## 4 Discussion  
With the increasing requirements of precision agriculture for massive and various kinds of data, the data acquisition process has evolved from truth-surveying to sensors [73]. Remote sensing technology can provide a convenient approach to acquire the necessary data for precision agriculture, such as calculating the NDVI and LAI. The accurate determination of spatial variability of the target soil variable is a critical issue in precision agriculture. Thus, the selection of representative soil samples for higher soil mapping accuracy was the main focus of this paper. Traditional farm field soil sampling only considers good spatial coverage, ignoring the impact of good feature space coverage of the covariates on the farm field soil mapping accuracy. Some remote sensing measures, such as the NDVI and crop yield estimations, can explain the variation of the farm field's target soil variable. These measures can be used as covariates to recognize the representative soil samples for good feature space coverage. In this paper, we collected the crop yield data produced using the WOFOST model, DEM, slope, and aspect as the covariates. In the future, we will explore the potential use of more remote sensing measures for farm field soil sampling, such as gross primary production (GPP) and LAI. This paper developed a sampling design method considering both good spatial coverage and feature space coverage for farm field-level soil mapping. Good feature space coverage can be achieved  
Figure 8: The MAE and RMSE results for the SOM content mapping using the three sampling design methods. Figure 7: The predicted SOM content maps using the three groups of samples: (**a**) The proposed method with the optimal super-grid size, (**b**) regular grid sampling, and (**c**) k-means sampling. by assessing the aforementioned remote sensing measures. In this paper, good spatial coverage was achieved using the super-grid. However, the selection of the appropriate super-grid size is important in forming a satisfactory sampling design. Thus, this paper compared the influence of six different super-grid sizes (i.e., \(200\times 200\) m\({}^{2}\), \(300\times 300\) m\({}^{2}\), \(400\times 400\) m\({}^{2}\), \(500\times 500\) m\({}^{2}\), \(600\times 600\) m\({}^{2}\), and \(700\times 700\) m\({}^{2}\)) on the soil mapping accuracies. Figure 5 shows that the soil mapping accuracy of the \(400\times 400\) m\({}^{2}\) super-grid was the highest, as indicated by the lowest MAE and average RMSE. A possible reason may be that the \(400\times 400\) m\({}^{2}\) super-grid balanced the potential trade-offs between good spatial coverage and good feature space coverage. Thus, the balance of good spatial coverage and good feature space coverage for one sampling design must be carefully preserved. As described in Section 1, the determination of sample size and sample locations are critical to obtaining reliable soil mapping results. Large sample sizes are likely to result in higher soil mapping accuracy than small sample sizes [35]. [PERSON] et al. [35] analyzed the influence of the sample size on variogram parameters and believed that at least 50 samples were required to produce reliable interpolation results in a farm field with an area of 24-ha. However, the available resources and precision requirements are usually in conflict when we determine the sample size. In most cases, the sample size is restricted by available resources. Thus, the focus of this paper was to recognize representative sample locations at a given limited sample size to improve soil mapping accuracy. This paper selected two percent of all the candidate sampling points as the given limited sample size, and we compared the proposed method with two other sampling design methods (i.e., regular grid sampling and k-means sampling) to demonstrate the effectiveness of the proposed method. Note that the determination of two percent of all the candidate sampling points satisfied the limited sample size precondition.