4.4 Article

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

Journal

SOIL SCIENCE SOCIETY OF AMERICA JOURNAL
Volume 73, Issue 5, Pages 1676-1681

Publisher

WILEY
DOI: 10.2136/sssaj2008.0205

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Funding

  1. Department of Science and Technology, Zhejiang Province, China [2006C22026]
  2. Chinese Academy of Sciences Innovative Research International Partnership Project [CXTD-Z2005-4]
  3. National Basic Research Priorities Program [2002CB410810]
  4. China Scholarship Council
  5. Zhejiang University

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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 I 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 km(2) 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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