Mapping soil organic matter using the topographic wetness index: A comparative study based on different flow-direction algorithms and kriging methods
Mapping soil organic matter using the topographic wetness index: A comparative study based on different flow-direction algorithms and kriging methods
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DOI：
10.1016/j.ecolind.2009.10.005
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发表时间：
2010-05
影响因子：
6.9
通讯作者：
T. Pei;C. Qin;A. Zhu;L. Yang;Ming Luo;Baolin Li;Chenghu Zhou
中科院分区：
文献类型：
--
作者：
T. Pei;C. Qin;A. Zhu;L. Yang;Ming Luo;Baolin Li;Chenghu Zhou
从数字高程模型中提取的地形属性已被广泛应用于土壤有机质（SOM）制图。在这些属性中，地形湿度指数（TWI），一个指标，定量地表明在当地尺度上的水积累和排水条件之间的平衡，已被证明与SOM。然而，大多数研究中使用的TWI是使用单流向（SFD）算法计算的，该算法假设来自网格单元的所有水仅流入一个相邻单元。这一假设并不总是正确的，特别是在地势较低的地区，那里的水流可能是分散的。为了克服这种SFD的限制，一个多流向（MFD）算法已经开发出来，它分配流量从一个网格单元到几个下坡邻居。以嫩江县51.76km2的土壤有机质为例，比较了基于SFD和MFD的TWI计算方法在土壤有机质预测制图中的应用效果。我们发现，基于MFD的TWI与SOM的相关性优于基于SFD的指数。然后，我们比较了SOM地图的准确性，这是来自MFD为基础的TWI和SFD为基础的TWI合并普通克里格（OK），简单克里格与变化的局部均值（SKlm），克里格与外部漂移（KED）和同位协同克里格（CC）。在SKlm和CC中用作次要变量的基于MFD的TWI优于基于SFD的TWI。对于不同的克里金方法，CC（包括基于MFD的TWI或基于SFD的TWI）表现出最好的性能，OK生成的结果优于SKlm和KED。事实证明，基于MFD的TWI和基于SFD的TWI都与KED和SKlm不兼容，因为它们的数值不稳定性是由粗糙的TWI表面引起的。在所有预测方法中，CC结合基于MFD的TWI产生了最好的结果。这是因为：（1）基于MFD的TWI最能定量表征土壤水分，与土壤有机质的相关性最强;（2）CC能有效利用土壤有机质的空间自相关性以及与基于MFD的TWI的互相关性。
Terrain attributes derived from digital elevation models have been used widely for mapping soil organic matter (SOM). Among these attributes, the topographic wetness index (TWI), an index for quantitatively indicating the balance between water accumulation and drainage conditions at the local scale, has been shown to correlate with SOM. However, TWIs used in most studies are calculated using a single-flow-direction (SFD) algorithm, which assumes that all water from a grid cell flows into only one neighboring cell. This assumption is not always valid, especially in areas with low relief where movement of water may be divergent. To overcome this SFD limitation, a multiple-flow-direction (MFD) algorithm has been developed, which distributes flow from a grid cell to several downslope neighbors. In this study we compared the effect of TWI calculations based on SFD and MFD in predictive mapping of SOM by incorporating them into different kriging methods over a 51.76km2area in Nenjiang County of northeastern China. We found that the MFD-based TWI was better correlated with SOM than was the SFD-based index. We then compared the accuracies of SOM maps which were derived from MFD-based TWI and SFD-based TWI incorporated by ordinary kriging (OK), simple kriging with varying local means (SKlm), kriging with external drift (KED) and collocated cokriging (CC). The MFD-based TWI, used as a secondary variable in SKlm and CC, outperforms the SFD-based TWI. For the different kriging methods, CC (incorporating either MFD-based TWI or SFD-based TWI) showed the best performance, and OK generated a better result than SKlm and KED. Both the MFD-based TWI and SFD-based TWI proved to be incompatible with KED and SKlm due to their numerical instability caused by the rough TWI surfaces. Among all predictive methods, CC incorporating the MFD-based TWI produced the best results. This is because: (1) the MFD-based TWI is best able to indicate quantitatively soil moisture and therefore has the strongest correlation with SOM; (2) CC is capable of utilizing effectively the spatial auto-correlation of SOM and the cross-correlation between SOM and the MFD-based TWI.