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SOIL FERTILITY & PLANT NUTRITION

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

^a formerly with:, Dep. Natural Resources and Environ., Zhejiang Univ., Hangzhou 310029, China
^b currently at, Yantai Institute of Coastal Zone Research for, Sustainable Development, Chinese Academy of Sciences, Yantai, 264003, China
^c Key Lab. of Soil Environment and Pollution, Remediation, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
^d Haining Agricultural Extension Service, Haining 314400, China
^e Dep. Crop and Soil Sciences, Cornell Univ., Ithaca, NY 14853

^* Corresponding author (wchf1680{at}sina.com).

Accurately measuring soil organic matter content (SOM) in paddy fields is important because SOM is one of the key soil properties controlling nutrient budgets in agricultural production systems. Estimation of this soil property at an acceptable level of accuracy is important; especially in the case when SOM exhibits strong spatial dependence and its measurement is a time- and labor-consuming procedure. This study was conducted to evaluate and compare spatial estimation by kriging and cokriging with remotely sensed data to predict SOM using limited available data for a 367-km² study area in Haining City, Zhejiang Province, China. Measured SOM ranged from 5.7 to 40.4 g kg^–1, with a mean of 19.5 g kg^–1. Correlation analysis between the SOM content of 131 soil samples and the corresponding digital number (DN) of six bands (Band 1–5 and Band 7) of Landsat Enhanced Thematic Mapper (ETM) imagery showed that correlation between SOM and DN of Band 1 was the highest (r = –0.587). We used the DN of Band 1 as auxiliary data for the SOM prediction, and used descriptive statistics and the kriging standard deviation (STD) to compare the reliabilities of the predictions. We also used cross-validation to validate the SOM prediction. Results indicate that cokriging with remotely sensed data was superior to kriging in the case of limited available data and the moderately strong linear relationship between remotely sensed data and SOM content. Remotely sensed data such as Landsat ETM imagery have the potential as useful auxiliary variables for improving the precision and reliability of SOM prediction.

Abbreviations: CV, coefficient of variation • DN, digital number • ETM, Landsat Enhanced Thematic Mapper • NDVI, normalized difference vegetation index • SE, standard error of the estimate • SOM, soil organic matter content • STD, standard deviation • TM, Landsat Thematic Mapper

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				C. Wu, J. Wu, Y. Luo, L. Zhang, and S. D. DeGloria Spatial Estimation of Soil Total Nitrogen Using Cokriging with Predicted Soil Organic Matter Content Soil Sci. Soc. Am. J., August 19, 2009; 73(5): 1676 - 1681. [Abstract] [Full Text] [PDF]