Estimating soil organic carbon density in plains using landscape metric-based regression Kriging model
Estimating soil organic carbon density in plains using landscape metric-based regression Kriging model
复制标题
使用基于景观度量的回归克里金模型估算平原土壤有机碳密度
DOI：
10.1016/j.still.2019.104381
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发表时间：
2019-12
影响因子：
6.5
通讯作者：
Liu Yanfang
中科院分区：
文献类型：
--
作者：
Wu Zihao;Wang Bozhi;Huang Junlong;An Zihao;Jiang Ping;Chen Yiyun;Liu Yanfang
土壤有机碳密度(SOCD)的空间分布是了解土地利用对碳收支影响的关键。平原地区SOCD的空间估计和精确制图仍然具有挑战性，部分原因是相对不变的地形和缺乏对景观格局的考虑。在这里，我们提出了一种新的基于景观度量的回归克立格法(LMRK)用于平原地区SOCD的空间估计。利用江汉平原采集的2 42个表土样品，我们(1)研究了土壤有机质含量与24种景观指数之间的尺度依赖关系；(2)建立了多尺度缓冲(10 0~1 0 0 0 m)的线性马尔可夫模型，并与结合土地利用类型的普通克立格法(OK)和回归克立格法(RK)进行了性能比较。结果表明，LMRK模型的性能优于其他模型。SOCD和景观指数之间的关系被发现是尺度相关的，在我们的例子中，300 m的缓冲区显示出最佳的尺度。LMRK还表明，高度连通和水分充足的景观有利于农田土壤有机碳的积累。这些结果表明，景观指数具有良好的预测性，所提出的LMRK方法对于平原地区的SOCD制图是有效的。我们的发现突出了景观度量和SOCD之间的尺度依赖关系，并为平原土壤制图提供了一个新的视角。
The spatial distribution of soil organic carbon density (SOCD) is crucial for understanding land use impact on carbon budget. The spatial estimation and accurate mapping of SOCD in plains remain challenging, partly due to the relatively invariant topography and the lack of consideration of landscape patterns. Here, we propose a novel landscape metric-based regression Kriging (LMRK) for the spatial estimation of SOCD in plains. Using 242 topsoil samples collected in the Jianghan Plain, China, we (i) investigate the scale-dependent relationship between SOCD and 24 landscape metrics and (ii) develop LMRK models with multi-scale buffers (100–1000 m) for SOCD estimation and compare their performance with ordinary Kriging (OK) and regression Kriging (RK) that integrates land use types. Results showed that LMRK outperformed other models. The relationships between SOCD and landscape metrics were found to be scale-dependent, and the buffer of 300 m exhibited the optimal scale in our case. The LMRK also revealed that a highly connected and water-sufficient landscape was conducive to the accumulation of soil organic carbon in farmlands. These results indicated that landscape metrics serve as good predictors, and the proposed LMRK method is effective for SOCD mapping in plains. Our findings highlight the scale-dependent relationship between landscape metrics and SOCD and provide a new perspective for soil mapping in plains.
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