Comparisons of spatial and non-spatial models for predicting soil carbon content based on visible and near-infrared spectral technology

Guo, Long; Chang, Zhao; Zhang, Haitao; Chen, Yiyun; Linderman, Marc; Zhang, Qing; Liu, Yanfang

doi:10.1016/j.geoderma.2016.10.010

Cited by 48 publications

(25 citation statements)

References 47 publications

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“…In electromagnetic theory, SOM is represented as an organic compound containing functional groups with relatively discrete absorption characteristics in the Vis-NIR range [51]. In the visible region (400-760 nm), the transition of the outer electrons from the ground states to high energy states is the primary process of soil energy absorption [11].…”

Section: Discussionmentioning

confidence: 99%

Assessing Soil Organic Matter Content in a Coal Mining Area through Spectral Variables of Different Numbers of Dimensions

Zhu

Zhang

Wang

et al. 2020

Sensors

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Soil organic matter (SOM) is a crucial indicator for evaluating soil quality and an important component of soil carbon pools, which play a vital role in terrestrial ecosystems. Rapid, non-destructive and accurate monitoring of SOM content is of great significance for the environmental management and ecological restoration of mining areas. Visible-near-infrared (Vis-NIR) spectroscopy has proven its applicability in estimating SOM over the years. In this study, 168 soil samples were collected from the Zhundong coal field of Xinjiang Province, Northwest China. The SOM content (g kg−1) was determined by the potassium dichromate external heating method and the soil reflectance spectra were measured by the spectrometer. Two spectral feature extraction strategies, namely, principal component analysis (PCA) and the optimal band combination algorithm, were introduced to choose spectral variables. Linear models and random forests (RF) were used for predictive models. The coefficient of determination (R2), root mean square error (RMSE), and the ratio of the performance to the interquartile distance (RPIQ) were used to evaluate the predictive performance of the model. The results indicated that the variables (2DI and 3DI) derived from the optimal band combination algorithm outperformed the PCA variables (1DV) regardless of whether linear or RF models were used. An inherent gap exists between 2DI and 3DI, and the performance of 2DI is significantly poorer than that of 3DI. The accuracy of the prediction model increases with the increasing number of spectral variable dimensions (in the following order: 1DV < 2DI < 3DI). This study proves that the 3DI is the first choice for the optimal band combination algorithm to derive sensitive parameters related to SOM in the coal mining area. Furthermore, the optimal band combination algorithm can be applied to hyperspectral or multispectral images and to convert the spectral response into image pixels, which may be helpful for a soil property spatial distribution map.

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Section: Discussionmentioning

confidence: 99%

Assessing Soil Organic Matter Content in a Coal Mining Area through Spectral Variables of Different Numbers of Dimensions

Zhu

Zhang

Wang

et al. 2020

Sensors

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“…The mean, minimum, maximum, and standard deviation (SD) of all soil properties in the calibration set were similar to the corresponding values in the validation set, suggesting that it was reasonable to divide the LIBS spectra dataset into calibration and validation sets. The coefficient of variation (CV) is a normalized dispersion measure of a probability or frequency distribution and is defined as the ratio of the SD to the mean [59]. A high CV value indicates large variability in the data set.…”

Section: Calibration and Validationmentioning

confidence: 99%

Fast and Simultaneous Determination of Soil Properties Using Laser-Induced Breakdown Spectroscopy (LIBS): A Case Study of Typical Farmland Soils in China

et al. 2019

Soil Systems

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Accurate management of soil nutrients and fast and simultaneous acquisition of soil properties are crucial in the development of sustainable agriculture. However, the conventional methods of soil analysis are generally labor-intensive, environmentally unfriendly, as well as time- and cost-consuming. Laser-induced breakdown spectroscopy (LIBS) is a “superstar” technique that has yielded outstanding results in the elemental analysis of a wide range of materials. However, its application for analysis of farmland soil faces the challenges of matrix effects, lack of large-scale soil samples with distinct origin and nature, and problems with simultaneous determination of multiple soil properties. Therefore, LIBS technique, in combination with partial least squares regression (PLSR), was applied to simultaneously determinate soil pH, cation exchange capacity (CEC), soil organic matter (SOM), total nitrogen (TN), total phosphorus (TP), total potassium (TK), available phosphorus (AP), and available potassium (AK) in 200 soils from different farmlands in China. The prediction performances of full spectra and characteristic lines were evaluated and compared. Based on full spectra, the estimates of pH, CEC, SOM, TN, and TK achieved excellent prediction abilities with the residual prediction deviation (RPDV) values > 2.0 and the estimate of TP featured good performance with RPDV value of 1.993. However, using characteristic lines only improved the predicted accuracy of SOM, but reduced the prediction accuracies of TN, TP, and TK. In addition, soil AP and AK were predicted poorly with RPDV values of < 1.4 based on both full spectra and characteristic lines. The weak correlations between conventionally analyzed soil AP and AK and soil LIBS spectra are responsible for the poor prediction abilities of AP and AK contents. Findings from this study demonstrated that the LIBS technique combined with multivariate methods is a promising alternative for fast and simultaneous detection of some properties (i.e., pH and CEC) and nutrient contents (i.e., SOM, TN, TP, and TK) in farmland soils because of the extraordinary prediction performances achieved for these attributes.

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“…GWR, as a local spatial regression model, considers the spatial autocorrelation of explanatory variables (spectra or environmental factors) and explained variables (soil properties). This method reflects the variation of the spatial weights of PCs to the SOCD in different geographical locations [22]. The predictive ability of GWR outperforms that of traditional regression models because these models contain no spatial information on the input variables.…”

Section: Advantages Of Gwrmentioning

confidence: 99%

“…Meanwhile, the ordinary kriging was used by Ge et al [16,21] to weaken the influence of the spatial autocorrelation of spectral reflectance on the prediction accuracy of soil spectral models. Guo et al [22] also showed that GWR can consider the spatial autocorrelation of soil properties and spectral reflectance to improve its prediction accuracy over that of PLSR. These investigations offered important theoretical basis to explore the application of spatial autocorrelation in predicting one complicated composition (SOCD).…”

Section: Advantages Of Gwrmentioning

confidence: 99%

Exploring the Role of the Spatial Characteristics of Visible and Near-Infrared Reflectance in Predicting Soil Organic Carbon Density

Guo

Chen

Shi

et al. 2017

IJGI

Self Cite

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Soil organic carbon stock plays a key role in the global carbon cycle and the precision agriculture. Visible and near-infrared reflectance spectroscopy (VNIRS) can directly reflect the internal physical construction and chemical substances of soil. The partial least squares regression (PLSR) is a classical and highly commonly used model in constructing soil spectral models and predicting soil properties. Nevertheless, using PLSR alone may not consider soil as characterized by strong spatial heterogeneity and dependence. However, considering the spatial characteristics of soil can offer valuable spatial information to guarantee the prediction accuracy of soil spectral models. Thus, this study aims to construct a rapid and accurate soil spectral model in predicting soil organic carbon density (SOCD) with the aid of the spatial autocorrelation of soil spectral reflectance. A total of 231 topsoil samples (0-30 cm) were collected from the Jianghan Plain, Wuhan, China. The spectral reflectance (350-2500 nm) was used as auxiliary variable. A geographically-weighted regression (GWR) model was used to evaluate the potential improvement of SOCD prediction when the spatial information of the spectral features was considered. Results showed that: (1) The principal components extracted from PLSR have a strong relationship with the regression coefficients at the average sampling distance (300 m) based on the Moran's I values. (2) The eigenvectors of the principal components exhibited strong relationships with the absorption spectral features, and the regression coefficients of GWR varied with the geographical locations. (3) GWR displayed a higher accuracy than that of PLSR in predicting the SOCD by VNIRS. This study aimed to help people realize the importance of the spatial characteristics of soil properties and their spectra. This work also introduced guidelines for the application of GWR in predicting soil properties by VNIRS.

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Comparisons of spatial and non-spatial models for predicting soil carbon content based on visible and near-infrared spectral technology

Cited by 48 publications

References 47 publications

Assessing Soil Organic Matter Content in a Coal Mining Area through Spectral Variables of Different Numbers of Dimensions

Assessing Soil Organic Matter Content in a Coal Mining Area through Spectral Variables of Different Numbers of Dimensions

Fast and Simultaneous Determination of Soil Properties Using Laser-Induced Breakdown Spectroscopy (LIBS): A Case Study of Typical Farmland Soils in China

Exploring the Role of the Spatial Characteristics of Visible and Near-Infrared Reflectance in Predicting Soil Organic Carbon Density

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