Estimation of the Spatial Distribution of Soil Chemicals Using Pseudo-Cross-Variograms

Zhang, R.; Myers, Donald E.; Warrick, A. W.

doi:10.2136/sssaj1992.03615995005600050018x

Cited by 39 publications

(18 citation statements)

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“…IMPROVING MAP ACCURACY OF SOIL VARIABLES where RMSE k and RMSE ck are the root mean square errors of kriging and cokriging, respectively (adapted from Zhang et al, 1992). These findings were comparable to the observations by Yates and Warrick (1987), which found that a reduction in kriging variance is observed as the correlation between the covariate and target variable increased.…”

Section: Quantitative Resultssupporting

confidence: 53%

“…It is hypothesized that the benefit of cokriging was not observed because of the random choice of fifteen points or because semivariogram and cross-variogram models were not well defined with such few sample pairs at a low density (n = 15). Absolute values of RMSE were generally higher and correlation between predicted and actual values were generally lower with the lower sampling density, as also shown by Zhang et al (1992) (Table 3).…”

Section: Sampling Density Comparisonsupporting

confidence: 52%

“…However, both methods generated similar trends in the variability of the soil parameters. Comparable results have been found for topsoil silt cokriged with subsoil silt and sand as covariates (McBratney and Webster, 1983a), NaHCO 3 -extractable P cokriged with 25% HCl-extractable P (Trangmar et al, 1986), NO 3 cokriged with soil EC, (Zhang et al, 1992) and soil EC of soil paste extract cokriged with apparent soil EC (Vaughan et al, 1995). Improved local detail of the cokriging maps was due to the finer sampling grid of the covariate, soil EC (McBratney and Webster, 1983a).…”

Section: Map Resultsmentioning

confidence: 70%

See 2 more Smart Citations

Improving Map Accuracy of Soil Variables Using Soil Electrical Conductivity as a Covariate

Tarr¹,

Moore

Bullock

et al. 2005

Precision Agric

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Section: Quantitative Resultssupporting

confidence: 53%

Section: Sampling Density Comparisonsupporting

confidence: 52%

Section: Map Resultsmentioning

confidence: 70%

See 1 more Smart Citation

Improving Map Accuracy of Soil Variables Using Soil Electrical Conductivity as a Covariate

Tarr¹,

Moore

Bullock

et al. 2005

Precision Agric

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“…Cokriging (COK) is a multivariate extension of OK to the situations where one or more secondary variables are spatially cross-correlated with the primary variable. In such cases, the readily available secondary information is used to improve the quality of soil properties estimates (Yates & Warrick 1987;Zhang et al 1992Zhang et al , 1997Vaughan et al 1995;Gotway & Hartford 1996;Juang & Lee 1998). For example, Ersahin (2003) compared OK and COK for estimating the infiltration rate.…”

mentioning

confidence: 99%

Geostatistical analysis of soil texture fractions on the field scale

Delbari¹,

Afrasiab²,

Loiskandl³

2011

Soil & Water Res.

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Geostatistical estimation methods including ordinary kriging (OK), lognormal ordinary kriging (LOK), cokriging (COK), and indicator kriging (IK) are compared for the purposes of prediction and, in particular, uncertainty assessment of the soil texture fractions, i.e. sand, silt, and clay proportions, in an erosion experimental field in Lower Austria. The soil samples were taken on 136 sites, about 30-m apart. The validation technique was cross-validation, and the comparison criteria were the mean bias error (MBE) and root mean squared error (RMSE). Statistical analysis revealed that the sand content is positively skewed, thus persuading us to use LOK for the estimation. COK was also used due to a good negative correlation seen between the texture fractions. The autocorrelation analysis showed that the soil texture fractions in the study area are strongly to moderately correlated in space. Cross-validation indicated that COK is the most accurate method for estimating the silt and clay contents; RMSE equalling to 3.17% and 1.85%, respectively. For the sand content, IK with RMSE (12%) slightly smaller than COK (RMSE = 14%) was the best estimation method. However, COK maps presented the true variability of the soil texture fractions much better than the other approaches, i.e. they achieved the smallest smoothness. Regarding the local uncertainty, the estimation variance maps produced by OK, LOK, and COK methods similarly indicated that the lowest uncertainty occurred near the data locations, and that the highest uncertainty was seen in the areas of sparse sampling. The uncertainty, however, varied much less across the study area compared to conditional variance for IK. The IK conditional variance maps showed, in contrast, some relations to the data values. The estimation uncertainty needs to be evaluated for the incorporation into the risk analysis in the soil management.

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“…Geostatistics, especially kriging, has been used widely in soil nutrient studies (Burrough, 1983;Zhang et al, 1992;Cambardella et al, 1994;Yanai et al, 2003;Gallardo, 2003;Ouyang et al, 2006). Cokriging is an extended technique of kriging.…”

mentioning

confidence: 99%

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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Estimation of the Spatial Distribution of Soil Chemicals Using Pseudo-Cross-Variograms

Cited by 39 publications

References 0 publications

Improving Map Accuracy of Soil Variables Using Soil Electrical Conductivity as a Covariate

Improving Map Accuracy of Soil Variables Using Soil Electrical Conductivity as a Covariate

Geostatistical analysis of soil texture fractions on the field scale

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

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