Some Peculiarities of Arable Soil Organic Matter Detection Using Optical Remote Sensing Data

Prudnikova, E. Yu.; Savin, I. Yu.

doi:10.3390/rs13122313

Cited by 17 publications

(5 citation statements)

References 63 publications

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“…(iii) Limitation of soil surface conditions: A recent study by Prudnikova et al [82] demonstrated that rainfall negatively affected the accuracy of SOM predictions based on Sentinel-2 data. Accordingly, we conclude that there is a need for minimization of the effect of soil surface variations in large-scale satellite data.…”

Section: Current Limitationsmentioning

confidence: 99%

Earth Observation Data-Driven Cropland Soil Monitoring: A Review

et al. 2021

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We conducted a systematic review and inventory of recent research achievements related to spaceborne and aerial Earth Observation (EO) data-driven monitoring in support of soil-related strategic goals for a three-year period (2019–2021). Scaling, resolution, data characteristics, and modelling approaches were summarized, after reviewing 46 peer-reviewed articles in international journals. Inherent limitations associated with an EO-based soil mapping approach that hinder its wider adoption were recognized and divided into four categories: (i) area covered and data to be shared; (ii) thresholds for bare soil detection; (iii) soil surface conditions; and (iv) infrastructure capabilities. Accordingly, we tried to redefine the meaning of what is expected in the next years for EO data-driven topsoil monitoring by performing a thorough analysis driven by the upcoming technological waves. The review concludes that the best practices for the advancement of an EO data-driven soil mapping include: (i) a further leverage of recent artificial intelligence techniques to achieve the desired representativeness and reliability; (ii) a continued effort to share harmonized labelled datasets; (iii) data fusion with in situ sensing systems; (iv) a continued effort to overcome the current limitations in terms of sensor resolution and processing limitations of this wealth of EO data; and (v) political and administrative issues (e.g., funding, sustainability). This paper may help to pave the way for further interdisciplinary research and multi-actor coordination activities and to generate EO-based benefits for policy and economy.

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Section: Current Limitationsmentioning

confidence: 99%

Earth Observation Data-Driven Cropland Soil Monitoring: A Review

et al. 2021

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“…Imaging spectra are the most important data source, and their characteristic response to soil chemical composition is an important foundation for hyperspectral remote sensing-based SOM content prediction [18][19][20]. However, imaging spectra are not affected by soil composition alone but comprehensively reflect the soil physical properties and chemical composition within the ground sample, and soil physical properties and chemical composition exert a coupling effect on the response to the spectrum [21][22][23]. Research has shown that the scattering contribution of soil physical properties, such as soil moisture (SM) and surface roughness properties (e.g., root mean squared height, RMSH), to spectral reflectance seriously affects the sensitivity of the hyperspectral data to the SOM content [24].…”

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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“…As an important dyeing material of the soil, SOM can significantly absorb the incident light in the visible-light band, or even the short-wave infrared band, and there is usually a significant negative correlation between its content and the soil spectral reflectance. However, imaging spectra are not affected by the soil composition alone but comprehensively reflect the soil physical properties and chemical composition within the ground sample, and the soil physical properties and chemical composition exert a coupling effect on the response to the spectrum [21][22][23]. Previous studies on SOM prediction lack consideration of soil physical properties, ignoring the spectral response of soil physical properties, such as soil moisture (SM), soil bulk weight (SBW), and surface roughness properties (e.g., root-mean-square height, RMSH), resulting in poor accuracy and poor spatiotemporal transferability of SOM prediction models.…”

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: 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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Some Peculiarities of Arable Soil Organic Matter Detection Using Optical Remote Sensing Data

Cited by 17 publications

References 63 publications

Earth Observation Data-Driven Cropland Soil Monitoring: A Review

Earth Observation Data-Driven Cropland Soil Monitoring: A Review

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

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