Abstract
With the increasing requirements of precision agriculture for massive and various kinds of data, remote sensing technology has become indispensable in acquiring the necessary data for precision agriculture. Understanding the spatial variability of a target soil variable (i.e., soil mapping) is a critical issue in solving many agricultural problems. Field sampling is one of the most commonly used technologies for soil mapping, but sample sizes are restricted by resources, such as field labor, soil physicochemical analysis, and funding. In this paper, we proposed a sampling design method with both good spatial coverage and feature space coverage to achieve more precise spatial variability of farm field-level target soil variables for limited sample sizes. The proposed method used the super-grid to achieve good spatial coverage, and it took advantage of remote sensing products that were highly correlated with the target soil property (SOM content) to achieve good feature space coverage. For the experiments, we employed the ordinary kriging (OK) method to map the soil organic matter (SOM) content. The different sized super-grid comparison experiments showed that the 400 × 400 m2 super-grid had the highest SOM content mapping accuracy. Then, we compared the proposed method to regular grid sampling (good spatial coverage) and k-means sampling (good feature space coverage), and the experimental results indicated that the proposed method had greater potential in the selection of representative samples that could improve the SOM content mapping accuracy.
Highlights
Precision agriculture is a new trend in modern agricultural practices as a way to improve yields and quality, thereby increasing the benefits to farmers and guaranteeing sufficient environmental protection [1,2,3,4,5]
Since the sample size was restricted by resources, including field labor, soil physicochemical analysis, and funding, this paper proposed a new sampling design method that recognized representative sample locations to improve soil mapping accuracy for a limited sample size (i.e., 36 sampling points in this paper)
As the super-grid size increased, the difference in the number of sampling points between the super-grids increased. It indicated that the spatial coverage gradually worsened, whereas the feature space coverage improved as the super-grid size increased
Summary
Precision agriculture is a new trend in modern agricultural practices as a way to improve yields and quality, thereby increasing the benefits to farmers and guaranteeing sufficient environmental protection [1,2,3,4,5]. Field sampling, which is a critical issue in soil surveys, helps to obtain reliable soil mapping results by optimizing the sample size and recognizing the representative sampling locations [14,15,16]. Extensive sampling methods have been developed for soil mapping, ranging from simple random sampling to complex, advanced sampling [14,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32]. When the sample size is limited by the available resources and cannot meet the requirements of the precision constraints, the determination of the representative sample locations will become critical to obtaining more accurate soil maps. The study by An et al [19] indicated that soil mapping results using representative samples had comparable accuracies to conclusions that were produced using full samples
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