Soil organic matter and clay predictions by laboratory spectroscopy: Data spatial correlation

Silva-Sangoi, Daniely Vaz da; Horst, Taciara Zborowski; Moura-Bueno, Jean Michel; Dalmolin, Ricardo Simão Diniz; Sebem, Elódio; Gebler, Luciano; Santos, Márcio da Silva

doi:10.1016/j.geodrs.2022.e00486

Cited by 7 publications

(3 citation statements)

References 55 publications

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“…We hypothesize that this observation is mainly an effect of our sampling design and the specific agricultural management and is therefore not generalizable. Clay and soil organic matter are claimed to be modeled with a high success rate with vis-NIR spectroscopy since they have strong absorption features (da Silva-Sangoi et al, 2022). Unfortunately, soil texture was measured using different samples than the reference dataset for the spectral modeling, so we cannot check for the correlation between soil texture and target variables.…”

Section: Variability Of Clay Contentmentioning

confidence: 99%

Best performances of visible–near-infrared models in soils with little carbonate – a field study in Switzerland

Oberholzer,

Summerauer,

Steffens

et al. 2024

SOIL

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Abstract. Conventional laboratory analysis of soil properties is often expensive and requires much time if various soil properties are to be measured. Visual and near-infrared (vis–NIR) spectroscopy offers a complementary and cost-efficient way to gain a wide variety of soil information at high spatial and temporal resolutions. Yet, applying vis–NIR spectroscopy requires confidence in the prediction accuracy of the infrared models. In this study, we used soil data from six agricultural fields in eastern Switzerland and calibrated (i) field-specific (local) models and (ii) general models (combining all fields) for soil organic carbon (SOC), permanganate oxidizable carbon (POXC), total nitrogen (N), total carbon (C) and pH using partial least-squares regression. The 30 local models showed a ratio of performance to deviation (RPD) between 1.14 and 5.27, and the root mean square errors (RMSE) were between 1.07 and 2.43 g kg−1 for SOC, between 0.03 and 0.07 g kg−1 for POXC, between 0.09 and 0.14 g kg−1 for total N, between 1.29 and 2.63 g kg−1 for total C, and between 0.04 and 0.19 for pH. Two fields with high carbonate content and poor correlation between the target properties were responsible for six local models with a low performance (RPD < 2). Analysis of variable importance in projection, as well as of correlations between spectral variables and target soil properties, confirmed that high carbonate content masked absorption features for SOC. Field sites with low carbonate content can be combined with general models with only a limited loss in prediction accuracy compared to the field-specific models. On the other hand, for fields with high carbonate contents, the prediction accuracy substantially decreased in general models. Whether the combination of soils with high carbonate contents in one prediction model leads to satisfying prediction accuracies needs further investigation.

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Section: Variability Of Clay Contentmentioning

confidence: 99%

Best performances of visible–near-infrared models in soils with little carbonate – a field study in Switzerland

Oberholzer,

Summerauer,

Steffens

et al. 2024

SOIL

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“…Notably, sand has a noticeable effect on light scattering, which results in increased reflectivity in the visible and near-infrared spectral ranges. The reflectivity within these spectral regions is lower in silt and clay, which exhibit a relatively lesser scattering effect but absorb a significant quantity of light energy [7]. Therefore, via the analysis of soil reflectance data, the content of particles with different particle sizes in the soil and their interactions with light can be inferred.…”

Section: Introductionmentioning

confidence: 99%

Predicting the Surface Soil Texture of Cultivated Land via Hyperspectral Remote Sensing and Machine Learning: A Case Study in Jianghuai Hilly Area

Pan,

Cai,

Zhao

et al. 2023

Applied Sciences

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Soil reflectance spectra and hyperspectral images have great potential to monitor and evaluate soil texture in large-scale scenarios. In hilly areas, sand, clay, and silt have similar spectral characteristics in visible, near-infrared, and short-wave infrared (VNIR-SWIR) reflection spectra. Soil texture spectra belong to mixed spectra despite some differences in particle size, mineral composition, and water content, making their distinction difficult. The accurate identification of the content within different particle sizes is difficult as it involves capturing spectral reflection features. Therefore, this study aimed to predict soil texture content through machine learning and unmixing the soil texture’s spectra while also comparing their respective modelling performances. Taking typical cultivated land in the Jianghuai hills as an example, the GaoFen-5 Advanced Hyperspectral Imaging (GF-5 AHSI) laboratory spectra of soil samples were used to predict sand, silt, and clay particle contents using partial least squares regression (PLSR) and convolutional neural networks (CNNs). The entire spectra of VNIR-SWIR regions were smoothed, and the dimensions were reduced via principal component analysis (PCA). The prediction models of sand, silt, and clay particle content were constructed, and inversion maps were generated using AHSI. The results showed that the PCA-CNN model achieved a higher prediction precision than the PCA-PLSR in both ASD and GF-5 data. Clay content exhibited the highest predictive performance with a coefficient of determination (R2) of 0.948 and 0.908 and a root mean square error (RMSE) of 26.51 g/kg and 31.24 g/kg, respectively, which represented a 39.0% and 79.8% increase in R2 and a 57% and 57.1% decrease in RMSE compared to that of the PCA-PLSR. This method indicates that the PCA-CNN model can effectively achieve nonlinear interactions between multiple spectral components and better model and fit spectral mixing processes; moreover, it provides an alternative method for investigating the spatial distribution of soil texture.

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“…Studies have shown promising results with the use of Vis-NIR-SWIR spectroscopy for the prediction of some soil physical-chemical attributes, such as organic carbon (Silva-Sangoi et al, 2022;Santos et al, 2020;Naimi et al, 2022;Lu et al, 2013), electrical conductivity (Kodaira & Shibusawa, 2013), soil texture (Naimi et al, 2022 ), pH, phosphorus and cation exchange capacity (Kodaira & Shibusawa, 2013;Lu et al, 2013), N, P, K (Mukherjee & Laskar, 2019), Ca and Mg (Vašát et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Vis-NIR-SWIR spectroscopy in mapping of soil physical-chemical attributes in areas cultivated with irrigated mango in the Brazilian semi-arid region

Souza

Rodrigues

Costa

et al. 2023

Preprint

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The adoption of specific soil management in agricultural areas requires a series of soil analyses, which is time-consuming and costly. In this context, Vis-NIR-SWIR spectroscopy (visible - near infrared - short-wave infrared) emerges as an alternative to determine soil attributes quickly, with lower cost and few environmental impacts. Thus, the objective of this study was to map the physical-chemical attributes of the soil in areas cultivated with irrigated mango in different soil classes in the Brazilian semi-arid region using Vis-NIR-SWIR spectroscopy. In total 318 soil samples were used. For these samples, the reflectance spectra were obtained (350 to 2500 nm) and the values of pH, EC (electrical condutivicty), Ca2+, Mg2+, K+, Na+, Al3+, P, H + Al, TOC (total organic carbon), sand and clay were determined by standard analytical methods. For the development of predictive models, the techniques of Partial Least Squares Regression (PLSR) and Multiple Linear Regression (MLR) were used. For the predictive models that had R2 above 0.50, the semivariograms and maps of the soil attributes determined by the reference methods and by Vis-NIR-SWIR spectroscopy were constructed. The PLSR and MLR regression models provided strong predictions for sand, clay and TOC, moderate for Na+, Ca2+ and Mg2+, weak for pH, CE, K+ and Al3+ and very weak for H + Al and P. The maps of soil attributes showed the existence of spatial correlation with each other. Therefore, the Vis-NIR-SWIR spectroscopy is a potential tool for evaluation of soil and mapping of fruit growing areas.

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Soil organic matter and clay predictions by laboratory spectroscopy: Data spatial correlation

Cited by 7 publications

References 55 publications

Best performances of visible–near-infrared models in soils with little carbonate – a field study in Switzerland

Best performances of visible–near-infrared models in soils with little carbonate – a field study in Switzerland

Predicting the Surface Soil Texture of Cultivated Land via Hyperspectral Remote Sensing and Machine Learning: A Case Study in Jianghuai Hilly Area

Vis-NIR-SWIR spectroscopy in mapping of soil physical-chemical attributes in areas cultivated with irrigated mango in the Brazilian semi-arid region

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