Assessment of soil water retention characteristics based on VNIR/SWIR hyperspectral imaging of soil surface

Krzyszczak, Jaromir; Baranowski, Piotr; Pastuszka, Joanna; Wesołowska, Monika; Cymerman, Jacek; Sławiński, Cezary; Siedliska, Anna

doi:10.1016/j.still.2023.105789

Cited by 8 publications

(2 citation statements)

References 84 publications

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“…In addition, this experiment only considered the influence of soil properties within 5 cm of the surface layer on the spectrum, while the spectrum may have different sensing depths for different soil properties [63]. Even though the spectrum only directly detects SM changes in shallow soil (about 0 to 2 cm), this depth also changes under different SBW and RMSH conditions [20,64]. The vertical heterogeneity of SM and SBW may be the main factor causing soil spectral correction errors.…”

Section: The Potential and Limitations Of The Soil Spectral Correctio...mentioning

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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Section: The Potential and Limitations Of The Soil Spectral Correctio...mentioning

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

View full text Add to dashboard Cite

show abstract

“…In addition, this experiment only considered the influence of soil properties within 5 cm of the surface layer on the spectrum, while the spectrum may have different sensing depths for different soil properties [60]. Even though the spectrum only directly detects SM changes in shallow soil (from about 0 to 2 cm), this depth also changes under different SBW and RMSH conditions [20,61]. The vertical heterogeneity of the SM and SBW may be the main factor causing soil spectral correction errors.…”

Section: The Potential and Limitations Of The Soil Spectral Correctio...mentioning

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: A Case Study in Northeast China

Sui,

Jiang,

Lin

et al. 2024

Agronomy

View full text Add to dashboard Cite

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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Diversity, phylogeny and taxonomy of forest fungal communities

Salazar-Vidal,

Dibán,

Rugolo

et al. 2025

Forest Fungi

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Assessment of soil water retention characteristics based on VNIR/SWIR hyperspectral imaging of soil surface

Cited by 8 publications

References 84 publications

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

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

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

Diversity, phylogeny and taxonomy of forest fungal communities

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