Assessing soil organic matter of reclaimed soil from a large surface coal mine using a field spectroradiometer in laboratory

Bao, Nisha; Wu, Lei; Ye, Baoying; Yang, Ke; Zhou, Wei

doi:10.1016/j.geoderma.2016.10.033

Cited by 79 publications

(31 citation statements)

References 38 publications

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“…Common tools for mining site reclamation include optical multispectral sensors, satellite imagery, and hyperspectral data. For example, these tools are used in land change detection [4], vegetation health assessment [2], and soil property prediction [5]. However, the use of optical sensors for the AGB estimation in semiarid mining locations is limited by the vegetation structure as well as the saturation of the high-vegetation cover [6].…”

Section: Introductionmentioning

confidence: 99%

Biomass Estimation for Semiarid Vegetation and Mine Rehabilitation Using Worldview-3 and Sentinel-1 SAR Imagery

Bao

et al. 2019

Remote Sensing

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The surface mining activities in grassland and rangeland zones directly affect the livestock production, forage quality, and regional grassland resources. Mine rehabilitation is necessary for accelerating the recovery of the grassland ecosystem. In this work, we investigate the integration of data obtained via a synthetic aperture radar (Sentinel-1 SAR) with data obtained by optical remote sensing (Worldview-3, WV-3) in order to monitor the conditions of a vegetation area rehabilitated after coal mining in North China. The above-ground biomass (AGB) is used as an indicator of the rehabilitated vegetation conditions and the success of mine rehabilitation. The wavelet principal component analysis is used for the fusion of the WV-3 and Sentinel-1 SAR images. Furthermore, a multiple linear regression model is applied based on the relationship between the remote sensing features and the AGB field measurements. Our results show that WV-3 enhanced vegetation indices (EVI), mean texture from band8 (near infrared band2, NIR2), the SAR vertical and horizon (VH) polarization, and band 8 (NIR2) from the fused image have higher correlation coefficient value with the field-measured AGB. The proposed AGB estimation model combining WV-3 and Sentinel 1A SAR imagery yields higher model accuracy (R 2 = 0.79 and RMSE = 22.82 g/m 2 ) compared to that obtained with any of the two datasets only. Besides improving AGB estimation, the proposed model can also reduce the uncertainty range by 7 g m −2 on average. These results demonstrate the potential of new multispectral high-resolution datasets, such as Sentinel-1 SAR and Worldview-3, in providing timely and accurate AGB estimation for mine rehabilitation planning and management.

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Section: Introductionmentioning

confidence: 99%

Biomass Estimation for Semiarid Vegetation and Mine Rehabilitation Using Worldview-3 and Sentinel-1 SAR Imagery

Bao

et al. 2019

Remote Sensing

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“…In recent years, visible (VIS, 400-700 nm) and near-infrared (NIR, 700-2500 nm) spectroscopy combined with multivariate modeling techniques, provided an alternative tool to characterize SOM [4,5]. Besides, this VIS-NIR technique can be applied either in the laboratory or in the field, and when a calibration model between spectral data and their corresponding soil property reference value is developed, this model can be used to predict other soil samples in specific areas only containing their VIS-NIR spectra [6].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Soil Organic Matter by VIS–NIR Spectroscopy Using Normalized Soil Moisture Index as a Proxy of Soil Moisture

Hong

Chen

et al. 2017

Remote Sensing

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Soil organic matter (SOM) is an important parameter of soil fertility, and visible and near-infrared (VIS-NIR) spectroscopy combined with multivariate modeling techniques have provided new possibilities to estimate SOM. However, the spectral signal is strongly influenced by soil moisture (SM) in the field. Interest in using spectral classification to predict soils in the moist conditions to minimize the influence of SM is growing. The objective of this study was to investigate the transferability of two approaches, SM-based cluster method with known SM (classifying the VIS-NIR spectra into different SM clusters to develop models separately), the normalized soil moisture index (NSMI)-based cluster method with unknown SM (utilizing NSMI to indicate the SM and establish models separately), to predict SOM directly in moist soil spectra. One hundred and twenty one soil samples were collected from Central China, and eight SM levels were obtained for each sample through rewetting experiments. Their reflectance spectra and SOM concentrations were measured in the laboratory. Partial least square-support vector machine (PLS-SVM) was employed to construct SOM prediction models. Specifically, prediction models were developed for NSMI-based clusters with unknown SM data. The models were assessed through three statistics in the processes of calibration and validation: the coefficient of determination (R 2 ), root mean square error (RMSE) and the ratio of the performance to deviation (RPD). Results showed that the variable SM led to reduced VIS-NIR reflectance nonlinearly across the entire spectral range. NSMI was an effective spectral index to indicate the SM. Classifying the VIS-NIR spectra into different SM clusters in known SM states could improve the performance of PLS-SVM models to acceptable prediction accuracies (R 2 cv = 0.69-0.77, RPD = 1.79-2.08). The estimation of SOM, when using the NSMI-based cluster method with unknown SM (RPD = 1.95-2.04), was similar to the use of the SM-based cluster method with known SM (RPD = 1.79-2.08). The predictive results (RPD = 1.87-2.06) demonstrated that the NSMI-based cluster method has potential for application outside the laboratory for SOM prediction without knowing the SM explicitly, and this method is also easy to carry out and only requires spectral information.

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“…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 (R v 2 ) = 0.91, root mean square error of validation (RMSE v ) = 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.2 of 16 information and monitor the soil environment has been an urgent problem in precise management. With the development of technology, non-destructive, fast, accurate and large-scale remote sensing monitoring makes up for the shortcomings of traditional monitoring methods [4,5], while hyperspectral technology is also widely used in soil quality monitoring [6].Previous studies have shown that soil spectral reflectance is significantly correlated with soil properties, such as soil moisture, iron oxides, clay minerals, and organic matter, while the absorption in the range of 400-1000 nm is mainly caused by iron oxide and organic matter [7]. The reflectance is significantly negatively correlated with organic matter [8][9][10], it has a high correlation with SOM in the visible bands, especially in the red bands [11,12], and the most sensitive bands are mainly in the 550-710 nm range [13][14][15][16][17].…”

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confidence: 99%

“…2 of 16 information and monitor the soil environment has been an urgent problem in precise management. With the development of technology, non-destructive, fast, accurate and large-scale remote sensing monitoring makes up for the shortcomings of traditional monitoring methods [4,5], while hyperspectral technology is also widely used in soil quality monitoring [6].…”

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confidence: 99%

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

Chen

Wang

Liu

et al. 2019

Forests

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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.

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Assessing soil organic matter of reclaimed soil from a large surface coal mine using a field spectroradiometer in laboratory

Cited by 79 publications

References 38 publications

Biomass Estimation for Semiarid Vegetation and Mine Rehabilitation Using Worldview-3 and Sentinel-1 SAR Imagery

Biomass Estimation for Semiarid Vegetation and Mine Rehabilitation Using Worldview-3 and Sentinel-1 SAR Imagery

Prediction of Soil Organic Matter by VIS–NIR Spectroscopy Using Normalized Soil Moisture Index as a Proxy of Soil Moisture

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

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