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.
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机译：准确测量稻田中的土壤有机质含量非常重要，因为SOM是控制农业生产系统中养分收支的关键土壤特性之一。以可接受的准确度估算土壤性质非常重要;特别是在SOM表现出强烈的空间依赖性并且其测量是一项既费时又费力的过程中。这项研究的目的是通过有限的可用数据对中国浙江省海宁市一个367 km2的研究区域进行克里金法和共克里金法的遥感估计，以评估和比较空间估计，从而预测SOM。测得的SOM为5.7至40.4 g kg-1，平均值为19.5 g kg-1。 131个土壤样品的SOM含量与Landsat增强专题测绘仪（ETM）图像的6个波段（1-5和7波段）的相应数字值（DN）之间的相关分析表明，波段1的SOM与DN之间的相关性为最高（r = -0.587）。我们使用波段1的DN作为SOM预测的辅助数据，并使用描述性统计量和克里金标准差（STD）来比较预测的可靠性。我们还使用交叉验证来验证SOM预测。结果表明，在可用数据有限以及遥感数据与SOM内容之间存在中等强的线性关系的情况下，使用遥感数据进行协同克里金法要优于克里金法。诸如Landsat ETM影像之类的遥感数据有潜力作为有用的辅助变量，以提高SOM预测的精度和可靠性。
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