg [39]. The mean spectral reflectance curves corresponding to seven SOM content levels were calculated (Figure 3a).…”

Section: Characteristic Of Soil Spectral Curvesmentioning

confidence: 99%

“…FDR transformation showed better model performance than the second derivative transformation for SOM estimations in several modeling methods [36]. Some research has also explored the prediction effect of SOM content using characteristic wavelength screening combined with different spectral transformation techniques [38,39]. As shown above, characteristic wavelength screening, spectral transformation and combinations of two means have been widely applied to improve the accuracy of SOM spectral modeling.…”

Section: Introductionmentioning

confidence: 99%

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Hyperspectral Modeling of Soil Organic Matter Based on Characteristic Wavelength in East China

et al. 2022

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Soil organic matter (SOM) is a key index of soil fertility. Visible and near-infrared (VNIR, 350–2500 nm) reflectance spectroscopy is an effective method for modeling SOM content. Characteristic wavelength screening and spectral transformation may improve the performance of SOM prediction. This study aimed to explore the optimal combination of characteristic wavelength selection and spectral transformation for hyperspectral modeling of SOM. A total of 219 topsoil (0–20 cm) samples were collected from two soil types in the East China. VNIR reflectance spectra were measured in the laboratory. Firstly, after spectral transformation (inverse-log reflectance (LR), continuum removal (CR) and first-order derivative reflectance (FDR)) of VNIR spectra, characteristic wavelengths were selected by competitive adaptive reweighted sampling (CARS) and uninformative variables elimination (UVE) algorithms. Secondly, the SOM prediction models were constructed based on the partial least squares regression (PLSR), random forest (RF) and support vector regression (SVR) methods using the full spectra and selected wavelengths, respectively. Finally, optimal SOM prediction models were selected for two soil types. The results were as follows: (1) The CARS algorithm screened 40–125 characteristic wavelengths from the full spectra. The UVE algorithm screened 105–884 characteristic wavelengths. (2) For two soil types and full spectra, CARS and UVE improved the SOM modeling precision based on the PLSR and SVR methods. The coefficient of determination (R2) value in the validation of the CARS-PLSR (PLSR model combined with CARS) and CARS-SVR (SVR model combined CARS) models ranged from 0.69 to 0.95, and the relative percent deviation (RPD) value ranged from 1.74 to 4.31. Lin’s concordance correlation coefficient (LCCC) values ranged from 0.83 to 0.97. The UVE-PLSR and UVE-SVR models showed moderate precision. (3) The PLSR and SVR modeling accuracies of Paddy soil were better than those for Shajiang black soil. RF models performed worse for both soil types, with the R2 values of validation ranging from 0.22 to 0.68 and RPD values ranging from 1.01 to 1.60. (4) For Paddy soil, the optimal SOM prediction models (highest R2 and RPD, lowest root mean square error (RMSE)) were CR-CARS-PLSR (R2 and RMSE: 0.97 and 1.21 g/kg in calibration sets, 0.95 and 1.72 g/kg in validation sets, RPD: 4.31) and CR-CARS-SVR (R2 and RMSE: 0.98 and 1.04 g/kg in calibration sets, 0.91 and 2.24 g/kg in validation sets, RPD: 3.37). For Shajiang black soil, the optimal SOM prediction models were LR-CARS-PLSR (R2 and RMSE: 0.95 and 0.93 g/kg in calibration sets, 0.86 and 1.44 g/kg in validation sets, RPD: 2.62) and FDR-CARS-SVR (R2 and RMSE: 0.99 and 0.45 g/kg in calibration sets, 0.83 and 1.58 g/kg in validation sets, RPD: 2.38). The results suggested that the CARS algorithm combined CR and FDR can significantly improve the modeling accuracy of SOM content.

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Section: Characteristic Of Soil Spectral Curvesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hyperspectral Modeling of Soil Organic Matter Based on Characteristic Wavelength in East China

et al. 2022

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“…On the other hand, due to many HS absorbance band ranges for the SOC and NC dataset, eliminating and selecting specific band ranges to obtain the best prediction accuracy became complex. Hence, the least absolute shrinkage and selection operator (Lasso) algorithm was applied to determine the significant band range [ 70 ]. In this experiment, we selected the 575.5–1062 nm, 1100 nm, 1852–1885 nm, 1945–2017.5 nm, 2053–2208 nm, and 2454–2499.5 nm HS bands.…”

Section: Methodsmentioning

confidence: 99%

Soil Moisture, Organic Carbon, and Nitrogen Content Prediction with Hyperspectral Data Using Regression Models

Datta

Paul

Murshed

et al. 2022

Sensors

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Soil moisture, soil organic carbon, and nitrogen content prediction are considered significant fields of study as they are directly related to plant health and food production. Direct estimation of these soil properties with traditional methods, for example, the oven-drying technique and chemical analysis, is a time and resource-consuming approach and can predict only smaller areas. With the significant development of remote sensing and hyperspectral (HS) imaging technologies, soil moisture, carbon, and nitrogen can be estimated over vast areas. This paper presents a generalized approach to predicting three different essential soil contents using a comprehensive study of various machine learning (ML) models by considering the dimensional reduction in feature spaces. In this study, we have used three popular benchmark HS datasets captured in Germany and Sweden. The efficacy of different ML algorithms is evaluated to predict soil content, and significant improvement is obtained when a specific range of bands is selected. The performance of ML models is further improved by applying principal component analysis (PCA), a dimensional reduction method that works with an unsupervised learning method. The effect of soil temperature on soil moisture prediction is evaluated in this study, and the results show that when the soil temperature is considered with the HS band, the soil moisture prediction accuracy does not improve. However, the combined effect of band selection and feature transformation using PCA significantly enhances the prediction accuracy for soil moisture, carbon, and nitrogen content. This study represents a comprehensive analysis of a wide range of established ML regression models using data preprocessing, effective band selection, and data dimension reduction and attempt to understand which feature combinations provide the best accuracy. The outcomes of several ML models are verified with validation techniques and the best- and worst-case scenarios in terms of soil content are noted. The proposed approach outperforms existing estimation techniques.

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“…Many modeling methods, such as PLSR [ 3 , 8 , 36 ], multivariate adaptive regression spline [ 11 , 37 ], machine learning [ 11 , 13 , 38 , 39 ], deep learning [ 40 ], were used for predicting SOM content. Through systematic comparison of modeling accuracy, these models can yield acceptable accuracy of SOM prediction in different soil types and geographical areas, of which PLSR model has relatively high accuracy and robust prediction, and is widely used [ 11 , 13 , 20 , 36 ].…”

Section: Introductionmentioning

confidence: 99%

Improved multivariate modeling for soil organic matter content estimation using hyperspectral indexes and characteristic bands

et al. 2023

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Soil organic matter (SOM) is a key index of soil fertility. Calculating spectral index and screening characteristic band reduce redundancy information of hyperspectral data, and improve the accuracy of SOM prediction. This study aimed to compare the improvement of model accuracy by spectral index and characteristic band. This study collected 178 samples of topsoil (0–20 cm) in the central plain of Jiangsu, East China. Firstly, visible and near-infrared (VNIR, 350–2500 nm) reflectance spectra were measured using ASD FieldSpec 4 Std-Res spectral radiometer in the laboratory, and inverse-log reflectance (LR), continuum removal (CR), first-order derivative reflectance (FDR) were applied to transform the original reflectance (R). Secondly, optimal spectral indexes (including deviation of arch, difference index, ratio index, and normalized difference index) were calculated from each type of VNIR spectra. Characteristic bands were selected from each type of spectra by the competitive adaptive reweighted sampling (CARS) algorithm, respectively. Thirdly, SOM prediction models were established based on random forest (RF), support vector regression (SVR), deep neural networks (DNN) and partial least squares regression (PLSR) methods using optimal spectral indexes, denoted here as SI-based models. Meanwhile, SOM prediction models were established using characteristic wavelengths, denoted here as CARS-based models. Finally, this research compared and assessed accuracy of SI-based models and CARS-based models, and selected optimal model. Results showed: (1) The correlation between optimal spectral indexes and SOM was enhanced, with absolute value of correlation coefficient between 0.66 and 0.83. The SI-based models predicted SOM content accurately, with the coefficient of determination (R2) and root mean square error (RMSE) values ranging from 0.80 to 0.87, 2.40 g/kg to 2.88 g/kg in validation sets, and relative percent deviation (RPD) value between 2.14 and 2.52. (2) The accuracy of CARS-based models differed with models and spectral transformations. For all spectral transformations, PLSR and SVR combined with CARS displayed the best prediction (R2 and RMSE values ranged from 0.87 to 0.92, 1.91 g/kg to 2.56 g/kg in validation sets, and RPD value ranged from 2.41 to 3.23). For FDR and CR spectra, DNN and RF models achieved more accuracy (R2 and RMSE values ranged from 0.69 to 0.91, 1.90 g/kg to 3.57 g/kg in validation sets, and RPD value ranged from 1.73 to 3.25) than LR and R spectra (R2 and RMSE values from 0.20 to 0.35, 5.08 g/kg to 6.44 g/kg in validation sets, and RPD value ranged from 0.96 to 1.21). (3) Overall, the accuracy of SI-based models was slightly lower than that of CARS-based models. But spectral index had a good adaptability to the models, and each SI-based model displayed the similar accuracy. For different spectra, the accuracy of CARS-based model differed from modeling methods. (4) The optimal CARS-based model was model CARS-CR-SVR (R2 and RMSE: 0.92 and 1.91 g/kg in validation set, RPD: 3.23). The optimal SI-based model was model SI3-SVR (R2 and RMSE: 0.87 and 2.40 g/kg in validation set, RPD: 2.57) and model SI-SVR (R2 and RMSE: 0.84 and 2.63 g/kg in validation set, RPD: 2.35).

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Hyperspectral band selection and modeling of soil organic matter content in a forest using the Ranger algorithm

Cited by 12 publications

References 62 publications

Hyperspectral Modeling of Soil Organic Matter Based on Characteristic Wavelength in East China

Hyperspectral Modeling of Soil Organic Matter Based on Characteristic Wavelength in East China

Soil Moisture, Organic Carbon, and Nitrogen Content Prediction with Hyperspectral Data Using Regression Models

Improved multivariate modeling for soil organic matter content estimation using hyperspectral indexes and characteristic bands

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