基于湿度分布特征的小尺度土壤碳通量空间采样策略

由于土壤碳通量的空间异质性很强,传统的随机抽样方法无法对区域土壤碳通量进行准确估算,而多点采样需耗费大量的人力及设备成本,因此确定适当的采样点数量及分布策略对于区域土壤碳通量的测算非常重要。提出一种基于湿度空间分布特征的小尺度土壤碳通量空间采样策略:首先利用无线传感网密集测量区域的土壤湿度,根据湿度数据的空间分布特征划分监测区域,通过Hammond Mc Cullagh方程计算各子区域内的最优采样点数量,最终确定整个监测区域的空间采样点部署策略。提出的方法考虑了各子区域间土壤碳通量空间分布的差异,使得采样点的部署位置与土壤碳通量的分布具有较好的相关性。研究结果证明:土壤碳通量部署策略能够获得比均匀部署策略、随机部署策略更高的区域土壤碳通量估算准确度。

A sampling strategy for fine-scale regional soil carbon flux estimation based on spatial distribution of soil moisture

Soil respiration is a key ecological process during which CO₂ is emitted from the soil and released into the atmosphere. It includes processes such as soil microbial respiration, root respiration, and respiration of heterotrophic animals. The regional soil carbon flux cannot be accurately estimated using traditional random sampling methods, because of its strong spatial heterogeneity. Because multi-point sampling involves massive manpower and equipment costs, it is crucial to determine the appropriate number and the distribution of sampling positions to include in studies estimating regional soil carbon flux. As a complex ecological process, soil respiration is not only affected by environmental factors such as soil temperature and humidity but also by biological factors such as vegetation, microorganisms, and land usage. Because of the correlation between soil respiration and soil moisture, we propose a spatial sampling strategy based on soil moisture distribution characteristic (SMTC) for use in the estimation of fine-scale regional soil carbon flux. Regional soil moisture data are collected using densely deployed sensors nodes, and the monitored area is divided into several sub-regions according to the spatial distribution of the soil moisture data. Then, the optimal number of sampling positions in each sub-region is calculated using the Hammond McCullagh method. As a result, the optimal sampling strategy of the whole monitoring area is determined. We simultaneously applied the SMTC method, random sampling strategy, and uniform sampling strategy to estimate the regional soil carbon flux. In the experiment, we determined that 23 sampling points would be required to measure soil carbon flux in the monitored area, according to the SMTC method. In the same experimental environment, 23 sampling points were selected using a random sampling strategy, and 25 sampling points arranged in a 5 m×5 m grid pattern were selected using a uniform sampling strategy. Regional soil carbon flux is determined via interpolation using the Kriging method based on the measurements taken at all sampling points by using each strategy described above. The experimental results show that SMTC performs better than the other two sampling strategies. The mean squared errors of SMTC, random sampling strategy, and uniform sampling strategy were 8.78%, 13.32%, and 11.56%, respectively. Furthermore, the SMTC method also produced the smallest mean squared error among these three strategies. The SMTC strategy takes the variation of the soil carbon flux among various sub-regions into account, which leads to a better correlation between sampling positions and the distribution of soil carbon flux. Using the SMTC strategy, more sampling points are selected in regions where the soil carbon flux is strongly heterogeneous, allowing the heterogeneity to be captured more fully, and allowing the estimation error to be reduced. In addition, it allows for the use of fewer sampling points in regions of weak heterogeneity. Thus, the SMTC sampling strategy can be used for fine-scale regional soil carbon flux estimation, needing comparatively fewer sampling points because of its strategy of setting each sampling point in a more optimal position than traditional methods.