Estimation of soil organic matter content based on spectral indices constructed by improved Hapke model

Yuan, Jing; Gao, Jichao; Yu, Bo; Yan, Changxiang; Ma, Chaoran; Xu, Jiawei; Liu, Yuteng

doi:10.1016/j.geoderma.2024.116823

Cited by 3 publications

(2 citation statements)

References 43 publications

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“…To further enhance the pretreatment method for spectral reflectance and improve the accuracy of SOM estimation, future research could focus on establishing an effective physical model combined with statistical methods or other physical models [41]. Additionally, expanding the sample set used in this study could provide more comprehensive insights for further in-depth discussion.…”

Section: The Model Advantage Of Spectral Index Combined With Water Re...mentioning

confidence: 99%

Estimation of Soil Organic Matter Based on Spectral Indices Combined with Water Removal Algorithm

Xu,

Liu,

Yan

et al. 2024

Remote Sensing

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Soil moisture strongly interferes with the spectra of soil organic matter (SOM) in the near-infrared region, which reduces the correlation between organic matter and spectra and decreases accuracy in the prediction of SOM. In this study, we explored the feasibility of two types of spectral indices, two- and three-band mixed (SI) and three-band spectral indices (SI3), and two water removal algorithms, direct standardization (DS) and external parameter orthogonalization (EPO), to estimate SOM in wet soils using a total of 192 soil samples at six water content gradients. The estimation accuracies of spectral indices combined with water removal algorithms were better than those of full spectral data combined with water removal algorithms: the prediction accuracies of SI-EPO (R2 = 0.735, RMSEp = 3.4102 g/kg) were higher than those of EPO (R2 = 0.63, RMSEp = 4.1021 g/kg), and those of SI-DS (R2 = 0.70, RMSEp = 3.7085 g/kg) were higher than those of DS (R2 = 0.61, RMSEp = 4.2806 g/kg); SI3-EPO (R2 = 0.752, RMSEp = 3.1344 g/kg) was better than SI-EPO; both EPO and DS effectively mitigated the influence of soil moisture, with EPO demonstrating superior performance in small-sample prediction scenarios. This study introduces a novel approach to counteract the impact of soil moisture on SOM estimation.

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Section: The Model Advantage Of Spectral Index Combined With Water Re...mentioning

confidence: 99%

Estimation of Soil Organic Matter Based on Spectral Indices Combined with Water Removal Algorithm

Xu,

Liu,

Yan

et al. 2024

Remote Sensing

Self Cite

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“…Through an analysis of 120 soil samples collected in Xuzhou City, Jiangsu Province, not only were the heavy metal content and spectral characteristics of the samples determined, but attempts were also made to enhance the spectral information of soil heavy metals through fractional order derivative spectral preprocessing methods (FOD) and a novel three-band index, concurrently addressing issues of collinearity and redundancy inherent in hyperspectral data. Jing Yuan et al (Yuan, J., et al 2024) proposed a soil organic matter content estimation method based on an improved Hapke model, anchored in the radiative transfer process of soil reflectance spectra. By transforming reflectance and single scattering albedo, they constructed spectral indices, thus facilitating the remote sensing estimation of soil organic matter content.…”

Section: Introductionmentioning

confidence: 99%

Application Model of Hyperspectral Technology Based on Novel Spectral Indices for Salinity Assessment in Soil Heritage Sites

Liu,

Ren

2024

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. The Dunhuang murals are a precious treasure of China's cultural heritage, yet they have long been affected by salt damage. Traditional methods for detecting salt content are costly, inefficient, and may cause physical harm to the murals. Among current techniques for measuring salt content in murals, hyperspectral remote sensing technology offers a non-invasive , circumventing issues of high costs, low efficiency. Building on this, our study developed a high-spectral feature inversion model for mural phosphate content using Fractional Order Differentiation (FOD), a novel three-band spectral index, and Partial Least Squares Regression (PLSR) algorithm. The specific research contents include: 1) Exploring the absorption mechanism of phosphates and their characteristic bands, combined with the optimal spectral index to construct a univariate linear regression model, providing a basis for rapid quantitative measurement of mural phosphate content. 2) By comparing the accuracy of the PSR and PNDI spectral indices based on the linear regression model, the first six orders of the highest accuracy spectral index were selected as the optimal three-band spectral index combination, used as explanatory variables, with mural plaster electrical conductivity as the response variable, employing the PLSR method to construct the mural phosphate content high-spectral feature inversion model. The study's findings include: 1) By comparing the outcomes of different orders of fractional differentiation, it was found that the model performance reached its optimum at a 0.3 order of differentiation for both PSR and PNDI data, with a determination coefficient (R2) of 0.728. 2) Utilizing PLSR, this study employed the previously determined optimal six-order three-band spectral index combination as explanatory variables, with salt content as the response variable, successfully constructing the high-spectral feature inversion model for mural phosphate content with a determination coefficient (R2) of 0.815. This provides an effective technical means for monitoring the salt damage conditions of precious cultural heritage such as murals.

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The spectral inversion model for electrical conductivity in mural plaster following phosphate erosion based on fractional order differentiation and novel spectral indices

Ren,

Liu

2024

Herit Sci

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The Dunhuang murals are a precious treasure of China’s cultural heritage, yet they have long been affected by salt damage. Traditional methods for detecting salt content are costly, inefficient, and may cause physical harm to the murals. Among current techniques for measuring salt content in murals, hyperspectral remote sensing technology offers a non-invasive, circumventing issues of high costs, low efficiency. Building on this, the study constructs an inversion model for the Electrical Conductivity (EC) values of mural plaster subjected to phosphate erosion, through the integration of Fractional Order Differentiation (FOD), a novel three-band spectral index, and the Partial Least Squares Regression algorithm. The specific research contents include: (1) Initially, in preparation for the experiments, the materials used to create the samples underwent a rigorous desalting process, and phosphate solutions were prepared using deionized water to ensure uniform experimental conditions and the accuracy of the results. These meticulous preprocessing steps guaranteed that the measured EC values exhibited a clear correlation with the phosphate content. Subsequently, by employing qualitative experimental analysis techniques, this study was able to more accurately simulate the real-world scenarios of mural plaster affected by salt damage, enabling a deeper investigation into the mechanisms by which salts inflict microscopic damage to murals. (2) Explores the absorption mechanisms and characteristic spectral bands of the Electrical Conductivity (EC) values measured after the phosphate erosion of mural plaster. By integrating the optimal spectral indices, a univariate linear regression model is constructed, providing a basis for the rapid quantitative measurement of electrical conductivity in murals. (3) By comparing the accuracy of the Phosphate Simple Ratio (PSR) and Phosphate Normalized Difference Index (PNDI) spectral indices based on the linear regression model, the first six orders of the highest accuracy spectral index were selected as the optimal three-band spectral index combination, used as explanatory variables, with mural plaster electrical conductivity as the response variable, employing the PLSR method to construct the mural phosphate content high-spectral feature inversion model. The study’s findings include: (1) Surfaces of samples deteriorated by phosphate erosion formed numerous irregularly shaped crystal clusters, exhibiting uneven characteristics. (2) By comparing the outcomes of different orders of fractional differentiation, it was found that the model performance reached its optimum at a 0.3 order of differentiation for both PSR and PNDI data, with a determination coefficient (Q2) of 0.728. (3) Utilizing PLSR, this study employed the previously determined optimal six-order three-band spectral index combination as explanatory variables, with salt content as the response variable, successfully constructing the high-spectral feature inversion model for mural electrical conductivity with a determination coefficient (Q2) of 0.815. This provides an effective technical means for monitoring the salt damage conditions of precious cultural heritage such as murals.

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Estimation of soil organic matter content based on spectral indices constructed by improved Hapke model

Cited by 3 publications

References 43 publications

Estimation of Soil Organic Matter Based on Spectral Indices Combined with Water Removal Algorithm

Estimation of Soil Organic Matter Based on Spectral Indices Combined with Water Removal Algorithm

Application Model of Hyperspectral Technology Based on Novel Spectral Indices for Salinity Assessment in Soil Heritage Sites

The spectral inversion model for electrical conductivity in mural plaster following phosphate erosion based on fractional order differentiation and novel spectral indices

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