Prediction of soil organic carbon in a coal mining area by Vis-NIR spectroscopy

Sun, Wenke; Li, Xinju; Niu, Beibei

doi:10.1371/journal.pone.0196198

Cited by 20 publications

(10 citation statements)

References 15 publications

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“…Coal mining operations reduced the mean content of soil nutrients, which were consistent with previous studies [36,37], showing that land subsidence caused by coal mining could induce losses in surface soil nutrients. This might be due to the subsidence in the coal mining area resulting in the vertical leakage of soil nutrients from the top layer to the deeper layer [38].…”

Section: Discussionsupporting

confidence: 92%

Prediction of Soil Nutrients Based on Topographic Factors and Remote Sensing Index in a Coal Mining Area, China

et al. 2020

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1) Background: Coal mining operations caused severe land subsidence and altered the distributions of soil nutrients that influenced by multiple environmental factors at different scales. However, the prediction performances for soil nutrients based on their scale-specific relationships with influencing factors remains undefined in the coal mining area. The objective of this study was to establish prediction models of soil nutrients based on their scale-specific relationships with influencing factors in a coal mining area. (2) Methods: Soil samples were collected based on a 1 × 1 km regular grid, and contents of soil organic matter, soil available nitrogen, soil available phosphorus, and soil available potassium were measured. The scale components of soil nutrients and the influencing factors collected from remote sensing and topographic factors were decomposed by two-dimensional empirical mode decomposition (2D-EMD), and the predictions for soil nutrients were established using the methods of multiple linear stepwise regression or partial least squares regression based on original samples (MLSR Ori or PLSR Ori ), partial least squares regression based on bi-dimensional intrinsic mode function (PLSR BIMF ), and the combined method of 2D-EMD, PLSR, and MLSR (2D-EMD PM ).(3) Results: The correlation types and correlation coefficients between soil nutrients and influencing factors were scale-dependent. The variances of soil nutrients at smaller scale were stochastic and non-significantly correlated with influencing factors, while their variances at the larger scales were stable. The prediction performances in the coal mining area were better than those in the non-coal mining area, and 2D-EMD PM had the most stable performance. (4) Conclusions: The scale-dependent predictions can be used for soil nutrients in the coal mining areas.

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Section: Discussionsupporting

confidence: 92%

Prediction of Soil Nutrients Based on Topographic Factors and Remote Sensing Index in a Coal Mining Area, China

et al. 2020

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“…ASTAVI and EVI were the second and third most influential variables for the prediction of SOC distribution. The reason for their influence may be due to their ability to provide proxies for measuring aboveground biomass, which may influence SOC contents stored in the soil as a litter [63]. Additionally, the characteristics of ASTAVI (i.e., low sensitivity to soil backgrounds) may contribute to deriving more information related to SOC contents than other vegetation indices [64].…”

Section: Importance Of Environmental Variablesmentioning

confidence: 99%

Modeling the Spatial Dynamics of Soil Organic Carbon Using Remotely-Sensed Predictors in Fuzhou City, China

Sodango

Sha

et al. 2021

Remote Sensing

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Assessing the spatial dynamics of soil organic carbon (SOC) is essential for carbon monitoring. Since variability of SOC is mainly attributed to biophysical land surface variables, integrating a compressive set of such indices may support the pursuit of an optimum set of predictor variables. Therefore, this study was aimed at predicting the spatial distribution of SOC in relation to remotely sensed variables and other covariates. Hence, the land surface variables were combined from remote sensing, topographic, and soil spectral sources. Moreover, the most influential variables for prediction were selected using the random forest (RF) and classification and regression tree (CART). The results indicated that the RF model has good prediction performance with corresponding R2 and root-mean-square error (RMSE) values of 0.96 and 0.91 mg·g−1, respectively. The distribution of SOC content showed variability across landforms (CV = 78.67%), land use (CV = 93%), and lithology (CV = 64.67%). Forestland had the highest SOC (13.60 mg·g−1) followed by agriculture (10.43 mg·g−1), urban (9.74 mg·g−1), and water body (4.55 mg·g−1) land uses. Furthermore, soils developed in bauxite and laterite lithology had the highest SOC content (14.69 mg·g−1). The SOC content was remarkably lower in soils developed in sandstones; however, the values obtained in soils from the rest of the lithologies could not be significantly differentiated. The mean SOC concentration was 11.70 mg·g−1, where the majority of soils in the study area were classified as highly humus and extremely humus. The soils with the highest SOC content (extremely humus) were distributed in the mountainous regions of the study area. The biophysical land surface indices, brightness removed vegetation indices, topographic indices, and soil spectral bands were the most influential predictors of SOC in the study area. The spatial variability of SOC may be influenced by landform, land use, and lithology of the study area. Remotely sensed predictors including land moisture, land surface temperature, and built-up indices added valuable information for the prediction of SOC. Hence, the land surface indices may provide new insights into SOC modeling in complex landscapes of warm subtropical urban regions.

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“…Typically, soil spectrum characterized with reflectance increasing with increasing wavelengths, the wavelengths contributing most to the prediction of SOC content were between 1050 to 2380 nm (Milos and Bensa 2017). Sun and Niu, (2018) discovered that the spectral curves of the soil with different SOC content grades exhibited a uniform pattern, which increased rapidly in the visible band (400-760 nm), but then ascended gradually in the short NIR and NIR long wave bands (780-1300 nm). Then the curves formed a high reflectivity platform until it began to decline after 2100 nm.…”

Section: Soil Laboratory Analysis Resultsmentioning

confidence: 99%

A Studying the Possibility of Estimating Soil Organic Carbon of Soils Under PINUS BRUTIA and QUERCUS AEGILOPS L. Trees in Sarke-Duhok By Using ASD FieldSpec 3 Spectroradiometer

Gravi

Ibrahim

2020

SJUOZ

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Visible and Near-infrared (VNIRS) spectroscopy is a very fast non-destructive and environmentally friendly analytical technique. It has been suggested as an alternative to conventional methods for assessing and monitoring soil quality. Accurate VNIRS prediction of soil organic carbon (SOC) has been reported by many researchers in different world regions. Sixteen surface soil samples (0-30cm depth) from Sarke - Duhok were collected, eight samples under Zawita Pine (Pinus brutia) and the other eight-under oak (Quercus aegilops L.) trees. Soil colour was measured in the field and soil samples were air-dried and sieved by using a 2.0 mm sieve and analyzed in the laboratory to estimate the PH, EC, CaCo3, texture, and SOC according to Walkley and Black methods. Laboratory reflectance spectra were acquired for each sample from 350 to 2500 nm by using the Analytical Spectral Device (ASD) FieldSpec 3spectroradiometer for each of the different spectrum bands (410, 570, 660, 849, 1543 and 2187nm). Results showed that the reflectivity values of soil under both Oak and Pine trees in the IR region (849, 1543, 2187nm) were almost more than that at the visible portion (410,570, 660nm). Also, the determination coefficient (R2) results indicated that the bands 570, 1543 and 2187nm showed significant relationships between soil reflectivity and SOC% under Oak trees, R2 was 58.2 %, 89.9%, and 93.9% respectively. While under the Pine tree, the only band that showed a significant relationship was band 1543nm, its R2 was 58.3%. From the current results, as the main objectives, it is obvious that the ASD FieldSpec 3 spectroradiometer is quite an efficient and un-destructive tool that can be used for SOC estimation under Oak & pine trees especially at the IR spectrum (band 1543nm). The relationship between the variables was moderately strong; R2 was 89.88 and 58.3 alternatively. The standard error was low (0.0399, 0.0185), which indicates that the predicted values are close to the real values. Besides that, under Oak trees results indicated that there was a high R2 between the variables at bands 570 & 2187 nm, the R2 values were 58.21 and 93.92, alternatively.

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Prediction of soil organic carbon in a coal mining area by Vis-NIR spectroscopy

Cited by 20 publications

References 15 publications

Prediction of Soil Nutrients Based on Topographic Factors and Remote Sensing Index in a Coal Mining Area, China

Prediction of Soil Nutrients Based on Topographic Factors and Remote Sensing Index in a Coal Mining Area, China

Modeling the Spatial Dynamics of Soil Organic Carbon Using Remotely-Sensed Predictors in Fuzhou City, China

A Studying the Possibility of Estimating Soil Organic Carbon of Soils Under PINUS BRUTIA and QUERCUS AEGILOPS L. Trees in Sarke-Duhok By Using ASD FieldSpec 3 Spectroradiometer

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