Arable land quality has been evaluated through weighted average of indicators related to soil properties and tillage technics to present the most basic function of cropland: food production potential. A hybrid sampling method spatial coverage sampling and random sampling–conditioned Latin hypercube sampling (SPCOSA–CLHS) was designed for arable land quality observation network in this paper. The SPCOSA–CLHS integrates the uniform spatial partitioning results generated by the spatial coverage sampling and random sampling (SPCOSA) into the conditioned Latin hypercube sampling (CLHS) method along with other arable land quality indicators such as field slope, soil bulk density, organic matter content, thickness of plough layer and irrigation. Then, SPCOSA, CLHS, SPCOSA–CLHS, CLHS with x and y coordinates as covariates (XY-CLHS), random sampling method (RSM) were compared using the example of Heilongjiang Province. The sample population covers 12,147,008 grid cells and 17 arable land quality indicators. Five parameters: information entropy, Kullback–Leibler divergence, similarity distance, expression ability to local spatial heterogeneity of arable land quality and distribution homogeneity of sampling results were used to compare the applicability of these methods for overall arable land quality. The SPCOSA–CLHS can better trade off sampling result's representative ability to the population and spatial heterogeneity of arable land quality, as well as its samples have advantages of spatially uniform distribution. When the sample size is between 5000 and 20,000, all methods show good applicability. When the sample size is below 5000, however, the differences among these methods become significant. SPCOSA and random sampling method offset most dramatically. Based on this detailed comparison of the five sampling strategy approaches, we strongly recommend to use SPCOSA–CLHS to design arable land quality observation.

中文翻译：

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基于 SPCOSA-CLHS 混合方法的耕地质量观测土壤采样设计

耕地质量通过与土壤性质和耕作技术相关的指标的加权平均来评估，以呈现耕地最基本的功能：粮食生产潜力。本文针对耕地质量观测网络设计了空间覆盖抽样和随机抽样条件拉丁超立方抽样（SPCOSA-CLHS）的混合抽样方法。SPCOSA–CLHS 将空间覆盖抽样和随机抽样 (SPCOSA) 生成的均匀空间分区结果与其他耕地质量指标（如田间坡度、土壤容重、有机质）一起整合到条件拉丁超立方抽样 (CLHS) 方法中含量、耕层厚度和灌溉。然后，SPCOSA、CLHS、SPCOSA-CLHS、以 x 和 y 坐标作为协变量的 CLHS (XY-CLHS)，以黑龙江省为例，对随机抽样方法（RSM）进行了比较。样本人口覆盖12,147,008个网格单元和17个耕地质量指标。使用信息熵、Kullback-Leibler 散度、相似距离、对耕地质量局部空间异质性的表达能力和抽样结果的分布同质性五个参数来比较这些方法对总体耕地质量的适用性。SPCOSA-CLHS 可以更好地权衡抽样结果对人口的代表能力和耕地质量的空间异质性，并且其样本具有空间分布均匀的优势。当样本量在 5000 到 20000 之间时，所有方法都表现出良好的适用性。然而，当样本量低于 5000 时，这些方法之间的差异变得显着。SPCOSA 和随机抽样方法的抵消幅度最大。基于对五种采样策略方法的详细比较，我们强烈建议使用 SPCOSA-CLHS 来设计耕地质量观测。