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篇目详细内容 |
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【篇名】 |
Regression-kriging for characterizing soils with remote-sensing data |
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【刊名】 |
Frontiers of Earth Science |
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【刊名缩写】 |
Front. Earth Sci |
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【ISSN】 |
2095-0195 |
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【EISSN】 |
2095-0209 |
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【DOI】 |
10.1007/s11707-011-0174-1 |
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【出版社】 |
Higher Education Press and Springer-Verlag Berlin
Heidelberg |
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【出版年】 |
2011 |
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【卷期】 |
5
卷3期 |
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【页码】 |
239-244
页,共
6
页 |
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【作者】 |
Yufeng GE;
J. Alex THOMASSON;
Ruixiu SUI;
James WOOTEN;
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【关键词】 |
precision agriculture; regression-kriging; remote sensing; soil sensors |
【摘要】 |
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In precision agriculture regression has been used widely to quantify the relationship between soil attributes and other environmental variables. However, spatial correlation existing in soil samples usually violates a basic assumption of regression: sample independence. In this study, a regression-kriging method was attempted in relating soil properties to the remote sensing image of a cotton field near Vance, Mississippi, USA. The regression-kriging model was developed and tested by using 273 soil samples collected from the field. The result showed that by properly incorporating the spatial correlation information of regression residuals, the regression-kriging model generally achieved higher prediction accuracy than the stepwise multiple linear regression model. Most strikingly, a 50% increase in prediction accuracy was shown in soil sodium concentration. Potential usages of regression-kriging in future precision agriculture applications include real-time soil sensor development and digital soil mapping. |
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