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

Guo, Long; Chen, Yiyun; Shi, Tiezhu; Chang, Zhao; Liu, Yanfang; Wang, Shanqin; Zhang, Haitao

doi:10.3390/ijgi6100308

Cited by 10 publications

(8 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Subsequently, the SOC content was measured using potassium dichromate [52]. All these methods can be found in the studies of Guo et al [53] and Liu et al [22].…”

Section: Study Area and Samplingmentioning

confidence: 99%

“…PLSR is a popular modeling technique used in chemometric and quantitative spectral analyses. It is based on a linear transition from numerous original descriptors to a new variable space based on a small number of orthogonal factors [53,59]. This method is used to create predictive models when many highly collinear predictor variables exist [60].…”

Section: Partial Least Squares Regression (Plsr) Modelmentioning

confidence: 99%

“…Digital mapping has become a crucial and popular method to investigate the spatial distribution of soil properties, which provide substantial details. Presently, numerous studies have focused on exploring a feasible and reliable method to acquire spatial SOC information [24,53,98]. However, all these methods have considered topography as a necessary influential factor.…”

Section: Effect Of Terrain Factors On Soc Predictionmentioning

confidence: 99%

See 2 more Smart Citations

Prediction of Soil Organic Carbon based on Landsat 8 Monthly NDVI Data for the Jianghan Plain in Hubei Province, China

et al. 2019

Self Cite

View full text Add to dashboard Cite

High-precision maps of soil organic carbon (SOC) are beneficial for managing soil fertility and understanding the global carbon cycle. Digital soil mapping plays an important role in efficiently obtaining the spatial distribution of SOC, which contributes to precision agriculture. However, traditional soil-forming factors (i.e., terrain or climatic factors) have weak variability in low-relief areas, such as plains, and cannot reflect the spatial variation of soil attributes. Meanwhile, vegetation cover hinders the acquisition of the direct information of farmland soil. Thus, useful environmental variables should be utilized for SOC prediction and the digital mapping of such areas. SOC has an important effect on crop growth status, and remote sensing data can record the apparent spectral characteristics of crops. The normalized difference vegetation index (NDVI) is an important index reflecting crop growth and biomass. This study used NDVI time series data rather than traditional soil-forming factors to map SOC. Honghu City, located in the middle of the Jianghan Plain, was selected as the study region, and the NDVI time series data extracted from Landsat 8 were used as the auxiliary variables. SOC maps were estimated through stepwise linear regression (SLR), partial least squares regression (PLSR), support vector machine (SVM), and artificial neural network (ANN). Ordinary kriging (OK) was used as the reference model, while root mean square error of prediction (RMSE P ) and coefficient of determination of prediction (R 2 P ) were used to evaluate the model performance. Results showed that SOC had a significant positive correlation in July and August (0.17, 0.29) and a significant negative correlation in January, April, and December (−0.23, −0.27, and −0.23) with NDVI time series data. The best model for SOC prediction was generated by ANN, with the lowest RMSE P of 3.718 and highest R 2 P of 0.391, followed by SVM (RMSE P = 3.753, R 2 P = 0.361) and PLSR (RMSE P = 4.087, R 2 P = 0.283). The SLR model was the worst model, with the lowest R 2 P of 0.281 and highest RMSE P of 3.930. ANN and SVM were better than OK (RMSE P = 3.727, R 2 P = 0.372), whereas PLSR and SLR were worse than OK. Moreover, the prediction results using single-data NDVI or short time series NDVI showed low accuracy. The effect of the terrain factor on SOC prediction represented unsatisfactory results. All these results indicated that the NDVI time series data can be used for SOC mapping in plain areas and that the ANN model can maximally extract additional associated information between NDVI time series data and SOC. This study presented an effective method to overcome the selection of auxiliary variables for digital soil mapping in plain areas when the soil was covered with vegetation. This finding indicated that the time series characteristics of NDVI were conducive for predicting SOC in plains.

show abstract

“…Subsequently, the SOC content was measured using potassium dichromate [52]. All these methods can be found in the studies of Guo et al [53] and Liu et al [22].…”

Section: Study Area and Samplingmentioning

confidence: 99%

Section: Partial Least Squares Regression (Plsr) Modelmentioning

confidence: 99%

Section: Effect Of Terrain Factors On Soc Predictionmentioning

confidence: 99%

See 1 more Smart Citation

Prediction of Soil Organic Carbon based on Landsat 8 Monthly NDVI Data for the Jianghan Plain in Hubei Province, China

et al. 2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, for the other soil properties, differences in RPIQ V were less between the algorithms (Figure 3). These effects of sample size on RPIQ V can be attributed to the combined effect of spectral information in the cross‐validation and spatial autocorrelation (Guo, Chen, et al., 2017; Guo, Zhao, et al., 2017), since both increase with increasing sample size in this field‐scale study.…”

Section: Resultsmentioning

confidence: 83%

Diffuse reflectance infrared spectroscopy estimates for soil properties using multiple partitions: Effects of the range of contents, sample size, and algorithms

Ludwig

Greenberg

Sawallisch

et al. 2021

Soil Science Soc of Amer J

View full text Add to dashboard Cite

The RMSE of validation (RMSE V ) and ratio of the interquartile range to RMSE V (RPIQ V ) are key quality parameters in diffuse reflectance infrared (IR) spectroscopy studies, but the effects of different factors on these parameters are often not sufficiently considered. Our objectives were to reveal the effects of range of contents, sample size, data pretreatment, wavenumber region selection, and algorithms on the evaluation of IR spectra in the wavenumber range from 1,000 to 7,000 cm −1 (mid-and long-wave near IR) estimations. Contents of soil organic C (SOC), N, clay, and sand and pH values were determined for surface soils of an arable field in India, and IR spectra were recorded for four samples consisting of 71-263 soils. For each of the four samples, five random partitions into calibration and validation datasets were carried out, and partial least squares regression (PLSR) or support vector machine regression was performed. A plot of the RMSE V values against the interquartile ranges of measured values for the validation samples (IQR V ) indicated that the IQR V was a key parameter for all soil properties: a sufficiently high IQR V -which is affected by sample size and random partitioning-resulted in generally good estimation accuracies (RPIQ V ≥ 2.70). Optimized data pretreatment and wavenumber region selection improved estimation accuracy for SOC and pH. Support vector machine regression was superior to PLSR for the estimation of SOC, clay, and sand, but worse for pH. Overall, this study indicates that multiple partitioning of the data is essential in IR studies and suggests that RPIQ V and RMSE V need to be interpreted in the context of the respective IQR V values.

show abstract

“…The important work of this group is to choose representative auxiliary variables and remove multicollinearity and random noise among them. The third group is about combining spatial characteristics and soil covariates to construct soil prediction models, including COK, GWR and other extensional models (regression kriging and geographically weighted regression kriging model), which consider spatial autocorrelation of residuals of the prediction results (Guo et al, 2017a). Various prediction models of soil properties have been constructed in different regions of interest, and most of the publications have shown that the third class of models have better predictive accuracy and can be easily interpreted (Wang et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Combining Environmental Factors and Lab VNIR Spectral Data to Predict SOM by Geospatial Techniques

et al. 2019

Self Cite

View full text Add to dashboard Cite

Soil organic matter (SOM) is an important parameter related to soil nutrient and miscellaneous ecosystem services. This paper attempts to improve the performance of traditional partial least square regression (PLSR) model by considering the spatial autocorrelation and soil forming factors. Surface soil samples (n = 180) were collected from Honghu City located in the middle of Jianghan Plain, China. The visible and near infrared (VNIR) spectra and six environmental factors (elevation, land use types, roughness, relief amplitude, enhanced vegetation index, and land surface water index) were used as the auxiliary variables to construct the multiple linear regression (MLR), PLSR and geographically weighted regression (GWR) models. Results showed that: 1) the VNIR spectra can increase about 39.62% prediction accuracy than the environmental factors in predicting SOM; 2) the comprehensive variables of VNIR spectra and the environmental factors can improve about 5.78% and 44.90% relative to soil spectral models and soil environmental models, respectively; 3) the spatial model (GWR) can improve about 3.28% accuracy than MLR and PLSR. Our results suggest that the combination of spectral reflectance and the environmental variables can be used as the suitable auxiliary variables in predicting SOM, and GWR is a promising model for predicting soil properties.

show abstract

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

Cited by 10 publications

References 50 publications

Prediction of Soil Organic Carbon based on Landsat 8 Monthly NDVI Data for the Jianghan Plain in Hubei Province, China

Prediction of Soil Organic Carbon based on Landsat 8 Monthly NDVI Data for the Jianghan Plain in Hubei Province, China

Diffuse reflectance infrared spectroscopy estimates for soil properties using multiple partitions: Effects of the range of contents, sample size, and algorithms

Combining Environmental Factors and Lab VNIR Spectral Data to Predict SOM by Geospatial Techniques

Contact Info

Product

Resources

About