Prediction of soil carbon and nitrogen contents using visible and near infrared diffuse reflectance spectroscopy in varying salt-affected soils in Sine Saloum (Senegal)

Cambou, Aurélie; Barthès, Bernard; Moulin, Patricia; Chauvin, Laure; Faye, Elhadji; Massé, Dominique; Chevallier, Tiphaine; Chapuis‐Lardy, Lydie

doi:10.1016/j.catena.2022.106075

Cited by 14 publications

(5 citation statements)

References 49 publications

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“…N cal and N val were limited by the most restrictive DB p dataset, i.e., DB 9 , which corresponded to the bare soil fractional cover greater than 0.7 and consisted of 46 samples. By adopting an 80-20% split for calibration-validation datasets [61,62], each M p model was calibrated on 36 samples (N cal ) and validated on 10 samples (N val ), belonging to the corresponding dataset DB p . This equal number of samples in both the calibration (N cal = 36) and validation (N val = 10) datasets among the models allowed a robust comparison of the effect of bare soil fractional cover on the model performance.…”

Section: Dataset Preparation For Plsr Training and Validationmentioning

confidence: 99%

Adapting Prediction Models to Bare Soil Fractional Cover for Extending Topsoil Clay Content Mapping Based on AVIRIS-NG Hyperspectral Data

George,

Gomez,

Kumar

2024

Remote Sensing

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The deployment of remote sensing platforms has facilitated the mapping of soil properties to a great extent. However, the accuracy of these soil property estimates is compromised by the presence of non-soil cover, which introduces interference with the acquired reflectance spectra over pixels. Therefore, current soil property estimation by remote sensing is limited to bare soil pixels, which are identified based on spectral indices of vegetation. Our study proposes a composite mapping approach to extend the soil properties mapping beyond bare soil pixels, associated with an uncertainty map. The proposed approach first classified the pixels based on their bare soil fractional cover by spectral unmixing. Then, a specific regression model was built and applied to each bare soil fractional cover class to estimate clay content. Finally, the clay content maps created for each bare soil fractional cover class were mosaicked to create a composite map of clay content estimations. A bootstrap procedure was used to estimate the standard deviation of clay content predictions per bare soil fractional cover dataset, which represented the uncertainty of estimations. This study used a hyperspectral image acquired by the Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) sensor over cultivated fields in South India. The proposed approach provided modest performances in prediction (Rval2 ranging from 0.53 to 0.63) depending on the bare soil fractional cover class and showed a correct spatial pattern, regardless of the bare soil fraction classes. The model’s performance was observed to increase with the adoption of higher bare soil fractional cover thresholds. The mapped area ranged from 10.4% for pixels with bare soil fractional cover >0.7 to 52.7% for pixels with bare soil fractional cover >0.3. The approach thus extended the mapped surface by 42.4%, while maintaining acceptable prediction performances. Finally, the proposed approach could be adopted to extend the mapping capability of planned and current hyperspectral satellite missions.

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Section: Dataset Preparation For Plsr Training and Validationmentioning

confidence: 99%

Adapting Prediction Models to Bare Soil Fractional Cover for Extending Topsoil Clay Content Mapping Based on AVIRIS-NG Hyperspectral Data

George,

Gomez,

Kumar

2024

Remote Sensing

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“…With an increase in ginsenosides concentration, soil physicochemical properties had a significant effect on the soil microbial community (Shukla et al, 2011) and EC indicating that the application of exogenous ginsenosides led to increased salinization of the soil. Increased salinization is one of the important features associated with soil quality degradation (Cambou et al, 2022). The organic matter content was significantly reduced and the sucrase activity, which is a response to the rate and content of soil organic matter conversion, was also gradually reduced.…”

Section: Coupling Environmental Factors To Soil Microbial Community S...mentioning

confidence: 99%

Effect of ginsenosides on microbial community and enzyme activity in continuous cropping soil of ginseng

et al. 2023

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Root exudates contain plant metabolites secreted by the roots into the soil, such as ginsenosides secreted by the ginseng root. However, little is known about ginseng root exudate and its impact on the chemical and microbial properties of soil. In this study, the effect of increasing concentrations of ginsenosides on the chemical and microbial properties of soil was tested. Chemical analysis and high-throughput sequencing techniques were used to evaluate the soil chemical properties and microbial characteristics following exogenous application of 0.1 mg·L−1, 1 mg·L−1, and 10 mg·L−1 ginsenosides. Ginsenosides application significantly altered soil enzyme activities; SOM-dominated physicochemical properties were significantly reduced which altered the composition and structure of the soil microbial community. In particular, treatment with 10 mg∙L−1 ginsenosides significantly increased the relative abundance of pathogenic fungi such as Fusarium, Gibberella and Neocosmospora. These findings indicate that ginsenosides in root exudates are important factors that may lead to increased deterioration of soil during ginseng cultivation and provided new research direction for the subsequent study on the mechanism of interaction between ginsenosides and soil microbial communities.

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“…Carbon (C) is often analyzed in combination with Nitrogen (N) in studies of this nature. C and N contents has been successfully predicted in salt-aff ected soils, suggesting a relationship between salinity and C and N prediction [27]. Carbon to Nitrogen ratio (C:N) is an attribute related to soil nutrient availability which has been tested for quantifi cation and depth distribution in forest soils using national-scale spectral data, yielding good results for soils with low to moderate C:N and poor results for high C:N values [28].…”

Section: Soil Carbonmentioning

confidence: 99%

Recent Trends on the use of Infrared Spectroscopy for Soil Assessment

Jamil Maia*

2023

J Biomed Res Environ Sci

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Infrared spectroscopy has emerged as a powerful tool to assess soil properties for both environmental science and agriculture. Here, we explore its recent trends and developments for soil assessment. This technique is an alternative that counters the limitations of traditional laboratory methods, offering a cost-effective and non-destructive approach. Here, the latest trends in the innovation landscape of infrared spectroscopy for soil assessment are explored, providing insights on its broad range of applications and into the future trajectory of this technology. Firstly, we delve into its applications in agriculture, highlighting its potential for prediction of many soil attributes. Next, we explore soil carbon assessment, emphasizing the importance of estimating soil organic carbon and soil carbon stock for soil quality. Soil pollution and elemental contents are addressed, focusing on the prediction of potentially toxic elements concentrations in soil, strongly relevant for environmental monitoring. Infrared spectroscopy emerges as a valuable tool for rapid and non-hazardous elemental content assessment. Soil physical properties prediction, traditionally limited to soil texture analysis, is extended through the application of novel approaches, shedding light on the broader potential of this technology for soil quality assessment. The ongoing developments in statistical modeling and technological innovation are also showcased, mainly focused on machine learning methods. Lastly, the importance of soil spectral libraries is emphasized, such as the Global Soil Spectral Calibration Library and Estimation Service, and Brazilian Soil Spectral Library. In conclusion, infrared spectroscopy has become an important tool in soil assessment, offering a multitude of applications across environmental and agricultural contexts. This review underscores the growing potential of this technology in advancing the standardization and reproducibility of sustainable soil assessment procedures, ensuring a brighter future for soil science.

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Prediction of soil carbon and nitrogen contents using visible and near infrared diffuse reflectance spectroscopy in varying salt-affected soils in Sine Saloum (Senegal)

Cited by 14 publications

References 49 publications

Adapting Prediction Models to Bare Soil Fractional Cover for Extending Topsoil Clay Content Mapping Based on AVIRIS-NG Hyperspectral Data

Adapting Prediction Models to Bare Soil Fractional Cover for Extending Topsoil Clay Content Mapping Based on AVIRIS-NG Hyperspectral Data

Effect of ginsenosides on microbial community and enzyme activity in continuous cropping soil of ginseng

Recent Trends on the use of Infrared Spectroscopy for Soil Assessment

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