scholarly journals Hyperspectral Estimation Model of Forest Soil Organic Matter in Northwest Yunnan Province, China

Forests ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 217 ◽  
Author(s):  
Yun Chen ◽  
Jinliang Wang ◽  
Guangjie Liu ◽  
Yanlin Yang ◽  
Zhiyuan Liu ◽  
...  
Keyword(s):  
Particle Size ◽  
Organic Matter ◽  
Soil Organic Matter ◽  
Linear Regression ◽  
Forest Soil ◽  
Spectral Reflectance ◽  
Soil Particle ◽  
Estimation Model ◽  
Soil Particle Size ◽  
First Derivative

Soil organic matter (SOM) is an important index to evaluate soil fertility and soil quality, while playing an important role in the terrestrial carbon cycle. The technology of hyperspectral remote sensing is an important method to estimate SOM content efficiently and accurately. This study researched the best hyperspectral estimation model for SOM content in Shangri-La forest soil. The spectral reflectance of soils with sizes of 2 mm, 1 mm, 0.50 mm, and 0.25 mm were measured indoors. After smoothing and de-noising, the reciprocal reflectance (RR), logarithmic reflectance (LR), first-derivative reflectance (FR), reciprocal first-derivative reflectance (RFR), logarithmic first-derivative reflectance (LFR), and mathematical transformations of the original spectral reflectance (REF) were carried out to analyze the relevance of spectral reflectance and SOM content and extract the characteristic bands. Finally the simple linear regression (SLR), multiple stepwise linear regression (SMLR), and partial least squares regression (PLSR) models for SOM content estimation were established. The results showed that: (1) With the decrease of soil particle size, the spectral reflectance increased. The smaller the soil particle sizes, the more obvious was the increase in spectral reflectance. (2) The sensitive bands of SOM were mainly in the 580–690 nm range (correlation coefficient (R) > 0.6, p-value (p) < 0.01), and the spectral information of SOM could be significantly enhanced by first-order differential transformation. (3) Comparing the three models, PLSR had better estimation ability than SMLR and SLR. The precision of the 0.25 mm soil particle size and the LFR index in the PLSR estimation model of SOM content was the best (coefficient of determination of validation (Rv2) = 0.91, root mean square error of validation (RMSEv) = 13.41, the ratio of percent deviation (RPD) = 3.33). The results provide a basis for monitoring SOM content rapidly in the forests of Northwest Yunnan, and provide a reference for forest SOM estimation in other areas.

Contamination of a calcareous soil by battery industry wastes. I. Characterization

2001 ◽  
Vol 28 (3) ◽  
pp. 341-348 ◽  
Author(s):  
S A Wasay ◽  
W J Parker ◽  
P J Van Geel
Keyword(s):  
Heavy Metals ◽  
Particle Size ◽  
Organic Matter ◽  
Contaminated Soil ◽  
Buffering Capacity ◽  
Water Soluble ◽  
Soil Particle ◽  
Soil Particle Size ◽  
Native Soil ◽  
Battery Industry

A study of soil contamination due to the disposal of waste from a battery industry was conducted. The soil particle size, organic matter content, and buffering capacity were characterized. The heavy metal content of the soil was characterized with soil depth, soil particle size, and with respect to the fraction of the soil by which it was retained. Lead was found to be the dominant contaminant with all other metals present at considerably lower concentrations. Most of the lead was retained in the fraction of the soil that had a particle size less than 2 mm. This fraction represented 40.8% of the soil and contained 24 600 mg Pb/kg of soil. A particle size analysis indicated that 45.3% of soil particles were found to be greater than 4.75 mm. The pH of the contaminated soil in water was found to be 7.6 and was similar to the background soil. The similarity in pH was attributed to the high calcium content of the native soil. The lead content in the native soil that was collected 100 m away from the contaminated site was found to be 1967 mg/kg in the soil with particle sizes less than 2 mm (contaminated soil). The difference in pH between KCl solution (pH 7.0) and in water was found to be –0.6 indicating that the pH value was above the point of zero salt effect. An evaluation of the buffering capacity revealed that 297 mL of 0.5 M HNO3 per kg of soil was required to substantially modify the soil pH. The heavy metals in the soil were sequentially extracted to quantify the water soluble, exchangeable, carbonate, oxides, organic matter, and residual fractions. The Pb concentrations were mainly found in the carbonate and oxide fractions of the soil.Key words: heavy metals, soil pollution, characterization, retention form.

Turnover of soil organic matter and storage of corn residue carbon estimated from natural 13C abundance

1995 ◽  
Vol 75 (2) ◽  
pp. 161-167 ◽  
Author(s):  
E. G. Gregorich ◽  
C. M. Monreal ◽  
B. H. Ellert
Keyword(s):  
Particle Size ◽  
Organic Matter ◽  
Soil Organic Matter ◽  
Forest Soil ◽  
Half Life ◽  
Light Fraction ◽  
Organic C ◽  
Size Fractions ◽  
Particle Size Fractions ◽  
Corn Residue

Total organic C and natural C abundance were measured in a forest soil and a soil under corn (Zea mays L.) to assess management-induced changes in the quantity and initial source of organic matter. The total mass of organic C in the cultivated soil was 19% lower than in the forest soil. It was estimated that after 25 yr of continuous corn, 100 Mg C ha−1 was returned to the soil as residues, of which only 23 Mg ha−1 remained in the soil; 88% of the remaining corn-derived C (C4-derived C) was in the plow layer. About 30% of the soil organic C in the plow layer (0–27 cm) was derived from corn. Assuming first order kinetics, the half-life of C3-derived C in the 0- to 15-cm layer was 13 yr. The half-life of C3-derived C in the 0- to 30-cm layer, which included organic C below the plow layer, was 24 yr. Mineralization of the light fraction (LF) was faster than that of organic matter associated with particle-size fractions. More than 70% of the LF had turned over since the start of corn cropping, and 45% of organic matter in the sand fraction comprised corn residue. The half-life of C3-derived C in the LF was 8 yr. The mineralization of C from native organic matter associated with the coarse silt fraction was the slowest of all particle-size fractions. Key words: Soil organic matter, carbon storage, natural 13C abundance, light fraction, particle-size fractions, mineralization

scholarly journals Ectomycorrhizal Influence on Particle Size, Surface Structure, Mineral Crystallinity, Functional Groups, and Elemental Composition of Soil Colloids from Different Soil Origins

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Yanhong Li ◽  
Huimei Wang ◽  
Wenjie Wang ◽  
Lei Yang ◽  
Yuangang Zu
Keyword(s):  
Particle Size ◽  
Surface Structure ◽  
Forest Soil ◽  
Functional Groups ◽  
Soil Particle ◽  
Brown Forest Soil ◽  
Soil Minerals ◽  
Soil Particle Size ◽  
Alkali Soil ◽  
Soil Colloids

Limited data are available on the ectomycorrhizae-induced changes in surface structure and composition of soil colloids, the most active portion in soil matrix, although such data may benefit the understanding of mycorrhizal-aided soil improvements. By using ectomycorrhizae (Gomphidius viscidus) and soil colloids from dark brown forest soil (a good loam) and saline-alkali soil (heavily degraded soil), we tried to approach the changes here. For the good loam either from the surface or deep soils, the fungus treatment induced physical absorption of covering materials on colloid surface with nonsignificant increases in soil particle size (P>0.05). These increased the amount of variable functional groups (O–H stretching and bending, C–H stretching, C=O stretching, etc.) by 3–26% and the crystallinity of variable soil minerals (kaolinite, hydromica, and quartz) by 40–300%. However, the fungus treatment of saline-alkali soil obviously differed from the dark brown forest soil. There were 12–35% decreases in most functional groups, 15–55% decreases in crystallinity of most soil minerals but general increases in their grain size, and significant increases in soil particle size (P<0.05). These different responses sharply decreased element ratios (C : O, C : N, and C : Si) in soil colloids from saline-alkali soil, moving them close to those of the good loam of dark brown forest soil.

scholarly journals Hyperspectral Estimation of Soil Organic Matter Content using Different Spectral Preprocessing Techniques and PLSR Method

2020 ◽  
Vol 12 (7) ◽  
pp. 1206
Author(s):  
Lanzhi Shen ◽  
Maofang Gao ◽  
Jingwen Yan ◽  
Zhao-Liang Li ◽  
Pei Leng ◽  
...  
Keyword(s):  
Organic Matter ◽  
Soil Organic Matter ◽  
Dimensionality Reduction ◽  
Spectral Data ◽  
Wavelet Packet ◽  
Estimation Model ◽  
Data Transformations ◽  
First Derivative ◽  
Model Based ◽  
Reduction Methods

Soil organic matter (SOM) is the main source of soil nutrients, which are essential for the growth and development of agricultural crops. Hyperspectral remote sensing is one of the most efficient ways of estimating the SOM content. Visible, near infrared, and mid-infrared reflectance spectroscopy, combined with the partial least squares regression (PLSR) method is considered to be an effective way of determining soil properties. In this study, we used 54 different spectral pretreatments to preprocess soil spectral data. These spectral pretreatments were composed of three denoising methods, six data transformations, and three dimensionality reduction methods. The three denoising methods included no denoising (ND), Savitzky–Golay denoising (SGD), and wavelet packet denoising (WPD). The six data transformations included original spectral data, R; reciprocal, 1/R; logarithmic, log(R); reciprocal logarithmic, log(1/R); first derivative, R’; and first derivative of reciprocal, (1/R)’. The three dimensionality reduction methods included no dimensionality reduction (NDR), sensitive waveband dimensionality reduction (SWDR), and principal component analysis (PCA) dimensionality reduction (PCADR). The processed spectra were then employed to construct PLSR models for predicting the SOM content. The main results were as follows—(1) the wavelet packet denoising (WPD)-R’ and WPD-(1/R)’ data showed stronger correlations with the SOM content. Furthermore, these methods could effectively limit the correlation between the adjacent bands and, thus, prevent “overfitting”. (2) Of the 54 pretreatments investigated, WPD-(1/R)’-PCADR yielded the model with the highest accuracy and stability. (3) For the same denoising method and spectral transformation data, the accuracy of the SOM content estimation model based on SWDR was higher than that of the model based on NDR. Furthermore, the accuracy in the case of PCADR was higher than that for SWDR. (4) Dimensionality reduction was effective in preventing data overfitting. (5) The quality of the spectral data could be improved and the accuracy of the SOM content estimation model could be enhanced effectively, by using some appropriate preprocessing methods (one combining WPD and PCADR in this study).

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