Predictive Mapping of Soil Organic Matter at a Regional Scale Using Local Topographic Variables: A Comparison of Different Polynomial Models

Variability in <sup>14</sup>C contents of soil organic matter at the plot and regional scale across climatic and geologic gradients

10.5194/bg-2015-649 ◽

2016 ◽

Cited By ~ 2

Author(s):

T. S. van der Voort ◽

F. Hagedorn ◽

C. McIntyre ◽

C. Zell ◽

L. Walthert ◽

...

Keyword(s):

Organic Matter ◽

Soil Organic Matter ◽

Soil Depth ◽

Regional Scale ◽

Mean Annual Temperature ◽

Spatial And Temporal Variability ◽

Climatic Parameters ◽

Soil Carbon Dynamics ◽

Modelling Assumptions ◽

Micro Topography

Abstract. Soil organic matter (SOM) forms the largest terrestrial pool of carbon outside of sedimentary rocks. Radiocarbon is a powerful tool for assessing soil organic matter dynamics. However, due to the nature of the measurement, extensive 14C studies of soils systems remain relatively rare. In particular, information on the extent of spatial and temporal variability in 14C contents of soils is limited, yet this information is crucial for establishing the range of baseline properties and for detecting potential modifications to the SOM pool. This study describes a comprehensive approach to explore heterogeneity in bulk SOM 14C in Swiss forest soils that encompass diverse landscapes and climates. We examine spatial variability in soil organic carbon (SOC) 14C, SOC content and C:N ratios over both regional climatic and geologic gradients, on the watershed- and plot-scale and within soil profiles. Results reveal (1) a relatively uniform radiocarbon signal across climatic and geologic gradients in Swiss forest topsoils (0-5 cm, Δ14C=159±36.4, n=12 sites), (2) similar radiocarbon trends with soil depth despite dissimilar environmental conditions, and (3) micro-topography dependent, plot-scale variability that is similar in magnitude to regional-scale variability (e.g., Gleysol, 0-5 cm, Δ14C 126±35.2, n=8 adjacent plots of 10x10m). Statistical analyses have additionally shown that Δ14C signature in the topsoil is not significantly correlated to climatic parameters (precipitation, elevation, primary production) except mean annual temperature at 0-5 cm. These observations have important consequences for SOM carbon stability modelling assumptions, as well as for the understanding of controls on past and current soil carbon dynamics.

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Digital mapping of soil organic matter for rubber plantation at regional scale: An application of random forest plus residuals kriging approach

Geoderma ◽

10.1016/j.geoderma.2014.08.009 ◽

2015 ◽

Vol 237-238 ◽

pp. 49-59 ◽

Cited By ~ 63

Author(s):

Peng-Tao Guo ◽

Mao-Fen Li ◽

Wei Luo ◽

Qun-Feng Tang ◽

Zhi-Wei Liu ◽

...

Keyword(s):

Organic Matter ◽

Soil Organic Matter ◽

Random Forest ◽

Regional Scale ◽

Rubber Plantation ◽

Digital Mapping ◽

Kriging Approach

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Predicting spatial distribution of soil organic matter using regression approaches at the regional scale (Eastern Azerbaijan, Iran)

Environmental Monitoring and Assessment ◽

10.1007/s10661-021-09416-0 ◽

2021 ◽

Vol 193 (9) ◽

Author(s):

Golnaz Ebrahimzadeh ◽

Nafiseh Yaghmaeian Mahabadi ◽

Kamal Khosravi Aqdam ◽

Farrokh Asadzadeh

Keyword(s):

Organic Matter ◽

Spatial Distribution ◽

Soil Organic Matter ◽

Regional Scale

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Variability in <sup>14</sup>C contents of soil organic matter at the plot and regional scale across climatic and geologic gradients

Biogeosciences ◽

10.5194/bg-13-3427-2016 ◽

2016 ◽

Vol 13 (11) ◽

pp. 3427-3439 ◽

Cited By ~ 12

Author(s):

Tessa Sophia van der Voort ◽

Frank Hagedorn ◽

Cameron McIntyre ◽

Claudia Zell ◽

Lorenz Walthert ◽

...

Keyword(s):

Organic Matter ◽

Soil Organic Matter ◽

Soil Depth ◽

Regional Scale ◽

Mean Annual Temperature ◽

Spatial And Temporal Variability ◽

Climatic Parameters ◽

Soil Systems ◽

Modelling Assumptions ◽

Micro Topography

Abstract. Soil organic matter (SOM) forms the largest terrestrial pool of carbon outside of sedimentary rocks. Radiocarbon is a powerful tool for assessing soil organic matter dynamics. However, due to the nature of the measurement, extensive 14C studies of soil systems remain relatively rare. In particular, information on the extent of spatial and temporal variability in 14C contents of soils is limited, yet this information is crucial for establishing the range of baseline properties and for detecting potential modifications to the SOM pool. This study describes a comprehensive approach to explore heterogeneity in bulk SOM 14C in Swiss forest soils that encompass diverse landscapes and climates. We examine spatial variability in soil organic carbon (SOC) 14C, SOC content and C : N ratios over both regional climatic and geologic gradients, on the watershed- and plot-scale and within soil profiles. Results reveal (1) a relatively uniform radiocarbon signal across climatic and geologic gradients in Swiss forest topsoils (0–5 cm, Δ14C  =  130 ± 28.6, n = 12 sites), (2) similar radiocarbon trends with soil depth despite dissimilar environmental conditions, and (3) micro-topography dependent, plot-scale variability that is similar in magnitude to regional-scale variability (e.g., Gleysol, 0–5 cm, Δ14C 126 ± 35.2, n = 8 adjacent plots of 10  ×  10 m). Statistical analyses have additionally shown that Δ14C signature in the topsoil is not significantly correlated to climatic parameters (precipitation, elevation, primary production) except mean annual temperature at 0–5 cm. These observations have important consequences for SOM carbon stability modelling assumptions, as well as for the understanding of controls on past and current soil carbon dynamics.

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Soil Organic Matter Prediction Model with Satellite Hyperspectral Image Based on Optimized Denoising Method

Remote Sensing ◽

10.3390/rs13122273 ◽

2021 ◽

Vol 13 (12) ◽

pp. 2273

Author(s):

Xiangtian Meng ◽

Yilin Bao ◽

Qiang Ye ◽

Huanjun Liu ◽

Xinle Zhang ◽

...

Keyword(s):

Organic Matter ◽

Soil Organic Matter ◽

Prediction Model ◽

Hyperspectral Image ◽

Signal To Noise Ratio ◽

Regional Scale ◽

Hyperspectral Data ◽

Recursive Feature Elimination ◽

Discrete Wavelet ◽

Denoising Method

In order to improve the signal-to-noise ratio of the hyperspectral sensors and exploit the potential of satellite hyperspectral data for predicting soil properties, we took MingShui County as the study area, which the study area is approximately 1481 km2, and we selected Gaofen-5 (GF-5) satellite hyperspectral image of the study area to explore an applicable and accurate denoising method that can effectively improve the prediction accuracy of soil organic matter (SOM) content. First, fractional-order derivative (FOD) processing is performed on the original reflectance (OR) to evaluate the optimal FOD. Second, singular value decomposition (SVD), Fourier transform (FT) and discrete wavelet transform (DWT) are used to denoise the OR and optimal FOD reflectance. Third, the spectral indexes of the reflectance under different denoising methods are extracted by optimal band combination algorithm, and the input variables of different denoising methods are selected by the recursive feature elimination (RFE) algorithm. Finally, the SOM content is predicted by a random forest prediction model. The results reveal that 0.6-order reflectance describes more useful details in satellite hyperspectral data. Five spectral indexes extracted from the reflectance under different denoising methods have a strong correlation with the SOM content, which is helpful for realizing high-accuracy SOM predictions. All three denoising methods can reduce the noise in hyperspectral data, and the accuracies of the different denoising methods are ranked DWT > FT > SVD, where 0.6-order-DWT has the highest accuracy (R2 = 0.84, RMSE = 3.36 g kg−1, and RPIQ = 1.71). This paper is relatively novel, in that GF-5 satellite hyperspectral data based on different denoising methods are used to predict SOM, and the results provide a highly robust and novel method for mapping the spatial distribution of SOM content at the regional scale.

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