Evaluation of different approaches for identifying optimal sites to predict mean hillslope soil moisture content
Evaluation of different approaches for identifying optimal sites to predict mean hillslope soil moisture content
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评估确定预测平均山坡土壤含水量最佳地点的不同方法
DOI：
10.1016/j.jhydrol.2017.01.043
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发表时间：
2017-04
影响因子：
6.4
通讯作者：
Feng Huihui
中科院分区：
文献类型：
--
作者：
Liao Kaihua;Zhou Zhiwen;Lai Xiaoming;Zhu Qing;Feng Huihui
确定代表性土壤湿度采样点对于验证特定区域的遥感平均土壤湿度以及流域或山坡水文研究中的地面土壤湿度测量非常重要。人们已经开发了许多方法来确定预测平均土壤湿度的最佳地点。每种方法都有一定的优点和缺点，但很少对其进行评估和比较。在我们的研究中，2013年1月至2016年3月（共43个采样日）的地表（0-20厘米）土壤湿度数据是在中国太湖流域丘陵地区混合土地利用（茶竹）山坡上的77个采样点收集的。总共评估了 10 种方法（基于 2 个指数的时间稳定性 (TS) 分析、基于 6 种输入的 K 均值聚类和 2 种随机抽样策略），以确定平均土壤湿度估计的最佳采样点。它们是基于最小时间稳定性指数（ITS，平均相对差值和相对差值标准差（SDRD）的组合）和基于最小SDRD的TS分析，基于土壤性质和地形指数（EFs）的K均值聚类，重复土壤湿度测量（Theta），EFs加一次性土壤湿度数据（EFsTheta），以及导出的主成分 来自 EF (EFs-PCA)、Theta (Theta-PCA) 和 EFsTheta (EFsTheta-PCA) 以及全局和分层随机抽样策略。结果表明，基于最小 ITS 的 TS（RMSE = 0.023 m3m−3）优于基于最小 SDRD 的 TS（RMSE = 0.034 m3m−3）。基于 EFsTheta (-PCA) 的 K 均值聚类比基于 EF (-PCA) 和 Theta (-PCA) 的 K 均值聚类更好（RMSE <0.020 m3m−3）。按土地利用分层的抽样设计比全局随机方法更有效。分层采样和全局采样分别需要 40 个和 60 个采样点，才能使其性能与最佳 K 均值方法 (EFsTheta-PCA) 相当。总体而言，TS只需要一个站点，但其准确性有限。最好的 K 均值方法需要 <8 个站点并产生高精度，但使用此方法时需要额外的土壤和地形信息。仅当没有关于土壤湿度变化的预先知识时，才能使用分层采样策略。该信息将有助于选择估计区域平均土壤湿度的最佳方法。
The identification of representative soil moisture sampling sites is important for the validation of remotely sensed mean soil moisture in a certain area and ground-based soil moisture measurements in catchment or hillslope hydrological studies. Numerous approaches have been developed to identify optimal sites for predicting mean soil moisture. Each method has certain advantages and disadvantages, but they have rarely been evaluated and compared. In our study, surface (0–20 cm) soil moisture data from January 2013 to March 2016 (a total of 43 sampling days) were collected at 77 sampling sites on a mixed land-use (tea and bamboo) hillslope in the hilly area of Taihu Lake Basin, China. A total of 10 methods (temporal stability (TS) analyses based on 2 indices, K-means clustering based on 6 kinds of inputs and 2 random sampling strategies) were evaluated for determining optimal sampling sites for mean soil moisture estimation. They were TS analyses based on the smallest index of temporal stability (ITS, a combination of the mean relative difference and standard deviation of relative difference (SDRD)) and based on the smallest SDRD, K-means clustering based on soil properties and terrain indices (EFs), repeated soil moisture measurements (Theta), EFs plus one-time soil moisture data (EFsTheta), and the principal components derived from EFs (EFs-PCA), Theta (Theta-PCA), and EFsTheta (EFsTheta-PCA), and global and stratified random sampling strategies. Results showed that the TS based on the smallest ITS was better (RMSE = 0.023 m3m−3) than that based on the smallest SDRD (RMSE = 0.034 m3m−3). The K-means clustering based on EFsTheta (-PCA) was better (RMSE <0.020 m3m−3) than these based on EFs (-PCA) and Theta (-PCA). The sampling design stratified by the land use was more efficient than the global random method. Forty and 60 sampling sites are needed for stratified sampling and global sampling respectively to make their performances comparable to the best K-means method (EFsTheta-PCA). Overall, TS required only one site, but its accuracy was limited. The best K-means method required <8 sites and yielded high accuracy, but extra soil and terrain information is necessary when using this method. The stratified sampling strategy can only be used if no pre-knowledge about soil moisture variation is available. This information will help in selecting the optimal methods for estimation the area mean soil moisture.
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影响因子：
6.4
作者：
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