Prediction of soil organic matter by Kubelka-Munk based airborne hyperspectral moisture removal model

Ou, Depin; Tan, Kun; Li, Jie; Wu, Zhifeng; Zhao, Liangbo; Ding, Jianwei; Wang, Xue; Zou, Bin

doi:10.1016/j.jag.2023.103493

Cited by 6 publications

(7 citation statements)

References 40 publications

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“…In the same year, Wu proposed the SESMRT model, which improved the R 2 by 0.0064 [38]. The Kubelka-Munk theory-based spectral correction model for soil moisture removal proposed by Ou increased the R 2 by 0.22 [39]. In 2024, Wu proposed an RF machine learning model combined with CARS selected feature bands to increase the R 2 by 0.2 [40].…”

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

View full text Add to dashboard Cite

show abstract

“…The concomitant changes in SM, SBW, and spectral reflectance exhibit a complex relationship. Generally, increases in SM, SBW, and RMSH decrease the spectral reflectance, showing a coupling effect [27,28]. Noteworthily, the effect of the SOM content on the soil spectrum is far weaker than that of soil physical properties [29].…”

Section: Introductionmentioning

confidence: 95%

Improving the Spatiotemporal Transferability of Hyperspectral Remote Sensing for Estimating Soil Organic Matter by Minimizing the Coupling Effect of Soil Physical Properties on the Spectrum: A Case Study in Northeast China

Sui,

Jiang,

Lin

et al. 2024

Agronomy

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Soil organic matter (SOM) is important for the global carbon cycle, and hyperspectral remote sensing has proven to be a promising method for fast SOM content estimation. However, because of the neglect of the spectral response of soil physical properties, the accuracy and spatiotemporal transferability of the SOM prediction model are poor. This study aims to improve the spatiotemporal transferability of the SOM prediction model by alleviating the coupling effect of soil physical properties on spectra. Based on satellite hyperspectral images and soil physical variables, including soil moisture (SM), soil surface roughness (root-mean-square height, RMSH), and soil bulk weight (SBW), a soil spectral correction model was established based on the information unmixing method. Two important grain-producing areas in Northeast China were selected as study areas to verify the performance and transferability of the spectral correction model and SOM content prediction model. The results showed that soil spectral corrections based on fourth-order polynomials and the XG-Boost algorithm had excellent accuracy and generalization ability, with residual predictive deviations (RPDs) exceeding 1.4 in almost all the bands. In addition, when the soil spectral correction strategy was adopted, the accuracy of the SOM prediction model and the generalization ability after the model migration were significantly improved. The SOM prediction accuracy based on the XG-Boost-corrected spectrum was the highest, with a coefficient of determination (R2) of 0.76, a root-mean-square error (RMSE) of 5.74 g/kg, and an RPD of 1.68. The prediction accuracy, R2 value, RMSE, and RPD of the model after the migration were 0.72, 6.71 g/kg, and 1.53, respectively. Compared with the direct migration prediction of the model, adopting the soil spectral correction model based on fourth-order polynomials and XG-Boost reduced the RMSE of the SOM prediction results by 57.90% and 60.27%, respectively. This performance comparison highlighted the advantages for considering soil physical properties in regional-scale SOM predictions.

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“…The concomitant changes in SM, SBW, and spectral reflectance exhibit a complex relationship. Generally, the increase in SM, SBW, and RMSH decreases the spectral reflectance, showing a coupling effect [27,28]. Noteworthy, the effect of SOM content on the soil spectrum is far weaker than that of soil physical properties [29].…”

Section: Introductionmentioning

confidence: 97%

“…Developing a soil spectral correction method alleviating the coupling effect of surface physical properties on soil pixel spectra is a long-term solution to improve the spatiotemporal transferability of the SOM prediction model. The complex interactions between the various surface physical properties and electromagnetic waves make it difficult to simulate the relationship between soil physical properties and the spectrum with physical models [28]. Therefore, this study aims to separate the physical and chemical soil information in the spectral data with data-driven methods.…”

Section: Introductionmentioning

confidence: 99%

Improving The Spatiotemporal Transferability of Hyperspectral Remote Sensing for Estimating Soil Organic Matter by Minimizing The Coupling Effect of Soil Physical Properties on The Spectrum

Sui,

Jiang,

Lin

et al. 2024

Preprint

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Soil organic matter (SOM) is important for the global carbon cycle, and hyperspectral remote sensing has proven a promising method for fast SOM content estimation. However, soil physical properties significantly affect the sensitivity of satellite hyperspectral imaging to SOM, leading to poor generalization ability of the estimation model. This study aims to improve the spatiotemporal transferability of the SOM prediction model by alleviating the coupling effect of soil physical properties on the spectra. Based on satellite hyperspectral images and soil physical variables, including soil moisture (SM), soil surface roughness (root mean squared height, RMSH), and soil bulk weight (SBW), a soil spectral correction strategy was established based on the information unmixing method. Two important grain-producing areas in Northeast China were selected as study areas to verify the performance and transferability of the spectral correction model and SOM content prediction model. The results showed that soil spectral corrections based on fourth-order polynomials and the XG-Boost algorithm had excellent accuracy and generalization ability, with residual predictive deviations (RPD) exceeding 1.4 in almost all bands. In addition, when the soil spectral correction strategy was adopted, the accuracy of the SOM prediction model and the generalization ability after model migration were significantly improved. The SOM prediction accuracy based on the XG-Boost corrected spectrum was the highest, with a coefficient of determination (R2) of 0.76, root mean square error (RMSE) of 5.74 g/kg, and RPD of 1.68. The prediction accuracy, R2, RMSE, and RPD of the model after migration were 0.72, 6.71 g/kg, and 1.53, respectively. Compared with the direct migration prediction of the model, adopting the soil spectral correction strategy based on fourth-order polynomials and XG-Boost reduced the RMSE of the SOM prediction results by 57.90% and 60.27%, respectively. The performance comparison highlighted the advantages of considering soil physical properties in regional-scale SOM prediction.

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Prediction of soil organic matter by Kubelka-Munk based airborne hyperspectral moisture removal model

Cited by 6 publications

References 40 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

Improving the Spatiotemporal Transferability of Hyperspectral Remote Sensing for Estimating Soil Organic Matter by Minimizing the Coupling Effect of Soil Physical Properties on the Spectrum: A Case Study in Northeast China

Improving The Spatiotemporal Transferability of Hyperspectral Remote Sensing for Estimating Soil Organic Matter by Minimizing The Coupling Effect of Soil Physical Properties on The Spectrum

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