Spatial Prediction of Soil Organic Matter Content Using Cokriging with Remotely Sensed Data

Wu, Chunfa; Wu, Jun; Luo, Yongming; Zhang, Limin; DeGloria, Stephen D.

doi:10.2136/sssaj2008.0045

Cited by 80 publications

(37 citation statements)

References 44 publications

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“…Th en, we compared the reliability in SOM estimation by the methods of kriging and cokriging based on the maps of kriging standard deviations, and validated them by using cross-validation. Th e detail processes and results are described in another paper (Wu et al, 2009). Th e results indicate that cokriging signifi cantly improved the precision and reliability of SOM prediction.…”

Section: Sampling Design and Soil Analysismentioning

confidence: 93%

Spatial Estimation of Soil Total Nitrogen Using Cokriging with Predicted Soil Organic Matter Content

Luo

et al. 2009

Soil Science Soc of Amer J

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Accurate measurement of soil total N (TN) content in agricultural fields is important to guide reasonable application of nitrogenous fertilizer. Estimation of soil TN content with limited in situ data at an acceptable level of accuracy is important because laboratory measurement of N is a time‐ and labor‐consuming procedure. This study was conducted to evaluate cokriging of soil TN with predicted soil organic matter (SOM) content as auxiliary data. The SOM content was predicted by cokriging with a digital number (DN) of Band 1 of Landsat Enhanced Thematic Mapper (ETM) imagery. Soil TN content was estimated by using 88 soil samples for prediction and 43 soil samples for validation in a study area of 367 km2 in Haining City, China. Field‐measured soil TN content ranged from 0.47 to 2.48 g kg−1, with a mean of 1.25 g kg−1 Soil TN content of all 131 soil samples including samples for prediction and validation was highly correlated with measured (r = 0.81, p < 0.01) and predicted (r = 0.81, p < 0.01) SOM content in paddy fields. Then, the predicted SOM content was used as auxiliary variable for the prediction of soil TN content. By using the 43 samples for validation, we had a mean error (ME) of 0.03 g kg−1 and a root mean square error (RMSE) of 0.31 g kg−1 for kriging, and a mean error of 0.00 g kg−1 and a root mean square error of 0.25 g kg−1 for cokriging, respectively. Our results indicate cokriging with predicted SOM content data was superior to kriging. In addition, predicted data of the auxiliary variable have the potential to be useful for cokriging when the predicted auxiliary data have high prediction accuracy.

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Section: Sampling Design and Soil Analysismentioning

confidence: 93%

Spatial Estimation of Soil Total Nitrogen Using Cokriging with Predicted Soil Organic Matter Content

Luo

et al. 2009

Soil Science Soc of Amer J

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“…To reveal the spatiotemporal distribution and evolution characteristics of groundwater salinization, geostatistical methods prove to be of great power to predict and display the distribution, variation and relevancy of different variants by interpolation from points data, especially for ordinary kriging (OK) [47,48]. However, recent studies have shown that the interpolation results by OK were not accurate with relatively few data, and co-kriging (COK) could improve the predict results notably, while the independent variable was highly-correlated with the coordination variables [49][50][51]. Based on the CT results of major ions and TDS and comparing the results of the mean error (ME), mean-squared error (MSE) and mean-square standard error (MSSE) of interpolation results in cross-validation by OK and COK, COK is finally used to display the spatial distribution of groundwater TDS among decades to reflect the variation tendency of groundwater quality in the study area.…”

Section: Data Analysis Methodsmentioning

confidence: 99%

Assessment of Long-Term Evolution of Groundwater Hydrochemical Characteristics Using Multiple Approaches: A Case Study in Cangzhou, Northern China

Wang

Liu³

et al. 2015

Water

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Water shortage is severe in the North China Plain (NCP). In addition to a deficiency of water resources, deterioration of groundwater quality should be of great concern. In this study, hydrogeological analysis was conducted in combination with principal component analysis, correlation analysis and the co-kriging method to identify factors controlling the content of major ions and total dissolved solids (TDS) in areal shallow and deep groundwater and to assess groundwater evolution in Cangzhou, China. The results suggested that groundwater quality degradation occurred and developed in the study area, as indicated by increasing concentrations of major ions, TDS and hardness in both shallow and deep groundwater. In shallow groundwater, whose hydrochemical water types changed from HCO3-Ca.Na.Mg and HCO3.Cl-Na in the west (Zone II) to Cl.SO4-Na and Cl-Na in the east (Zone III). Areas with TDS concentrations between 1500 and 2000 mg/L occupied 79.76% of the total in the 1980s, while areas with a TDS concentration ranging from 2500 to 3000 mg/L comprised 59.11% of the total in the 2010s. In deep groundwater, the area with TDS over 1000 mg/L expanded from 5366.39 km groundwater. Dissolution of dolomite, calcite, feldspar and gypsum were the primary sources of major ions in groundwater, and the ion exchange reaction had a strong effect on the cation content, especially for deep groundwater.

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“…A 0 is the absorbance in the bland test, which generally uses silica sand or burnt soil as the soil samples for comparison; and details of the calculation of SOM content are given elsewhere (e.g., [4,20]). The spectral reflectance (350-2500 nm) of the soil samples was measured using an ASD FieldSpec3 portable spectral radiometer.…”

Section: Sample Production and Spectral Measurementmentioning

confidence: 99%

“…is the absorbance in the bland test, which generally uses silica sand or burnt soil as the soil samples for comparison; and details of the calculation of SOM content are given elsewhere (e.g., [4,20]). …”

Section: = × 100mentioning

confidence: 99%

“…SOM is also an important source of carbon circulation due to the large amount of soil organic carbon (SOC) in SOM. Slight changes in SOC will significantly increase atmospheric CO 2 concentrations and exacerbate the greenhouse effect and global climate because of the large amounts of C stored in SOM [3,4]. Hence, predicting the SOM content with a relatively high accuracy is of crucial importance for land management.…”

Section: Introductionmentioning

confidence: 99%

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Construction of the Calibration Set through Multivariate Analysis in Visible and Near-Infrared Prediction Model for Estimating Soil Organic Matter

et al. 2017

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Abstract:The visible and near-infrared (VNIR) spectroscopy prediction model is an effective tool for the prediction of soil organic matter (SOM) content. The predictive accuracy of the VNIR model is highly dependent on the selection of the calibration set. However, conventional methods for selecting the calibration set for constructing the VNIR prediction model merely consider either the gradients of SOM or the soil VNIR spectra and neglect the influence of environmental variables. However, soil samples generally present a strong spatial variability, and, thus, the relationship between the SOM content and VNIR spectra may vary with respect to locations and surrounding environments. Hence, VNIR prediction models based on conventional calibration set selection methods would be biased, especially for estimating highly spatially variable soil content (e.g., SOM). To equip the calibration set selection method with the ability to consider SOM spatial variation and environmental influence, this paper proposes an improved method for selecting the calibration set. The proposed method combines the improved multi-variable association relationship clustering mining (MVARC) method and the Rank-Kennard-Stone (Rank-KS) method in order to synthetically consider the SOM gradient, spectral information, and environmental variables. In the proposed MVARC-R-KS method, MVARC integrates the Apriori algorithm, a density-based clustering algorithm, and the Delaunay triangulation. The MVARC method is first utilized to adaptively mine clustering distribution zones in which environmental variables exert a similar influence on soil samples. The feasibility of the MVARC method is proven by conducting an experiment on a simulated dataset. The calibration set is evenly selected from the clustering zones and the remaining zone by using the Rank-KS algorithm in order to avoid a single property in the selected calibration set. The proposed MVARC-R-KS approach is applied to select a calibration set in order to construct a VNIR prediction model of SOM content in the riparian areas of the Jianghan Plain in China. Results indicate that the calibration set selected using the MVARC-R-KS method is representative of the component concentration, spectral information, and environmental variables. The MVARC-R-KS method can also select the calibration set for constructing a VNIR model of SOM content with a relatively higher-fitting degree and accuracy by comparing it to classical calibration set selection methods.

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Spatial Prediction of Soil Organic Matter Content Using Cokriging with Remotely Sensed Data

Cited by 80 publications

References 44 publications

Spatial Estimation of Soil Total Nitrogen Using Cokriging with Predicted Soil Organic Matter Content

Spatial Estimation of Soil Total Nitrogen Using Cokriging with Predicted Soil Organic Matter Content

Assessment of Long-Term Evolution of Groundwater Hydrochemical Characteristics Using Multiple Approaches: A Case Study in Cangzhou, Northern China

Construction of the Calibration Set through Multivariate Analysis in Visible and Near-Infrared Prediction Model for Estimating Soil Organic Matter

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