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相关概念视频

Sampling Plans01:23

Sampling Plans

155
Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
155
Stratified Sampling Method01:16

Stratified Sampling Method

11.6K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a stratified sample, divide the population into groups called strata and then take a...
11.6K
Cluster Sampling Method01:20

Cluster Sampling Method

11.5K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
11.5K
Sampling Methods: Overview01:06

Sampling Methods: Overview

240
A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
In analytical chemistry, the choice of...
240
Random Sampling Method01:09

Random Sampling Method

10.9K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
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Convenience Sampling Method00:55

Convenience Sampling Method

8.5K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population.
Convenience sampling is a non-random method of sample selection; this method selects individuals that are easily accessible and may result in biased data. For example, a marketing...
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相关实验视频

Updated: May 13, 2025

Sampling Soils in a Heterogeneous Research Plot
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一种分区条件拉丁式超立方体采样方法,考虑到数字土壤绘图中的空间异质性.

Biao Huang1, Guijian Yang2, Jiancong Lei1

  • 1School of Geographic Sciences, Hunan Normal University, 36 Lushan Road, Changsha, 410081, China.

Scientific reports
|April 14, 2025
PubMed
概括

一种新的分区条件拉丁式超立方体采样 (PcLHS) 方法通过考虑空间变化来改善土壤有机碳 (SOC) 映射. 这种方法提高了数字土壤绘图的预测准确性,以改善土壤管理.

关键词:
分区条件拉丁式超立方体采样方法土壤有机碳土壤有机碳空间异质性 空间异质性

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Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon
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Soil Lysimeter Excavation for Coupled Hydrological, Geochemical, and Microbiological Investigations
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相关实验视频

Last Updated: May 13, 2025

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Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon
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Soil Lysimeter Excavation for Coupled Hydrological, Geochemical, and Microbiological Investigations
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科学领域:

  • 数字土壤科学 数字土壤科学
  • 地质统计学 在地质统计学
  • 环境建模环境建模

背景情况:

  • 准确的土壤有机碳 (SOC) 预测对于数字土壤绘图和土地管理至关重要.
  • 传统的采样方法与空间异质性作斗争,影响预测精度和可靠性.
  • 环境变量影响土壤特性,但它们的驱动因素在各次区域之间有所不同.

研究的目的:

  • 引入和评估一种新的采样方法,分区条件拉丁式超立方体采样 (PcLHS),旨在解决数字土壤绘图中的空间异质性.
  • 在复杂的景观中提高土壤有机碳 (SOC) 预测的准确性和可靠性.

主要方法:

  • 该研究提出了PcLHS方法,该方法将区域化与动态约束的聚合集群和分区 (REDCAP) 整合为区域分区.
  • 使用Boruta和变量通胀因子 (VIF) 方法确定了关键的环境变量.
  • 条件拉丁超立方体采样 (cLHS) 在每个次区域内应用,以选择培训点,形成一个全面的数据集.

主要成果:

  • 在法国东北部的SOC采样案例研究中,PcLHS表现优于传统方法.
  • 与其他方法相比,PcLHS实现了显著较低的根平均平方误差 (RMSE),更高的确定系数 (R2) 和更好的一致性相关系数 (CCC).
  • 具体来说,PcLHS减少了RMSE的4-11%,增加了R2的18-46%,并改善了CCC的14-29%.

结论:

  • 这些发现强调了将空间异质性纳入土壤采样设计,以准确地绘制数字土壤地图的关键重要性.
  • 验证了PcLHS方法是提高SOC预测准确性的有效策略,特别是在异质环境中.
  • 这种方法为优化各种景观中的土壤采样策略提供了一个强大的框架.