Abstract
Soil moisture is crucial in governing land surface processes that have an important influence on the yield and quality of crops. Therefore, it is essential to establish criteria for appropriate mean soil moisture monitoring strategy in order to obtain a representative mean soil moisture value of a farmer’s field. In this study, the plant available water capacity was introduced as an auxiliary variable and a stratified soil moisture sampling method based on the spatial autocorrelation of auxiliary variables (SSAV) was proposed by integrating classical statistics and geostatistics. The results of the proposed methods were compared with those of the international common simple random sampling (SRS) and stratified random sampling (STRS) methods at the field and regional scale. The results showed that the range of mean relative error and the standard deviation of the soil moisture obtained with the SSAV method were significantly lower than those of the soil moisture obtained with the SRS and STRS methods at both the field and regional scales. The root mean squared error between the observed and estimated soil moisture at the field and regional scales were found to be 0.0104 and 0.0125 cm3/cm3, respectively, with the SSAV method, which are significantly lower than those obtained with the SRS method (0.0124 and 0.0139 cm3/cm3, respectively) and STRS method (0.0116 and 0.0130 cm3/cm3, respectively). The standard deviation of the relative difference, mean absolute bias error, and root-mean-squared difference of the SSAV method, which were used as stability indices of the monitoring points, were all lower than those of the SRS and STRS methods. These results demonstrated that the SSAV could promote the monitoring accuracy and precision, and the soil moisture estimated based on the SSAV could represent the mean soil moisture for several years. The use of the SSAV is recommended as an effective method for the placement of soil moisture sampling points to estimate the mean soil moisture.
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